feat: DeepSeek V4 thinking-mode round-trip via DeepSeekChatOpenAI subclass

Resolves #599: thinking-mode models require reasoning_content to be echoed back across turns; multi-turn agent runs failed with HTTP 400. The fix isolates DeepSeek's quirks (reasoning_content round-trip and the deepseek-reasoner no-tool_choice limitation) into a subclass so the general OpenAI-compatible client stays untouched. Adds DeepSeek V4 Pro/Flash to the catalog. 9 new tests; rationale documented in the class docstrings. Design adapted from #600; #611 closed in favour of this approach.
fix(security): validate ticker before using as path component (#618 )
2026-05-02 06:53:16 +03:00 · 2026-05-01 19:23:23 +00:00 · 2026-05-01 18:56:36 +00:00 · 2026-04-25 22:16:09 +00:00 · 2026-04-25 20:54:19 +00:00 · 2026-04-25 20:27:23 +00:00
64 changed files with 4030 additions and 905 deletions
--- a/.dockerignore
+++ b/.dockerignore
@@ -0,0 +1,15 @@
+.git
+.venv
+.env
+.claude
+.idea
+.vscode
+.DS_Store
+__pycache__
+*.egg-info
+build
+dist
+results
+eval_results
+Dockerfile
+docker-compose.yml
--- a/.env.enterprise.example
+++ b/.env.enterprise.example
@@ -0,0 +1,5 @@
+# Azure OpenAI
+AZURE_OPENAI_API_KEY=
+AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
+AZURE_OPENAI_DEPLOYMENT_NAME=
+# OPENAI_API_VERSION=2024-10-21  # optional, required for non-v1 API
--- a/.env.example
+++ b/.env.example
@@ -3,4 +3,7 @@ OPENAI_API_KEY=
 GOOGLE_API_KEY=
 ANTHROPIC_API_KEY=
 XAI_API_KEY=
+DEEPSEEK_API_KEY=
+DASHSCOPE_API_KEY=
+ZHIPU_API_KEY=
 OPENROUTER_API_KEY=
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -0,0 +1,266 @@
+# Changelog
+
+All notable changes to TradingAgents are documented here.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project follows [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+Breaking changes within the 0.x line are called out explicitly.
+
+## [0.2.4] — 2026-04-25
+
+### Added
+
+- **Structured-output decision agents.** Research Manager, Trader, and Portfolio
+  Manager now use `llm.with_structured_output(Schema)` on their primary call
+  and return typed Pydantic instances. Each provider's native structured-output
+  mode is used (`json_schema` for OpenAI / xAI, `response_schema` for Gemini,
+  tool-use for Anthropic, function-calling for OpenAI-compatible providers).
+  Render helpers preserve the existing markdown shape so memory log, CLI
+  display, and saved reports keep working unchanged. (#434)
+- **LangGraph checkpoint resume** — opt-in via `--checkpoint`. State is saved
+  after each node so crashed or interrupted runs resume from the last
+  successful step. Per-ticker SQLite databases under
+  `~/.tradingagents/cache/checkpoints/`. `--clear-checkpoints` resets them. (#594)
+- **Persistent decision log** replacing the per-agent BM25 memory. Decisions
+  are stored automatically at the end of `propagate()`; the next same-ticker
+  run resolves prior pending entries with realised return, alpha vs SPY, and
+  a one-paragraph reflection. Override path with `TRADINGAGENTS_MEMORY_LOG_PATH`.
+  Optional `memory_log_max_entries` config caps resolved entries; pending
+  entries are never pruned. (#578, #563, #564, #579)
+- **DeepSeek, Qwen (Alibaba DashScope), GLM (Zhipu), and Azure OpenAI**
+  providers, plus dynamic OpenRouter model selection.
+- **Docker support** — multi-stage build with separate dev and runtime images.
+- **`scripts/smoke_structured_output.py`** — diagnostic that exercises the
+  three structured-output agents against any provider so contributors can
+  verify their setup with one command.
+- **5-tier rating scale** (Buy / Overweight / Hold / Underweight / Sell) used
+  consistently by Research Manager, Portfolio Manager, signal processor, and
+  the memory log; Trader keeps 3-tier (Buy / Hold / Sell) since transaction
+  direction is naturally ternary.
+- **Pytest fixtures** — lazy LLM client imports plus placeholder API keys so
+  the test suite runs cleanly without credentials. (#588)
+
+### Changed
+
+- **`backend_url` default is now `None`** rather than the OpenAI URL. Each
+  provider client falls back to its native default. The previous default
+  leaked the OpenAI URL into non-OpenAI clients (e.g. Gemini), producing
+  malformed request URLs for Python users who switched providers without
+  overriding `backend_url`. The CLI flow is unaffected.
+- All file I/O passes explicit `encoding="utf-8"` so Windows users no longer
+  hit `UnicodeEncodeError` with the cp1252 default. (#543, #550, #576)
+- Cache and log directories moved to `~/.tradingagents/` to resolve Docker
+  permission issues. (#519)
+- `SignalProcessor` reads the rating from the Portfolio Manager's rendered
+  markdown via a deterministic heuristic — no extra LLM call.
+- OpenAI structured-output calls default to `method="function_calling"` to
+  avoid noisy `PydanticSerializationUnexpectedValue` warnings emitted by
+  langchain-openai's Responses-API parse path. Same typed result, no warnings.
+
+### Fixed
+
+- Empty memory no longer triggers fabricated past-lessons in agent prompts;
+  the memory-log redesign makes this structurally impossible since only the
+  Portfolio Manager consults memory and only when entries exist. (#572)
+- Tool-call logging processes every chunk message, not just the last one, and
+  memory score normalization handles empty score arrays. (#534, #531)
+
+### Removed
+
+- `FinancialSituationMemory` (the per-agent BM25 system) and the dead
+  `reflect_and_remember()` plumbing; subsumed by the persistent decision log.
+- Hardcoded Google endpoint that caused 404 when `langchain-google-genai`
+  changed its API path. (#493, #496)
+
+### Contributors
+
+Thanks to everyone who shaped this release through code, design, and reports:
+
+- [@claytonbrown](https://github.com/claytonbrown) — checkpoint resume (#594), test fixtures (#588), design feedback on cost tracking (#582) and structured validation (#583)
+- [@Bcardo](https://github.com/Bcardo) — memory-log redesign (#579), empty-memory hallucination report (#572), encoding fix proposal (#570)
+- [@voidborne-d](https://github.com/voidborne-d) — memory persistence design (#564), portfolio manager state fix (#503)
+- [@mannubaveja007](https://github.com/mannubaveja007) — structured-output feature request (#434)
+- [@kelder66](https://github.com/kelder66) — RAM-only memory issue (#563)
+- [@Gujiassh](https://github.com/Gujiassh) — tool-call logging fix (#534), test stub PR (#533)
+- [@iuyup](https://github.com/iuyup) — memory score normalization fix (#531)
+- [@kaihg](https://github.com/kaihg) — Google base_url fix (#496)
+- [@32ryh98yfe](https://github.com/32ryh98yfe) — Gemini 404 report (#493)
+- [@uppb](https://github.com/uppb) — OpenRouter dynamic model selection (#482)
+- [@guoz14](https://github.com/guoz14) — OpenRouter limited-model report (#337)
+- [@samchenku](https://github.com/samchenku) — indicator name normalization (#490)
+- [@JasonOA888](https://github.com/JasonOA888) — y_finance pandas import fix (#488)
+- [@tiffanychum](https://github.com/tiffanychum) — stale import cleanup (#499)
+- [@zaizou](https://github.com/zaizou) — Docker permission issue (#519)
+- [@Stosman123](https://github.com/Stosman123), [@mauropuga](https://github.com/mauropuga), [@hotwind2015](https://github.com/hotwind2015) — Windows encoding bug reports (#543, #550, #576)
+- [@nnishad](https://github.com/nnishad), [@atharvajoshi01](https://github.com/atharvajoshi01) — encoding fix proposals (#568, #549)
+
+## [0.2.3] — 2026-03-29
+
+### Added
+
+- **Multi-language output** for analyst reports and final decisions, with a
+  CLI selector. Internal agent debate stays in English for reasoning quality. (#472)
+- **GPT-5.4 family models** in the default catalog, with deep/quick model split.
+- **Unified model catalog** as a single source of truth for CLI options and
+  provider validation.
+
+### Changed
+
+- `base_url` is forwarded to Google and Anthropic clients so corporate proxies
+  work consistently across providers. (#427)
+- Standardised the Google `api_key` parameter to the unified `api_key` form.
+
+### Fixed
+
+- Backtesting fetchers no longer leak look-ahead data when `curr_date` is in
+  the middle of a fetched window. (#475)
+- Invalid indicator names from the LLM are caught at the tool boundary instead
+  of crashing the run. (#429)
+- yfinance news fetchers respect the same exponential-backoff retry as price
+  fetchers. (#445)
+
+### Contributors
+
+- [@ahmedk20](https://github.com/ahmedk20) — multi-language output (#472)
+- [@CadeYu](https://github.com/CadeYu) — model catalog typing (#464)
+- [@javierdejesusda](https://github.com/javierdejesusda) — unified Google API key parameter (#453)
+- [@voidborne-d](https://github.com/voidborne-d) — yfinance news retry (#445)
+- [@kostakost2](https://github.com/kostakost2) — look-ahead bias report (#475)
+- [@lu-zhengda](https://github.com/lu-zhengda) — proxy/base_url support request (#427)
+- [@VamsiKrishna2021](https://github.com/VamsiKrishna2021) — invalid indicator crash report (#429)
+
+## [0.2.2] — 2026-03-22
+
+### Added
+
+- **Five-tier rating scale** (Buy / Overweight / Hold / Underweight / Sell)
+  introduced for the Portfolio Manager.
+- **Anthropic effort level** support for Claude models.
+- **OpenAI Responses API** path for native OpenAI models.
+
+### Changed
+
+- `risk_manager` renamed to `portfolio_manager` to match the role description
+  shown in the CLI display.
+- Exchange-qualified tickers (e.g. `7203.T`, `BRK.B`) preserved across all
+  agent prompts and tool calls.
+- Process-level UTF-8 default attempted for cross-platform consistency
+  (note: this approach did not actually take effect; replaced in v0.2.4 with
+  explicit per-call `encoding="utf-8"` arguments).
+
+### Fixed
+
+- yfinance rate-limit errors are retried with exponential backoff. (#426)
+- HTTP client SSL customisation is supported for environments that need
+  custom certificate bundles. (#379)
+- Report-section writes handle list-of-string content gracefully.
+
+### Contributors
+
+- [@CadeYu](https://github.com/CadeYu) — exchange-qualified ticker preservation (#413)
+- [@yang1002378395-cmyk](https://github.com/yang1002378395-cmyk) — HTTP client SSL customisation (#379)
+
+## [0.2.1] — 2026-03-15
+
+### Security
+
+- Patched `langchain-core` vulnerability (LangGrinch). (#335)
+- Removed `chainlit` dependency affected by CVE-2026-22218.
+
+### Added
+
+- `pyproject.toml` build-system configuration; the project now installs via
+  modern packaging tooling.
+
+### Removed
+
+- `setup.py` — dependencies consolidated to `pyproject.toml`.
+
+### Fixed
+
+- Risk manager reads the correct fundamental report source. (#341)
+- All `open()` calls receive an explicit UTF-8 encoding (initial pass).
+- `get_indicators` tool handles comma-separated indicator names from the LLM. (#368)
+- `Propagation` initialises every debate-state field so risk debaters never
+  see missing keys.
+- Stock data parsing tolerates malformed CSVs and NaN values.
+- Conditional debate logic respects the configured round count. (#361)
+
+### Contributors
+
+- [@RinZ27](https://github.com/RinZ27) — `langchain-core` security patch (#335)
+- [@Ljx-007](https://github.com/Ljx-007) — risk manager fundamental-report fix (#341)
+- [@makk9](https://github.com/makk9) — debate-rounds config issue (#361)
+
+## [0.2.0] — 2026-02-04
+
+This is the largest release since the initial public version. The framework
+moved from single-provider to a multi-provider architecture and grew several
+production-ready surfaces.
+
+### Added
+
+- **Multi-provider LLM support** (OpenAI, Google, Anthropic, xAI, OpenRouter,
+  Ollama) via a factory pattern, with provider-specific thinking configurations.
+- **Alpha Vantage** integration as a configurable primary data provider, with
+  yfinance as a community-stability fallback.
+- **Footer statistics** in the CLI: real-time tracking of LLM calls, tool
+  calls, and token usage via LangChain callbacks.
+- **Post-analysis report saving** — the framework writes per-section markdown
+  files (analyst reports, debate transcripts, final decision) when a run
+  completes.
+- **Announcements panel** — fetches updates from `api.tauric.ai/v1/announcements`
+  for the CLI welcome screen.
+- **Tool fallbacks** so a single vendor outage does not stop the pipeline.
+
+### Changed
+
+- Risky / Safe risk debaters renamed to **Aggressive / Conservative** for
+  consistency with the displayed agent labels.
+- Default data vendor switched to balance reliability and quota across
+  community deployments.
+- Ollama and OpenRouter model lists updated; default endpoints clarified.
+
+### Fixed
+
+- Analyst status tracking and message deduplication in the live display.
+- Infinite-loop guard in the agent loop; reflection and logging hardened.
+- Various data-vendor implementation bugs and tool-signature mismatches.
+
+### Contributors
+
+This release is the first with substantial outside contributions; many community
+PRs from late 2025 also landed here.
+
+- [@luohy15](https://github.com/luohy15) — Alpha Vantage data-vendor integration (#235)
+- [@EdwardoSunny](https://github.com/EdwardoSunny) — yfinance fetching optimisations (#245)
+- [@Mirza-Samad-Ahmed-Baig](https://github.com/Mirza-Samad-Ahmed-Baig) — infinite-loop guard, reflection, and logging fixes (#89)
+- [@ZeroAct](https://github.com/ZeroAct) — saved results path support (#29)
+- [@Zhongyi-Lu](https://github.com/Zhongyi-Lu) — `.env` gitignore (#49)
+- [@csoboy](https://github.com/csoboy) — local Ollama setup (#53)
+- [@chauhang](https://github.com/chauhang) — initial Docker support attempt (#47, later reverted; the merged Docker support shipped in v0.2.4)
+
+## [0.1.1] — 2025-06-07
+
+### Removed
+
+- Static site assets that had been bundled with v0.1.0; the public site now
+  lives separately.
+
+## [0.1.0] — 2025-06-05
+
+### Added
+
+- **Initial public release** of the TradingAgents multi-agent trading
+  framework: market / sentiment / news / fundamentals analysts; bull and bear
+  researchers; trader; aggressive, conservative, and neutral risk debaters;
+  portfolio manager. LangGraph orchestration, yfinance data, per-agent
+  BM25 memory, single-provider OpenAI integration, interactive CLI.
+
+[0.2.4]: https://github.com/TauricResearch/TradingAgents/compare/v0.2.3...v0.2.4
+[0.2.3]: https://github.com/TauricResearch/TradingAgents/compare/v0.2.2...v0.2.3
+[0.2.2]: https://github.com/TauricResearch/TradingAgents/compare/v0.2.1...v0.2.2
+[0.2.1]: https://github.com/TauricResearch/TradingAgents/compare/v0.2.0...v0.2.1
+[0.2.0]: https://github.com/TauricResearch/TradingAgents/compare/v0.1.1...v0.2.0
+[0.1.1]: https://github.com/TauricResearch/TradingAgents/compare/v0.1.0...v0.1.1
+[0.1.0]: https://github.com/TauricResearch/TradingAgents/releases/tag/v0.1.0
--- a/27
+++ b/27
@@ -0,0 +1,27 @@
+FROM python:3.12-slim AS builder
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1
+
+RUN python -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+WORKDIR /build
+COPY . .
+RUN pip install --no-cache-dir .
+
+FROM python:3.12-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+COPY --from=builder /opt/venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+RUN useradd --create-home appuser
+USER appuser
+WORKDIR /home/appuser/app
+
+COPY --from=builder --chown=appuser:appuser /build .
+
+ENTRYPOINT ["tradingagents"]
--- a/README.md
+++ b/README.md
@@ -28,6 +28,8 @@
 # TradingAgents: Multi-Agents LLM Financial Trading Framework

 ## News
+- [2026-04] **TradingAgents v0.2.4** released with structured-output agents (Research Manager, Trader, Portfolio Manager), LangGraph checkpoint resume, persistent decision log, DeepSeek/Qwen/GLM/Azure provider support, Docker, and a Windows UTF-8 encoding fix. See [CHANGELOG.md](CHANGELOG.md) for the full list.
+- [2026-03] **TradingAgents v0.2.3** released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.
 - [2026-03] **TradingAgents v0.2.2** released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.
 - [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
 - [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon.
@@ -117,6 +119,19 @@ Install the package and its dependencies:
 pip install .
 ```

+### Docker
+
+Alternatively, run with Docker:
+```bash
+cp .env.example .env  # add your API keys
+docker compose run --rm tradingagents
+```
+
+For local models with Ollama:
+```bash
+docker compose --profile ollama run --rm tradingagents-ollama
+```
+
 ### Required APIs

 TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:
@@ -126,10 +141,15 @@ export OPENAI_API_KEY=...          # OpenAI (GPT)
 export GOOGLE_API_KEY=...          # Google (Gemini)
 export ANTHROPIC_API_KEY=...       # Anthropic (Claude)
 export XAI_API_KEY=...             # xAI (Grok)
+export DEEPSEEK_API_KEY=...        # DeepSeek
+export DASHSCOPE_API_KEY=...       # Qwen (Alibaba DashScope)
+export ZHIPU_API_KEY=...           # GLM (Zhipu)
 export OPENROUTER_API_KEY=...      # OpenRouter
 export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage
 ```

+For enterprise providers (e.g. Azure OpenAI, AWS Bedrock), copy `.env.enterprise.example` to `.env.enterprise` and fill in your credentials.
+
 For local models, configure Ollama with `llm_provider: "ollama"` in your config.

 Alternatively, copy `.env.example` to `.env` and fill in your keys:
@@ -164,7 +184,7 @@ An interface will appear showing results as they load, letting you track the age

 ### Implementation Details

-We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, OpenRouter, and Ollama.
+We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen (Alibaba DashScope), GLM (Zhipu), OpenRouter, Ollama for local models, and Azure OpenAI for enterprise.

 ### Python Usage

@@ -188,9 +208,9 @@ from tradingagents.graph.trading_graph import TradingAgentsGraph
 from tradingagents.default_config import DEFAULT_CONFIG

 config = DEFAULT_CONFIG.copy()
-config["llm_provider"] = "openai"        # openai, google, anthropic, xai, openrouter, ollama
-config["deep_think_llm"] = "gpt-5.2"     # Model for complex reasoning
-config["quick_think_llm"] = "gpt-5-mini" # Model for quick tasks
+config["llm_provider"] = "openai"        # openai, google, anthropic, xai, deepseek, qwen, glm, openrouter, ollama, azure
+config["deep_think_llm"] = "gpt-5.4"     # Model for complex reasoning
+config["quick_think_llm"] = "gpt-5.4-mini" # Model for quick tasks
 config["max_debate_rounds"] = 2

 ta = TradingAgentsGraph(debug=True, config=config)
@@ -200,10 +220,40 @@ print(decision)

 See `tradingagents/default_config.py` for all configuration options.

+## Persistence and Recovery
+
+TradingAgents persists two kinds of state across runs.
+
+### Decision log
+
+The decision log is always on. Each completed run appends its decision to `~/.tradingagents/memory/trading_memory.md`. On the next run for the same ticker, TradingAgents fetches the realised return (raw and alpha vs SPY), generates a one-paragraph reflection, and injects the most recent same-ticker decisions plus recent cross-ticker lessons into the Portfolio Manager prompt, so each analysis carries forward what worked and what didn't.
+
+Override the path with `TRADINGAGENTS_MEMORY_LOG_PATH`.
+
+### Checkpoint resume
+
+Checkpoint resume is opt-in via `--checkpoint`. When enabled, LangGraph saves state after each node so a crashed or interrupted run resumes from the last successful step instead of starting over. On a resume run you will see `Resuming from step N for <TICKER> on <date>` in the logs; on a new run you will see `Starting fresh`. Checkpoints are cleared automatically on successful completion.
+
+Per-ticker SQLite databases live at `~/.tradingagents/cache/checkpoints/<TICKER>.db` (override the base with `TRADINGAGENTS_CACHE_DIR`). Use `--clear-checkpoints` to reset all of them before a run.
+
+```bash
+tradingagents analyze --checkpoint           # enable for this run
+tradingagents analyze --clear-checkpoints    # reset before running
+```
+
+```python
+config = DEFAULT_CONFIG.copy()
+config["checkpoint_enabled"] = True
+ta = TradingAgentsGraph(config=config)
+_, decision = ta.propagate("NVDA", "2026-01-15")
+```
+
 ## Contributing

 We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/).

+Past contributions, including code, design feedback, and bug reports, are credited per release in [`CHANGELOG.md`](CHANGELOG.md).
+
 ## Citation

 Please reference our work if you find *TradingAgents* provides you with some help :)
--- a/cli/main.py
+++ b/cli/main.py
@@ -6,8 +6,9 @@ from functools import wraps
 from rich.console import Console
 from dotenv import load_dotenv

-# Load environment variables from .env file
+# Load environment variables
 load_dotenv()
+load_dotenv(".env.enterprise", override=False)
 from rich.panel import Panel
 from rich.spinner import Spinner
 from rich.live import Live
@@ -79,7 +80,7 @@ class MessageBuffer:
        self.current_agent = None
        self.report_sections = {}
        self.selected_analysts = []
-        self._last_message_id = None
+        self._processed_message_ids = set()

    def init_for_analysis(self, selected_analysts):
        """Initialize agent status and report sections based on selected analysts.
@@ -114,7 +115,7 @@ class MessageBuffer:
        self.current_agent = None
        self.messages.clear()
        self.tool_calls.clear()
-        self._last_message_id = None
+        self._processed_message_ids.clear()

    def get_completed_reports_count(self):
        """Count reports that are finalized (their finalizing agent is completed).
@@ -462,7 +463,7 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
 def get_user_selections():
    """Get all user selections before starting the analysis display."""
    # Display ASCII art welcome message
-    with open(Path(__file__).parent / "static" / "welcome.txt", "r") as f:
+    with open(Path(__file__).parent / "static" / "welcome.txt", "r", encoding="utf-8") as f:
        welcome_ascii = f.read()

    # Create welcome box content
@@ -519,10 +520,19 @@ def get_user_selections():
    )
    analysis_date = get_analysis_date()

-    # Step 3: Select analysts
+    # Step 3: Output language
    console.print(
        create_question_box(
-            "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis"
+            "Step 3: Output Language",
+            "Select the language for analyst reports and final decision"
+        )
+    )
+    output_language = ask_output_language()
+
+    # Step 4: Select analysts
+    console.print(
+        create_question_box(
+            "Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
        )
    )
    selected_analysts = select_analysts()
@@ -530,32 +540,32 @@ def get_user_selections():
        f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
    )

-    # Step 4: Research depth
+    # Step 5: Research depth
    console.print(
        create_question_box(
-            "Step 4: Research Depth", "Select your research depth level"
+            "Step 5: Research Depth", "Select your research depth level"
        )
    )
    selected_research_depth = select_research_depth()

-    # Step 5: OpenAI backend
+    # Step 6: LLM Provider
    console.print(
        create_question_box(
-            "Step 5: OpenAI backend", "Select which service to talk to"
+            "Step 6: LLM Provider", "Select your LLM provider"
        )
    )
    selected_llm_provider, backend_url = select_llm_provider()
-    
-    # Step 6: Thinking agents
+
+    # Step 7: Thinking agents
    console.print(
        create_question_box(
-            "Step 6: Thinking Agents", "Select your thinking agents for analysis"
+            "Step 7: Thinking Agents", "Select your thinking agents for analysis"
        )
    )
    selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
    selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)

-    # Step 7: Provider-specific thinking configuration
+    # Step 8: Provider-specific thinking configuration
    thinking_level = None
    reasoning_effort = None
    anthropic_effort = None
@@ -564,7 +574,7 @@ def get_user_selections():
    if provider_lower == "google":
        console.print(
            create_question_box(
-                "Step 7: Thinking Mode",
+                "Step 8: Thinking Mode",
                "Configure Gemini thinking mode"
            )
        )
@@ -572,7 +582,7 @@ def get_user_selections():
    elif provider_lower == "openai":
        console.print(
            create_question_box(
-                "Step 7: Reasoning Effort",
+                "Step 8: Reasoning Effort",
                "Configure OpenAI reasoning effort level"
            )
        )
@@ -580,7 +590,7 @@ def get_user_selections():
    elif provider_lower == "anthropic":
        console.print(
            create_question_box(
-                "Step 7: Effort Level",
+                "Step 8: Effort Level",
                "Configure Claude effort level"
            )
        )
@@ -598,6 +608,7 @@ def get_user_selections():
        "google_thinking_level": thinking_level,
        "openai_reasoning_effort": reasoning_effort,
        "anthropic_effort": anthropic_effort,
+        "output_language": output_language,
    }


@@ -635,19 +646,19 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
    analyst_parts = []
    if final_state.get("market_report"):
        analysts_dir.mkdir(exist_ok=True)
-        (analysts_dir / "market.md").write_text(final_state["market_report"])
+        (analysts_dir / "market.md").write_text(final_state["market_report"], encoding="utf-8")
        analyst_parts.append(("Market Analyst", final_state["market_report"]))
    if final_state.get("sentiment_report"):
        analysts_dir.mkdir(exist_ok=True)
-        (analysts_dir / "sentiment.md").write_text(final_state["sentiment_report"])
+        (analysts_dir / "sentiment.md").write_text(final_state["sentiment_report"], encoding="utf-8")
        analyst_parts.append(("Social Analyst", final_state["sentiment_report"]))
    if final_state.get("news_report"):
        analysts_dir.mkdir(exist_ok=True)
-        (analysts_dir / "news.md").write_text(final_state["news_report"])
+        (analysts_dir / "news.md").write_text(final_state["news_report"], encoding="utf-8")
        analyst_parts.append(("News Analyst", final_state["news_report"]))
    if final_state.get("fundamentals_report"):
        analysts_dir.mkdir(exist_ok=True)
-        (analysts_dir / "fundamentals.md").write_text(final_state["fundamentals_report"])
+        (analysts_dir / "fundamentals.md").write_text(final_state["fundamentals_report"], encoding="utf-8")
        analyst_parts.append(("Fundamentals Analyst", final_state["fundamentals_report"]))
    if analyst_parts:
        content = "\n\n".join(f"### {name}\n{text}" for name, text in analyst_parts)
@@ -660,15 +671,15 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
        research_parts = []
        if debate.get("bull_history"):
            research_dir.mkdir(exist_ok=True)
-            (research_dir / "bull.md").write_text(debate["bull_history"])
+            (research_dir / "bull.md").write_text(debate["bull_history"], encoding="utf-8")
            research_parts.append(("Bull Researcher", debate["bull_history"]))
        if debate.get("bear_history"):
            research_dir.mkdir(exist_ok=True)
-            (research_dir / "bear.md").write_text(debate["bear_history"])
+            (research_dir / "bear.md").write_text(debate["bear_history"], encoding="utf-8")
            research_parts.append(("Bear Researcher", debate["bear_history"]))
        if debate.get("judge_decision"):
            research_dir.mkdir(exist_ok=True)
-            (research_dir / "manager.md").write_text(debate["judge_decision"])
+            (research_dir / "manager.md").write_text(debate["judge_decision"], encoding="utf-8")
            research_parts.append(("Research Manager", debate["judge_decision"]))
        if research_parts:
            content = "\n\n".join(f"### {name}\n{text}" for name, text in research_parts)
@@ -678,7 +689,7 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
    if final_state.get("trader_investment_plan"):
        trading_dir = save_path / "3_trading"
        trading_dir.mkdir(exist_ok=True)
-        (trading_dir / "trader.md").write_text(final_state["trader_investment_plan"])
+        (trading_dir / "trader.md").write_text(final_state["trader_investment_plan"], encoding="utf-8")
        sections.append(f"## III. Trading Team Plan\n\n### Trader\n{final_state['trader_investment_plan']}")

    # 4. Risk Management
@@ -688,15 +699,15 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
        risk_parts = []
        if risk.get("aggressive_history"):
            risk_dir.mkdir(exist_ok=True)
-            (risk_dir / "aggressive.md").write_text(risk["aggressive_history"])
+            (risk_dir / "aggressive.md").write_text(risk["aggressive_history"], encoding="utf-8")
            risk_parts.append(("Aggressive Analyst", risk["aggressive_history"]))
        if risk.get("conservative_history"):
            risk_dir.mkdir(exist_ok=True)
-            (risk_dir / "conservative.md").write_text(risk["conservative_history"])
+            (risk_dir / "conservative.md").write_text(risk["conservative_history"], encoding="utf-8")
            risk_parts.append(("Conservative Analyst", risk["conservative_history"]))
        if risk.get("neutral_history"):
            risk_dir.mkdir(exist_ok=True)
-            (risk_dir / "neutral.md").write_text(risk["neutral_history"])
+            (risk_dir / "neutral.md").write_text(risk["neutral_history"], encoding="utf-8")
            risk_parts.append(("Neutral Analyst", risk["neutral_history"]))
        if risk_parts:
            content = "\n\n".join(f"### {name}\n{text}" for name, text in risk_parts)
@@ -706,12 +717,12 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
        if risk.get("judge_decision"):
            portfolio_dir = save_path / "5_portfolio"
            portfolio_dir.mkdir(exist_ok=True)
-            (portfolio_dir / "decision.md").write_text(risk["judge_decision"])
+            (portfolio_dir / "decision.md").write_text(risk["judge_decision"], encoding="utf-8")
            sections.append(f"## V. Portfolio Manager Decision\n\n### Portfolio Manager\n{risk['judge_decision']}")

    # Write consolidated report
    header = f"# Trading Analysis Report: {ticker}\n\nGenerated: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
-    (save_path / "complete_report.md").write_text(header + "\n\n".join(sections))
+    (save_path / "complete_report.md").write_text(header + "\n\n".join(sections), encoding="utf-8")
    return save_path / "complete_report.md"


@@ -915,7 +926,7 @@ def format_tool_args(args, max_length=80) -> str:
        return result[:max_length - 3] + "..."
    return result

-def run_analysis():
+def run_analysis(checkpoint: bool = False):
    # First get all user selections
    selections = get_user_selections()

@@ -931,6 +942,8 @@ def run_analysis():
    config["google_thinking_level"] = selections.get("google_thinking_level")
    config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort")
    config["anthropic_effort"] = selections.get("anthropic_effort")
+    config["output_language"] = selections.get("output_language", "English")
+    config["checkpoint_enabled"] = checkpoint

    # Create stats callback handler for tracking LLM/tool calls
    stats_handler = StatsCallbackHandler()
@@ -968,7 +981,7 @@ def run_analysis():
            func(*args, **kwargs)
            timestamp, message_type, content = obj.messages[-1]
            content = content.replace("\n", " ")  # Replace newlines with spaces
-            with open(log_file, "a") as f:
+            with open(log_file, "a", encoding="utf-8") as f:
                f.write(f"{timestamp} [{message_type}] {content}\n")
        return wrapper
    
@@ -979,7 +992,7 @@ def run_analysis():
            func(*args, **kwargs)
            timestamp, tool_name, args = obj.tool_calls[-1]
            args_str = ", ".join(f"{k}={v}" for k, v in args.items())
-            with open(log_file, "a") as f:
+            with open(log_file, "a", encoding="utf-8") as f:
                f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
        return wrapper

@@ -993,7 +1006,7 @@ def run_analysis():
                if content:
                    file_name = f"{section_name}.md"
                    text = "\n".join(str(item) for item in content) if isinstance(content, list) else content
-                    with open(report_dir / file_name, "w") as f:
+                    with open(report_dir / file_name, "w", encoding="utf-8") as f:
                        f.write(text)
        return wrapper

@@ -1041,28 +1054,24 @@ def run_analysis():
        # Stream the analysis
        trace = []
        for chunk in graph.graph.stream(init_agent_state, **args):
-            # Process messages if present (skip duplicates via message ID)
-            if len(chunk["messages"]) > 0:
-                last_message = chunk["messages"][-1]
-                msg_id = getattr(last_message, "id", None)
+            # Process all messages in chunk, deduplicating by message ID
+            for message in chunk.get("messages", []):
+                msg_id = getattr(message, "id", None)
+                if msg_id is not None:
+                    if msg_id in message_buffer._processed_message_ids:
+                        continue
+                    message_buffer._processed_message_ids.add(msg_id)

-                if msg_id != message_buffer._last_message_id:
-                    message_buffer._last_message_id = msg_id
+                msg_type, content = classify_message_type(message)
+                if content and content.strip():
+                    message_buffer.add_message(msg_type, content)

-                    # Add message to buffer
-                    msg_type, content = classify_message_type(last_message)
-                    if content and content.strip():
-                        message_buffer.add_message(msg_type, content)
-
-                    # Handle tool calls
-                    if hasattr(last_message, "tool_calls") and last_message.tool_calls:
-                        for tool_call in last_message.tool_calls:
-                            if isinstance(tool_call, dict):
-                                message_buffer.add_tool_call(
-                                    tool_call["name"], tool_call["args"]
-                                )
-                            else:
-                                message_buffer.add_tool_call(tool_call.name, tool_call.args)
+                if hasattr(message, "tool_calls") and message.tool_calls:
+                    for tool_call in message.tool_calls:
+                        if isinstance(tool_call, dict):
+                            message_buffer.add_tool_call(tool_call["name"], tool_call["args"])
+                        else:
+                            message_buffer.add_tool_call(tool_call.name, tool_call.args)

            # Update analyst statuses based on report state (runs on every chunk)
            update_analyst_statuses(message_buffer, chunk)
@@ -1189,8 +1198,23 @@ def run_analysis():


@app.command()
-def analyze():
-    run_analysis()
+def analyze(
+    checkpoint: bool = typer.Option(
+        False,
+        "--checkpoint",
+        help="Enable checkpoint/resume: save state after each node so a crashed run can resume.",
+    ),
+    clear_checkpoints: bool = typer.Option(
+        False,
+        "--clear-checkpoints",
+        help="Delete all saved checkpoints before running (force fresh start).",
+    ),
+):
+    if clear_checkpoints:
+        from tradingagents.graph.checkpointer import clear_all_checkpoints
+        n = clear_all_checkpoints(DEFAULT_CONFIG["data_cache_dir"])
+        console.print(f"[yellow]Cleared {n} checkpoint(s).[/yellow]")
+    run_analysis(checkpoint=checkpoint)


 if __name__ == "__main__":
--- a/cli/utils.py
+++ b/cli/utils.py
@@ -4,6 +4,7 @@ from typing import List, Optional, Tuple, Dict
 from rich.console import Console

 from cli.models import AnalystType
+from tradingagents.llm_clients.model_catalog import get_model_options

 console = Console()

@@ -133,51 +134,70 @@ def select_research_depth() -> int:
    return choice


-def select_shallow_thinking_agent(provider) -> str:
-    """Select shallow thinking llm engine using an interactive selection."""
+def _fetch_openrouter_models() -> List[Tuple[str, str]]:
+    """Fetch available models from the OpenRouter API."""
+    import requests
+    try:
+        resp = requests.get("https://openrouter.ai/api/v1/models", timeout=10)
+        resp.raise_for_status()
+        models = resp.json().get("data", [])
+        return [(m.get("name") or m["id"], m["id"]) for m in models]
+    except Exception as e:
+        console.print(f"\n[yellow]Could not fetch OpenRouter models: {e}[/yellow]")
+        return []

-    # Define shallow thinking llm engine options with their corresponding model names
-    # Ordering: medium → light → heavy (balanced first for quick tasks)
-    # Within same tier, newer models first
-    SHALLOW_AGENT_OPTIONS = {
-        "openai": [
-            ("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
-            ("GPT-5 Nano - High-throughput, simple tasks", "gpt-5-nano"),
-            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
-            ("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
-        ],
-        "anthropic": [
-            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
-            ("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
-            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
-        ],
-        "google": [
-            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
-            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
-            ("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
-            ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
-        ],
-        "xai": [
-            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
-            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
-            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
-        ],
-        "openrouter": [
-            ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
-            ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
-        ],
-        "ollama": [
-            ("Qwen3:latest (8B, local)", "qwen3:latest"),
-            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
-            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
-        ],
-    }
+
+def select_openrouter_model() -> str:
+    """Select an OpenRouter model from the newest available, or enter a custom ID."""
+    models = _fetch_openrouter_models()
+
+    choices = [questionary.Choice(name, value=mid) for name, mid in models[:5]]
+    choices.append(questionary.Choice("Custom model ID", value="custom"))

    choice = questionary.select(
-        "Select Your [Quick-Thinking LLM Engine]:",
+        "Select OpenRouter Model (latest available):",
+        choices=choices,
+        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
+        style=questionary.Style([
+            ("selected", "fg:magenta noinherit"),
+            ("highlighted", "fg:magenta noinherit"),
+            ("pointer", "fg:magenta noinherit"),
+        ]),
+    ).ask()
+
+    if choice is None or choice == "custom":
+        return questionary.text(
+            "Enter OpenRouter model ID (e.g. google/gemma-4-26b-a4b-it):",
+            validate=lambda x: len(x.strip()) > 0 or "Please enter a model ID.",
+        ).ask().strip()
+
+    return choice
+
+
+def _prompt_custom_model_id() -> str:
+    """Prompt user to type a custom model ID."""
+    return questionary.text(
+        "Enter model ID:",
+        validate=lambda x: len(x.strip()) > 0 or "Please enter a model ID.",
+    ).ask().strip()
+
+
+def _select_model(provider: str, mode: str) -> str:
+    """Select a model for the given provider and mode (quick/deep)."""
+    if provider.lower() == "openrouter":
+        return select_openrouter_model()
+
+    if provider.lower() == "azure":
+        return questionary.text(
+            f"Enter Azure deployment name ({mode}-thinking):",
+            validate=lambda x: len(x.strip()) > 0 or "Please enter a deployment name.",
+        ).ask().strip()
+
+    choice = questionary.select(
+        f"Select Your [{mode.title()}-Thinking LLM Engine]:",
        choices=[
            questionary.Choice(display, value=value)
-            for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
+            for display, value in get_model_options(provider, mode)
        ],
        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
        style=questionary.Style(
@@ -190,95 +210,45 @@ def select_shallow_thinking_agent(provider) -> str:
    ).ask()

    if choice is None:
-        console.print(
-            "\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
-        )
+        console.print(f"\n[red]No {mode} thinking llm engine selected. Exiting...[/red]")
        exit(1)

+    if choice == "custom":
+        return _prompt_custom_model_id()
+
    return choice


+def select_shallow_thinking_agent(provider) -> str:
+    """Select shallow thinking llm engine using an interactive selection."""
+    return _select_model(provider, "quick")
+
+
 def select_deep_thinking_agent(provider) -> str:
    """Select deep thinking llm engine using an interactive selection."""
+    return _select_model(provider, "deep")

-    # Define deep thinking llm engine options with their corresponding model names
-    # Ordering: heavy → medium → light (most capable first for deep tasks)
-    # Within same tier, newer models first
-    DEEP_AGENT_OPTIONS = {
-        "openai": [
-            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
-            ("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
-            ("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
-            ("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
-        ],
-        "anthropic": [
-            ("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
-            ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
-            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
-            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
-        ],
-        "google": [
-            ("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
-            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
-            ("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
-            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
-        ],
-        "xai": [
-            ("Grok 4 - Flagship model", "grok-4-0709"),
-            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
-            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
-            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
-        ],
-        "openrouter": [
-            ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
-            ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
-        ],
-        "ollama": [
-            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
-            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
-            ("Qwen3:latest (8B, local)", "qwen3:latest"),
-        ],
-    }
-
-    choice = questionary.select(
-        "Select Your [Deep-Thinking LLM Engine]:",
-        choices=[
-            questionary.Choice(display, value=value)
-            for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
-        ],
-        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
-        style=questionary.Style(
-            [
-                ("selected", "fg:magenta noinherit"),
-                ("highlighted", "fg:magenta noinherit"),
-                ("pointer", "fg:magenta noinherit"),
-            ]
-        ),
-    ).ask()
-
-    if choice is None:
-        console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
-        exit(1)
-
-    return choice
-
-def select_llm_provider() -> tuple[str, str]:
-    """Select the OpenAI api url using interactive selection."""
-    # Define OpenAI api options with their corresponding endpoints
-    BASE_URLS = [
-        ("OpenAI", "https://api.openai.com/v1"),
-        ("Google", "https://generativelanguage.googleapis.com/v1"),
-        ("Anthropic", "https://api.anthropic.com/"),
-        ("xAI", "https://api.x.ai/v1"),
-        ("Openrouter", "https://openrouter.ai/api/v1"),
-        ("Ollama", "http://localhost:11434/v1"),
+def select_llm_provider() -> tuple[str, str | None]:
+    """Select the LLM provider and its API endpoint."""
+    # (display_name, provider_key, base_url)
+    PROVIDERS = [
+        ("OpenAI", "openai", "https://api.openai.com/v1"),
+        ("Google", "google", None),
+        ("Anthropic", "anthropic", "https://api.anthropic.com/"),
+        ("xAI", "xai", "https://api.x.ai/v1"),
+        ("DeepSeek", "deepseek", "https://api.deepseek.com"),
+        ("Qwen", "qwen", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
+        ("GLM", "glm", "https://open.bigmodel.cn/api/paas/v4/"),
+        ("OpenRouter", "openrouter", "https://openrouter.ai/api/v1"),
+        ("Azure OpenAI", "azure", None),
+        ("Ollama", "ollama", "http://localhost:11434/v1"),
    ]
-    
+
    choice = questionary.select(
        "Select your LLM Provider:",
        choices=[
-            questionary.Choice(display, value=(display, value))
-            for display, value in BASE_URLS
+            questionary.Choice(display, value=(provider_key, url))
+            for display, provider_key, url in PROVIDERS
        ],
        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
        style=questionary.Style(
@@ -291,13 +261,11 @@ def select_llm_provider() -> tuple[str, str]:
    ).ask()
    
    if choice is None:
-        console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
+        console.print("\n[red]No LLM provider selected. Exiting...[/red]")
        exit(1)
-    
-    display_name, url = choice
-    print(f"You selected: {display_name}\tURL: {url}")

-    return display_name, url
+    provider, url = choice
+    return provider, url


 def ask_openai_reasoning_effort() -> str:
@@ -356,3 +324,37 @@ def ask_gemini_thinking_config() -> str | None:
            ("pointer", "fg:green noinherit"),
        ]),
    ).ask()
+
+
+def ask_output_language() -> str:
+    """Ask for report output language."""
+    choice = questionary.select(
+        "Select Output Language:",
+        choices=[
+            questionary.Choice("English (default)", "English"),
+            questionary.Choice("Chinese (中文)", "Chinese"),
+            questionary.Choice("Japanese (日本語)", "Japanese"),
+            questionary.Choice("Korean (한국어)", "Korean"),
+            questionary.Choice("Hindi (हिन्दी)", "Hindi"),
+            questionary.Choice("Spanish (Español)", "Spanish"),
+            questionary.Choice("Portuguese (Português)", "Portuguese"),
+            questionary.Choice("French (Français)", "French"),
+            questionary.Choice("German (Deutsch)", "German"),
+            questionary.Choice("Arabic (العربية)", "Arabic"),
+            questionary.Choice("Russian (Русский)", "Russian"),
+            questionary.Choice("Custom language", "custom"),
+        ],
+        style=questionary.Style([
+            ("selected", "fg:yellow noinherit"),
+            ("highlighted", "fg:yellow noinherit"),
+            ("pointer", "fg:yellow noinherit"),
+        ]),
+    ).ask()
+
+    if choice == "custom":
+        return questionary.text(
+            "Enter language name (e.g. Turkish, Vietnamese, Thai, Indonesian):",
+            validate=lambda x: len(x.strip()) > 0 or "Please enter a language name.",
+        ).ask().strip()
+
+    return choice
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,35 @@
+services:
+  tradingagents:
+    build: .
+    env_file:
+      - .env
+    volumes:
+      - tradingagents_data:/home/appuser/.tradingagents
+    tty: true
+    stdin_open: true
+
+  ollama:
+    image: ollama/ollama:latest
+    volumes:
+      - ollama_data:/root/.ollama
+    profiles:
+      - ollama
+
+  tradingagents-ollama:
+    build: .
+    env_file:
+      - .env
+    environment:
+      - LLM_PROVIDER=ollama
+    volumes:
+      - tradingagents_data:/home/appuser/.tradingagents
+    depends_on:
+      - ollama
+    tty: true
+    stdin_open: true
+    profiles:
+      - ollama
+
+volumes:
+  tradingagents_data:
+  ollama_data:
--- a/main.py
+++ b/main.py
@@ -8,8 +8,8 @@ load_dotenv()

 # Create a custom config
 config = DEFAULT_CONFIG.copy()
-config["deep_think_llm"] = "gpt-5-mini"  # Use a different model
-config["quick_think_llm"] = "gpt-5-mini"  # Use a different model
+config["deep_think_llm"] = "gpt-5.4-mini"  # Use a different model
+config["quick_think_llm"] = "gpt-5.4-mini"  # Use a different model
 config["max_debate_rounds"] = 1  # Increase debate rounds

 # Configure data vendors (default uses yfinance, no extra API keys needed)
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "tradingagents"
-version = "0.2.2"
+version = "0.2.4"
 description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -13,14 +13,14 @@ dependencies = [
    "backtrader>=1.9.78.123",
    "langchain-anthropic>=0.3.15",
    "langchain-experimental>=0.3.4",
-    "langchain-google-genai>=2.1.5",
+    "langchain-google-genai>=4.0.0",
    "langchain-openai>=0.3.23",
    "langgraph>=0.4.8",
+    "langgraph-checkpoint-sqlite>=2.0.0",
    "pandas>=2.3.0",
    "parsel>=1.10.0",
    "pytz>=2025.2",
    "questionary>=2.1.0",
-    "rank-bm25>=0.2.2",
    "redis>=6.2.0",
    "requests>=2.32.4",
    "rich>=14.0.0",
@@ -40,3 +40,15 @@ include = ["tradingagents*", "cli*"]

 [tool.setuptools.package-data]
 cli = ["static/*"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-ra --strict-markers"
+markers = [
+    "unit: fast isolated unit tests",
+    "integration: tests requiring external services",
+    "smoke: quick sanity-check tests",
+]
+filterwarnings = [
+    "ignore::DeprecationWarning",
+]
--- a/scripts/smoke_structured_output.py
+++ b/scripts/smoke_structured_output.py
@@ -0,0 +1,176 @@
+"""End-to-end smoke for structured-output agents against a real LLM provider.
+
+Runs the three decision-making agents (Research Manager, Trader, Portfolio
+Manager) directly with their structured-output bindings and prints the
+typed Pydantic instance + the rendered markdown for each.  Use this to
+verify a provider's native structured-output mode (json_schema for
+OpenAI / xAI / DeepSeek / Qwen / GLM, response_schema for Gemini, tool-use
+for Anthropic) returns clean instances on the schemas we ship.
+
+Usage:
+    OPENAI_API_KEY=... python scripts/smoke_structured_output.py openai
+    GOOGLE_API_KEY=... python scripts/smoke_structured_output.py google
+    ANTHROPIC_API_KEY=... python scripts/smoke_structured_output.py anthropic
+    DEEPSEEK_API_KEY=... python scripts/smoke_structured_output.py deepseek
+
+The script does NOT call propagate(), to keep the surface tight and the
+cost low — it exercises only the three structured-output calls we just
+added, plus the heuristic SignalProcessor.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+
+from tradingagents.agents.managers.portfolio_manager import create_portfolio_manager
+from tradingagents.agents.managers.research_manager import create_research_manager
+from tradingagents.agents.trader.trader import create_trader
+from tradingagents.graph.signal_processing import SignalProcessor
+from tradingagents.llm_clients import create_llm_client
+
+
+PROVIDER_DEFAULTS = {
+    "openai": ("gpt-5.4-mini", None),
+    "google": ("gemini-2.5-flash", None),
+    "anthropic": ("claude-sonnet-4-6", None),
+    "deepseek": ("deepseek-chat", None),
+    "qwen": ("qwen-plus", None),
+    "glm": ("glm-5", None),
+    "xai": ("grok-4", None),
+}
+
+
+# Minimal but realistic state for the three agents.
+DEBATE_HISTORY = """
+Bull Analyst: NVDA's data-center revenue grew 60% YoY last quarter, driven by
+Blackwell ramp; sovereign AI deals with multiple governments add a $40B+
+multi-year tailwind. Margins remain above peer average.
+
+Bear Analyst: Concentration risk is real — top three customers are >40% of
+revenue. Any pause in hyperscaler capex would compress the multiple. China
+export restrictions still cap a meaningful portion of demand.
+"""
+
+
+def _make_rm_state():
+    return {
+        "company_of_interest": "NVDA",
+        "investment_debate_state": {
+            "history": DEBATE_HISTORY,
+            "bull_history": "Bull Analyst: NVDA's data-center revenue grew 60% YoY...",
+            "bear_history": "Bear Analyst: Concentration risk is real...",
+            "current_response": "",
+            "judge_decision": "",
+            "count": 1,
+        },
+    }
+
+
+def _make_trader_state(investment_plan: str):
+    return {
+        "company_of_interest": "NVDA",
+        "investment_plan": investment_plan,
+    }
+
+
+def _make_pm_state(investment_plan: str, trader_plan: str):
+    return {
+        "company_of_interest": "NVDA",
+        "past_context": "",
+        "risk_debate_state": {
+            "history": "Aggressive: lean in. Conservative: trim. Neutral: balanced sizing.",
+            "aggressive_history": "Aggressive: ...",
+            "conservative_history": "Conservative: ...",
+            "neutral_history": "Neutral: ...",
+            "judge_decision": "",
+            "current_aggressive_response": "",
+            "current_conservative_response": "",
+            "current_neutral_response": "",
+            "count": 1,
+        },
+        "market_report": "Market report.",
+        "sentiment_report": "Sentiment report.",
+        "news_report": "News report.",
+        "fundamentals_report": "Fundamentals report.",
+        "investment_plan": investment_plan,
+        "trader_investment_plan": trader_plan,
+    }
+
+
+def _print_section(title: str, content: str) -> None:
+    bar = "=" * 70
+    print(f"\n{bar}\n{title}\n{bar}\n{content}")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("provider", choices=list(PROVIDER_DEFAULTS.keys()))
+    parser.add_argument("--deep-model", default=None, help="Override deep_think_llm")
+    parser.add_argument("--quick-model", default=None, help="Override quick_think_llm")
+    args = parser.parse_args()
+
+    default_model, _ = PROVIDER_DEFAULTS[args.provider]
+    deep_model = args.deep_model or default_model
+    quick_model = args.quick_model or default_model
+
+    print(f"Provider: {args.provider}")
+    print(f"Deep model:  {deep_model}")
+    print(f"Quick model: {quick_model}")
+
+    # Build the LLM clients via the framework's factory.
+    deep_client = create_llm_client(provider=args.provider, model=deep_model)
+    quick_client = create_llm_client(provider=args.provider, model=quick_model)
+    deep_llm = deep_client.get_llm()
+    quick_llm = quick_client.get_llm()
+
+    # 1) Research Manager
+    rm = create_research_manager(deep_llm)
+    rm_result = rm(_make_rm_state())
+    investment_plan = rm_result["investment_plan"]
+    _print_section("[1] Research Manager — investment_plan", investment_plan)
+
+    # 2) Trader (consumes RM's plan)
+    trader = create_trader(quick_llm)
+    trader_result = trader(_make_trader_state(investment_plan))
+    trader_plan = trader_result["trader_investment_plan"]
+    _print_section("[2] Trader — trader_investment_plan", trader_plan)
+
+    # 3) Portfolio Manager (consumes both)
+    pm = create_portfolio_manager(deep_llm)
+    pm_result = pm(_make_pm_state(investment_plan, trader_plan))
+    final_decision = pm_result["final_trade_decision"]
+    _print_section("[3] Portfolio Manager — final_trade_decision", final_decision)
+
+    # 4) SignalProcessor extracts the rating with zero LLM calls.
+    sp = SignalProcessor()
+    rating = sp.process_signal(final_decision)
+    _print_section("[4] SignalProcessor → rating", rating)
+
+    # 5) Lightweight checks: each rendered output should carry the expected
+    #    section headers so downstream consumers (memory log, CLI display,
+    #    saved reports) keep working.
+    checks = [
+        ("Research Manager", investment_plan, ["**Recommendation**:"]),
+        ("Trader",           trader_plan,     ["**Action**:", "FINAL TRANSACTION PROPOSAL:"]),
+        ("Portfolio Manager", final_decision, ["**Rating**:", "**Executive Summary**:", "**Investment Thesis**:"]),
+    ]
+    print("\n" + "=" * 70 + "\nStructure checks\n" + "=" * 70)
+    failures = 0
+    for name, text, required in checks:
+        for marker in required:
+            ok = marker in text
+            print(f"  {'PASS' if ok else 'FAIL'}  {name}: contains {marker!r}")
+            failures += int(not ok)
+
+    print()
+    if failures:
+        print(f"Smoke FAILED: {failures} structure check(s) missing.")
+        return 1
+    print("Smoke PASSED: structured output → rendered markdown chain works for", args.provider)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -0,0 +1,42 @@
+"""Shared pytest fixtures that prevent CI hangs when API keys are absent."""
+
+import os
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+
+def pytest_configure(config):
+    for marker in ("unit", "integration", "smoke"):
+        config.addinivalue_line("markers", f"{marker}: {marker}-level tests")
+
+
+_API_KEY_ENV_VARS = (
+    "OPENAI_API_KEY",
+    "GOOGLE_API_KEY",
+    "ANTHROPIC_API_KEY",
+    "XAI_API_KEY",
+    "DEEPSEEK_API_KEY",
+    "DASHSCOPE_API_KEY",
+    "ZHIPU_API_KEY",
+    "OPENROUTER_API_KEY",
+    "AZURE_OPENAI_API_KEY",
+    "ALPHA_VANTAGE_API_KEY",
+)
+
+
+@pytest.fixture(autouse=True)
+def _dummy_api_keys(monkeypatch):
+    for env_var in _API_KEY_ENV_VARS:
+        monkeypatch.setenv(env_var, os.environ.get(env_var, "placeholder"))
+
+
+@pytest.fixture()
+def mock_llm_client():
+    client = MagicMock()
+    client.get_llm.return_value = MagicMock()
+    with patch(
+        "tradingagents.llm_clients.factory.create_llm_client",
+        return_value=client,
+    ):
+        yield client
--- a/tests/test_checkpoint_resume.py
+++ b/tests/test_checkpoint_resume.py
@@ -0,0 +1,147 @@
+"""Test checkpoint resume: crash mid-analysis, re-run resumes from last node."""
+
+import sqlite3
+import tempfile
+import unittest
+from pathlib import Path
+from typing import TypedDict
+
+from langgraph.checkpoint.sqlite import SqliteSaver
+from langgraph.graph import END, StateGraph
+
+from tradingagents.graph.checkpointer import (
+    checkpoint_step,
+    clear_checkpoint,
+    get_checkpointer,
+    has_checkpoint,
+    thread_id,
+)
+
+# Mutable flag to simulate crash on first run
+_should_crash = False
+
+
+class _SimpleState(TypedDict):
+    count: int
+
+
+def _node_a(state: _SimpleState) -> dict:
+    return {"count": state["count"] + 1}
+
+
+def _node_b(state: _SimpleState) -> dict:
+    if _should_crash:
+        raise RuntimeError("simulated mid-analysis crash")
+    return {"count": state["count"] + 10}
+
+
+def _build_graph() -> StateGraph:
+    builder = StateGraph(_SimpleState)
+    builder.add_node("analyst", _node_a)
+    builder.add_node("trader", _node_b)
+    builder.set_entry_point("analyst")
+    builder.add_edge("analyst", "trader")
+    builder.add_edge("trader", END)
+    return builder
+
+
+class TestCheckpointResume(unittest.TestCase):
+    def setUp(self):
+        self.tmpdir = tempfile.mkdtemp()
+        self.ticker = "TEST"
+        self.date = "2026-04-20"
+
+    def test_crash_and_resume(self):
+        """Crash at 'trader' node, then resume from checkpoint."""
+        global _should_crash
+        builder = _build_graph()
+        tid = thread_id(self.ticker, self.date)
+        cfg = {"configurable": {"thread_id": tid}}
+
+        # Run 1: crash at trader node
+        _should_crash = True
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            with self.assertRaises(RuntimeError):
+                graph.invoke({"count": 0}, config=cfg)
+
+        # Checkpoint should exist at step 1 (analyst completed)
+        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
+        step = checkpoint_step(self.tmpdir, self.ticker, self.date)
+        self.assertEqual(step, 1)
+
+        # Run 2: resume — trader succeeds this time
+        _should_crash = False
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            result = graph.invoke(None, config=cfg)
+
+        # analyst added 1, trader added 10 → 11
+        self.assertEqual(result["count"], 11)
+
+    def test_clear_checkpoint_allows_fresh_start(self):
+        """After clearing, the graph starts from scratch."""
+        global _should_crash
+        builder = _build_graph()
+        tid = thread_id(self.ticker, self.date)
+        cfg = {"configurable": {"thread_id": tid}}
+
+        # Create a checkpoint by crashing
+        _should_crash = True
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            with self.assertRaises(RuntimeError):
+                graph.invoke({"count": 0}, config=cfg)
+
+        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
+
+        # Clear it
+        clear_checkpoint(self.tmpdir, self.ticker, self.date)
+        self.assertFalse(has_checkpoint(self.tmpdir, self.ticker, self.date))
+
+        # Fresh run succeeds from scratch
+        _should_crash = False
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            result = graph.invoke({"count": 0}, config=cfg)
+
+        self.assertEqual(result["count"], 11)
+
+
+    def test_different_date_starts_fresh(self):
+        """A different date must NOT resume from an existing checkpoint."""
+        global _should_crash
+        builder = _build_graph()
+        date2 = "2026-04-21"
+
+        # Run with date1 — crash to leave a checkpoint
+        _should_crash = True
+        tid1 = thread_id(self.ticker, self.date)
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            with self.assertRaises(RuntimeError):
+                graph.invoke({"count": 0}, config={"configurable": {"thread_id": tid1}})
+
+        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
+
+        # date2 should have no checkpoint
+        self.assertFalse(has_checkpoint(self.tmpdir, self.ticker, date2))
+
+        # Run with date2 — should start fresh and succeed
+        _should_crash = False
+        tid2 = thread_id(self.ticker, date2)
+        self.assertNotEqual(tid1, tid2)
+
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            result = graph.invoke({"count": 0}, config={"configurable": {"thread_id": tid2}})
+
+        # Fresh run: analyst +1, trader +10 = 11
+        self.assertEqual(result["count"], 11)
+
+        # Original date checkpoint still exists (untouched)
+        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/tests/test_deepseek_reasoning.py
+++ b/tests/test_deepseek_reasoning.py
@@ -0,0 +1,169 @@
+"""Tests for DeepSeekChatOpenAI thinking-mode behaviour.
+
+Two pieces verified:
+
+1. ``reasoning_content`` is captured on receive into the AIMessage's
+   ``additional_kwargs`` and re-attached on send so DeepSeek's API
+   sees the same value across turns.
+2. ``with_structured_output`` raises NotImplementedError for
+   ``deepseek-reasoner`` so the agent factories' free-text fallback
+   handles the request instead of failing at runtime.
+"""
+
+import os
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.prompt_values import ChatPromptValue
+
+from tradingagents.llm_clients.openai_client import (
+    DeepSeekChatOpenAI,
+    NormalizedChatOpenAI,
+    _input_to_messages,
+)
+
+
+# ---------------------------------------------------------------------------
+# _input_to_messages — the helper that handles list / ChatPromptValue / other
+# (Gemini bot review note: non-list inputs must also work)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestInputToMessages:
+    def test_list_input_returned_as_is(self):
+        msgs = [HumanMessage(content="hi")]
+        assert _input_to_messages(msgs) is msgs
+
+    def test_chat_prompt_value_unwrapped(self):
+        msgs = [HumanMessage(content="hi")]
+        prompt_value = ChatPromptValue(messages=msgs)
+        assert _input_to_messages(prompt_value) == msgs
+
+    def test_string_input_yields_empty_list(self):
+        # A bare string isn't a message-bearing input; the caller's normal
+        # langchain conversion happens upstream of _get_request_payload.
+        assert _input_to_messages("hello") == []
+
+
+# ---------------------------------------------------------------------------
+# Reasoning content propagation across turns
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestDeepSeekReasoningContent:
+    def _client(self):
+        os.environ.setdefault("DEEPSEEK_API_KEY", "placeholder")
+        return DeepSeekChatOpenAI(
+            model="deepseek-v4-flash",
+            api_key="placeholder",
+            base_url="https://api.deepseek.com",
+        )
+
+    def test_capture_on_receive(self):
+        """When the response carries reasoning_content, it lands on the
+        AIMessage's additional_kwargs so the next turn can echo it back."""
+        client = self._client()
+        result = client._create_chat_result(
+            {
+                "model": "deepseek-v4-flash",
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {
+                            "role": "assistant",
+                            "content": "Plan: buy NVDA.",
+                            "reasoning_content": "Step 1: trend is up. Step 2: ...",
+                        },
+                        "finish_reason": "stop",
+                    }
+                ],
+                "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+            }
+        )
+        ai = result.generations[0].message
+        assert ai.additional_kwargs["reasoning_content"] == "Step 1: trend is up. Step 2: ..."
+
+    def test_propagate_on_send(self):
+        """When an outgoing AIMessage carries reasoning_content, the request
+        payload echoes it on the corresponding message dict."""
+        client = self._client()
+        prior = AIMessage(
+            content="Plan",
+            additional_kwargs={"reasoning_content": "weighed bull case"},
+        )
+        new_user = HumanMessage(content="Refine.")
+        payload = client._get_request_payload([prior, new_user])
+        # Find the assistant message in the payload
+        assistant_dicts = [m for m in payload["messages"] if m.get("role") == "assistant"]
+        assert assistant_dicts, "assistant message missing from outgoing payload"
+        assert assistant_dicts[0]["reasoning_content"] == "weighed bull case"
+
+    def test_propagate_through_chat_prompt_value(self):
+        """Gemini bot review note: non-list inputs (ChatPromptValue) must
+        also propagate reasoning_content."""
+        client = self._client()
+        prior = AIMessage(
+            content="Plan",
+            additional_kwargs={"reasoning_content": "weighed bull case"},
+        )
+        prompt_value = ChatPromptValue(messages=[prior, HumanMessage(content="Refine.")])
+        payload = client._get_request_payload(prompt_value)
+        assistant_dicts = [m for m in payload["messages"] if m.get("role") == "assistant"]
+        assert assistant_dicts[0]["reasoning_content"] == "weighed bull case"
+
+
+# ---------------------------------------------------------------------------
+# deepseek-reasoner: structured output unavailable, falls through to free-text
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestDeepSeekReasonerStructuredOutput:
+    def test_with_structured_output_raises_for_reasoner(self):
+        client = DeepSeekChatOpenAI(
+            model="deepseek-reasoner",
+            api_key="placeholder",
+            base_url="https://api.deepseek.com",
+        )
+        from pydantic import BaseModel
+
+        class _Sample(BaseModel):
+            answer: str
+
+        with pytest.raises(NotImplementedError):
+            client.with_structured_output(_Sample)
+
+    def test_with_structured_output_works_for_v4(self):
+        """V4 models (non-reasoner) accept tool_choice; structured output works."""
+        client = DeepSeekChatOpenAI(
+            model="deepseek-v4-flash",
+            api_key="placeholder",
+            base_url="https://api.deepseek.com",
+        )
+        from pydantic import BaseModel
+
+        class _Sample(BaseModel):
+            answer: str
+
+        # Should return a Runnable, not raise. (The actual API call would
+        # require a real key; we only assert binding succeeds.)
+        wrapped = client.with_structured_output(_Sample)
+        assert wrapped is not None
+
+
+# ---------------------------------------------------------------------------
+# Base class isolation: NormalizedChatOpenAI does NOT have DeepSeek behaviour
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestBaseClassIsolation:
+    def test_normalized_does_not_propagate_reasoning_content(self):
+        """The general-purpose NormalizedChatOpenAI must not carry
+        DeepSeek-specific behaviour. Only the subclass does."""
+        assert not hasattr(NormalizedChatOpenAI, "_get_request_payload") or (
+            NormalizedChatOpenAI._get_request_payload
+            is NormalizedChatOpenAI.__bases__[0]._get_request_payload
+        )
--- a/tests/test_google_api_key.py
+++ b/tests/test_google_api_key.py
@@ -0,0 +1,31 @@
+import unittest
+from unittest.mock import patch
+
+import pytest
+
+from tradingagents.llm_clients.google_client import GoogleClient
+
+
+@pytest.mark.unit
+class TestGoogleApiKeyStandardization(unittest.TestCase):
+    """Verify GoogleClient accepts unified api_key parameter."""
+
+    @patch("tradingagents.llm_clients.google_client.NormalizedChatGoogleGenerativeAI")
+    def test_api_key_handling(self, mock_chat):
+        test_cases = [
+            ("unified api_key is mapped", {"api_key": "test-key-123"}, "test-key-123"),
+            ("legacy google_api_key still works", {"google_api_key": "legacy-key-456"}, "legacy-key-456"),
+            ("unified api_key takes precedence", {"api_key": "unified", "google_api_key": "legacy"}, "unified"),
+        ]
+
+        for msg, kwargs, expected_key in test_cases:
+            with self.subTest(msg=msg):
+                mock_chat.reset_mock()
+                client = GoogleClient("gemini-2.5-flash", **kwargs)
+                client.get_llm()
+                call_kwargs = mock_chat.call_args[1]
+                self.assertEqual(call_kwargs.get("google_api_key"), expected_key)
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/tests/test_memory_log.py
+++ b/tests/test_memory_log.py
@@ -0,0 +1,773 @@
+"""Tests for TradingMemoryLog — storage, deferred reflection, PM injection, legacy removal."""
+
+import pytest
+import pandas as pd
+from unittest.mock import MagicMock, patch
+
+from tradingagents.agents.utils.memory import TradingMemoryLog
+from tradingagents.agents.schemas import PortfolioDecision, PortfolioRating
+from tradingagents.graph.reflection import Reflector
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.graph.propagation import Propagator
+from tradingagents.agents.managers.portfolio_manager import create_portfolio_manager
+
+_SEP = TradingMemoryLog._SEPARATOR
+
+DECISION_BUY = "Rating: Buy\nEnter at $189-192, 6% portfolio cap."
+DECISION_OVERWEIGHT = (
+    "Rating: Overweight\n"
+    "Executive Summary: Moderate position, await confirmation.\n"
+    "Investment Thesis: Strong fundamentals but near-term headwinds."
+)
+DECISION_SELL = "Rating: Sell\nExit position immediately."
+DECISION_NO_RATING = (
+    "Executive Summary: Complex situation with multiple competing factors.\n"
+    "Investment Thesis: No clear directional signal at this time."
+)
+
+
+# ---------------------------------------------------------------------------
+# Shared helpers
+# ---------------------------------------------------------------------------
+
+def make_log(tmp_path, filename="trading_memory.md"):
+    config = {"memory_log_path": str(tmp_path / filename)}
+    return TradingMemoryLog(config)
+
+
+def _seed_completed(tmp_path, ticker, date, decision_text, reflection_text, filename="trading_memory.md"):
+    """Write a completed entry directly to file, bypassing the API."""
+    entry = (
+        f"[{date} | {ticker} | Buy | +1.0% | +0.5% | 5d]\n\n"
+        f"DECISION:\n{decision_text}\n\n"
+        f"REFLECTION:\n{reflection_text}"
+        + _SEP
+    )
+    with open(tmp_path / filename, "a", encoding="utf-8") as f:
+        f.write(entry)
+
+
+def _resolve_entry(log, ticker, date, decision, reflection="Good call."):
+    """Store a decision then immediately resolve it via the API."""
+    log.store_decision(ticker, date, decision)
+    log.update_with_outcome(ticker, date, 0.05, 0.02, 5, reflection)
+
+
+def _price_df(prices):
+    """Minimal DataFrame matching yfinance .history() output shape."""
+    return pd.DataFrame({"Close": prices})
+
+
+def _make_pm_state(past_context=""):
+    """Minimal AgentState dict for portfolio_manager_node."""
+    return {
+        "company_of_interest": "NVDA",
+        "past_context": past_context,
+        "risk_debate_state": {
+            "history": "Risk debate history.",
+            "aggressive_history": "",
+            "conservative_history": "",
+            "neutral_history": "",
+            "judge_decision": "",
+            "current_aggressive_response": "",
+            "current_conservative_response": "",
+            "current_neutral_response": "",
+            "count": 1,
+        },
+        "market_report": "Market report.",
+        "sentiment_report": "Sentiment report.",
+        "news_report": "News report.",
+        "fundamentals_report": "Fundamentals report.",
+        "investment_plan": "Research plan.",
+        "trader_investment_plan": "Trader plan.",
+    }
+
+
+def _structured_pm_llm(captured: dict, decision: PortfolioDecision | None = None):
+    """Build a MagicMock LLM whose with_structured_output binding captures the
+    prompt and returns a real PortfolioDecision (so render_pm_decision works).
+    """
+    if decision is None:
+        decision = PortfolioDecision(
+            rating=PortfolioRating.HOLD,
+            executive_summary="Hold the position; await catalyst.",
+            investment_thesis="Balanced view; neither side carried the debate.",
+        )
+    structured = MagicMock()
+    structured.invoke.side_effect = lambda prompt: (
+        captured.__setitem__("prompt", prompt) or decision
+    )
+    llm = MagicMock()
+    llm.with_structured_output.return_value = structured
+    return llm
+
+
+# ---------------------------------------------------------------------------
+# Core: storage and read path
+# ---------------------------------------------------------------------------
+
+class TestTradingMemoryLogCore:
+
+    def test_store_creates_file(self, tmp_path):
+        log = make_log(tmp_path)
+        assert not (tmp_path / "trading_memory.md").exists()
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        assert (tmp_path / "trading_memory.md").exists()
+
+    def test_store_appends_not_overwrites(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        log.store_decision("AAPL", "2026-01-11", DECISION_OVERWEIGHT)
+        entries = log.load_entries()
+        assert len(entries) == 2
+        assert entries[0]["ticker"] == "NVDA"
+        assert entries[1]["ticker"] == "AAPL"
+
+    def test_store_decision_idempotent(self, tmp_path):
+        """Calling store_decision twice with same (ticker, date) stores only one entry."""
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        assert len(log.load_entries()) == 1
+
+    def test_batch_update_resolves_multiple_entries(self, tmp_path):
+        """batch_update_with_outcomes resolves multiple pending entries in one write."""
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-05", DECISION_BUY)
+        log.store_decision("NVDA", "2026-01-12", DECISION_SELL)
+
+        updates = [
+            {"ticker": "NVDA", "trade_date": "2026-01-05",
+             "raw_return": 0.05, "alpha_return": 0.02, "holding_days": 5,
+             "reflection": "First correct."},
+            {"ticker": "NVDA", "trade_date": "2026-01-12",
+             "raw_return": -0.03, "alpha_return": -0.01, "holding_days": 5,
+             "reflection": "Second correct."},
+        ]
+        log.batch_update_with_outcomes(updates)
+
+        entries = log.load_entries()
+        assert len(entries) == 2
+        assert all(not e["pending"] for e in entries)
+        assert entries[0]["reflection"] == "First correct."
+        assert entries[1]["reflection"] == "Second correct."
+
+    def test_pending_tag_format(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        text = (tmp_path / "trading_memory.md").read_text(encoding="utf-8")
+        assert "[2026-01-10 | NVDA | Buy | pending]" in text
+
+    # Rating parsing
+
+    def test_rating_parsed_buy(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        assert log.load_entries()[0]["rating"] == "Buy"
+
+    def test_rating_parsed_overweight(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("AAPL", "2026-01-11", DECISION_OVERWEIGHT)
+        assert log.load_entries()[0]["rating"] == "Overweight"
+
+    def test_rating_fallback_hold(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("MSFT", "2026-01-12", DECISION_NO_RATING)
+        assert log.load_entries()[0]["rating"] == "Hold"
+
+    def test_rating_priority_over_prose(self, tmp_path):
+        """'Rating: X' label wins even when an opposing rating word appears earlier in prose."""
+        decision = (
+            "The sell thesis is weak. The hold case is marginal.\n\n"
+            "Rating: Buy\n\n"
+            "Executive Summary: Strong fundamentals support the position."
+        )
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", decision)
+        assert log.load_entries()[0]["rating"] == "Buy"
+
+    # Delimiter robustness
+
+    def test_decision_with_markdown_separator(self, tmp_path):
+        """LLM decision containing '---' must not corrupt the entry."""
+        decision = "Rating: Buy\n\n---\n\nRisk: elevated volatility."
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", decision)
+        entries = log.load_entries()
+        assert len(entries) == 1
+        assert "Risk: elevated volatility" in entries[0]["decision"]
+
+    # load_entries
+
+    def test_load_entries_empty_file(self, tmp_path):
+        log = make_log(tmp_path)
+        assert log.load_entries() == []
+
+    def test_load_entries_single(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        entries = log.load_entries()
+        assert len(entries) == 1
+        e = entries[0]
+        assert e["date"] == "2026-01-10"
+        assert e["ticker"] == "NVDA"
+        assert e["rating"] == "Buy"
+        assert e["pending"] is True
+        assert e["raw"] is None
+
+    def test_load_entries_multiple(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        log.store_decision("AAPL", "2026-01-11", DECISION_OVERWEIGHT)
+        log.store_decision("MSFT", "2026-01-12", DECISION_NO_RATING)
+        entries = log.load_entries()
+        assert len(entries) == 3
+        assert [e["ticker"] for e in entries] == ["NVDA", "AAPL", "MSFT"]
+
+    def test_decision_content_preserved(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        assert log.load_entries()[0]["decision"] == DECISION_BUY.strip()
+
+    # get_pending_entries
+
+    def test_get_pending_returns_pending_only(self, tmp_path):
+        log = make_log(tmp_path)
+        _seed_completed(tmp_path, "NVDA", "2026-01-05", "Buy NVDA.", "Correct.")
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        pending = log.get_pending_entries()
+        assert len(pending) == 1
+        assert pending[0]["ticker"] == "NVDA"
+        assert pending[0]["date"] == "2026-01-10"
+
+    # get_past_context
+
+    def test_get_past_context_empty(self, tmp_path):
+        log = make_log(tmp_path)
+        assert log.get_past_context("NVDA") == ""
+
+    def test_get_past_context_pending_excluded(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        assert log.get_past_context("NVDA") == ""
+
+    def test_get_past_context_same_ticker(self, tmp_path):
+        log = make_log(tmp_path)
+        _seed_completed(tmp_path, "NVDA", "2026-01-05", "Buy NVDA — AI capex thesis intact.", "Directionally correct.")
+        ctx = log.get_past_context("NVDA")
+        assert "Past analyses of NVDA" in ctx
+        assert "Buy NVDA" in ctx
+
+    def test_get_past_context_cross_ticker(self, tmp_path):
+        log = make_log(tmp_path)
+        _seed_completed(tmp_path, "AAPL", "2026-01-05", "Buy AAPL — Services growth.", "Correct.")
+        ctx = log.get_past_context("NVDA")
+        assert "Recent cross-ticker lessons" in ctx
+        assert "Past analyses of NVDA" not in ctx
+
+    def test_n_same_limit_respected(self, tmp_path):
+        """Only the n_same most recent same-ticker entries are included."""
+        log = make_log(tmp_path)
+        for i in range(6):
+            _seed_completed(tmp_path, "NVDA", f"2026-01-{i+1:02d}", f"Buy entry {i}.", "Correct.")
+        ctx = log.get_past_context("NVDA", n_same=5)
+        assert "Buy entry 0" not in ctx
+        assert "Buy entry 5" in ctx
+
+    def test_n_cross_limit_respected(self, tmp_path):
+        """Only the n_cross most recent cross-ticker entries are included."""
+        log = make_log(tmp_path)
+        for i, ticker in enumerate(["AAPL", "MSFT", "GOOG", "META"]):
+            _seed_completed(tmp_path, ticker, f"2026-01-{i+1:02d}", f"Buy {ticker}.", "Correct.")
+        ctx = log.get_past_context("NVDA", n_cross=3)
+        assert "AAPL" not in ctx
+        assert "META" in ctx
+
+    # No-op when config is None
+
+    def test_no_log_path_is_noop(self):
+        log = TradingMemoryLog(config=None)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        assert log.load_entries() == []
+        assert log.get_past_context("NVDA") == ""
+
+    # Rotation: opt-in cap on resolved entries
+
+    def test_rotation_disabled_by_default(self, tmp_path):
+        """Without max_entries, all resolved entries are kept."""
+        log = make_log(tmp_path)
+        for i in range(7):
+            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
+        assert len(log.load_entries()) == 7
+
+    def test_rotation_prunes_oldest_resolved(self, tmp_path):
+        """When max_entries is set and exceeded, oldest resolved entries are pruned."""
+        log = TradingMemoryLog({
+            "memory_log_path": str(tmp_path / "trading_memory.md"),
+            "memory_log_max_entries": 3,
+        })
+        # Resolve 5 entries; rotation should keep only the 3 most recent.
+        for i in range(5):
+            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
+        entries = log.load_entries()
+        assert len(entries) == 3
+        # Confirm the OLDEST were dropped, not the newest.
+        dates = [e["date"] for e in entries]
+        assert dates == ["2026-01-03", "2026-01-04", "2026-01-05"]
+
+    def test_rotation_never_prunes_pending(self, tmp_path):
+        """Pending entries (unresolved) are kept regardless of the cap."""
+        log = TradingMemoryLog({
+            "memory_log_path": str(tmp_path / "trading_memory.md"),
+            "memory_log_max_entries": 2,
+        })
+        # 3 resolved + 2 pending. With cap=2, only 2 resolved survive; both pending stay.
+        for i in range(3):
+            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Resolved {i}.")
+        log.store_decision("NVDA", "2026-02-01", DECISION_BUY)
+        log.store_decision("NVDA", "2026-02-02", DECISION_OVERWEIGHT)
+        # Trigger rotation by resolving one more entry — pending entries must stay.
+        _resolve_entry(log, "NVDA", "2026-01-04", DECISION_BUY, "Resolved 3.")
+        entries = log.load_entries()
+        pending = [e for e in entries if e["pending"]]
+        resolved = [e for e in entries if not e["pending"]]
+        assert len(pending) == 2, "pending entries must never be pruned"
+        assert len(resolved) == 2, f"expected 2 resolved after rotation, got {len(resolved)}"
+
+    def test_rotation_under_cap_is_noop(self, tmp_path):
+        """No rotation when resolved count <= max_entries."""
+        log = TradingMemoryLog({
+            "memory_log_path": str(tmp_path / "trading_memory.md"),
+            "memory_log_max_entries": 10,
+        })
+        for i in range(3):
+            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
+        assert len(log.load_entries()) == 3
+
+    # Rating parsing: markdown bold and numbered list formats
+
+    def test_rating_parsed_from_bold_markdown(self, tmp_path):
+        """**Rating**: Buy — markdown bold around the label must not prevent parsing."""
+        decision = "**Rating**: Buy\nEnter at $190."
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", decision)
+        assert log.load_entries()[0]["rating"] == "Buy"
+
+    def test_rating_parsed_from_bold_value(self, tmp_path):
+        """Rating: **Sell** — markdown bold around the value must not prevent parsing."""
+        decision = "Rating: **Sell**\nExit immediately."
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", decision)
+        assert log.load_entries()[0]["rating"] == "Sell"
+
+    def test_rating_label_wins_over_prose_with_markdown(self, tmp_path):
+        """Rating: **Sell** must win even when prose contains a conflicting rating word."""
+        decision = (
+            "The buy thesis is weakened by guidance.\n"
+            "Rating: **Sell**\n"
+            "Exit before earnings."
+        )
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", decision)
+        assert log.load_entries()[0]["rating"] == "Sell"
+
+    def test_rating_parsed_from_numbered_list(self, tmp_path):
+        """1. Rating: Buy — numbered list prefix must not prevent parsing."""
+        decision = "1. Rating: Buy\nEnter at $190."
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", decision)
+        assert log.load_entries()[0]["rating"] == "Buy"
+
+
+# ---------------------------------------------------------------------------
+# Deferred reflection: update_with_outcome, Reflector, _fetch_returns
+# ---------------------------------------------------------------------------
+
+class TestDeferredReflection:
+
+    # update_with_outcome
+
+    def test_update_replaces_pending_tag(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        log.update_with_outcome("NVDA", "2026-01-10", 0.042, 0.021, 5, "Momentum confirmed.")
+        text = (tmp_path / "trading_memory.md").read_text(encoding="utf-8")
+        assert "[2026-01-10 | NVDA | Buy | pending]" not in text
+        assert "+4.2%" in text
+        assert "+2.1%" in text
+        assert "5d" in text
+
+    def test_update_appends_reflection(self, tmp_path):
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        log.update_with_outcome("NVDA", "2026-01-10", 0.042, 0.021, 5, "Momentum confirmed.")
+        entries = log.load_entries()
+        assert len(entries) == 1
+        e = entries[0]
+        assert e["pending"] is False
+        assert e["reflection"] == "Momentum confirmed."
+        assert e["decision"] == DECISION_BUY.strip()
+
+    def test_update_preserves_other_entries(self, tmp_path):
+        """Only the matching entry is modified; all other entries remain unchanged."""
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        log.store_decision("AAPL", "2026-01-11", "Rating: Hold\nHold AAPL.")
+        log.store_decision("MSFT", "2026-01-12", DECISION_SELL)
+        log.update_with_outcome("AAPL", "2026-01-11", 0.01, -0.01, 5, "Neutral result.")
+        entries = log.load_entries()
+        assert len(entries) == 3
+        nvda, aapl, msft = entries
+        assert nvda["ticker"] == "NVDA" and nvda["pending"] is True
+        assert aapl["ticker"] == "AAPL" and aapl["pending"] is False
+        assert aapl["reflection"] == "Neutral result."
+        assert msft["ticker"] == "MSFT" and msft["pending"] is True
+
+    def test_update_atomic_write(self, tmp_path):
+        """A pre-existing .tmp file is overwritten; the log is correctly updated."""
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        stale_tmp = tmp_path / "trading_memory.tmp"
+        stale_tmp.write_text("GARBAGE CONTENT — should be overwritten", encoding="utf-8")
+        log.update_with_outcome("NVDA", "2026-01-10", 0.042, 0.021, 5, "Correct.")
+        assert not stale_tmp.exists()
+        entries = log.load_entries()
+        assert len(entries) == 1
+        assert entries[0]["reflection"] == "Correct."
+        assert entries[0]["pending"] is False
+
+    def test_update_noop_when_no_log_path(self):
+        log = TradingMemoryLog(config=None)
+        log.update_with_outcome("NVDA", "2026-01-10", 0.05, 0.02, 5, "Reflection")
+
+    def test_formatting_roundtrip_after_update(self, tmp_path):
+        """All fields intact and blank line between tag and DECISION preserved after update."""
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
+        log.update_with_outcome("NVDA", "2026-01-10", 0.042, 0.021, 5, "Momentum confirmed.")
+        entries = log.load_entries()
+        assert len(entries) == 1
+        e = entries[0]
+        assert e["pending"] is False
+        assert e["decision"] == DECISION_BUY.strip()
+        assert e["reflection"] == "Momentum confirmed."
+        assert e["raw"] == "+4.2%"
+        assert e["alpha"] == "+2.1%"
+        assert e["holding"] == "5d"
+        raw_text = (tmp_path / "trading_memory.md").read_text(encoding="utf-8")
+        assert "[2026-01-10 | NVDA | Buy | +4.2% | +2.1% | 5d]\n\nDECISION:" in raw_text
+
+    # Reflector.reflect_on_final_decision
+
+    def test_reflect_on_final_decision_returns_llm_output(self):
+        mock_llm = MagicMock()
+        mock_llm.invoke.return_value.content = "Directionally correct. Thesis confirmed."
+        reflector = Reflector(mock_llm)
+        result = reflector.reflect_on_final_decision(
+            final_decision=DECISION_BUY, raw_return=0.042, alpha_return=0.021
+        )
+        assert result == "Directionally correct. Thesis confirmed."
+        mock_llm.invoke.assert_called_once()
+
+    def test_reflect_on_final_decision_includes_returns_in_prompt(self):
+        """Return figures are present in the human message sent to the LLM."""
+        mock_llm = MagicMock()
+        mock_llm.invoke.return_value.content = "Incorrect call."
+        reflector = Reflector(mock_llm)
+        reflector.reflect_on_final_decision(
+            final_decision=DECISION_SELL, raw_return=-0.08, alpha_return=-0.05
+        )
+        messages = mock_llm.invoke.call_args[0][0]
+        human_content = next(content for role, content in messages if role == "human")
+        assert "-8.0%" in human_content
+        assert "-5.0%" in human_content
+        assert "Exit position immediately." in human_content
+
+    # TradingAgentsGraph._fetch_returns
+
+    def test_fetch_returns_valid_ticker(self):
+        stock_prices = [100.0, 102.0, 104.0, 103.0, 105.0, 106.0]
+        spy_prices   = [400.0, 402.0, 404.0, 403.0, 405.0, 406.0]
+        mock_graph = MagicMock(spec=TradingAgentsGraph)
+        with patch("yfinance.Ticker") as mock_ticker_cls:
+            def _make_ticker(sym):
+                m = MagicMock()
+                m.history.return_value = _price_df(spy_prices if sym == "SPY" else stock_prices)
+                return m
+            mock_ticker_cls.side_effect = _make_ticker
+            raw, alpha, days = TradingAgentsGraph._fetch_returns(mock_graph, "NVDA", "2026-01-05")
+        assert raw is not None and alpha is not None and days is not None
+        assert isinstance(raw, float) and isinstance(alpha, float) and isinstance(days, int)
+        assert days == 5
+
+    def test_fetch_returns_too_recent(self):
+        """Only 1 data point available → returns (None, None, None), no crash."""
+        mock_graph = MagicMock(spec=TradingAgentsGraph)
+        with patch("yfinance.Ticker") as mock_ticker_cls:
+            m = MagicMock()
+            m.history.return_value = _price_df([100.0])
+            mock_ticker_cls.return_value = m
+            raw, alpha, days = TradingAgentsGraph._fetch_returns(mock_graph, "NVDA", "2026-04-19")
+        assert raw is None and alpha is None and days is None
+
+    def test_fetch_returns_delisted(self):
+        """Empty DataFrame → returns (None, None, None), no crash."""
+        mock_graph = MagicMock(spec=TradingAgentsGraph)
+        with patch("yfinance.Ticker") as mock_ticker_cls:
+            m = MagicMock()
+            m.history.return_value = pd.DataFrame({"Close": []})
+            mock_ticker_cls.return_value = m
+            raw, alpha, days = TradingAgentsGraph._fetch_returns(mock_graph, "XXXXXFAKE", "2026-01-10")
+        assert raw is None and alpha is None and days is None
+
+    def test_fetch_returns_spy_shorter_than_stock(self):
+        """SPY having fewer rows than the stock must not raise IndexError."""
+        stock_prices = [100.0, 102.0, 104.0, 103.0, 105.0, 106.0]
+        spy_prices   = [400.0, 402.0, 403.0]
+        mock_graph = MagicMock(spec=TradingAgentsGraph)
+        with patch("yfinance.Ticker") as mock_ticker_cls:
+            def _make_ticker(sym):
+                m = MagicMock()
+                m.history.return_value = _price_df(spy_prices if sym == "SPY" else stock_prices)
+                return m
+            mock_ticker_cls.side_effect = _make_ticker
+            raw, alpha, days = TradingAgentsGraph._fetch_returns(mock_graph, "NVDA", "2026-01-05")
+        assert raw is not None and alpha is not None and days is not None
+        assert days == 2
+
+    # TradingAgentsGraph._resolve_pending_entries
+
+    def test_resolve_skips_other_tickers(self, tmp_path):
+        """Pending AAPL entry is not resolved when the run is for NVDA."""
+        log = make_log(tmp_path)
+        log.store_decision("AAPL", "2026-01-10", DECISION_BUY)
+        mock_graph = MagicMock(spec=TradingAgentsGraph)
+        mock_graph.memory_log = log
+        mock_graph._fetch_returns = MagicMock(return_value=(0.05, 0.02, 5))
+        TradingAgentsGraph._resolve_pending_entries(mock_graph, "NVDA")
+        mock_graph._fetch_returns.assert_not_called()
+        assert len(log.get_pending_entries()) == 1
+
+    def test_resolve_marks_entry_completed(self, tmp_path):
+        """After resolve, get_pending_entries() is empty and the entry has a REFLECTION."""
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-05", DECISION_BUY)
+        mock_reflector = MagicMock()
+        mock_reflector.reflect_on_final_decision.return_value = "Momentum confirmed."
+        mock_graph = MagicMock(spec=TradingAgentsGraph)
+        mock_graph.memory_log = log
+        mock_graph.reflector = mock_reflector
+        mock_graph._fetch_returns = MagicMock(return_value=(0.05, 0.02, 5))
+        TradingAgentsGraph._resolve_pending_entries(mock_graph, "NVDA")
+        assert log.get_pending_entries() == []
+        entries = log.load_entries()
+        assert len(entries) == 1
+        assert entries[0]["pending"] is False
+        assert entries[0]["reflection"] == "Momentum confirmed."
+        assert "+5.0%" in entries[0]["raw"]
+        assert "+2.0%" in entries[0]["alpha"]
+
+
+# ---------------------------------------------------------------------------
+# Portfolio Manager injection: past_context in state and prompt
+# ---------------------------------------------------------------------------
+
+class TestPortfolioManagerInjection:
+
+    # past_context in initial state
+
+    def test_past_context_in_initial_state(self):
+        propagator = Propagator()
+        state = propagator.create_initial_state("NVDA", "2026-01-10", past_context="some context")
+        assert "past_context" in state
+        assert state["past_context"] == "some context"
+
+    def test_past_context_defaults_to_empty(self):
+        propagator = Propagator()
+        state = propagator.create_initial_state("NVDA", "2026-01-10")
+        assert state["past_context"] == ""
+
+    # PM prompt
+
+    def test_pm_prompt_includes_past_context(self):
+        captured = {}
+        llm = _structured_pm_llm(captured)
+        pm_node = create_portfolio_manager(llm)
+        state = _make_pm_state(past_context="[2026-01-05 | NVDA | Buy | +5.0% | +2.0% | 5d]\nGreat call.")
+        pm_node(state)
+        assert "Lessons from prior decisions and outcomes" in captured["prompt"]
+        assert "Great call." in captured["prompt"]
+
+    def test_pm_no_past_context_no_section(self):
+        """PM prompt omits the lessons section entirely when past_context is empty."""
+        captured = {}
+        llm = _structured_pm_llm(captured)
+        pm_node = create_portfolio_manager(llm)
+        state = _make_pm_state(past_context="")
+        pm_node(state)
+        assert "Lessons from prior decisions" not in captured["prompt"]
+
+    def test_pm_returns_rendered_markdown_with_rating(self):
+        """The structured PortfolioDecision is rendered to markdown that
+        downstream consumers (memory log, signal processor, CLI display)
+        can parse without any extra LLM call."""
+        captured = {}
+        decision = PortfolioDecision(
+            rating=PortfolioRating.OVERWEIGHT,
+            executive_summary="Build position gradually over the next two weeks.",
+            investment_thesis="AI capex cycle remains intact; institutional flows constructive.",
+            price_target=215.0,
+            time_horizon="3-6 months",
+        )
+        llm = _structured_pm_llm(captured, decision)
+        pm_node = create_portfolio_manager(llm)
+        result = pm_node(_make_pm_state())
+        md = result["final_trade_decision"]
+        assert "**Rating**: Overweight" in md
+        assert "**Executive Summary**: Build position gradually" in md
+        assert "**Investment Thesis**: AI capex cycle" in md
+        assert "**Price Target**: 215.0" in md
+        assert "**Time Horizon**: 3-6 months" in md
+
+    def test_pm_falls_back_to_freetext_when_structured_unavailable(self):
+        """If a provider does not support with_structured_output, the agent
+        falls back to a plain invoke and returns whatever prose the model
+        produced, so the pipeline never blocks."""
+        plain_response = "**Rating**: Sell\n\nExit ahead of guidance."
+        llm = MagicMock()
+        llm.with_structured_output.side_effect = NotImplementedError("provider unsupported")
+        llm.invoke.return_value = MagicMock(content=plain_response)
+        pm_node = create_portfolio_manager(llm)
+        result = pm_node(_make_pm_state())
+        assert result["final_trade_decision"] == plain_response
+
+    # get_past_context ordering and limits
+
+    def test_same_ticker_prioritised(self, tmp_path):
+        """Same-ticker entries in same-ticker section; cross-ticker entries in cross-ticker section."""
+        log = make_log(tmp_path)
+        _resolve_entry(log, "NVDA", "2026-01-05", DECISION_BUY, "Momentum confirmed.")
+        _resolve_entry(log, "AAPL", "2026-01-06", DECISION_SELL, "Overvalued.")
+        result = log.get_past_context("NVDA")
+        assert "Past analyses of NVDA" in result
+        assert "Recent cross-ticker lessons" in result
+        same_block, cross_block = result.split("Recent cross-ticker lessons")
+        assert "NVDA" in same_block
+        assert "AAPL" in cross_block
+
+    def test_cross_ticker_reflection_only(self, tmp_path):
+        """Cross-ticker entries show only the REFLECTION text, not the full DECISION."""
+        log = make_log(tmp_path)
+        _resolve_entry(log, "AAPL", "2026-01-06", DECISION_SELL, "Overvalued correction.")
+        result = log.get_past_context("NVDA")
+        assert "Overvalued correction." in result
+        assert "Exit position immediately." not in result
+
+    def test_n_same_limit_respected(self, tmp_path):
+        """More than 5 same-ticker completed entries → only 5 injected."""
+        log = make_log(tmp_path)
+        for i in range(7):
+            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
+        result = log.get_past_context("NVDA", n_same=5)
+        lessons_present = sum(1 for i in range(7) if f"Lesson {i}." in result)
+        assert lessons_present == 5
+
+    def test_n_cross_limit_respected(self, tmp_path):
+        """More than 3 cross-ticker completed entries → only 3 injected."""
+        log = make_log(tmp_path)
+        tickers = ["AAPL", "MSFT", "TSLA", "AMZN", "GOOG"]
+        for i, ticker in enumerate(tickers):
+            _resolve_entry(log, ticker, f"2026-01-{i+1:02d}", DECISION_BUY, f"{ticker} lesson.")
+        result = log.get_past_context("NVDA", n_cross=3)
+        cross_count = sum(result.count(f"{t} lesson.") for t in tickers)
+        assert cross_count == 3
+
+    # Full A→B→C integration cycle
+
+    def test_full_cycle_store_resolve_inject(self, tmp_path):
+        """store pending → resolve with outcome → past_context non-empty for PM."""
+        log = make_log(tmp_path)
+        log.store_decision("NVDA", "2026-01-05", DECISION_BUY)
+        assert len(log.get_pending_entries()) == 1
+        assert log.get_past_context("NVDA") == ""
+        log.update_with_outcome("NVDA", "2026-01-05", 0.05, 0.02, 5, "Correct call.")
+        assert log.get_pending_entries() == []
+        past_ctx = log.get_past_context("NVDA")
+        assert past_ctx != ""
+        assert "NVDA" in past_ctx
+        assert "Correct call." in past_ctx
+        assert "DECISION:" in past_ctx
+        assert "REFLECTION:" in past_ctx
+
+
+# ---------------------------------------------------------------------------
+# Legacy removal: BM25 / FinancialSituationMemory fully gone
+# ---------------------------------------------------------------------------
+
+class TestLegacyRemoval:
+
+    def test_financial_situation_memory_removed(self):
+        """FinancialSituationMemory must not be importable from the memory module."""
+        import tradingagents.agents.utils.memory as m
+        assert not hasattr(m, "FinancialSituationMemory")
+
+    def test_bm25_not_imported(self):
+        """rank_bm25 must not be present in the memory module namespace."""
+        import tradingagents.agents.utils.memory as m
+        assert not hasattr(m, "BM25Okapi")
+
+    def test_reflect_and_remember_removed(self):
+        """TradingAgentsGraph must not expose reflect_and_remember."""
+        assert not hasattr(TradingAgentsGraph, "reflect_and_remember")
+
+    def test_portfolio_manager_no_memory_param(self):
+        """create_portfolio_manager accepts only llm; passing memory= raises TypeError."""
+        mock_llm = MagicMock()
+        create_portfolio_manager(mock_llm)
+        with pytest.raises(TypeError):
+            create_portfolio_manager(mock_llm, memory=MagicMock())
+
+    def test_full_pipeline_no_regression(self, tmp_path):
+        """propagate() completes and stores the decision after the redesign."""
+        import functools
+
+        fake_state = {
+            "final_trade_decision": "Rating: Buy\nBuy NVDA.",
+            "company_of_interest": "NVDA",
+            "trade_date": "2026-01-10",
+            "market_report": "",
+            "sentiment_report": "",
+            "news_report": "",
+            "fundamentals_report": "",
+            "investment_debate_state": {
+                "bull_history": "", "bear_history": "", "history": "",
+                "current_response": "", "judge_decision": "",
+            },
+            "investment_plan": "",
+            "trader_investment_plan": "",
+            "risk_debate_state": {
+                "aggressive_history": "", "conservative_history": "",
+                "neutral_history": "", "history": "", "judge_decision": "",
+                "current_aggressive_response": "", "current_conservative_response": "",
+                "current_neutral_response": "", "count": 1, "latest_speaker": "",
+            },
+        }
+        mock_graph = MagicMock()
+        mock_graph.memory_log = TradingMemoryLog({"memory_log_path": str(tmp_path / "mem.md")})
+        mock_graph.log_states_dict = {}
+        mock_graph.debug = False
+        mock_graph.config = {"results_dir": str(tmp_path)}
+        mock_graph.graph.invoke.return_value = fake_state
+        mock_graph.propagator.create_initial_state.return_value = fake_state
+        mock_graph.propagator.get_graph_args.return_value = {}
+        mock_graph.signal_processor.process_signal.return_value = "Buy"
+        # Bind the real _run_graph so propagate's call to self._run_graph executes
+        # the actual write path instead of the auto-MagicMock.
+        mock_graph._run_graph = functools.partial(
+            TradingAgentsGraph._run_graph, mock_graph
+        )
+        TradingAgentsGraph.propagate(mock_graph, "NVDA", "2026-01-10")
+        entries = mock_graph.memory_log.load_entries()
+        assert len(entries) == 1
+        assert entries[0]["ticker"] == "NVDA"
+        assert entries[0]["pending"] is True
--- a/tests/test_model_validation.py
+++ b/tests/test_model_validation.py
@@ -0,0 +1,55 @@
+import unittest
+import warnings
+
+import pytest
+
+from tradingagents.llm_clients.base_client import BaseLLMClient
+from tradingagents.llm_clients.model_catalog import get_known_models
+from tradingagents.llm_clients.validators import validate_model
+
+
+class DummyLLMClient(BaseLLMClient):
+    def __init__(self, provider: str, model: str):
+        self.provider = provider
+        super().__init__(model)
+
+    def get_llm(self):
+        self.warn_if_unknown_model()
+        return object()
+
+    def validate_model(self) -> bool:
+        return validate_model(self.provider, self.model)
+
+
+@pytest.mark.unit
+class ModelValidationTests(unittest.TestCase):
+    def test_cli_catalog_models_are_all_validator_approved(self):
+        for provider, models in get_known_models().items():
+            if provider in ("ollama", "openrouter"):
+                continue
+
+            for model in models:
+                with self.subTest(provider=provider, model=model):
+                    self.assertTrue(validate_model(provider, model))
+
+    def test_unknown_model_emits_warning_for_strict_provider(self):
+        client = DummyLLMClient("openai", "not-a-real-openai-model")
+
+        with warnings.catch_warnings(record=True) as caught:
+            warnings.simplefilter("always")
+            client.get_llm()
+
+        self.assertEqual(len(caught), 1)
+        self.assertIn("not-a-real-openai-model", str(caught[0].message))
+        self.assertIn("openai", str(caught[0].message))
+
+    def test_openrouter_and_ollama_accept_custom_models_without_warning(self):
+        for provider in ("openrouter", "ollama"):
+            client = DummyLLMClient(provider, "custom-model-name")
+
+            with self.subTest(provider=provider):
+                with warnings.catch_warnings(record=True) as caught:
+                    warnings.simplefilter("always")
+                    client.get_llm()
+
+                self.assertEqual(caught, [])
--- a/tests/test_safe_ticker_component.py
+++ b/tests/test_safe_ticker_component.py
@@ -0,0 +1,52 @@
+"""Tests for the ticker path-component validator that blocks directory traversal."""
+
+import os
+import unittest
+
+import pytest
+
+from tradingagents.dataflows.utils import safe_ticker_component
+
+
+@pytest.mark.unit
+class TestSafeTickerComponent(unittest.TestCase):
+    def test_accepts_common_ticker_formats(self):
+        for ticker in ("AAPL", "BRK-B", "BRK.A", "0700.HK", "7203.T", "BHP.AX", "^GSPC"):
+            self.assertEqual(safe_ticker_component(ticker), ticker)
+
+    def test_rejects_path_separators(self):
+        for bad in (".", "..", "../etc", "a/b", "a\\b", "/abs", "..\\..\\x"):
+            with self.assertRaises(ValueError):
+                safe_ticker_component(bad)
+
+    def test_rejects_null_byte_and_whitespace(self):
+        for bad in ("AAP L", "AAPL\x00", "AAPL\n", "\tAAPL"):
+            with self.assertRaises(ValueError):
+                safe_ticker_component(bad)
+
+    def test_rejects_empty_or_non_string(self):
+        for bad in ("", None, 123, b"AAPL"):
+            with self.assertRaises(ValueError):
+                safe_ticker_component(bad)
+
+    def test_rejects_overlong_input(self):
+        with self.assertRaises(ValueError):
+            safe_ticker_component("A" * 33)
+
+    def test_rejects_dot_only_values(self):
+        # '.' and '..' pass the regex but traverse when used as a path
+        # component (e.g. ``Path(results_dir) / ticker / "logs"``).
+        for bad in (".", "..", "...", "...."):
+            with self.assertRaises(ValueError):
+                safe_ticker_component(bad)
+
+    def test_traversal_string_does_not_escape_join(self):
+        """Sanity: sanitized values stay within base when joined."""
+        base = os.path.realpath("/tmp/cache")
+        ticker = safe_ticker_component("AAPL")
+        joined = os.path.realpath(os.path.join(base, f"{ticker}.csv"))
+        self.assertTrue(joined.startswith(base + os.sep))
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/tests/test_signal_processing.py
+++ b/tests/test_signal_processing.py
@@ -0,0 +1,90 @@
+"""Tests for the shared rating heuristic and the SignalProcessor adapter.
+
+The Portfolio Manager produces a typed PortfolioDecision via structured
+output and renders it to markdown that always contains a ``**Rating**: X``
+header.  The deterministic heuristic in ``tradingagents.agents.utils.rating``
+is therefore sufficient to extract the rating downstream — no second LLM
+call is needed — and SignalProcessor is now a thin adapter that delegates
+to it.
+"""
+
+import pytest
+
+from tradingagents.agents.utils.rating import RATINGS_5_TIER, parse_rating
+from tradingagents.graph.signal_processing import SignalProcessor
+
+
+# ---------------------------------------------------------------------------
+# Heuristic parser
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestParseRating:
+    def test_explicit_label_buy(self):
+        assert parse_rating("Rating: Buy\nReasoning here.") == "Buy"
+
+    def test_explicit_label_overweight(self):
+        assert parse_rating("Rating: Overweight\nDetails.") == "Overweight"
+
+    def test_explicit_label_with_markdown_bold_value(self):
+        # Regression: Rating: **Sell** — markdown around the value.
+        assert parse_rating("Rating: **Sell**\nExit immediately.") == "Sell"
+
+    def test_explicit_label_with_markdown_bold_label(self):
+        assert parse_rating("**Rating**: Underweight\nTrim exposure.") == "Underweight"
+
+    def test_rendered_pm_markdown_shape(self):
+        # The exact shape produced by render_pm_decision must always parse.
+        text = (
+            "**Rating**: Buy\n\n"
+            "**Executive Summary**: Enter at $189-192, 6% portfolio cap.\n\n"
+            "**Investment Thesis**: AI capex cycle intact; institutional flows constructive."
+        )
+        assert parse_rating(text) == "Buy"
+
+    def test_explicit_label_wins_over_prose_with_markdown(self):
+        text = (
+            "The buy thesis is weakened by guidance.\n"
+            "Rating: **Sell**\n"
+            "Exit before earnings."
+        )
+        assert parse_rating(text) == "Sell"
+
+    def test_no_rating_returns_default(self):
+        assert parse_rating("No clear directional signal at this time.") == "Hold"
+
+    def test_no_rating_custom_default(self):
+        assert parse_rating("Plain prose.", default="Underweight") == "Underweight"
+
+    def test_all_five_tiers_recognised(self):
+        for r in RATINGS_5_TIER:
+            assert parse_rating(f"Rating: {r}") == r
+
+
+# ---------------------------------------------------------------------------
+# SignalProcessor: thin adapter over the heuristic
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestSignalProcessor:
+    def test_returns_rating_from_pm_markdown(self):
+        sp = SignalProcessor()
+        md = "**Rating**: Overweight\n\n**Executive Summary**: Build gradually."
+        assert sp.process_signal(md) == "Overweight"
+
+    def test_makes_no_llm_calls(self):
+        """SignalProcessor must not invoke the LLM it was constructed with —
+        the rating is parseable from the rendered PM markdown directly."""
+        from unittest.mock import MagicMock
+
+        llm = MagicMock()
+        sp = SignalProcessor(llm)
+        sp.process_signal("Rating: Buy\nDetails.")
+        llm.invoke.assert_not_called()
+        llm.with_structured_output.assert_not_called()
+
+    def test_default_when_no_rating_present(self):
+        sp = SignalProcessor()
+        assert sp.process_signal("Plain prose without a recommendation.") == "Hold"
--- a/tests/test_structured_agents.py
+++ b/tests/test_structured_agents.py
@@ -0,0 +1,232 @@
+"""Tests for structured-output agents (Trader and Research Manager).
+
+The Portfolio Manager has its own coverage in tests/test_memory_log.py
+(which exercises the full memory-log → PM injection cycle).  This file
+covers the parallel schemas, render functions, and graceful-fallback
+behavior we added for the Trader and Research Manager so all three
+decision-making agents share the same shape.
+"""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from tradingagents.agents.managers.research_manager import create_research_manager
+from tradingagents.agents.schemas import (
+    PortfolioRating,
+    ResearchPlan,
+    TraderAction,
+    TraderProposal,
+    render_research_plan,
+    render_trader_proposal,
+)
+from tradingagents.agents.trader.trader import create_trader
+
+
+# ---------------------------------------------------------------------------
+# Render functions
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestRenderTraderProposal:
+    def test_minimal_required_fields(self):
+        p = TraderProposal(action=TraderAction.HOLD, reasoning="Balanced setup; no edge.")
+        md = render_trader_proposal(p)
+        assert "**Action**: Hold" in md
+        assert "**Reasoning**: Balanced setup; no edge." in md
+        # The trailing FINAL TRANSACTION PROPOSAL line is preserved for the
+        # analyst stop-signal text and any external code that greps for it.
+        assert "FINAL TRANSACTION PROPOSAL: **HOLD**" in md
+
+    def test_optional_fields_included_when_present(self):
+        p = TraderProposal(
+            action=TraderAction.BUY,
+            reasoning="Strong technicals + fundamentals.",
+            entry_price=189.5,
+            stop_loss=178.0,
+            position_sizing="6% of portfolio",
+        )
+        md = render_trader_proposal(p)
+        assert "**Action**: Buy" in md
+        assert "**Entry Price**: 189.5" in md
+        assert "**Stop Loss**: 178.0" in md
+        assert "**Position Sizing**: 6% of portfolio" in md
+        assert "FINAL TRANSACTION PROPOSAL: **BUY**" in md
+
+    def test_optional_fields_omitted_when_absent(self):
+        p = TraderProposal(action=TraderAction.SELL, reasoning="Guidance cut.")
+        md = render_trader_proposal(p)
+        assert "Entry Price" not in md
+        assert "Stop Loss" not in md
+        assert "Position Sizing" not in md
+        assert "FINAL TRANSACTION PROPOSAL: **SELL**" in md
+
+
+@pytest.mark.unit
+class TestRenderResearchPlan:
+    def test_required_fields(self):
+        p = ResearchPlan(
+            recommendation=PortfolioRating.OVERWEIGHT,
+            rationale="Bull case carried; tailwinds intact.",
+            strategic_actions="Build position over two weeks; cap at 5%.",
+        )
+        md = render_research_plan(p)
+        assert "**Recommendation**: Overweight" in md
+        assert "**Rationale**: Bull case carried" in md
+        assert "**Strategic Actions**: Build position" in md
+
+    def test_all_5_tier_ratings_render(self):
+        for rating in PortfolioRating:
+            p = ResearchPlan(
+                recommendation=rating,
+                rationale="r",
+                strategic_actions="s",
+            )
+            md = render_research_plan(p)
+            assert f"**Recommendation**: {rating.value}" in md
+
+
+# ---------------------------------------------------------------------------
+# Trader agent: structured happy path + fallback
+# ---------------------------------------------------------------------------
+
+
+def _make_trader_state():
+    return {
+        "company_of_interest": "NVDA",
+        "investment_plan": "**Recommendation**: Buy\n**Rationale**: ...\n**Strategic Actions**: ...",
+    }
+
+
+def _structured_trader_llm(captured: dict, proposal: TraderProposal | None = None):
+    """Build a MagicMock LLM whose with_structured_output binding captures the
+    prompt and returns a real TraderProposal so render_trader_proposal works.
+    """
+    if proposal is None:
+        proposal = TraderProposal(
+            action=TraderAction.BUY,
+            reasoning="Strong setup.",
+        )
+    structured = MagicMock()
+    structured.invoke.side_effect = lambda prompt: (
+        captured.__setitem__("prompt", prompt) or proposal
+    )
+    llm = MagicMock()
+    llm.with_structured_output.return_value = structured
+    return llm
+
+
+@pytest.mark.unit
+class TestTraderAgent:
+    def test_structured_path_produces_rendered_markdown(self):
+        captured = {}
+        proposal = TraderProposal(
+            action=TraderAction.BUY,
+            reasoning="AI capex cycle intact; institutional flows constructive.",
+            entry_price=189.5,
+            stop_loss=178.0,
+            position_sizing="6% of portfolio",
+        )
+        llm = _structured_trader_llm(captured, proposal)
+        trader = create_trader(llm)
+        result = trader(_make_trader_state())
+        plan = result["trader_investment_plan"]
+        assert "**Action**: Buy" in plan
+        assert "**Entry Price**: 189.5" in plan
+        assert "FINAL TRANSACTION PROPOSAL: **BUY**" in plan
+        # The same rendered markdown is also added to messages for downstream agents.
+        assert plan in result["messages"][0].content
+
+    def test_prompt_includes_investment_plan(self):
+        captured = {}
+        llm = _structured_trader_llm(captured)
+        trader = create_trader(llm)
+        trader(_make_trader_state())
+        # The investment plan is in the user message of the captured prompt.
+        prompt = captured["prompt"]
+        assert any("Proposed Investment Plan" in m["content"] for m in prompt)
+
+    def test_falls_back_to_freetext_when_structured_unavailable(self):
+        plain_response = (
+            "**Action**: Sell\n\nGuidance cut hits margins.\n\n"
+            "FINAL TRANSACTION PROPOSAL: **SELL**"
+        )
+        llm = MagicMock()
+        llm.with_structured_output.side_effect = NotImplementedError("provider unsupported")
+        llm.invoke.return_value = MagicMock(content=plain_response)
+        trader = create_trader(llm)
+        result = trader(_make_trader_state())
+        assert result["trader_investment_plan"] == plain_response
+
+
+# ---------------------------------------------------------------------------
+# Research Manager agent: structured happy path + fallback
+# ---------------------------------------------------------------------------
+
+
+def _make_rm_state():
+    return {
+        "company_of_interest": "NVDA",
+        "investment_debate_state": {
+            "history": "Bull and bear arguments here.",
+            "bull_history": "Bull says...",
+            "bear_history": "Bear says...",
+            "current_response": "",
+            "judge_decision": "",
+            "count": 1,
+        },
+    }
+
+
+def _structured_rm_llm(captured: dict, plan: ResearchPlan | None = None):
+    if plan is None:
+        plan = ResearchPlan(
+            recommendation=PortfolioRating.HOLD,
+            rationale="Balanced view across both sides.",
+            strategic_actions="Hold current position; reassess after earnings.",
+        )
+    structured = MagicMock()
+    structured.invoke.side_effect = lambda prompt: (
+        captured.__setitem__("prompt", prompt) or plan
+    )
+    llm = MagicMock()
+    llm.with_structured_output.return_value = structured
+    return llm
+
+
+@pytest.mark.unit
+class TestResearchManagerAgent:
+    def test_structured_path_produces_rendered_markdown(self):
+        captured = {}
+        plan = ResearchPlan(
+            recommendation=PortfolioRating.OVERWEIGHT,
+            rationale="Bull case is stronger; AI tailwind intact.",
+            strategic_actions="Build position gradually over two weeks.",
+        )
+        llm = _structured_rm_llm(captured, plan)
+        rm = create_research_manager(llm)
+        result = rm(_make_rm_state())
+        ip = result["investment_plan"]
+        assert "**Recommendation**: Overweight" in ip
+        assert "**Rationale**: Bull case" in ip
+        assert "**Strategic Actions**: Build position" in ip
+
+    def test_prompt_uses_5_tier_rating_scale(self):
+        """The RM prompt must list all five tiers so the schema enum matches user expectations."""
+        captured = {}
+        llm = _structured_rm_llm(captured)
+        rm = create_research_manager(llm)
+        rm(_make_rm_state())
+        prompt = captured["prompt"]
+        for tier in ("Buy", "Overweight", "Hold", "Underweight", "Sell"):
+            assert f"**{tier}**" in prompt, f"missing {tier} in prompt"
+
+    def test_falls_back_to_freetext_when_structured_unavailable(self):
+        plain_response = "**Recommendation**: Sell\n\n**Rationale**: ...\n\n**Strategic Actions**: ..."
+        llm = MagicMock()
+        llm.with_structured_output.side_effect = NotImplementedError("provider unsupported")
+        llm.invoke.return_value = MagicMock(content=plain_response)
+        rm = create_research_manager(llm)
+        result = rm(_make_rm_state())
+        assert result["investment_plan"] == plain_response
--- a/tests/test_ticker_symbol_handling.py
+++ b/tests/test_ticker_symbol_handling.py
@@ -1,9 +1,12 @@
 import unittest

+import pytest
+
 from cli.utils import normalize_ticker_symbol
 from tradingagents.agents.utils.agent_utils import build_instrument_context


+@pytest.mark.unit
 class TickerSymbolHandlingTests(unittest.TestCase):
    def test_normalize_ticker_symbol_preserves_exchange_suffix(self):
        self.assertEqual(normalize_ticker_symbol(" cnc.to "), "CNC.TO")
--- a/tradingagents/init.py
+++ b/tradingagents/init.py
@@ -1,2 +0,0 @@
-import os
-os.environ.setdefault("PYTHONUTF8", "1")
--- a/tradingagents/agents/init.py
+++ b/tradingagents/agents/init.py
@@ -1,6 +1,5 @@
 from .utils.agent_utils import create_msg_delete
 from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState
-from .utils.memory import FinancialSituationMemory

 from .analysts.fundamentals_analyst import create_fundamentals_analyst
 from .analysts.market_analyst import create_market_analyst
@@ -20,7 +19,6 @@ from .managers.portfolio_manager import create_portfolio_manager
 from .trader.trader import create_trader

 __all__ = [
-    "FinancialSituationMemory",
    "AgentState",
    "create_msg_delete",
    "InvestDebateState",
--- a/tradingagents/agents/analysts/fundamentals_analyst.py
+++ b/tradingagents/agents/analysts/fundamentals_analyst.py
@@ -1,6 +1,4 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import (
    build_instrument_context,
    get_balance_sheet,
@@ -8,6 +6,7 @@ from tradingagents.agents.utils.agent_utils import (
    get_fundamentals,
    get_income_statement,
    get_insider_transactions,
+    get_language_instruction,
 )
 from tradingagents.dataflows.config import get_config

@@ -27,7 +26,8 @@ def create_fundamentals_analyst(llm):
        system_message = (
            "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
            + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."
-            + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.",
+            + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements."
+            + get_language_instruction(),
        )

        prompt = ChatPromptTemplate.from_messages(
--- a/tradingagents/agents/analysts/market_analyst.py
+++ b/tradingagents/agents/analysts/market_analyst.py
@@ -1,9 +1,8 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import (
    build_instrument_context,
    get_indicators,
+    get_language_instruction,
    get_stock_data,
 )
 from tradingagents.dataflows.config import get_config
@@ -47,6 +46,7 @@ Volume-Based Indicators:

 - Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."""
            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + get_language_instruction()
        )

        prompt = ChatPromptTemplate.from_messages(
--- a/tradingagents/agents/analysts/news_analyst.py
+++ b/tradingagents/agents/analysts/news_analyst.py
@@ -1,9 +1,8 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import (
    build_instrument_context,
    get_global_news,
+    get_language_instruction,
    get_news,
 )
 from tradingagents.dataflows.config import get_config
@@ -22,6 +21,7 @@ def create_news_analyst(llm):
        system_message = (
            "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + get_language_instruction()
        )

        prompt = ChatPromptTemplate.from_messages(
--- a/tradingagents/agents/analysts/social_media_analyst.py
+++ b/tradingagents/agents/analysts/social_media_analyst.py
@@ -1,7 +1,5 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
-from tradingagents.agents.utils.agent_utils import build_instrument_context, get_news
+from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction, get_news
 from tradingagents.dataflows.config import get_config


@@ -17,6 +15,7 @@ def create_social_media_analyst(llm):
        system_message = (
            "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + get_language_instruction()
        )

        prompt = ChatPromptTemplate.from_messages(
--- a/tradingagents/agents/managers/portfolio_manager.py
+++ b/tradingagents/agents/managers/portfolio_manager.py
@@ -1,25 +1,43 @@
-from tradingagents.agents.utils.agent_utils import build_instrument_context
+"""Portfolio Manager: synthesises the risk-analyst debate into the final decision.
+
+Uses LangChain's ``with_structured_output`` so the LLM produces a typed
+``PortfolioDecision`` directly, in a single call.  The result is rendered
+back to markdown for storage in ``final_trade_decision`` so memory log,
+CLI display, and saved reports continue to consume the same shape they do
+today.  When a provider does not expose structured output, the agent falls
+back gracefully to free-text generation.
+"""
+
+from __future__ import annotations
+
+from tradingagents.agents.schemas import PortfolioDecision, render_pm_decision
+from tradingagents.agents.utils.agent_utils import (
+    build_instrument_context,
+    get_language_instruction,
+)
+from tradingagents.agents.utils.structured import (
+    bind_structured,
+    invoke_structured_or_freetext,
+)


-def create_portfolio_manager(llm, memory):
+def create_portfolio_manager(llm):
+    structured_llm = bind_structured(llm, PortfolioDecision, "Portfolio Manager")
+
    def portfolio_manager_node(state) -> dict:
-
        instrument_context = build_instrument_context(state["company_of_interest"])

        history = state["risk_debate_state"]["history"]
        risk_debate_state = state["risk_debate_state"]
-        market_research_report = state["market_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]
-        sentiment_report = state["sentiment_report"]
-        trader_plan = state["investment_plan"]
+        research_plan = state["investment_plan"]
+        trader_plan = state["trader_investment_plan"]

-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
+        past_context = state.get("past_context", "")
+        lessons_line = (
+            f"- Lessons from prior decisions and outcomes:\n{past_context}\n"
+            if past_context
+            else ""
+        )

        prompt = f"""As the Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.

@@ -35,27 +53,26 @@ def create_portfolio_manager(llm, memory):
 - **Sell**: Exit position or avoid entry

 **Context:**
- Trader's proposed plan: **{trader_plan}**
- Lessons from past decisions: **{past_memory_str}**
-
-**Required Output Structure:**
-1. **Rating**: State one of Buy / Overweight / Hold / Underweight / Sell.
-2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon.
-3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections.
-
---
-
+- Research Manager's investment plan: **{research_plan}**
+- Trader's transaction proposal: **{trader_plan}**
+{lessons_line}
 **Risk Analysts Debate History:**
 {history}

 ---

-Be decisive and ground every conclusion in specific evidence from the analysts."""
+Be decisive and ground every conclusion in specific evidence from the analysts.{get_language_instruction()}"""

-        response = llm.invoke(prompt)
+        final_trade_decision = invoke_structured_or_freetext(
+            structured_llm,
+            llm,
+            prompt,
+            render_pm_decision,
+            "Portfolio Manager",
+        )

        new_risk_debate_state = {
-            "judge_decision": response.content,
+            "judge_decision": final_trade_decision,
            "history": risk_debate_state["history"],
            "aggressive_history": risk_debate_state["aggressive_history"],
            "conservative_history": risk_debate_state["conservative_history"],
@@ -69,7 +86,7 @@ Be decisive and ground every conclusion in specific evidence from the analysts."

        return {
            "risk_debate_state": new_risk_debate_state,
-            "final_trade_decision": response.content,
+            "final_trade_decision": final_trade_decision,
        }

    return portfolio_manager_node
--- a/tradingagents/agents/managers/research_manager.py
+++ b/tradingagents/agents/managers/research_manager.py
@@ -1,60 +1,64 @@
-import time
-import json
+"""Research Manager: turns the bull/bear debate into a structured investment plan for the trader."""

+from __future__ import annotations
+
+from tradingagents.agents.schemas import ResearchPlan, render_research_plan
 from tradingagents.agents.utils.agent_utils import build_instrument_context
+from tradingagents.agents.utils.structured import (
+    bind_structured,
+    invoke_structured_or_freetext,
+)


-def create_research_manager(llm, memory):
+def create_research_manager(llm):
+    structured_llm = bind_structured(llm, ResearchPlan, "Research Manager")
+
    def research_manager_node(state) -> dict:
        instrument_context = build_instrument_context(state["company_of_interest"])
        history = state["investment_debate_state"].get("history", "")
-        market_research_report = state["market_report"]
-        sentiment_report = state["sentiment_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]

        investment_debate_state = state["investment_debate_state"]

-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
-
-        prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented.
-
-Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—Buy, Sell, or Hold—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments.
-
-Additionally, develop a detailed investment plan for the trader. This should include:
-
-Your Recommendation: A decisive stance supported by the most convincing arguments.
-Rationale: An explanation of why these arguments lead to your conclusion.
-Strategic Actions: Concrete steps for implementing the recommendation.
-Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting. 
-
-Here are your past reflections on mistakes:
-\"{past_memory_str}\"
+        prompt = f"""As the Research Manager and debate facilitator, your role is to critically evaluate this round of debate and deliver a clear, actionable investment plan for the trader.

 {instrument_context}

-Here is the debate:
-Debate History:
+---
+
+**Rating Scale** (use exactly one):
+- **Buy**: Strong conviction in the bull thesis; recommend taking or growing the position
+- **Overweight**: Constructive view; recommend gradually increasing exposure
+- **Hold**: Balanced view; recommend maintaining the current position
+- **Underweight**: Cautious view; recommend trimming exposure
+- **Sell**: Strong conviction in the bear thesis; recommend exiting or avoiding the position
+
+Commit to a clear stance whenever the debate's strongest arguments warrant one; reserve Hold for situations where the evidence on both sides is genuinely balanced.
+
+---
+
+**Debate History:**
 {history}"""
-        response = llm.invoke(prompt)
+
+        investment_plan = invoke_structured_or_freetext(
+            structured_llm,
+            llm,
+            prompt,
+            render_research_plan,
+            "Research Manager",
+        )

        new_investment_debate_state = {
-            "judge_decision": response.content,
+            "judge_decision": investment_plan,
            "history": investment_debate_state.get("history", ""),
            "bear_history": investment_debate_state.get("bear_history", ""),
            "bull_history": investment_debate_state.get("bull_history", ""),
-            "current_response": response.content,
+            "current_response": investment_plan,
            "count": investment_debate_state["count"],
        }

        return {
            "investment_debate_state": new_investment_debate_state,
-            "investment_plan": response.content,
+            "investment_plan": investment_plan,
        }

    return research_manager_node
--- a/tradingagents/agents/researchers/bear_researcher.py
+++ b/tradingagents/agents/researchers/bear_researcher.py
@@ -1,9 +1,6 @@
-from langchain_core.messages import AIMessage
-import time
-import json


-def create_bear_researcher(llm, memory):
+def create_bear_researcher(llm):
    def bear_node(state) -> dict:
        investment_debate_state = state["investment_debate_state"]
        history = investment_debate_state.get("history", "")
@@ -15,13 +12,6 @@ def create_bear_researcher(llm, memory):
        news_report = state["news_report"]
        fundamentals_report = state["fundamentals_report"]

-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
-
        prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.

 Key points to focus on:
@@ -40,8 +30,7 @@ Latest world affairs news: {news_report}
 Company fundamentals report: {fundamentals_report}
 Conversation history of the debate: {history}
 Last bull argument: {current_response}
-Reflections from similar situations and lessons learned: {past_memory_str}
-Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past.
+Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock.
 """

        response = llm.invoke(prompt)
--- a/tradingagents/agents/researchers/bull_researcher.py
+++ b/tradingagents/agents/researchers/bull_researcher.py
@@ -1,9 +1,6 @@
-from langchain_core.messages import AIMessage
-import time
-import json


-def create_bull_researcher(llm, memory):
+def create_bull_researcher(llm):
    def bull_node(state) -> dict:
        investment_debate_state = state["investment_debate_state"]
        history = investment_debate_state.get("history", "")
@@ -15,13 +12,6 @@ def create_bull_researcher(llm, memory):
        news_report = state["news_report"]
        fundamentals_report = state["fundamentals_report"]

-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
-
        prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.

 Key points to focus on:
@@ -38,8 +28,7 @@ Latest world affairs news: {news_report}
 Company fundamentals report: {fundamentals_report}
 Conversation history of the debate: {history}
 Last bear argument: {current_response}
-Reflections from similar situations and lessons learned: {past_memory_str}
-Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past.
+Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position.
 """

        response = llm.invoke(prompt)
--- a/tradingagents/agents/risk_mgmt/aggressive_debator.py
+++ b/tradingagents/agents/risk_mgmt/aggressive_debator.py
@@ -1,5 +1,3 @@
-import time
-import json


 def create_aggressive_debator(llm):
--- a/tradingagents/agents/risk_mgmt/conservative_debator.py
+++ b/tradingagents/agents/risk_mgmt/conservative_debator.py
@@ -1,6 +1,3 @@
-from langchain_core.messages import AIMessage
-import time
-import json


 def create_conservative_debator(llm):
--- a/tradingagents/agents/risk_mgmt/neutral_debator.py
+++ b/tradingagents/agents/risk_mgmt/neutral_debator.py
@@ -1,5 +1,3 @@
-import time
-import json


 def create_neutral_debator(llm):
--- a/tradingagents/agents/schemas.py
+++ b/tradingagents/agents/schemas.py
@@ -0,0 +1,228 @@
+"""Pydantic schemas used by agents that produce structured output.
+
+The framework's primary artifact is still prose: each agent's natural-language
+reasoning is what users read in the saved markdown reports and what the
+downstream agents read as context.  Structured output is layered onto the
+three decision-making agents (Research Manager, Trader, Portfolio Manager)
+so that:
+
+- Their outputs follow consistent section headers across runs and providers
+- Each provider's native structured-output mode is used (json_schema for
+  OpenAI/xAI, response_schema for Gemini, tool-use for Anthropic)
+- Schema field descriptions become the model's output instructions, freeing
+  the prompt body to focus on context and the rating-scale guidance
+- A render helper turns the parsed Pydantic instance back into the same
+  markdown shape the rest of the system already consumes, so display,
+  memory log, and saved reports keep working unchanged
+"""
+
+from __future__ import annotations
+
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+# ---------------------------------------------------------------------------
+# Shared rating types
+# ---------------------------------------------------------------------------
+
+
+class PortfolioRating(str, Enum):
+    """5-tier rating used by the Research Manager and Portfolio Manager."""
+
+    BUY = "Buy"
+    OVERWEIGHT = "Overweight"
+    HOLD = "Hold"
+    UNDERWEIGHT = "Underweight"
+    SELL = "Sell"
+
+
+class TraderAction(str, Enum):
+    """3-tier transaction direction used by the Trader.
+
+    The Trader's job is to translate the Research Manager's investment plan
+    into a concrete transaction proposal: should the desk execute a Buy, a
+    Sell, or sit on Hold this round.  Position sizing and the nuanced
+    Overweight / Underweight calls happen later at the Portfolio Manager.
+    """
+
+    BUY = "Buy"
+    HOLD = "Hold"
+    SELL = "Sell"
+
+
+# ---------------------------------------------------------------------------
+# Research Manager
+# ---------------------------------------------------------------------------
+
+
+class ResearchPlan(BaseModel):
+    """Structured investment plan produced by the Research Manager.
+
+    Hand-off to the Trader: the recommendation pins the directional view,
+    the rationale captures which side of the bull/bear debate carried the
+    argument, and the strategic actions translate that into concrete
+    instructions the trader can execute against.
+    """
+
+    recommendation: PortfolioRating = Field(
+        description=(
+            "The investment recommendation. Exactly one of Buy / Overweight / "
+            "Hold / Underweight / Sell. Reserve Hold for situations where the "
+            "evidence on both sides is genuinely balanced; otherwise commit to "
+            "the side with the stronger arguments."
+        ),
+    )
+    rationale: str = Field(
+        description=(
+            "Conversational summary of the key points from both sides of the "
+            "debate, ending with which arguments led to the recommendation. "
+            "Speak naturally, as if to a teammate."
+        ),
+    )
+    strategic_actions: str = Field(
+        description=(
+            "Concrete steps for the trader to implement the recommendation, "
+            "including position sizing guidance consistent with the rating."
+        ),
+    )
+
+
+def render_research_plan(plan: ResearchPlan) -> str:
+    """Render a ResearchPlan to markdown for storage and the trader's prompt context."""
+    return "\n".join([
+        f"**Recommendation**: {plan.recommendation.value}",
+        "",
+        f"**Rationale**: {plan.rationale}",
+        "",
+        f"**Strategic Actions**: {plan.strategic_actions}",
+    ])
+
+
+# ---------------------------------------------------------------------------
+# Trader
+# ---------------------------------------------------------------------------
+
+
+class TraderProposal(BaseModel):
+    """Structured transaction proposal produced by the Trader.
+
+    The trader reads the Research Manager's investment plan and the analyst
+    reports, then turns them into a concrete transaction: what action to
+    take, the reasoning that justifies it, and the practical levels for
+    entry, stop-loss, and sizing.
+    """
+
+    action: TraderAction = Field(
+        description="The transaction direction. Exactly one of Buy / Hold / Sell.",
+    )
+    reasoning: str = Field(
+        description=(
+            "The case for this action, anchored in the analysts' reports and "
+            "the research plan. Two to four sentences."
+        ),
+    )
+    entry_price: Optional[float] = Field(
+        default=None,
+        description="Optional entry price target in the instrument's quote currency.",
+    )
+    stop_loss: Optional[float] = Field(
+        default=None,
+        description="Optional stop-loss price in the instrument's quote currency.",
+    )
+    position_sizing: Optional[str] = Field(
+        default=None,
+        description="Optional sizing guidance, e.g. '5% of portfolio'.",
+    )
+
+
+def render_trader_proposal(proposal: TraderProposal) -> str:
+    """Render a TraderProposal to markdown.
+
+    The trailing ``FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**`` line is
+    preserved for backward compatibility with the analyst stop-signal text
+    and any external code that greps for it.
+    """
+    parts = [
+        f"**Action**: {proposal.action.value}",
+        "",
+        f"**Reasoning**: {proposal.reasoning}",
+    ]
+    if proposal.entry_price is not None:
+        parts.extend(["", f"**Entry Price**: {proposal.entry_price}"])
+    if proposal.stop_loss is not None:
+        parts.extend(["", f"**Stop Loss**: {proposal.stop_loss}"])
+    if proposal.position_sizing:
+        parts.extend(["", f"**Position Sizing**: {proposal.position_sizing}"])
+    parts.extend([
+        "",
+        f"FINAL TRANSACTION PROPOSAL: **{proposal.action.value.upper()}**",
+    ])
+    return "\n".join(parts)
+
+
+# ---------------------------------------------------------------------------
+# Portfolio Manager
+# ---------------------------------------------------------------------------
+
+
+class PortfolioDecision(BaseModel):
+    """Structured output produced by the Portfolio Manager.
+
+    The model fills every field as part of its primary LLM call; no separate
+    extraction pass is required. Field descriptions double as the model's
+    output instructions, so the prompt body only needs to convey context and
+    the rating-scale guidance.
+    """
+
+    rating: PortfolioRating = Field(
+        description=(
+            "The final position rating. Exactly one of Buy / Overweight / Hold / "
+            "Underweight / Sell, picked based on the analysts' debate."
+        ),
+    )
+    executive_summary: str = Field(
+        description=(
+            "A concise action plan covering entry strategy, position sizing, "
+            "key risk levels, and time horizon. Two to four sentences."
+        ),
+    )
+    investment_thesis: str = Field(
+        description=(
+            "Detailed reasoning anchored in specific evidence from the analysts' "
+            "debate. If prior lessons are referenced in the prompt context, "
+            "incorporate them; otherwise rely solely on the current analysis."
+        ),
+    )
+    price_target: Optional[float] = Field(
+        default=None,
+        description="Optional target price in the instrument's quote currency.",
+    )
+    time_horizon: Optional[str] = Field(
+        default=None,
+        description="Optional recommended holding period, e.g. '3-6 months'.",
+    )
+
+
+def render_pm_decision(decision: PortfolioDecision) -> str:
+    """Render a PortfolioDecision back to the markdown shape the rest of the system expects.
+
+    Memory log, CLI display, and saved report files all read this markdown,
+    so the rendered output preserves the exact section headers (``**Rating**``,
+    ``**Executive Summary**``, ``**Investment Thesis**``) that downstream
+    parsers and the report writers already handle.
+    """
+    parts = [
+        f"**Rating**: {decision.rating.value}",
+        "",
+        f"**Executive Summary**: {decision.executive_summary}",
+        "",
+        f"**Investment Thesis**: {decision.investment_thesis}",
+    ]
+    if decision.price_target is not None:
+        parts.extend(["", f"**Price Target**: {decision.price_target}"])
+    if decision.time_horizon:
+        parts.extend(["", f"**Time Horizon**: {decision.time_horizon}"])
+    return "\n".join(parts)
--- a/tradingagents/agents/trader/trader.py
+++ b/tradingagents/agents/trader/trader.py
@@ -1,48 +1,60 @@
+"""Trader: turns the Research Manager's investment plan into a concrete transaction proposal."""
+
+from __future__ import annotations
+
 import functools
-import time
-import json

+from langchain_core.messages import AIMessage
+
+from tradingagents.agents.schemas import TraderProposal, render_trader_proposal
 from tradingagents.agents.utils.agent_utils import build_instrument_context
+from tradingagents.agents.utils.structured import (
+    bind_structured,
+    invoke_structured_or_freetext,
+)


-def create_trader(llm, memory):
+def create_trader(llm):
+    structured_llm = bind_structured(llm, TraderProposal, "Trader")
+
    def trader_node(state, name):
        company_name = state["company_of_interest"]
        instrument_context = build_instrument_context(company_name)
        investment_plan = state["investment_plan"]
-        market_research_report = state["market_report"]
-        sentiment_report = state["sentiment_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]
-
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        if past_memories:
-            for i, rec in enumerate(past_memories, 1):
-                past_memory_str += rec["recommendation"] + "\n\n"
-        else:
-            past_memory_str = "No past memories found."
-
-        context = {
-            "role": "user",
-            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. {instrument_context} This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
-        }

        messages = [
            {
                "role": "system",
-                "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Apply lessons from past decisions to strengthen your analysis. Here are reflections from similar situations you traded in and the lessons learned: {past_memory_str}""",
+                "content": (
+                    "You are a trading agent analyzing market data to make investment decisions. "
+                    "Based on your analysis, provide a specific recommendation to buy, sell, or hold. "
+                    "Anchor your reasoning in the analysts' reports and the research plan."
+                ),
+            },
+            {
+                "role": "user",
+                "content": (
+                    f"Based on a comprehensive analysis by a team of analysts, here is an investment "
+                    f"plan tailored for {company_name}. {instrument_context} This plan incorporates "
+                    f"insights from current technical market trends, macroeconomic indicators, and "
+                    f"social media sentiment. Use this plan as a foundation for evaluating your next "
+                    f"trading decision.\n\nProposed Investment Plan: {investment_plan}\n\n"
+                    f"Leverage these insights to make an informed and strategic decision."
+                ),
            },
-            context,
        ]

-        result = llm.invoke(messages)
+        trader_plan = invoke_structured_or_freetext(
+            structured_llm,
+            llm,
+            messages,
+            render_trader_proposal,
+            "Trader",
+        )

        return {
-            "messages": [result],
-            "trader_investment_plan": result.content,
+            "messages": [AIMessage(content=trader_plan)],
+            "trader_investment_plan": trader_plan,
            "sender": name,
        }

--- a/tradingagents/agents/utils/agent_states.py
+++ b/tradingagents/agents/utils/agent_states.py
@@ -1,10 +1,6 @@
-from typing import Annotated, Sequence
-from datetime import date, timedelta, datetime
-from typing_extensions import TypedDict, Optional
-from langchain_openai import ChatOpenAI
-from tradingagents.agents import *
-from langgraph.prebuilt import ToolNode
-from langgraph.graph import END, StateGraph, START, MessagesState
+from typing import Annotated
+from typing_extensions import TypedDict
+from langgraph.graph import MessagesState


 # Researcher team state
@@ -74,3 +70,4 @@ class AgentState(MessagesState):
        RiskDebateState, "Current state of the debate on evaluating risk"
    ]
    final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"]
+    past_context: Annotated[str, "Memory log context injected at run start (same-ticker decisions + cross-ticker lessons)"]
--- a/tradingagents/agents/utils/agent_utils.py
+++ b/tradingagents/agents/utils/agent_utils.py
@@ -20,6 +20,20 @@ from tradingagents.agents.utils.news_data_tools import (
 )


+def get_language_instruction() -> str:
+    """Return a prompt instruction for the configured output language.
+
+    Returns empty string when English (default), so no extra tokens are used.
+    Only applied to user-facing agents (analysts, portfolio manager).
+    Internal debate agents stay in English for reasoning quality.
+    """
+    from tradingagents.dataflows.config import get_config
+    lang = get_config().get("output_language", "English")
+    if lang.strip().lower() == "english":
+        return ""
+    return f" Write your entire response in {lang}."
+
+
 def build_instrument_context(ticker: str) -> str:
    """Describe the exact instrument so agents preserve exchange-qualified tickers."""
    return (
--- a/tradingagents/agents/utils/memory.py
+++ b/tradingagents/agents/utils/memory.py
@@ -1,144 +1,300 @@
-"""Financial situation memory using BM25 for lexical similarity matching.
+"""Append-only markdown decision log for TradingAgents."""

-Uses BM25 (Best Matching 25) algorithm for retrieval - no API calls,
-no token limits, works offline with any LLM provider.
-"""
-
-from rank_bm25 import BM25Okapi
-from typing import List, Tuple
+from typing import List, Optional
+from pathlib import Path
 import re

+from tradingagents.agents.utils.rating import parse_rating

-class FinancialSituationMemory:
-    """Memory system for storing and retrieving financial situations using BM25."""

-    def __init__(self, name: str, config: dict = None):
-        """Initialize the memory system.
+class TradingMemoryLog:
+    """Append-only markdown log of trading decisions and reflections."""

-        Args:
-            name: Name identifier for this memory instance
-            config: Configuration dict (kept for API compatibility, not used for BM25)
-        """
-        self.name = name
-        self.documents: List[str] = []
-        self.recommendations: List[str] = []
-        self.bm25 = None
+    # HTML comment: cannot appear in LLM prose output, safe as a hard delimiter
+    _SEPARATOR = "\n\n<!-- ENTRY_END -->\n\n"
+    # Precompiled patterns — avoids re-compilation on every load_entries() call
+    _DECISION_RE = re.compile(r"DECISION:\n(.*?)(?=\nREFLECTION:|\Z)", re.DOTALL)
+    _REFLECTION_RE = re.compile(r"REFLECTION:\n(.*?)$", re.DOTALL)

-    def _tokenize(self, text: str) -> List[str]:
-        """Tokenize text for BM25 indexing.
+    def __init__(self, config: dict = None):
+        cfg = config or {}
+        self._log_path = None
+        path = cfg.get("memory_log_path")
+        if path:
+            self._log_path = Path(path).expanduser()
+            self._log_path.parent.mkdir(parents=True, exist_ok=True)
+        # Optional cap on resolved entries. None disables rotation.
+        self._max_entries = cfg.get("memory_log_max_entries")

-        Simple whitespace + punctuation tokenization with lowercasing.
-        """
-        # Lowercase and split on non-alphanumeric characters
-        tokens = re.findall(r'\b\w+\b', text.lower())
-        return tokens
+    # --- Write path (Phase A) ---

-    def _rebuild_index(self):
-        """Rebuild the BM25 index after adding documents."""
-        if self.documents:
-            tokenized_docs = [self._tokenize(doc) for doc in self.documents]
-            self.bm25 = BM25Okapi(tokenized_docs)
-        else:
-            self.bm25 = None
+    def store_decision(
+        self,
+        ticker: str,
+        trade_date: str,
+        final_trade_decision: str,
+    ) -> None:
+        """Append pending entry at end of propagate(). No LLM call."""
+        if not self._log_path:
+            return
+        # Idempotency guard: fast raw-text scan instead of full parse
+        if self._log_path.exists():
+            raw = self._log_path.read_text(encoding="utf-8")
+            for line in raw.splitlines():
+                if line.startswith(f"[{trade_date} | {ticker} |") and line.endswith("| pending]"):
+                    return
+        rating = parse_rating(final_trade_decision)
+        tag = f"[{trade_date} | {ticker} | {rating} | pending]"
+        entry = f"{tag}\n\nDECISION:\n{final_trade_decision}{self._SEPARATOR}"
+        with open(self._log_path, "a", encoding="utf-8") as f:
+            f.write(entry)

-    def add_situations(self, situations_and_advice: List[Tuple[str, str]]):
-        """Add financial situations and their corresponding advice.
+    # --- Read path (Phase A) ---

-        Args:
-            situations_and_advice: List of tuples (situation, recommendation)
-        """
-        for situation, recommendation in situations_and_advice:
-            self.documents.append(situation)
-            self.recommendations.append(recommendation)
-
-        # Rebuild BM25 index with new documents
-        self._rebuild_index()
-
-    def get_memories(self, current_situation: str, n_matches: int = 1) -> List[dict]:
-        """Find matching recommendations using BM25 similarity.
-
-        Args:
-            current_situation: The current financial situation to match against
-            n_matches: Number of top matches to return
-
-        Returns:
-            List of dicts with matched_situation, recommendation, and similarity_score
-        """
-        if not self.documents or self.bm25 is None:
+    def load_entries(self) -> List[dict]:
+        """Parse all entries from log. Returns list of dicts."""
+        if not self._log_path or not self._log_path.exists():
            return []
+        text = self._log_path.read_text(encoding="utf-8")
+        raw_entries = [e.strip() for e in text.split(self._SEPARATOR) if e.strip()]
+        entries = []
+        for raw in raw_entries:
+            parsed = self._parse_entry(raw)
+            if parsed:
+                entries.append(parsed)
+        return entries

-        # Tokenize query
-        query_tokens = self._tokenize(current_situation)
+    def get_pending_entries(self) -> List[dict]:
+        """Return entries with outcome:pending (for Phase B)."""
+        return [e for e in self.load_entries() if e.get("pending")]

-        # Get BM25 scores for all documents
-        scores = self.bm25.get_scores(query_tokens)
+    def get_past_context(self, ticker: str, n_same: int = 5, n_cross: int = 3) -> str:
+        """Return formatted past context string for agent prompt injection."""
+        entries = [e for e in self.load_entries() if not e.get("pending")]
+        if not entries:
+            return ""

-        # Get top-n indices sorted by score (descending)
-        top_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:n_matches]
+        same, cross = [], []
+        for e in reversed(entries):
+            if len(same) >= n_same and len(cross) >= n_cross:
+                break
+            if e["ticker"] == ticker and len(same) < n_same:
+                same.append(e)
+            elif e["ticker"] != ticker and len(cross) < n_cross:
+                cross.append(e)

-        # Build results
-        results = []
-        max_score = max(scores) if max(scores) > 0 else 1  # Normalize scores
+        if not same and not cross:
+            return ""

-        for idx in top_indices:
-            # Normalize score to 0-1 range for consistency
-            normalized_score = scores[idx] / max_score if max_score > 0 else 0
-            results.append({
-                "matched_situation": self.documents[idx],
-                "recommendation": self.recommendations[idx],
-                "similarity_score": normalized_score,
-            })
+        parts = []
+        if same:
+            parts.append(f"Past analyses of {ticker} (most recent first):")
+            parts.extend(self._format_full(e) for e in same)
+        if cross:
+            parts.append("Recent cross-ticker lessons:")
+            parts.extend(self._format_reflection_only(e) for e in cross)
+        return "\n\n".join(parts)

-        return results
+    # --- Update path (Phase B) ---

-    def clear(self):
-        """Clear all stored memories."""
-        self.documents = []
-        self.recommendations = []
-        self.bm25 = None
+    def update_with_outcome(
+        self,
+        ticker: str,
+        trade_date: str,
+        raw_return: float,
+        alpha_return: float,
+        holding_days: int,
+        reflection: str,
+    ) -> None:
+        """Replace pending tag and append REFLECTION section using atomic write.

+        Finds the first pending entry matching (trade_date, ticker), updates
+        its tag with return figures, and appends a REFLECTION section.  Uses
+        a temp-file + os.replace() so a crash mid-write never corrupts the log.
+        """
+        if not self._log_path or not self._log_path.exists():
+            return

-if __name__ == "__main__":
-    # Example usage
-    matcher = FinancialSituationMemory("test_memory")
+        text = self._log_path.read_text(encoding="utf-8")
+        blocks = text.split(self._SEPARATOR)

-    # Example data
-    example_data = [
-        (
-            "High inflation rate with rising interest rates and declining consumer spending",
-            "Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.",
-        ),
-        (
-            "Tech sector showing high volatility with increasing institutional selling pressure",
-            "Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.",
-        ),
-        (
-            "Strong dollar affecting emerging markets with increasing forex volatility",
-            "Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.",
-        ),
-        (
-            "Market showing signs of sector rotation with rising yields",
-            "Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.",
-        ),
-    ]
+        pending_prefix = f"[{trade_date} | {ticker} |"
+        raw_pct = f"{raw_return:+.1%}"
+        alpha_pct = f"{alpha_return:+.1%}"

-    # Add the example situations and recommendations
-    matcher.add_situations(example_data)
+        updated = False
+        new_blocks = []
+        for block in blocks:
+            stripped = block.strip()
+            if not stripped:
+                new_blocks.append(block)
+                continue

-    # Example query
-    current_situation = """
-    Market showing increased volatility in tech sector, with institutional investors
-    reducing positions and rising interest rates affecting growth stock valuations
-    """
+            lines = stripped.splitlines()
+            tag_line = lines[0].strip()

-    try:
-        recommendations = matcher.get_memories(current_situation, n_matches=2)
+            if (
+                not updated
+                and tag_line.startswith(pending_prefix)
+                and tag_line.endswith("| pending]")
+            ):
+                # Parse rating from the existing pending tag
+                fields = [f.strip() for f in tag_line[1:-1].split("|")]
+                rating = fields[2]
+                new_tag = (
+                    f"[{trade_date} | {ticker} | {rating}"
+                    f" | {raw_pct} | {alpha_pct} | {holding_days}d]"
+                )
+                rest = "\n".join(lines[1:])
+                new_blocks.append(
+                    f"{new_tag}\n\n{rest.lstrip()}\n\nREFLECTION:\n{reflection}"
+                )
+                updated = True
+            else:
+                new_blocks.append(block)

-        for i, rec in enumerate(recommendations, 1):
-            print(f"\nMatch {i}:")
-            print(f"Similarity Score: {rec['similarity_score']:.2f}")
-            print(f"Matched Situation: {rec['matched_situation']}")
-            print(f"Recommendation: {rec['recommendation']}")
+        if not updated:
+            return

-    except Exception as e:
-        print(f"Error during recommendation: {str(e)}")
+        new_blocks = self._apply_rotation(new_blocks)
+        new_text = self._SEPARATOR.join(new_blocks)
+        tmp_path = self._log_path.with_suffix(".tmp")
+        tmp_path.write_text(new_text, encoding="utf-8")
+        tmp_path.replace(self._log_path)
+
+    def batch_update_with_outcomes(self, updates: List[dict]) -> None:
+        """Apply multiple outcome updates in a single read + atomic write.
+
+        Each element of updates must have keys: ticker, trade_date,
+        raw_return, alpha_return, holding_days, reflection.
+        """
+        if not self._log_path or not self._log_path.exists() or not updates:
+            return
+
+        text = self._log_path.read_text(encoding="utf-8")
+        blocks = text.split(self._SEPARATOR)
+
+        # Build lookup keyed by (trade_date, ticker) for O(1) dispatch
+        update_map = {(u["trade_date"], u["ticker"]): u for u in updates}
+
+        new_blocks = []
+        for block in blocks:
+            stripped = block.strip()
+            if not stripped:
+                new_blocks.append(block)
+                continue
+
+            lines = stripped.splitlines()
+            tag_line = lines[0].strip()
+
+            matched = False
+            for (trade_date, ticker), upd in list(update_map.items()):
+                pending_prefix = f"[{trade_date} | {ticker} |"
+                if tag_line.startswith(pending_prefix) and tag_line.endswith("| pending]"):
+                    fields = [f.strip() for f in tag_line[1:-1].split("|")]
+                    rating = fields[2]
+                    raw_pct = f"{upd['raw_return']:+.1%}"
+                    alpha_pct = f"{upd['alpha_return']:+.1%}"
+                    new_tag = (
+                        f"[{trade_date} | {ticker} | {rating}"
+                        f" | {raw_pct} | {alpha_pct} | {upd['holding_days']}d]"
+                    )
+                    rest = "\n".join(lines[1:])
+                    new_blocks.append(
+                        f"{new_tag}\n\n{rest.lstrip()}\n\nREFLECTION:\n{upd['reflection']}"
+                    )
+                    del update_map[(trade_date, ticker)]
+                    matched = True
+                    break
+
+            if not matched:
+                new_blocks.append(block)
+
+        new_blocks = self._apply_rotation(new_blocks)
+        new_text = self._SEPARATOR.join(new_blocks)
+        tmp_path = self._log_path.with_suffix(".tmp")
+        tmp_path.write_text(new_text, encoding="utf-8")
+        tmp_path.replace(self._log_path)
+
+    # --- Helpers ---
+
+    def _apply_rotation(self, blocks: List[str]) -> List[str]:
+        """Drop oldest resolved blocks when their count exceeds max_entries.
+
+        Pending blocks are always kept (they represent unprocessed work).
+        Returns ``blocks`` unchanged when rotation is disabled or under cap.
+        """
+        if not self._max_entries or self._max_entries <= 0:
+            return blocks
+
+        # Tag each block with (kept, is_resolved) by parsing tag-line markers.
+        decisions = []
+        for block in blocks:
+            stripped = block.strip()
+            if not stripped:
+                decisions.append((block, False))
+                continue
+            tag_line = stripped.splitlines()[0].strip()
+            is_resolved = (
+                tag_line.startswith("[")
+                and tag_line.endswith("]")
+                and not tag_line.endswith("| pending]")
+            )
+            decisions.append((block, is_resolved))
+
+        resolved_count = sum(1 for _, r in decisions if r)
+        if resolved_count <= self._max_entries:
+            return blocks
+
+        to_drop = resolved_count - self._max_entries
+        kept: List[str] = []
+        for block, is_resolved in decisions:
+            if is_resolved and to_drop > 0:
+                to_drop -= 1
+                continue
+            kept.append(block)
+        return kept
+
+    def _parse_entry(self, raw: str) -> Optional[dict]:
+        lines = raw.strip().splitlines()
+        if not lines:
+            return None
+        tag_line = lines[0].strip()
+        if not (tag_line.startswith("[") and tag_line.endswith("]")):
+            return None
+        fields = [f.strip() for f in tag_line[1:-1].split("|")]
+        if len(fields) < 4:
+            return None
+        entry = {
+            "date": fields[0],
+            "ticker": fields[1],
+            "rating": fields[2],
+            "pending": fields[3] == "pending",
+            "raw": fields[3] if fields[3] != "pending" else None,
+            "alpha": fields[4] if len(fields) > 4 else None,
+            "holding": fields[5] if len(fields) > 5 else None,
+        }
+        body = "\n".join(lines[1:]).strip()
+        decision_match = self._DECISION_RE.search(body)
+        reflection_match = self._REFLECTION_RE.search(body)
+        entry["decision"] = decision_match.group(1).strip() if decision_match else ""
+        entry["reflection"] = reflection_match.group(1).strip() if reflection_match else ""
+        return entry
+
+    def _format_full(self, e: dict) -> str:
+        raw = e["raw"] or "n/a"
+        alpha = e["alpha"] or "n/a"
+        holding = e["holding"] or "n/a"
+        tag = f"[{e['date']} | {e['ticker']} | {e['rating']} | {raw} | {alpha} | {holding}]"
+        parts = [tag, f"DECISION:\n{e['decision']}"]
+        if e["reflection"]:
+            parts.append(f"REFLECTION:\n{e['reflection']}")
+        return "\n\n".join(parts)
+
+    def _format_reflection_only(self, e: dict) -> str:
+        tag = f"[{e['date']} | {e['ticker']} | {e['rating']} | {e['raw'] or 'n/a'}]"
+        if e["reflection"]:
+            return f"{tag}\n{e['reflection']}"
+        text = e["decision"][:300]
+        suffix = "..." if len(e["decision"]) > 300 else ""
+        return f"{tag}\n{text}{suffix}"
--- a/tradingagents/agents/utils/rating.py
+++ b/tradingagents/agents/utils/rating.py
@@ -0,0 +1,50 @@
+"""Shared 5-tier rating vocabulary and a deterministic heuristic parser.
+
+The same five-tier scale (Buy, Overweight, Hold, Underweight, Sell) is used by:
+- The Research Manager (investment plan recommendation)
+- The Portfolio Manager (final position decision)
+- The signal processor (rating extracted for downstream consumers)
+- The memory log (rating tag stored alongside each decision entry)
+
+Centralising it here avoids drift between those call sites.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Tuple
+
+
+# Canonical, ordered 5-tier scale (most bullish to most bearish).
+RATINGS_5_TIER: Tuple[str, ...] = (
+    "Buy", "Overweight", "Hold", "Underweight", "Sell",
+)
+
+_RATING_SET = {r.lower() for r in RATINGS_5_TIER}
+
+# Matches "Rating: X" / "rating - X" / "Rating: **X**" — tolerates markdown
+# bold wrappers and either a colon or hyphen separator.
+_RATING_LABEL_RE = re.compile(r"rating.*?[:\-][\s*]*(\w+)", re.IGNORECASE)
+
+
+def parse_rating(text: str, default: str = "Hold") -> str:
+    """Heuristically extract a 5-tier rating from prose text.
+
+    Two-pass strategy:
+    1. Look for an explicit "Rating: X" label (tolerant of markdown bold).
+    2. Fall back to the first 5-tier rating word found anywhere in the text.
+
+    Returns a Title-cased rating string, or ``default`` if no rating word appears.
+    """
+    for line in text.splitlines():
+        m = _RATING_LABEL_RE.search(line)
+        if m and m.group(1).lower() in _RATING_SET:
+            return m.group(1).capitalize()
+
+    for line in text.splitlines():
+        for word in line.lower().split():
+            clean = word.strip("*:.,")
+            if clean in _RATING_SET:
+                return clean.capitalize()
+
+    return default
--- a/tradingagents/agents/utils/structured.py
+++ b/tradingagents/agents/utils/structured.py
@@ -0,0 +1,73 @@
+"""Shared helpers for invoking an agent with structured output and a graceful fallback.
+
+The Portfolio Manager, Trader, and Research Manager all follow the same
+canonical pattern:
+
+1. At agent creation, wrap the LLM with ``with_structured_output(Schema)``
+   so the model returns a typed Pydantic instance. If the provider does
+   not support structured output (rare; mostly older Ollama models), the
+   wrap is skipped and the agent uses free-text generation instead.
+2. At invocation, run the structured call and render the result back to
+   markdown. If the structured call itself fails for any reason
+   (malformed JSON from a weak model, transient provider issue), fall
+   back to a plain ``llm.invoke`` so the pipeline never blocks.
+
+Centralising the pattern here keeps the agent factories small and ensures
+all three agents log the same warnings when fallback fires.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Callable, Optional, TypeVar
+
+from pydantic import BaseModel
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T", bound=BaseModel)
+
+
+def bind_structured(llm: Any, schema: type[T], agent_name: str) -> Optional[Any]:
+    """Return ``llm.with_structured_output(schema)`` or ``None`` if unsupported.
+
+    Logs a warning when the binding fails so the user understands the agent
+    will use free-text generation for every call instead of one-shot fallback.
+    """
+    try:
+        return llm.with_structured_output(schema)
+    except (NotImplementedError, AttributeError) as exc:
+        logger.warning(
+            "%s: provider does not support with_structured_output (%s); "
+            "falling back to free-text generation",
+            agent_name, exc,
+        )
+        return None
+
+
+def invoke_structured_or_freetext(
+    structured_llm: Optional[Any],
+    plain_llm: Any,
+    prompt: Any,
+    render: Callable[[T], str],
+    agent_name: str,
+) -> str:
+    """Run the structured call and render to markdown; fall back to free-text on any failure.
+
+    ``prompt`` is whatever the underlying LLM accepts (a string for chat
+    invocations, a list of message dicts for chat models that take that
+    shape). The same value is forwarded to the free-text path so the
+    fallback sees the same input the structured call did.
+    """
+    if structured_llm is not None:
+        try:
+            result = structured_llm.invoke(prompt)
+            return render(result)
+        except Exception as exc:
+            logger.warning(
+                "%s: structured-output invocation failed (%s); retrying once as free text",
+                agent_name, exc,
+            )
+
+    response = plain_llm.invoke(prompt)
+    return response.content
--- a/tradingagents/agents/utils/technical_indicators_tools.py
+++ b/tradingagents/agents/utils/technical_indicators_tools.py
@@ -22,10 +22,11 @@ def get_indicators(
    """
    # LLMs sometimes pass multiple indicators as a comma-separated string;
    # split and process each individually.
-    indicators = [i.strip() for i in indicator.split(",") if i.strip()]
-    if len(indicators) > 1:
-        results = []
-        for ind in indicators:
+    indicators = [i.strip().lower() for i in indicator.split(",") if i.strip()]
+    results = []
+    for ind in indicators:
+        try:
            results.append(route_to_vendor("get_indicators", symbol, ind, curr_date, look_back_days))
-        return "\n\n".join(results)
-    return route_to_vendor("get_indicators", symbol, indicator.strip(), curr_date, look_back_days)
+        except ValueError as e:
+            results.append(str(e))
+    return "\n\n".join(results)
--- a/tradingagents/dataflows/alpha_vantage_fundamentals.py
+++ b/tradingagents/dataflows/alpha_vantage_fundamentals.py
@@ -1,6 +1,23 @@
 from .alpha_vantage_common import _make_api_request


+def _filter_reports_by_date(result, curr_date: str):
+    """Filter annualReports/quarterlyReports to exclude entries after curr_date.
+
+    Prevents look-ahead bias by removing fiscal periods that end after
+    the simulation's current date.
+    """
+    if not curr_date or not isinstance(result, dict):
+        return result
+    for key in ("annualReports", "quarterlyReports"):
+        if key in result:
+            result[key] = [
+                r for r in result[key]
+                if r.get("fiscalDateEnding", "") <= curr_date
+            ]
+    return result
+
+
 def get_fundamentals(ticker: str, curr_date: str = None) -> str:
    """
    Retrieve comprehensive fundamental data for a given ticker symbol using Alpha Vantage.
@@ -19,59 +36,20 @@ def get_fundamentals(ticker: str, curr_date: str = None) -> str:
    return _make_api_request("OVERVIEW", params)


-def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
-    """
-    Retrieve balance sheet data for a given ticker symbol using Alpha Vantage.
-
-    Args:
-        ticker (str): Ticker symbol of the company
-        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
-        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
-
-    Returns:
-        str: Balance sheet data with normalized fields
-    """
-    params = {
-        "symbol": ticker,
-    }
-
-    return _make_api_request("BALANCE_SHEET", params)
+def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None):
+    """Retrieve balance sheet data for a given ticker symbol using Alpha Vantage."""
+    result = _make_api_request("BALANCE_SHEET", {"symbol": ticker})
+    return _filter_reports_by_date(result, curr_date)


-def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
-    """
-    Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage.
-
-    Args:
-        ticker (str): Ticker symbol of the company
-        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
-        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
-
-    Returns:
-        str: Cash flow statement data with normalized fields
-    """
-    params = {
-        "symbol": ticker,
-    }
-
-    return _make_api_request("CASH_FLOW", params)
+def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None):
+    """Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage."""
+    result = _make_api_request("CASH_FLOW", {"symbol": ticker})
+    return _filter_reports_by_date(result, curr_date)


-def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
-    """
-    Retrieve income statement data for a given ticker symbol using Alpha Vantage.
-
-    Args:
-        ticker (str): Ticker symbol of the company
-        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
-        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
-
-    Returns:
-        str: Income statement data with normalized fields
-    """
-    params = {
-        "symbol": ticker,
-    }
-
-    return _make_api_request("INCOME_STATEMENT", params)
+def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None):
+    """Retrieve income statement data for a given ticker symbol using Alpha Vantage."""
+    result = _make_api_request("INCOME_STATEMENT", {"symbol": ticker})
+    return _filter_reports_by_date(result, curr_date)

--- a/tradingagents/dataflows/stockstats_utils.py
+++ b/tradingagents/dataflows/stockstats_utils.py
@@ -8,6 +8,7 @@ from stockstats import wrap
 from typing import Annotated
 import os
 from .config import get_config
+from .utils import safe_ticker_component

 logger = logging.getLogger(__name__)

@@ -44,6 +45,68 @@ def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
    return data


+def load_ohlcv(symbol: str, curr_date: str) -> pd.DataFrame:
+    """Fetch OHLCV data with caching, filtered to prevent look-ahead bias.
+
+    Downloads 15 years of data up to today and caches per symbol. On
+    subsequent calls the cache is reused. Rows after curr_date are
+    filtered out so backtests never see future prices.
+    """
+    # Reject ticker values that would escape the cache directory when
+    # interpolated into the cache filename (e.g. ``../../tmp/x``).
+    safe_symbol = safe_ticker_component(symbol)
+
+    config = get_config()
+    curr_date_dt = pd.to_datetime(curr_date)
+
+    # Cache uses a fixed window (15y to today) so one file per symbol
+    today_date = pd.Timestamp.today()
+    start_date = today_date - pd.DateOffset(years=5)
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = today_date.strftime("%Y-%m-%d")
+
+    os.makedirs(config["data_cache_dir"], exist_ok=True)
+    data_file = os.path.join(
+        config["data_cache_dir"],
+        f"{safe_symbol}-YFin-data-{start_str}-{end_str}.csv",
+    )
+
+    if os.path.exists(data_file):
+        data = pd.read_csv(data_file, on_bad_lines="skip", encoding="utf-8")
+    else:
+        data = yf_retry(lambda: yf.download(
+            symbol,
+            start=start_str,
+            end=end_str,
+            multi_level_index=False,
+            progress=False,
+            auto_adjust=True,
+        ))
+        data = data.reset_index()
+        data.to_csv(data_file, index=False, encoding="utf-8")
+
+    data = _clean_dataframe(data)
+
+    # Filter to curr_date to prevent look-ahead bias in backtesting
+    data = data[data["Date"] <= curr_date_dt]
+
+    return data
+
+
+def filter_financials_by_date(data: pd.DataFrame, curr_date: str) -> pd.DataFrame:
+    """Drop financial statement columns (fiscal period timestamps) after curr_date.
+
+    yfinance financial statements use fiscal period end dates as columns.
+    Columns after curr_date represent future data and are removed to
+    prevent look-ahead bias.
+    """
+    if not curr_date or data.empty:
+        return data
+    cutoff = pd.Timestamp(curr_date)
+    mask = pd.to_datetime(data.columns, errors="coerce") <= cutoff
+    return data.loc[:, mask]
+
+
 class StockstatsUtils:
    @staticmethod
    def get_stock_stats(
@@ -55,42 +118,10 @@ class StockstatsUtils:
            str, "curr date for retrieving stock price data, YYYY-mm-dd"
        ],
    ):
-        config = get_config()
-
-        today_date = pd.Timestamp.today()
-        curr_date_dt = pd.to_datetime(curr_date)
-
-        end_date = today_date
-        start_date = today_date - pd.DateOffset(years=15)
-        start_date_str = start_date.strftime("%Y-%m-%d")
-        end_date_str = end_date.strftime("%Y-%m-%d")
-
-        # Ensure cache directory exists
-        os.makedirs(config["data_cache_dir"], exist_ok=True)
-
-        data_file = os.path.join(
-            config["data_cache_dir"],
-            f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
-        )
-
-        if os.path.exists(data_file):
-            data = pd.read_csv(data_file, on_bad_lines="skip")
-        else:
-            data = yf_retry(lambda: yf.download(
-                symbol,
-                start=start_date_str,
-                end=end_date_str,
-                multi_level_index=False,
-                progress=False,
-                auto_adjust=True,
-            ))
-            data = data.reset_index()
-            data.to_csv(data_file, index=False)
-
-        data = _clean_dataframe(data)
+        data = load_ohlcv(symbol, curr_date)
        df = wrap(data)
        df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
-        curr_date_str = curr_date_dt.strftime("%Y-%m-%d")
+        curr_date_str = pd.to_datetime(curr_date).strftime("%Y-%m-%d")

        df[indicator]  # trigger stockstats to calculate the indicator
        matching_rows = df[df["Date"].str.startswith(curr_date_str)]
--- a/tradingagents/dataflows/utils.py
+++ b/tradingagents/dataflows/utils.py
@@ -1,4 +1,5 @@
 import os
+import re
 import json
 import pandas as pd
 from datetime import date, timedelta, datetime
@@ -6,9 +7,43 @@ from typing import Annotated

 SavePathType = Annotated[str, "File path to save data. If None, data is not saved."]

+# Tickers can contain letters, digits, dot, dash, underscore, and caret
+# (for index symbols like ^GSPC). Anything else is rejected so the value
+# never escapes a containing directory when interpolated into a path.
+_TICKER_PATH_RE = re.compile(r"^[A-Za-z0-9._\-\^]+$")
+
+
+def safe_ticker_component(value: str, *, max_len: int = 32) -> str:
+    """Validate ``value`` is safe to interpolate into a filesystem path.
+
+    Tickers come from user CLI input or from LLM tool calls, both of which
+    can be influenced by attacker-controlled content (e.g. prompt injection
+    embedded in fetched news). Without validation, a value like
+    ``"../../../etc/foo"`` flows into ``os.path.join`` / ``Path /`` and
+    escapes the configured cache, checkpoint, or results directory.
+
+    Returns ``value`` unchanged when it matches the allowed pattern; raises
+    ``ValueError`` otherwise.
+    """
+    if not isinstance(value, str) or not value:
+        raise ValueError(f"ticker must be a non-empty string, got {value!r}")
+    if len(value) > max_len:
+        raise ValueError(f"ticker exceeds {max_len} chars: {value!r}")
+    if not _TICKER_PATH_RE.fullmatch(value):
+        raise ValueError(
+            f"ticker contains characters not allowed in a filesystem path: {value!r}"
+        )
+    # The regex above allows '.', so values like '.', '..', '...' would pass,
+    # and as a path component they traverse the parent directory. Reject any
+    # value that's only dots.
+    if set(value) == {"."}:
+        raise ValueError(f"ticker cannot consist solely of dots: {value!r}")
+    return value
+
+
 def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = None) -> None:
    if save_path:
-        data.to_csv(save_path)
+        data.to_csv(save_path, encoding="utf-8")
        print(f"{tag} saved to {save_path}")


--- a/tradingagents/dataflows/y_finance.py
+++ b/tradingagents/dataflows/y_finance.py
@@ -1,9 +1,10 @@
 from typing import Annotated
 from datetime import datetime
 from dateutil.relativedelta import relativedelta
+import pandas as pd
 import yfinance as yf
 import os
-from .stockstats_utils import StockstatsUtils, _clean_dataframe, yf_retry
+from .stockstats_utils import StockstatsUtils, _clean_dataframe, yf_retry, load_ohlcv, filter_financials_by_date

 def get_YFin_data_online(
    symbol: Annotated[str, "ticker symbol of the company"],
@@ -194,58 +195,9 @@ def _get_stock_stats_bulk(
    Fetches data once and calculates indicator for all available dates.
    Returns dict mapping date strings to indicator values.
    """
-    from .config import get_config
-    import pandas as pd
    from stockstats import wrap
-    import os
-    
-    config = get_config()
-    online = config["data_vendors"]["technical_indicators"] != "local"
-    
-    if not online:
-        # Local data path
-        try:
-            data = pd.read_csv(
-                os.path.join(
-                    config.get("data_cache_dir", "data"),
-                    f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
-                ),
-                on_bad_lines="skip",
-            )
-        except FileNotFoundError:
-            raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
-    else:
-        # Online data fetching with caching
-        today_date = pd.Timestamp.today()
-        curr_date_dt = pd.to_datetime(curr_date)

-        end_date = today_date
-        start_date = today_date - pd.DateOffset(years=15)
-        start_date_str = start_date.strftime("%Y-%m-%d")
-        end_date_str = end_date.strftime("%Y-%m-%d")
-
-        os.makedirs(config["data_cache_dir"], exist_ok=True)
-
-        data_file = os.path.join(
-            config["data_cache_dir"],
-            f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
-        )
-
-        if os.path.exists(data_file):
-            data = pd.read_csv(data_file, on_bad_lines="skip")
-        else:
-            data = yf_retry(lambda: yf.download(
-                symbol,
-                start=start_date_str,
-                end=end_date_str,
-                multi_level_index=False,
-                progress=False,
-                auto_adjust=True,
-            ))
-            data = data.reset_index()
-            data.to_csv(data_file, index=False)
-
-    data = _clean_dataframe(data)
+    data = load_ohlcv(symbol, curr_date)
    df = wrap(data)
    df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
    
@@ -353,7 +305,7 @@ def get_fundamentals(
 def get_balance_sheet(
    ticker: Annotated[str, "ticker symbol of the company"],
    freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
+    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
 ):
    """Get balance sheet data from yfinance."""
    try:
@@ -363,7 +315,9 @@ def get_balance_sheet(
            data = yf_retry(lambda: ticker_obj.quarterly_balance_sheet)
        else:
            data = yf_retry(lambda: ticker_obj.balance_sheet)
-            
+
+        data = filter_financials_by_date(data, curr_date)
+
        if data.empty:
            return f"No balance sheet data found for symbol '{ticker}'"
            
@@ -383,7 +337,7 @@ def get_balance_sheet(
 def get_cashflow(
    ticker: Annotated[str, "ticker symbol of the company"],
    freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
+    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
 ):
    """Get cash flow data from yfinance."""
    try:
@@ -393,7 +347,9 @@ def get_cashflow(
            data = yf_retry(lambda: ticker_obj.quarterly_cashflow)
        else:
            data = yf_retry(lambda: ticker_obj.cashflow)
-            
+
+        data = filter_financials_by_date(data, curr_date)
+
        if data.empty:
            return f"No cash flow data found for symbol '{ticker}'"
            
@@ -413,7 +369,7 @@ def get_cashflow(
 def get_income_statement(
    ticker: Annotated[str, "ticker symbol of the company"],
    freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
+    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
 ):
    """Get income statement data from yfinance."""
    try:
@@ -423,7 +379,9 @@ def get_income_statement(
            data = yf_retry(lambda: ticker_obj.quarterly_income_stmt)
        else:
            data = yf_retry(lambda: ticker_obj.income_stmt)
-            
+
+        data = filter_financials_by_date(data, curr_date)
+
        if data.empty:
            return f"No income statement data found for symbol '{ticker}'"
            
--- a/tradingagents/dataflows/yfinance_news.py
+++ b/tradingagents/dataflows/yfinance_news.py
@@ -4,6 +4,8 @@ import yfinance as yf
 from datetime import datetime
 from dateutil.relativedelta import relativedelta

+from .stockstats_utils import yf_retry
+

 def _extract_article_data(article: dict) -> dict:
    """Extract article data from yfinance news format (handles nested 'content' structure)."""
@@ -64,7 +66,7 @@ def get_news_yfinance(
    """
    try:
        stock = yf.Ticker(ticker)
-        news = stock.get_news(count=20)
+        news = yf_retry(lambda: stock.get_news(count=20))

        if not news:
            return f"No news found for {ticker}"
@@ -131,11 +133,11 @@ def get_global_news_yfinance(

    try:
        for query in search_queries:
-            search = yf.Search(
-                query=query,
+            search = yf_retry(lambda q=query: yf.Search(
+                query=q,
                news_count=limit,
                enable_fuzzy_query=True,
-            )
+            ))

            if search.news:
                for article in search.news:
@@ -167,6 +169,11 @@ def get_global_news_yfinance(
            # Handle both flat and nested structures
            if "content" in article:
                data = _extract_article_data(article)
+                # Skip articles published after curr_date (look-ahead guard)
+                if data.get("pub_date"):
+                    pub_naive = data["pub_date"].replace(tzinfo=None) if hasattr(data["pub_date"], "replace") else data["pub_date"]
+                    if pub_naive > curr_dt + relativedelta(days=1):
+                        continue
                title = data["title"]
                publisher = data["publisher"]
                link = data["link"]
--- a/tradingagents/default_config.py
+++ b/tradingagents/default_config.py
@@ -1,21 +1,36 @@
 import os

+_TRADINGAGENTS_HOME = os.path.join(os.path.expanduser("~"), ".tradingagents")
+
 DEFAULT_CONFIG = {
    "project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
-    "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
-    "data_cache_dir": os.path.join(
-        os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
-        "dataflows/data_cache",
-    ),
+    "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", os.path.join(_TRADINGAGENTS_HOME, "logs")),
+    "data_cache_dir": os.getenv("TRADINGAGENTS_CACHE_DIR", os.path.join(_TRADINGAGENTS_HOME, "cache")),
+    "memory_log_path": os.getenv("TRADINGAGENTS_MEMORY_LOG_PATH", os.path.join(_TRADINGAGENTS_HOME, "memory", "trading_memory.md")),
+    # Optional cap on the number of resolved memory log entries. When set,
+    # the oldest resolved entries are pruned once this limit is exceeded.
+    # Pending entries are never pruned. None disables rotation entirely.
+    "memory_log_max_entries": None,
    # LLM settings
    "llm_provider": "openai",
-    "deep_think_llm": "gpt-5.2",
-    "quick_think_llm": "gpt-5-mini",
-    "backend_url": "https://api.openai.com/v1",
+    "deep_think_llm": "gpt-5.4",
+    "quick_think_llm": "gpt-5.4-mini",
+    # When None, each provider's client falls back to its own default endpoint
+    # (api.openai.com for OpenAI, generativelanguage.googleapis.com for Gemini, ...).
+    # The CLI overrides this per provider when the user picks one. Keeping a
+    # provider-specific URL here would leak (e.g. OpenAI's /v1 was previously
+    # being forwarded to Gemini, producing malformed request URLs).
+    "backend_url": None,
    # Provider-specific thinking configuration
    "google_thinking_level": None,      # "high", "minimal", etc.
    "openai_reasoning_effort": None,    # "medium", "high", "low"
    "anthropic_effort": None,           # "high", "medium", "low"
+    # Checkpoint/resume: when True, LangGraph saves state after each node
+    # so a crashed run can resume from the last successful step.
+    "checkpoint_enabled": False,
+    # Output language for analyst reports and final decision
+    # Internal agent debate stays in English for reasoning quality
+    "output_language": "English",
    # Debate and discussion settings
    "max_debate_rounds": 1,
    "max_risk_discuss_rounds": 1,
--- a/tradingagents/graph/checkpointer.py
+++ b/tradingagents/graph/checkpointer.py
@@ -0,0 +1,90 @@
+"""LangGraph checkpoint support for resumable analysis runs.
+
+Per-ticker SQLite databases so concurrent tickers don't contend.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import sqlite3
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Generator
+
+from langgraph.checkpoint.sqlite import SqliteSaver
+
+from tradingagents.dataflows.utils import safe_ticker_component
+
+
+def _db_path(data_dir: str | Path, ticker: str) -> Path:
+    """Return the SQLite checkpoint DB path for a ticker."""
+    # Reject ticker values that would escape the checkpoints directory.
+    safe = safe_ticker_component(ticker).upper()
+    p = Path(data_dir) / "checkpoints"
+    p.mkdir(parents=True, exist_ok=True)
+    return p / f"{safe}.db"
+
+
+def thread_id(ticker: str, date: str) -> str:
+    """Deterministic thread ID for a ticker+date pair."""
+    return hashlib.sha256(f"{ticker.upper()}:{date}".encode()).hexdigest()[:16]
+
+
+@contextmanager
+def get_checkpointer(data_dir: str | Path, ticker: str) -> Generator[SqliteSaver, None, None]:
+    """Context manager yielding a SqliteSaver backed by a per-ticker DB."""
+    db = _db_path(data_dir, ticker)
+    conn = sqlite3.connect(str(db), check_same_thread=False)
+    try:
+        saver = SqliteSaver(conn)
+        saver.setup()
+        yield saver
+    finally:
+        conn.close()
+
+
+def has_checkpoint(data_dir: str | Path, ticker: str, date: str) -> bool:
+    """Check whether a resumable checkpoint exists for ticker+date."""
+    return checkpoint_step(data_dir, ticker, date) is not None
+
+
+def checkpoint_step(data_dir: str | Path, ticker: str, date: str) -> int | None:
+    """Return the step number of the latest checkpoint, or None if none exists."""
+    db = _db_path(data_dir, ticker)
+    if not db.exists():
+        return None
+    tid = thread_id(ticker, date)
+    with get_checkpointer(data_dir, ticker) as saver:
+        config = {"configurable": {"thread_id": tid}}
+        cp = saver.get_tuple(config)
+        if cp is None:
+            return None
+        return cp.metadata.get("step")
+
+
+def clear_all_checkpoints(data_dir: str | Path) -> int:
+    """Remove all checkpoint DBs. Returns number of files deleted."""
+    cp_dir = Path(data_dir) / "checkpoints"
+    if not cp_dir.exists():
+        return 0
+    dbs = list(cp_dir.glob("*.db"))
+    for db in dbs:
+        db.unlink()
+    return len(dbs)
+
+
+def clear_checkpoint(data_dir: str | Path, ticker: str, date: str) -> None:
+    """Remove checkpoint for a specific ticker+date by deleting the thread's rows."""
+    db = _db_path(data_dir, ticker)
+    if not db.exists():
+        return
+    tid = thread_id(ticker, date)
+    conn = sqlite3.connect(str(db))
+    try:
+        for table in ("writes", "checkpoints"):
+            conn.execute(f"DELETE FROM {table} WHERE thread_id = ?", (tid,))
+        conn.commit()
+    except sqlite3.OperationalError:
+        pass
+    finally:
+        conn.close()
--- a/tradingagents/graph/propagation.py
+++ b/tradingagents/graph/propagation.py
@@ -16,13 +16,14 @@ class Propagator:
        self.max_recur_limit = max_recur_limit

    def create_initial_state(
-        self, company_name: str, trade_date: str
+        self, company_name: str, trade_date: str, past_context: str = ""
    ) -> Dict[str, Any]:
        """Create the initial state for the agent graph."""
        return {
            "messages": [("human", company_name)],
            "company_of_interest": company_name,
            "trade_date": str(trade_date),
+            "past_context": past_context,
            "investment_debate_state": InvestDebateState(
                {
                    "bull_history": "",
--- a/tradingagents/graph/reflection.py
+++ b/tradingagents/graph/reflection.py
@@ -1,121 +1,53 @@
 # TradingAgents/graph/reflection.py

-from typing import Dict, Any
-from langchain_openai import ChatOpenAI
+from typing import Any


 class Reflector:
-    """Handles reflection on decisions and updating memory."""
+    """Handles reflection on trading decisions."""

-    def __init__(self, quick_thinking_llm: ChatOpenAI):
+    def __init__(self, quick_thinking_llm: Any):
        """Initialize the reflector with an LLM."""
        self.quick_thinking_llm = quick_thinking_llm
-        self.reflection_system_prompt = self._get_reflection_prompt()
+        self.log_reflection_prompt = self._get_log_reflection_prompt()

-    def _get_reflection_prompt(self) -> str:
-        """Get the system prompt for reflection."""
-        return """
-You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis. 
-Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines:
+    def _get_log_reflection_prompt(self) -> str:
+        """Concise prompt for reflect_on_final_decision (Phase B log entries).

-1. Reasoning:
-   - For each trading decision, determine whether it was correct or incorrect. A correct decision results in an increase in returns, while an incorrect decision does the opposite.
-   - Analyze the contributing factors to each success or mistake. Consider:
-     - Market intelligence.
-     - Technical indicators.
-     - Technical signals.
-     - Price movement analysis.
-     - Overall market data analysis 
-     - News analysis.
-     - Social media and sentiment analysis.
-     - Fundamental data analysis.
-     - Weight the importance of each factor in the decision-making process.
+        Produces 2-4 sentences of plain prose — compact enough to be re-injected
+        into future agent prompts without bloating the context window.
+        """
+        return (
+            "You are a trading analyst reviewing your own past decision now that the outcome is known.\n"
+            "Write exactly 2-4 sentences of plain prose (no bullets, no headers, no markdown).\n\n"
+            "Cover in order:\n"
+            "1. Was the directional call correct? (cite the alpha figure)\n"
+            "2. Which part of the investment thesis held or failed?\n"
+            "3. One concrete lesson to apply to the next similar analysis.\n\n"
+            "Be specific and terse. Your output will be stored verbatim in a decision log "
+            "and re-read by future analysts, so every word must earn its place."
+        )

-2. Improvement:
-   - For any incorrect decisions, propose revisions to maximize returns.
-   - Provide a detailed list of corrective actions or improvements, including specific recommendations (e.g., changing a decision from HOLD to BUY on a particular date).
-
-3. Summary:
-   - Summarize the lessons learned from the successes and mistakes.
-   - Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained.
-
-4. Query:
-   - Extract key insights from the summary into a concise sentence of no more than 1000 tokens.
-   - Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference.
-
-Adhere strictly to these instructions, and ensure your output is detailed, accurate, and actionable. You will also be given objective descriptions of the market from a price movements, technical indicator, news, and sentiment perspective to provide more context for your analysis.
-"""
-
-    def _extract_current_situation(self, current_state: Dict[str, Any]) -> str:
-        """Extract the current market situation from the state."""
-        curr_market_report = current_state["market_report"]
-        curr_sentiment_report = current_state["sentiment_report"]
-        curr_news_report = current_state["news_report"]
-        curr_fundamentals_report = current_state["fundamentals_report"]
-
-        return f"{curr_market_report}\n\n{curr_sentiment_report}\n\n{curr_news_report}\n\n{curr_fundamentals_report}"
-
-    def _reflect_on_component(
-        self, component_type: str, report: str, situation: str, returns_losses
+    def reflect_on_final_decision(
+        self,
+        final_decision: str,
+        raw_return: float,
+        alpha_return: float,
    ) -> str:
-        """Generate reflection for a component."""
+        """Single reflection call on the final trade decision with outcome context.
+
+        Used by Phase B deferred reflection. The final_trade_decision already
+        synthesises all analyst insights, so no separate market context is needed.
+        """
        messages = [
-            ("system", self.reflection_system_prompt),
+            ("system", self.log_reflection_prompt),
            (
                "human",
-                f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}",
+                (
+                    f"Raw return: {raw_return:+.1%}\n"
+                    f"Alpha vs SPY: {alpha_return:+.1%}\n\n"
+                    f"Final Decision:\n{final_decision}"
+                ),
            ),
        ]
-
-        result = self.quick_thinking_llm.invoke(messages).content
-        return result
-
-    def reflect_bull_researcher(self, current_state, returns_losses, bull_memory):
-        """Reflect on bull researcher's analysis and update memory."""
-        situation = self._extract_current_situation(current_state)
-        bull_debate_history = current_state["investment_debate_state"]["bull_history"]
-
-        result = self._reflect_on_component(
-            "BULL", bull_debate_history, situation, returns_losses
-        )
-        bull_memory.add_situations([(situation, result)])
-
-    def reflect_bear_researcher(self, current_state, returns_losses, bear_memory):
-        """Reflect on bear researcher's analysis and update memory."""
-        situation = self._extract_current_situation(current_state)
-        bear_debate_history = current_state["investment_debate_state"]["bear_history"]
-
-        result = self._reflect_on_component(
-            "BEAR", bear_debate_history, situation, returns_losses
-        )
-        bear_memory.add_situations([(situation, result)])
-
-    def reflect_trader(self, current_state, returns_losses, trader_memory):
-        """Reflect on trader's decision and update memory."""
-        situation = self._extract_current_situation(current_state)
-        trader_decision = current_state["trader_investment_plan"]
-
-        result = self._reflect_on_component(
-            "TRADER", trader_decision, situation, returns_losses
-        )
-        trader_memory.add_situations([(situation, result)])
-
-    def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory):
-        """Reflect on investment judge's decision and update memory."""
-        situation = self._extract_current_situation(current_state)
-        judge_decision = current_state["investment_debate_state"]["judge_decision"]
-
-        result = self._reflect_on_component(
-            "INVEST JUDGE", judge_decision, situation, returns_losses
-        )
-        invest_judge_memory.add_situations([(situation, result)])
-
-    def reflect_portfolio_manager(self, current_state, returns_losses, portfolio_manager_memory):
-        """Reflect on portfolio manager's decision and update memory."""
-        situation = self._extract_current_situation(current_state)
-        judge_decision = current_state["risk_debate_state"]["judge_decision"]
-
-        result = self._reflect_on_component(
-            "PORTFOLIO MANAGER", judge_decision, situation, returns_losses
-        )
-        portfolio_manager_memory.add_situations([(situation, result)])
+        return self.quick_thinking_llm.invoke(messages).content
--- a/tradingagents/graph/setup.py
+++ b/tradingagents/graph/setup.py
@@ -1,8 +1,7 @@
 # TradingAgents/graph/setup.py

-from typing import Dict, Any
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, StateGraph, START
+from typing import Any, Dict
+from langgraph.graph import END, START, StateGraph
 from langgraph.prebuilt import ToolNode

 from tradingagents.agents import *
@@ -16,25 +15,15 @@ class GraphSetup:

    def __init__(
        self,
-        quick_thinking_llm: ChatOpenAI,
-        deep_thinking_llm: ChatOpenAI,
+        quick_thinking_llm: Any,
+        deep_thinking_llm: Any,
        tool_nodes: Dict[str, ToolNode],
-        bull_memory,
-        bear_memory,
-        trader_memory,
-        invest_judge_memory,
-        portfolio_manager_memory,
        conditional_logic: ConditionalLogic,
    ):
        """Initialize with required components."""
        self.quick_thinking_llm = quick_thinking_llm
        self.deep_thinking_llm = deep_thinking_llm
        self.tool_nodes = tool_nodes
-        self.bull_memory = bull_memory
-        self.bear_memory = bear_memory
-        self.trader_memory = trader_memory
-        self.invest_judge_memory = invest_judge_memory
-        self.portfolio_manager_memory = portfolio_manager_memory
        self.conditional_logic = conditional_logic

    def setup_graph(
@@ -86,24 +75,16 @@ class GraphSetup:
            tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"]

        # Create researcher and manager nodes
-        bull_researcher_node = create_bull_researcher(
-            self.quick_thinking_llm, self.bull_memory
-        )
-        bear_researcher_node = create_bear_researcher(
-            self.quick_thinking_llm, self.bear_memory
-        )
-        research_manager_node = create_research_manager(
-            self.deep_thinking_llm, self.invest_judge_memory
-        )
-        trader_node = create_trader(self.quick_thinking_llm, self.trader_memory)
+        bull_researcher_node = create_bull_researcher(self.quick_thinking_llm)
+        bear_researcher_node = create_bear_researcher(self.quick_thinking_llm)
+        research_manager_node = create_research_manager(self.deep_thinking_llm)
+        trader_node = create_trader(self.quick_thinking_llm)

        # Create risk analysis nodes
        aggressive_analyst = create_aggressive_debator(self.quick_thinking_llm)
        neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
        conservative_analyst = create_conservative_debator(self.quick_thinking_llm)
-        portfolio_manager_node = create_portfolio_manager(
-            self.deep_thinking_llm, self.portfolio_manager_memory
-        )
+        portfolio_manager_node = create_portfolio_manager(self.deep_thinking_llm)

        # Create workflow
        workflow = StateGraph(AgentState)
@@ -198,5 +179,4 @@ class GraphSetup:

        workflow.add_edge("Portfolio Manager", END)

-        # Compile and return
-        return workflow.compile()
+        return workflow
--- a/tradingagents/graph/signal_processing.py
+++ b/tradingagents/graph/signal_processing.py
@@ -1,33 +1,31 @@
-# TradingAgents/graph/signal_processing.py
+"""Extract the 5-tier portfolio rating from the Portfolio Manager's decision.

-from langchain_openai import ChatOpenAI
+The Portfolio Manager produces a typed ``PortfolioDecision`` via structured
+output and renders it to markdown that always carries a ``**Rating**: X``
+header (see :func:`tradingagents.agents.schemas.render_pm_decision`).  The
+deterministic heuristic in :mod:`tradingagents.agents.utils.rating` is more
+than sufficient to extract that rating; no extra LLM call is needed.
+
+This module exists for backwards compatibility with callers that expect a
+``SignalProcessor.process_signal(text)`` interface.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from tradingagents.agents.utils.rating import parse_rating


 class SignalProcessor:
-    """Processes trading signals to extract actionable decisions."""
+    """Read the 5-tier rating out of a Portfolio Manager decision."""

-    def __init__(self, quick_thinking_llm: ChatOpenAI):
-        """Initialize with an LLM for processing."""
+    def __init__(self, quick_thinking_llm: Any = None):
+        # The LLM argument is accepted for backwards compatibility but no
+        # longer used: the PM's structured output guarantees the rating is
+        # parseable from the rendered markdown without a second LLM call.
        self.quick_thinking_llm = quick_thinking_llm

    def process_signal(self, full_signal: str) -> str:
-        """
-        Process a full trading signal to extract the core decision.
-
-        Args:
-            full_signal: Complete trading signal text
-
-        Returns:
-            Extracted rating (BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, or SELL)
-        """
-        messages = [
-            (
-                "system",
-                "You are an efficient assistant that extracts the trading decision from analyst reports. "
-                "Extract the rating as exactly one of: BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, SELL. "
-                "Output only the single rating word, nothing else.",
-            ),
-            ("human", full_signal),
-        ]
-
-        return self.quick_thinking_llm.invoke(messages).content
+        """Return one of Buy / Overweight / Hold / Underweight / Sell."""
+        return parse_rating(full_signal)
--- a/tradingagents/graph/trading_graph.py
+++ b/tradingagents/graph/trading_graph.py
@@ -1,18 +1,24 @@
 # TradingAgents/graph/trading_graph.py

+import logging
 import os
 from pathlib import Path
 import json
-from datetime import date
+from datetime import datetime, timedelta
 from typing import Dict, Any, Tuple, List, Optional

+import yfinance as yf
+
+logger = logging.getLogger(__name__)
+
 from langgraph.prebuilt import ToolNode

 from tradingagents.llm_clients import create_llm_client

 from tradingagents.agents import *
 from tradingagents.default_config import DEFAULT_CONFIG
-from tradingagents.agents.utils.memory import FinancialSituationMemory
+from tradingagents.agents.utils.memory import TradingMemoryLog
+from tradingagents.dataflows.utils import safe_ticker_component
 from tradingagents.agents.utils.agent_states import (
    AgentState,
    InvestDebateState,
@@ -33,6 +39,7 @@ from tradingagents.agents.utils.agent_utils import (
    get_global_news
 )

+from .checkpointer import checkpoint_step, clear_checkpoint, get_checkpointer, thread_id
 from .conditional_logic import ConditionalLogic
 from .setup import GraphSetup
 from .propagation import Propagator
@@ -66,10 +73,8 @@ class TradingAgentsGraph:
        set_config(self.config)

        # Create necessary directories
-        os.makedirs(
-            os.path.join(self.config["project_dir"], "dataflows/data_cache"),
-            exist_ok=True,
-        )
+        os.makedirs(self.config["data_cache_dir"], exist_ok=True)
+        os.makedirs(self.config["results_dir"], exist_ok=True)

        # Initialize LLMs with provider-specific thinking configuration
        llm_kwargs = self._get_provider_kwargs()
@@ -94,12 +99,7 @@ class TradingAgentsGraph:
        self.deep_thinking_llm = deep_client.get_llm()
        self.quick_thinking_llm = quick_client.get_llm()
        
-        # Initialize memories
-        self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
-        self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
-        self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
-        self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
-        self.portfolio_manager_memory = FinancialSituationMemory("portfolio_manager_memory", self.config)
+        self.memory_log = TradingMemoryLog(self.config)

        # Create tool nodes
        self.tool_nodes = self._create_tool_nodes()
@@ -113,11 +113,6 @@ class TradingAgentsGraph:
            self.quick_thinking_llm,
            self.deep_thinking_llm,
            self.tool_nodes,
-            self.bull_memory,
-            self.bear_memory,
-            self.trader_memory,
-            self.invest_judge_memory,
-            self.portfolio_manager_memory,
            self.conditional_logic,
        )

@@ -130,8 +125,10 @@ class TradingAgentsGraph:
        self.ticker = None
        self.log_states_dict = {}  # date to full state dict

-        # Set up the graph
-        self.graph = self.graph_setup.setup_graph(selected_analysts)
+        # Set up the graph: keep the workflow for recompilation with a checkpointer.
+        self.workflow = self.graph_setup.setup_graph(selected_analysts)
+        self.graph = self.workflow.compile()
+        self._checkpointer_ctx = None

    def _get_provider_kwargs(self) -> Dict[str, Any]:
        """Get provider-specific kwargs for LLM client creation."""
@@ -191,19 +188,133 @@ class TradingAgentsGraph:
            ),
        }

-    def propagate(self, company_name, trade_date):
-        """Run the trading agents graph for a company on a specific date."""
+    def _fetch_returns(
+        self, ticker: str, trade_date: str, holding_days: int = 5
+    ) -> Tuple[Optional[float], Optional[float], Optional[int]]:
+        """Fetch raw and alpha return for ticker over holding_days from trade_date.

+        Returns (raw_return, alpha_return, actual_holding_days) or
+        (None, None, None) if price data is unavailable (too recent, delisted,
+        or network error).
+        """
+        try:
+            start = datetime.strptime(trade_date, "%Y-%m-%d")
+            end = start + timedelta(days=holding_days + 7)  # buffer for weekends/holidays
+            end_str = end.strftime("%Y-%m-%d")
+
+            stock = yf.Ticker(ticker).history(start=trade_date, end=end_str)
+            spy = yf.Ticker("SPY").history(start=trade_date, end=end_str)
+
+            if len(stock) < 2 or len(spy) < 2:
+                return None, None, None
+
+            actual_days = min(holding_days, len(stock) - 1, len(spy) - 1)
+            raw = float(
+                (stock["Close"].iloc[actual_days] - stock["Close"].iloc[0])
+                / stock["Close"].iloc[0]
+            )
+            spy_ret = float(
+                (spy["Close"].iloc[actual_days] - spy["Close"].iloc[0])
+                / spy["Close"].iloc[0]
+            )
+            alpha = raw - spy_ret
+            return raw, alpha, actual_days
+        except Exception as e:
+            logger.warning(
+                "Could not resolve outcome for %s on %s (will retry next run): %s",
+                ticker, trade_date, e,
+            )
+            return None, None, None
+
+    def _resolve_pending_entries(self, ticker: str) -> None:
+        """Resolve pending log entries for ticker at the start of a new run.
+
+        Fetches returns for each same-ticker pending entry, generates reflections,
+        then writes all updates in a single atomic batch write to avoid redundant I/O.
+        Skips entries whose price data is not yet available (too recent or delisted).
+
+        Trade-off: only same-ticker entries are resolved per run.  Entries for
+        other tickers accumulate until that ticker is run again.
+        """
+        pending = [e for e in self.memory_log.get_pending_entries() if e["ticker"] == ticker]
+        if not pending:
+            return
+
+        updates = []
+        for entry in pending:
+            raw, alpha, days = self._fetch_returns(ticker, entry["date"])
+            if raw is None:
+                continue  # price not available yet — try again next run
+            reflection = self.reflector.reflect_on_final_decision(
+                final_decision=entry.get("decision", ""),
+                raw_return=raw,
+                alpha_return=alpha,
+            )
+            updates.append({
+                "ticker": ticker,
+                "trade_date": entry["date"],
+                "raw_return": raw,
+                "alpha_return": alpha,
+                "holding_days": days,
+                "reflection": reflection,
+            })
+
+        if updates:
+            self.memory_log.batch_update_with_outcomes(updates)
+
+    def propagate(self, company_name, trade_date):
+        """Run the trading agents graph for a company on a specific date.
+
+        When ``checkpoint_enabled`` is set in config, the graph is recompiled
+        with a per-ticker SqliteSaver so a crashed run can resume from the last
+        successful node on a subsequent invocation with the same ticker+date.
+        """
        self.ticker = company_name

-        # Initialize state
+        # Resolve any pending memory-log entries for this ticker before the pipeline runs.
+        self._resolve_pending_entries(company_name)
+
+        # Recompile with a checkpointer if the user opted in.
+        if self.config.get("checkpoint_enabled"):
+            self._checkpointer_ctx = get_checkpointer(
+                self.config["data_cache_dir"], company_name
+            )
+            saver = self._checkpointer_ctx.__enter__()
+            self.graph = self.workflow.compile(checkpointer=saver)
+
+            step = checkpoint_step(
+                self.config["data_cache_dir"], company_name, str(trade_date)
+            )
+            if step is not None:
+                logger.info(
+                    "Resuming from step %d for %s on %s", step, company_name, trade_date
+                )
+            else:
+                logger.info("Starting fresh for %s on %s", company_name, trade_date)
+
+        try:
+            return self._run_graph(company_name, trade_date)
+        finally:
+            if self._checkpointer_ctx is not None:
+                self._checkpointer_ctx.__exit__(None, None, None)
+                self._checkpointer_ctx = None
+                self.graph = self.workflow.compile()
+
+    def _run_graph(self, company_name, trade_date):
+        """Execute the graph and write the resulting state to disk and memory log."""
+        # Initialize state — inject memory log context for PM.
+        past_context = self.memory_log.get_past_context(company_name)
        init_agent_state = self.propagator.create_initial_state(
-            company_name, trade_date
+            company_name, trade_date, past_context=past_context
        )
        args = self.propagator.get_graph_args()

+        # Inject thread_id so same ticker+date resumes, different date starts fresh.
+        if self.config.get("checkpoint_enabled"):
+            tid = thread_id(company_name, str(trade_date))
+            args.setdefault("config", {}).setdefault("configurable", {})["thread_id"] = tid
+
        if self.debug:
-            # Debug mode with tracing
            trace = []
            for chunk in self.graph.stream(init_agent_state, **args):
                if len(chunk["messages"]) == 0:
@@ -211,19 +322,29 @@ class TradingAgentsGraph:
                else:
                    chunk["messages"][-1].pretty_print()
                    trace.append(chunk)
-
            final_state = trace[-1]
        else:
-            # Standard mode without tracing
            final_state = self.graph.invoke(init_agent_state, **args)

-        # Store current state for reflection
+        # Store current state for reflection.
        self.curr_state = final_state

-        # Log state
+        # Log state to disk.
        self._log_state(trade_date, final_state)

-        # Return decision and processed signal
+        # Store decision for deferred reflection on the next same-ticker run.
+        self.memory_log.store_decision(
+            ticker=company_name,
+            trade_date=trade_date,
+            final_trade_decision=final_state["final_trade_decision"],
+        )
+
+        # Clear checkpoint on successful completion to avoid stale state.
+        if self.config.get("checkpoint_enabled"):
+            clear_checkpoint(
+                self.config["data_cache_dir"], company_name, str(trade_date)
+            )
+
        return final_state, self.process_signal(final_state["final_trade_decision"])

    def _log_state(self, trade_date, final_state):
@@ -258,34 +379,15 @@ class TradingAgentsGraph:
            "final_trade_decision": final_state["final_trade_decision"],
        }

-        # Save to file
-        directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/")
+        # Save to file. Reject ticker values that would escape the
+        # results directory when joined as a path component.
+        safe_ticker = safe_ticker_component(self.ticker)
+        directory = Path(self.config["results_dir"]) / safe_ticker / "TradingAgentsStrategy_logs"
        directory.mkdir(parents=True, exist_ok=True)

-        with open(
-            f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
-            "w",
-            encoding="utf-8",
-        ) as f:
-            json.dump(self.log_states_dict, f, indent=4)
-
-    def reflect_and_remember(self, returns_losses):
-        """Reflect on decisions and update memory based on returns."""
-        self.reflector.reflect_bull_researcher(
-            self.curr_state, returns_losses, self.bull_memory
-        )
-        self.reflector.reflect_bear_researcher(
-            self.curr_state, returns_losses, self.bear_memory
-        )
-        self.reflector.reflect_trader(
-            self.curr_state, returns_losses, self.trader_memory
-        )
-        self.reflector.reflect_invest_judge(
-            self.curr_state, returns_losses, self.invest_judge_memory
-        )
-        self.reflector.reflect_portfolio_manager(
-            self.curr_state, returns_losses, self.portfolio_manager_memory
-        )
+        log_path = directory / f"full_states_log_{trade_date}.json"
+        with open(log_path, "w", encoding="utf-8") as f:
+            json.dump(self.log_states_dict[str(trade_date)], f, indent=4)

    def process_signal(self, full_signal):
        """Process a signal to extract the core decision."""
--- a/tradingagents/llm_clients/TODO.md
+++ b/tradingagents/llm_clients/TODO.md
@@ -5,20 +5,11 @@
 ### 1. `validate_model()` is never called
 - Add validation call in `get_llm()` with warning (not error) for unknown models

-### 2. Inconsistent parameter handling
-| Client | API Key Param | Special Params |
-|--------|---------------|----------------|
-| OpenAI | `api_key` | `reasoning_effort` |
-| Anthropic | `api_key` | `thinking_config` → `thinking` |
-| Google | `google_api_key` | `thinking_budget` |
+### 2. ~~Inconsistent parameter handling~~ (Fixed)
+- GoogleClient now accepts unified `api_key` and maps it to `google_api_key`

-**Fix:** Standardize with unified `api_key` that maps to provider-specific keys
+### 3. ~~`base_url` accepted but ignored~~ (Fixed)
+- All clients now pass `base_url` to their respective LLM constructors

-### 3. `base_url` accepted but ignored
- `AnthropicClient`: accepts `base_url` but never uses it
- `GoogleClient`: accepts `base_url` but never uses it (correct - Google doesn't support it)
-
-**Fix:** Remove unused `base_url` from clients that don't support it
-
-### 4. Update validators.py with models from CLI
- Sync `VALID_MODELS` dict with CLI model options after Feature 2 is complete
+### 4. ~~Update validators.py with models from CLI~~ (Fixed)
+- Synced in v0.2.2
--- a/tradingagents/llm_clients/anthropic_client.py
+++ b/tradingagents/llm_clients/anthropic_client.py
@@ -31,8 +31,12 @@ class AnthropicClient(BaseLLMClient):

    def get_llm(self) -> Any:
        """Return configured ChatAnthropic instance."""
+        self.warn_if_unknown_model()
        llm_kwargs = {"model": self.model}

+        if self.base_url:
+            llm_kwargs["base_url"] = self.base_url
+
        for key in _PASSTHROUGH_KWARGS:
            if key in self.kwargs:
                llm_kwargs[key] = self.kwargs[key]
--- a/tradingagents/llm_clients/azure_client.py
+++ b/tradingagents/llm_clients/azure_client.py
@@ -0,0 +1,52 @@
+import os
+from typing import Any, Optional
+
+from langchain_openai import AzureChatOpenAI
+
+from .base_client import BaseLLMClient, normalize_content
+from .validators import validate_model
+
+_PASSTHROUGH_KWARGS = (
+    "timeout", "max_retries", "api_key", "reasoning_effort",
+    "callbacks", "http_client", "http_async_client",
+)
+
+
+class NormalizedAzureChatOpenAI(AzureChatOpenAI):
+    """AzureChatOpenAI with normalized content output."""
+
+    def invoke(self, input, config=None, **kwargs):
+        return normalize_content(super().invoke(input, config, **kwargs))
+
+
+class AzureOpenAIClient(BaseLLMClient):
+    """Client for Azure OpenAI deployments.
+
+    Requires environment variables:
+        AZURE_OPENAI_API_KEY: API key
+        AZURE_OPENAI_ENDPOINT: Endpoint URL (e.g. https://<resource>.openai.azure.com/)
+        AZURE_OPENAI_DEPLOYMENT_NAME: Deployment name
+        OPENAI_API_VERSION: API version (e.g. 2025-03-01-preview)
+    """
+
+    def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
+        super().__init__(model, base_url, **kwargs)
+
+    def get_llm(self) -> Any:
+        """Return configured AzureChatOpenAI instance."""
+        self.warn_if_unknown_model()
+
+        llm_kwargs = {
+            "model": self.model,
+            "azure_deployment": os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", self.model),
+        }
+
+        for key in _PASSTHROUGH_KWARGS:
+            if key in self.kwargs:
+                llm_kwargs[key] = self.kwargs[key]
+
+        return NormalizedAzureChatOpenAI(**llm_kwargs)
+
+    def validate_model(self) -> bool:
+        """Azure accepts any deployed model name."""
+        return True
--- a/tradingagents/llm_clients/base_client.py
+++ b/tradingagents/llm_clients/base_client.py
@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import Any, Optional
+import warnings


 def normalize_content(response):
@@ -29,6 +30,27 @@ class BaseLLMClient(ABC):
        self.base_url = base_url
        self.kwargs = kwargs

+    def get_provider_name(self) -> str:
+        """Return the provider name used in warning messages."""
+        provider = getattr(self, "provider", None)
+        if provider:
+            return str(provider)
+        return self.__class__.__name__.removesuffix("Client").lower()
+
+    def warn_if_unknown_model(self) -> None:
+        """Warn when the model is outside the known list for the provider."""
+        if self.validate_model():
+            return
+
+        warnings.warn(
+            (
+                f"Model '{self.model}' is not in the known model list for "
+                f"provider '{self.get_provider_name()}'. Continuing anyway."
+            ),
+            RuntimeWarning,
+            stacklevel=2,
+        )
+
    @abstractmethod
    def get_llm(self) -> Any:
        """Return the configured LLM instance."""
--- a/tradingagents/llm_clients/factory.py
+++ b/tradingagents/llm_clients/factory.py
@@ -1,9 +1,11 @@
 from typing import Optional

 from .base_client import BaseLLMClient
-from .openai_client import OpenAIClient
-from .anthropic_client import AnthropicClient
-from .google_client import GoogleClient
+
+# Providers that use the OpenAI-compatible chat completions API
+_OPENAI_COMPATIBLE = (
+    "openai", "xai", "deepseek", "qwen", "glm", "ollama", "openrouter",
+)


 def create_llm_client(
@@ -14,17 +16,15 @@ def create_llm_client(
 ) -> BaseLLMClient:
    """Create an LLM client for the specified provider.

+    Provider modules are imported lazily so that simply importing this
+    factory (e.g. during test collection) does not pull in heavy LLM SDKs
+    or fail when their API keys are absent.
+
    Args:
-        provider: LLM provider (openai, anthropic, google, xai, ollama, openrouter)
+        provider: LLM provider name
        model: Model name/identifier
        base_url: Optional base URL for API endpoint
        **kwargs: Additional provider-specific arguments
-            - http_client: Custom httpx.Client for SSL proxy or certificate customization
-            - http_async_client: Custom httpx.AsyncClient for async operations
-            - timeout: Request timeout in seconds
-            - max_retries: Maximum retry attempts
-            - api_key: API key for the provider
-            - callbacks: LangChain callbacks

    Returns:
        Configured BaseLLMClient instance
@@ -34,16 +34,20 @@ def create_llm_client(
    """
    provider_lower = provider.lower()

-    if provider_lower in ("openai", "ollama", "openrouter"):
+    if provider_lower in _OPENAI_COMPATIBLE:
+        from .openai_client import OpenAIClient
        return OpenAIClient(model, base_url, provider=provider_lower, **kwargs)

-    if provider_lower == "xai":
-        return OpenAIClient(model, base_url, provider="xai", **kwargs)
-
    if provider_lower == "anthropic":
+        from .anthropic_client import AnthropicClient
        return AnthropicClient(model, base_url, **kwargs)

    if provider_lower == "google":
+        from .google_client import GoogleClient
        return GoogleClient(model, base_url, **kwargs)

+    if provider_lower == "azure":
+        from .azure_client import AzureOpenAIClient
+        return AzureOpenAIClient(model, base_url, **kwargs)
+
    raise ValueError(f"Unsupported LLM provider: {provider}")
--- a/tradingagents/llm_clients/google_client.py
+++ b/tradingagents/llm_clients/google_client.py
@@ -25,12 +25,21 @@ class GoogleClient(BaseLLMClient):

    def get_llm(self) -> Any:
        """Return configured ChatGoogleGenerativeAI instance."""
+        self.warn_if_unknown_model()
        llm_kwargs = {"model": self.model}

-        for key in ("timeout", "max_retries", "google_api_key", "callbacks", "http_client", "http_async_client"):
+        if self.base_url:
+            llm_kwargs["base_url"] = self.base_url
+
+        for key in ("timeout", "max_retries", "callbacks", "http_client", "http_async_client"):
            if key in self.kwargs:
                llm_kwargs[key] = self.kwargs[key]

+        # Unified api_key maps to provider-specific google_api_key
+        google_api_key = self.kwargs.get("api_key") or self.kwargs.get("google_api_key")
+        if google_api_key:
+            llm_kwargs["google_api_key"] = google_api_key
+
        # Map thinking_level to appropriate API param based on model
        # Gemini 3 Pro: low, high
        # Gemini 3 Flash: minimal, low, medium, high
--- a/tradingagents/llm_clients/model_catalog.py
+++ b/tradingagents/llm_clients/model_catalog.py
@@ -0,0 +1,136 @@
+"""Shared model catalog for CLI selections and validation."""
+
+from __future__ import annotations
+
+from typing import Dict, List, Tuple
+
+ModelOption = Tuple[str, str]
+ProviderModeOptions = Dict[str, Dict[str, List[ModelOption]]]
+
+
+MODEL_OPTIONS: ProviderModeOptions = {
+    "openai": {
+        "quick": [
+            ("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
+            ("GPT-5.4 Nano - Cheapest, high-volume tasks", "gpt-5.4-nano"),
+            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
+            ("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
+        ],
+        "deep": [
+            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
+            ("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
+            ("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
+            ("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
+        ],
+    },
+    "anthropic": {
+        "quick": [
+            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
+            ("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
+            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
+        ],
+        "deep": [
+            ("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
+            ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
+            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
+            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
+        ],
+    },
+    "google": {
+        "quick": [
+            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
+            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
+            ("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
+            ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
+        ],
+        "deep": [
+            ("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
+            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
+            ("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
+            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
+        ],
+    },
+    "xai": {
+        "quick": [
+            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
+            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
+            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
+        ],
+        "deep": [
+            ("Grok 4 - Flagship model", "grok-4-0709"),
+            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
+            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
+            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
+        ],
+    },
+    "deepseek": {
+        "quick": [
+            ("DeepSeek V4 Flash - Latest V4 fast model", "deepseek-v4-flash"),
+            ("DeepSeek V3.2", "deepseek-chat"),
+            ("Custom model ID", "custom"),
+        ],
+        "deep": [
+            ("DeepSeek V4 Pro - Latest V4 flagship model", "deepseek-v4-pro"),
+            ("DeepSeek V3.2 (thinking)", "deepseek-reasoner"),
+            ("DeepSeek V3.2", "deepseek-chat"),
+            ("Custom model ID", "custom"),
+        ],
+    },
+    "qwen": {
+        "quick": [
+            ("Qwen 3.5 Flash", "qwen3.5-flash"),
+            ("Qwen Plus", "qwen-plus"),
+            ("Custom model ID", "custom"),
+        ],
+        "deep": [
+            ("Qwen 3.6 Plus", "qwen3.6-plus"),
+            ("Qwen 3.5 Plus", "qwen3.5-plus"),
+            ("Qwen 3 Max", "qwen3-max"),
+            ("Custom model ID", "custom"),
+        ],
+    },
+    "glm": {
+        "quick": [
+            ("GLM-4.7", "glm-4.7"),
+            ("GLM-5", "glm-5"),
+            ("Custom model ID", "custom"),
+        ],
+        "deep": [
+            ("GLM-5.1", "glm-5.1"),
+            ("GLM-5", "glm-5"),
+            ("Custom model ID", "custom"),
+        ],
+    },
+    # OpenRouter: fetched dynamically. Azure: any deployed model name.
+    "ollama": {
+        "quick": [
+            ("Qwen3:latest (8B, local)", "qwen3:latest"),
+            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
+            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
+        ],
+        "deep": [
+            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
+            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
+            ("Qwen3:latest (8B, local)", "qwen3:latest"),
+        ],
+    },
+}
+
+
+def get_model_options(provider: str, mode: str) -> List[ModelOption]:
+    """Return shared model options for a provider and selection mode."""
+    return MODEL_OPTIONS[provider.lower()][mode]
+
+
+def get_known_models() -> Dict[str, List[str]]:
+    """Build known model names from the shared CLI catalog."""
+    return {
+        provider: sorted(
+            {
+                value
+                for options in mode_options.values()
+                for _, value in options
+            }
+        )
+        for provider, mode_options in MODEL_OPTIONS.items()
+    }
--- a/tradingagents/llm_clients/openai_client.py
+++ b/tradingagents/llm_clients/openai_client.py
@@ -1,6 +1,7 @@
 import os
 from typing import Any, Optional

+from langchain_core.messages import AIMessage
 from langchain_openai import ChatOpenAI

 from .base_client import BaseLLMClient, normalize_content
@@ -11,13 +12,97 @@ class NormalizedChatOpenAI(ChatOpenAI):
    """ChatOpenAI with normalized content output.

    The Responses API returns content as a list of typed blocks
-    (reasoning, text, etc.). This normalizes to string for consistent
-    downstream handling.
+    (reasoning, text, etc.). ``invoke`` normalizes to string for
+    consistent downstream handling. ``with_structured_output`` defaults
+    to function-calling so the Responses-API parse path is avoided
+    (langchain-openai's parse path emits noisy
+    PydanticSerializationUnexpectedValue warnings per call without
+    affecting correctness).
+
+    Provider-specific quirks (e.g. DeepSeek's thinking mode) live in
+    purpose-built subclasses below so this base class stays small.
    """

    def invoke(self, input, config=None, **kwargs):
        return normalize_content(super().invoke(input, config, **kwargs))

+    def with_structured_output(self, schema, *, method=None, **kwargs):
+        if method is None:
+            method = "function_calling"
+        return super().with_structured_output(schema, method=method, **kwargs)
+
+
+def _input_to_messages(input_: Any) -> list:
+    """Normalise a langchain LLM input to a list of message objects.
+
+    Accepts a list of messages, a ``ChatPromptValue`` (from a
+    ChatPromptTemplate), or anything else (treated as no messages).
+    Used by providers that need to walk the outgoing message history;
+    in particular DeepSeek thinking-mode propagation must work for
+    both bare-list invocations and ChatPromptTemplate-driven ones, so
+    treating only ``list`` here would silently skip half the call sites.
+    """
+    if isinstance(input_, list):
+        return input_
+    if hasattr(input_, "to_messages"):
+        return input_.to_messages()
+    return []
+
+
+class DeepSeekChatOpenAI(NormalizedChatOpenAI):
+    """DeepSeek-specific overrides on top of the OpenAI-compatible client.
+
+    Two quirks that don't apply to other OpenAI-compatible providers:
+
+    1. **Thinking-mode round-trip.** When DeepSeek's thinking models return
+       a response with ``reasoning_content``, that field must be echoed
+       back as part of the assistant message on the next turn or the API
+       fails with HTTP 400. ``_create_chat_result`` captures the field on
+       receive and ``_get_request_payload`` re-attaches it on send.
+
+    2. **deepseek-reasoner has no tool_choice.** Structured output via
+       function-calling is unavailable, so we raise NotImplementedError
+       and let the agent factories fall back to free-text generation
+       (see ``tradingagents/agents/utils/structured.py``).
+    """
+
+    def _get_request_payload(self, input_, *, stop=None, **kwargs):
+        payload = super()._get_request_payload(input_, stop=stop, **kwargs)
+        outgoing = payload.get("messages", [])
+        for message_dict, message in zip(outgoing, _input_to_messages(input_)):
+            if not isinstance(message, AIMessage):
+                continue
+            reasoning = message.additional_kwargs.get("reasoning_content")
+            if reasoning is not None:
+                message_dict["reasoning_content"] = reasoning
+        return payload
+
+    def _create_chat_result(self, response, generation_info=None):
+        chat_result = super()._create_chat_result(response, generation_info)
+        response_dict = (
+            response
+            if isinstance(response, dict)
+            else response.model_dump(
+                exclude={"choices": {"__all__": {"message": {"parsed"}}}}
+            )
+        )
+        for generation, choice in zip(
+            chat_result.generations, response_dict.get("choices", [])
+        ):
+            reasoning = choice.get("message", {}).get("reasoning_content")
+            if reasoning is not None:
+                generation.message.additional_kwargs["reasoning_content"] = reasoning
+        return chat_result
+
+    def with_structured_output(self, schema, *, method=None, **kwargs):
+        if self.model_name == "deepseek-reasoner":
+            raise NotImplementedError(
+                "deepseek-reasoner does not support tool_choice; structured "
+                "output is unavailable. Agent factories fall back to "
+                "free-text generation automatically."
+            )
+        return super().with_structured_output(schema, method=method, **kwargs)
+
 # Kwargs forwarded from user config to ChatOpenAI
 _PASSTHROUGH_KWARGS = (
    "timeout", "max_retries", "reasoning_effort",
@@ -27,6 +112,9 @@ _PASSTHROUGH_KWARGS = (
 # Provider base URLs and API key env vars
 _PROVIDER_CONFIG = {
    "xai": ("https://api.x.ai/v1", "XAI_API_KEY"),
+    "deepseek": ("https://api.deepseek.com", "DEEPSEEK_API_KEY"),
+    "qwen": ("https://dashscope-intl.aliyuncs.com/compatible-mode/v1", "DASHSCOPE_API_KEY"),
+    "glm": ("https://api.z.ai/api/paas/v4/", "ZHIPU_API_KEY"),
    "openrouter": ("https://openrouter.ai/api/v1", "OPENROUTER_API_KEY"),
    "ollama": ("http://localhost:11434/v1", None),
 }
@@ -53,12 +141,15 @@ class OpenAIClient(BaseLLMClient):

    def get_llm(self) -> Any:
        """Return configured ChatOpenAI instance."""
+        self.warn_if_unknown_model()
        llm_kwargs = {"model": self.model}

-        # Provider-specific base URL and auth
+        # Provider-specific base URL and auth. An explicit base_url on the
+        # client (e.g. a corporate proxy) takes precedence over the
+        # provider default so users can route through their own gateway.
        if self.provider in _PROVIDER_CONFIG:
-            base_url, api_key_env = _PROVIDER_CONFIG[self.provider]
-            llm_kwargs["base_url"] = base_url
+            default_base, api_key_env = _PROVIDER_CONFIG[self.provider]
+            llm_kwargs["base_url"] = self.base_url or default_base
            if api_key_env:
                api_key = os.environ.get(api_key_env)
                if api_key:
@@ -78,7 +169,10 @@ class OpenAIClient(BaseLLMClient):
        if self.provider == "openai":
            llm_kwargs["use_responses_api"] = True

-        return NormalizedChatOpenAI(**llm_kwargs)
+        # DeepSeek's thinking-mode quirks live in their own subclass so the
+        # base NormalizedChatOpenAI stays free of provider-specific branches.
+        chat_cls = DeepSeekChatOpenAI if self.provider == "deepseek" else NormalizedChatOpenAI
+        return chat_cls(**llm_kwargs)

    def validate_model(self) -> bool:
        """Validate model for the provider."""
--- a/tradingagents/llm_clients/validators.py
+++ b/tradingagents/llm_clients/validators.py
@@ -1,53 +1,12 @@
-"""Model name validators for each provider.
+"""Model name validators for each provider."""
+
+from .model_catalog import get_known_models

-Only validates model names - does NOT enforce limits.
-Let LLM providers use their own defaults for unspecified params.
-"""

 VALID_MODELS = {
-    "openai": [
-        # GPT-5 series
-        "gpt-5.4-pro",
-        "gpt-5.4",
-        "gpt-5.2",
-        "gpt-5.1",
-        "gpt-5",
-        "gpt-5-mini",
-        "gpt-5-nano",
-        # GPT-4.1 series
-        "gpt-4.1",
-        "gpt-4.1-mini",
-        "gpt-4.1-nano",
-    ],
-    "anthropic": [
-        # Claude 4.6 series (latest)
-        "claude-opus-4-6",
-        "claude-sonnet-4-6",
-        # Claude 4.5 series
-        "claude-opus-4-5",
-        "claude-sonnet-4-5",
-        "claude-haiku-4-5",
-    ],
-    "google": [
-        # Gemini 3.1 series (preview)
-        "gemini-3.1-pro-preview",
-        "gemini-3.1-flash-lite-preview",
-        # Gemini 3 series (preview)
-        "gemini-3-flash-preview",
-        # Gemini 2.5 series
-        "gemini-2.5-pro",
-        "gemini-2.5-flash",
-        "gemini-2.5-flash-lite",
-    ],
-    "xai": [
-        # Grok 4.1 series
-        "grok-4-1-fast-reasoning",
-        "grok-4-1-fast-non-reasoning",
-        # Grok 4 series
-        "grok-4-0709",
-        "grok-4-fast-reasoning",
-        "grok-4-fast-non-reasoning",
-    ],
+    provider: models
+    for provider, models in get_known_models().items()
+    if provider not in ("ollama", "openrouter")
 }
Author	SHA1	Message	Date
Yijia-Xiao	7e9e7b83c7	feat: DeepSeek V4 thinking-mode round-trip via DeepSeekChatOpenAI subclass Resolves #599: thinking-mode models require reasoning_content to be echoed back across turns; multi-turn agent runs failed with HTTP 400. The fix isolates DeepSeek's quirks (reasoning_content round-trip and the deepseek-reasoner no-tool_choice limitation) into a subclass so the general OpenAI-compatible client stays untouched. Adds DeepSeek V4 Pro/Flash to the catalog. 9 new tests; rationale documented in the class docstrings. Design adapted from #600; #611 closed in favour of this approach.	2026-05-01 19:23:23 +00:00
Yijia-Xiao	2c97bad45c	fix(security): validate ticker before using as path component (#618 ) The ticker symbol reaches three filesystem-path construction sites (load_ohlcv cache filename, checkpointer DB path, _log_state results directory) without validation. A value containing path separators or "../" escapes the configured cache / checkpoints / results directory. Two attack vectors: - Programmatic callers passing arbitrary ticker to propagate() - Prompt injection via fetched news content steering the LLM into tool calls with attacker-chosen ticker Fix: new safe_ticker_component() validator in tradingagents/dataflows/ utils.py applied at all three sites. Allows the standard ticker character set ([A-Za-z0-9._\-\^], up to 32 chars) and explicitly rejects dot-only values like "." and ".." which would otherwise pass the regex but traverse parent directories. Seven test cases cover the accepted formats (BRK-B, 7203.T, ^GSPC, etc.) and the rejected inputs (path separators, null bytes, whitespace, empty values, overlong strings, dot-only values). Closes #618.	2026-05-01 18:56:36 +00:00
Yijia-Xiao	7c37249f80	chore: release v0.2.4 — structured agents, checkpoint, memory log, providers This release bundles substantial work since v0.2.3: - Structured-output Research Manager, Trader, and Portfolio Manager (canonical with_structured_output pattern, single LLM call per agent, rendered markdown preserves the existing report shape). - LangGraph checkpoint resume for crash recovery (--checkpoint flag). - Persistent decision log replacing the per-agent BM25 memory, with deferred reflection driven by yfinance returns + alpha vs SPY. - DeepSeek, Qwen, GLM, and Azure OpenAI provider support; dynamic OpenRouter model selection. - Docker support; cache and logs moved to ~/.tradingagents/ to fix Docker permission issues. - Windows UTF-8 encoding fix on every file I/O site. - 5-tier rating consistency (Buy / Overweight / Hold / Underweight / Sell) across Research Manager, Portfolio Manager, signal processor, memory log. Plus the small quality items in this commit: 1. Suppress noisy Pydantic serializer warnings from OpenAI Responses-API parse path by defaulting structured-output to method="function_calling" (root-cause fix, not a warnings filter — same typed result, no warnings). 2. Ship scripts/smoke_structured_output.py so contributors can verify their provider's structured-output path with one command. 3. Add opt-in memory_log_max_entries config — when set, oldest resolved memory log entries are pruned once the cap is exceeded; pending entries (unresolved) are never pruned. 4. backend_url default changed from the OpenAI URL to None so the per-provider client falls back to its native endpoint instead of leaking OpenAI's URL into Gemini / other clients. CHANGELOG.md added with the full v0.2.4 entry. 92 tests pass without API keys.	2026-04-25 22:16:09 +00:00
Yijia-Xiao	4016fd4efa	fix: stop leaking OpenAI base_url into non-OpenAI provider clients Default config had backend_url='https://api.openai.com/v1' which was forwarded to every provider client, including Google. ChatGoogleGenerativeAI constructed requests against that base, producing malformed URLs like https://api.openai.com/v1/v1beta/models/gemini-2.5-flash:generateContent that 404 with empty body. Discovered while running propagate() against Gemini end-to-end. The structured-output smoke worked because that path constructed the LLM without going through the factory and without forwarding backend_url; propagate() goes through TradingAgentsGraph.__init__ which forwards config['backend_url'] to every provider. Fix: default to None. Each provider client falls back to its own endpoint (api.openai.com for OpenAI via _PROVIDER_CONFIG, Gemini's default for Google, and so on). The CLI flow already sets backend_url explicitly per provider when the user picks one, so that path is unchanged. Verified: full propagate() now passes end-to-end on both OpenAI gpt-5.4-mini and Gemini gemini-3-flash-preview, with all nine structure/log/signal checks green for each.	2026-04-25 20:54:19 +00:00
Yijia-Xiao	bba147798f	feat: structured-output Trader and Research Manager (#434 , finishes the trio) Extends the canonical structured-output pattern from the Portfolio Manager to the other two decision-making agents. Each of the three agents now returns a typed Pydantic instance via llm.with_structured_output() in a single primary call, and a render helper turns the result into the same markdown shape downstream agents and saved reports already consume. - ResearchPlan: 5-tier recommendation, conversational rationale, concrete strategic actions for the trader. - TraderProposal: 3-tier action (transaction direction is naturally Buy / Hold / Sell — position sizing happens later at the Portfolio Manager), reasoning, and optional entry_price / stop_loss / position_sizing. Rendered output preserves the trailing "FINAL TRANSACTION PROPOSAL: BUY/HOLD/SELL" line for backward compatibility with the analyst stop-signal text. - PortfolioDecision: 5-tier rating, executive summary, investment thesis, optional price_target / time_horizon (unchanged). The shared try-structured-then-fallback pattern is extracted into tradingagents/agents/utils/structured.py (bind_structured + invoke_structured_or_freetext) so all three agents go through the same code path and log the same warning when a provider lacks structured output and the agent falls back to free-text generation. Net effect for users: every saved markdown report (research/manager.md, trading/trader.md, portfolio/decision.md) now has consistent section headers across runs and providers, easier to scan. Net effect for the runtime: the rating extraction round-trip is gone — the rating comes from the structured response itself, not a second LLM call. SignalProcessor was already simplified to a heuristic adapter in the previous commit. 11 new tests in tests/test_structured_agents.py cover the Trader and Research Manager render functions, structured-output happy paths, and free-text fallback. Full suite: 88 tests pass in ~2s without API keys.	2026-04-25 20:27:23 +00:00
Yijia-Xiao	0fda24515f	feat: structured-output Portfolio Manager + 5-tier rating consistency (#434 ) Three related changes that take the rating pipeline from heuristic-only to type-safe at the source. 1) Research Manager prompt now uses the same 5-tier scale (Buy / Overweight / Hold / Underweight / Sell) as the Portfolio Manager, signal_processing, and the memory log. The prior 3-tier wording (Buy / Sell / Hold) was the only remaining inconsistency in the pipeline. 2) Centralise the 5-tier vocabulary and the heuristic prose-rating parser into tradingagents/agents/utils/rating.py. Both the memory log and the signal processor now share the same parser instead of duplicating regex and word-walker logic. 3) Make structured output a first-class part of the Portfolio Manager's primary call. The PM uses llm.with_structured_output(PortfolioDecision) so each provider's native structured-output mode (json_schema for OpenAI/xAI, response_schema for Gemini, tool-use for Anthropic, function_calling for OpenAI-compatible providers) yields a typed Pydantic instance directly. A render helper turns that instance back into the same markdown shape downstream consumers (memory log, CLI display, saved reports) already expect, so no other code has to know the PM now produces structured output. Providers without structured support fall back gracefully to free-text + the deterministic heuristic. The previous SignalProcessor had been making a second LLM call to re-extract the rating from the PM's prose; that round-trip is now eliminated. SignalProcessor is a thin adapter over parse_rating(), makes zero LLM calls, and stays for backwards compatibility with process_signal() callers. Schema (PortfolioDecision) captures rating + executive_summary + investment_thesis + optional price_target + time_horizon, with field descriptions doubling as output instructions. Agent prose remains the primary artifact; structured output is layered onto the PM only because it is the one agent whose output has machine-readable downstream consumers. 15 new tests cover the heuristic parser (markdown-bold edge cases that had no coverage before), the structured PM happy path, the free-text fallback path, and that SignalProcessor never invokes the LLM. Full suite: 77 tests pass in ~2s without API keys.	2026-04-25 19:57:26 +00:00
Yijia-Xiao	4cbd4b086f	feat: add LangGraph checkpoint resume for crash recovery (#594 ) Long analyses can take many minutes; a crash or interruption forced users to re-run from scratch and re-pay every LLM call. This adds an opt-in checkpoint layer backed by per-ticker SQLite databases so the graph resumes from the last successful node. How to use: - CLI: tradingagents analyze --checkpoint - CLI: tradingagents analyze --clear-checkpoints - Python: config["checkpoint_enabled"] = True Lifecycle: - propagate() recompiles the graph with a SqliteSaver when enabled and injects a deterministic thread_id derived from ticker+date so the same ticker+date resumes while a different date starts fresh. - On successful completion the per-thread checkpoint rows are cleared. - The context manager is closed in a try/finally so a crash never leaks the SQLite connection or leaves the graph in checkpoint mode. Storage: ~/.tradingagents/cache/checkpoints/<TICKER>.db (override via TRADINGAGENTS_CACHE_DIR). The checkpointer module is new (tradingagents/graph/checkpointer.py) and the GraphSetup now returns the uncompiled workflow so it can be recompiled with a saver when needed. Adds langgraph-checkpoint-sqlite>=2.0.0 dependency. 3 new tests verify the crash/resume cycle and that a different date starts fresh.	2026-04-25 08:47:15 +00:00
Yijia-Xiao	ebd2e12e67	feat: replace per-agent BM25 memory with persistent decision log (#578 , #563 , #564 , #579 ) The previous per-agent BM25 memory was effectively dead code — its only caller was a commented-out line in main.py. Replace it with a single append-only markdown decision log driven by the propagate() lifecycle. Lifecycle: - store_decision() appends a pending entry at the end of every run - _resolve_pending_entries() runs at the start of the next same-ticker run, fetches yfinance returns + alpha vs SPY, and writes one LLM reflection per resolved entry through an atomic temp-file rename - Portfolio Manager consumes state["past_context"] (5 most recent same-ticker entries plus 3 cross-ticker reflection-only excerpts) Storage at ~/.tradingagents/memory/trading_memory.md (override: TRADINGAGENTS_MEMORY_LOG_PATH). Tag schema: - Pending: [YYYY-MM-DD \| TICKER \| Rating \| pending] - Resolved: [YYYY-MM-DD \| TICKER \| Rating \| +X.X% \| +Y.Y% \| Nd] Removes rank-bm25 dependency and the legacy reflect_and_remember() plumbing across reflection.py, trading_graph.py, and the agent factories. 49 new tests in tests/test_memory_log.py cover the storage, deferred reflection, prompt injection, and legacy-removal paths. Full suite (58 tests) passes in under 2 seconds without API keys.	2026-04-25 08:24:03 +00:00
Yijia-Xiao	f85f5d9f5d	test: lazy-load LLM provider clients and add API-key fixtures so the test suite runs cleanly without credentials (#588 )	2026-04-25 07:41:36 +00:00
Yijia-Xiao	8e7654f0df	fix: drop past-memory directive and placeholder from agent prompts when memory is empty (#572 )	2026-04-25 07:41:36 +00:00
Yijia-Xiao	872b063e69	fix: use explicit encoding="utf-8" for all file I/O so Windows users avoid cp1252 crashes (#543 , #550 , #576 )	2026-04-25 07:25:32 +00:00
Zhigong Liu	6abc768c1d	feat: replace per-agent BM25 memory with persistent append-only decision log Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-04-19 22:43:14 -04:00
Zhigong Liu	8536ccacdd	chore: ignore CLAUDE.md (local AI assistant context file) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-04-17 18:57:31 -04:00
Yijia-Xiao	fa4d01c23a	fix: process all chunk messages for tool call logging, harden memory score normalization (#534 , #531 )	2026-04-13 07:21:33 +00:00
Yijia-Xiao	b0f6058299	feat: add DeepSeek, Qwen, GLM, and Azure OpenAI provider support	2026-04-13 07:12:07 +00:00
Yijia-Xiao	59d6b2152d	fix: use ~/.tradingagents/ for cache and logs, resolving Docker permission issue (#519 )	2026-04-13 05:26:04 +00:00
Yijia-Xiao	10c136f49c	feat: add Docker support for cross-platform deployment	2026-04-04 08:14:01 +00:00
Yijia-Xiao	4f965bf46a	feat: dynamic OpenRouter model selection with search (#482 , #337 )	2026-04-04 07:56:44 +00:00
Yijia-Xiao	bdb9c29d44	refactor: remove stale imports, use configurable results path (#499 )	2026-04-04 07:35:35 +00:00
Yijia-Xiao	bdc5fc62d3	chore: bump langchain-google-genai minimum to 4.0.0 for thought signature support	2026-04-04 07:28:03 +00:00
Yijia-Xiao	78fb66aed1	fix: normalize indicator names to lowercase (#490 )	2026-04-04 07:23:31 +00:00
Yijia-Xiao	7269f877c1	fix: portfolio manager reads trader's proposal and research plan (#503 )	2026-04-04 07:22:01 +00:00
Yijia-Xiao	28d5cc661f	fix: add missing pandas import in y_finance.py (#488 )	2026-04-04 07:14:10 +00:00
Yijia-Xiao	7004dfe554	fix: remove hardcoded Google endpoint that caused 404 (#493 , #496 )	2026-04-04 07:07:53 +00:00
Yijia-Xiao	4641c03340	TradingAgents v0.2.3	2026-03-29 19:50:46 +00:00
Yijia-Xiao	e75d17bc51	chore: update model lists and defaults to GPT-5.4 family	2026-03-29 19:45:36 +00:00
Yijia-Xiao	6cddd26d6e	feat: multi-language output support for analyst reports and final decision (#472 )	2026-03-29 19:19:01 +00:00
Yijia Xiao	c61242a28c	Merge pull request #464 from CadeYu/sync-validator-models sync model validation with cli catalog	2026-03-29 11:07:51 -07:00
Yijia-Xiao	58e99421bd	fix: pass base_url to Google and Anthropic clients for proxy support (#427 )	2026-03-29 17:59:52 +00:00
Yijia Xiao	46e1b600b8	Merge pull request #453 from javierdejesusda/fix/standardize-google-api-key fix(llm_clients): standardize Google API key to unified api_key param	2026-03-29 10:54:28 -07:00
Yijia-Xiao	ae8c8aebe8	fix: gracefully handle invalid indicator names in tool calls (#429 )	2026-03-29 17:50:30 +00:00
Yijia-Xiao	f3f58bdbdc	fix: add yf_retry to yfinance news fetchers (#445 )	2026-03-29 17:42:24 +00:00
Yijia-Xiao	e1113880a1	fix: prevent look-ahead bias in backtesting data fetchers (#475 )	2026-03-29 17:34:35 +00:00
CadeYu	bd6a5b75b5	fix model catalog typing and known-model helper	2026-03-25 21:46:56 +08:00
CadeYu	8793336dad	sync model validation with cli catalog	2026-03-25 21:23:02 +08:00
javierdejesusda	047b38971c	refactor: simplify api_key mapping and consolidate tests Apply review suggestions: use concise `or` pattern for API key resolution, consolidate tests into parameterized subTest, move import to module level per PEP 8.	2026-03-24 14:52:51 +01:00
javierdejesusda	f5026009f9	fix(llm_clients): standardize Google API key to unified api_key param GoogleClient now accepts the unified `api_key` parameter used by OpenAI and Anthropic clients, mapping it to the provider-specific `google_api_key` that ChatGoogleGenerativeAI expects. Legacy `google_api_key` still works for backward compatibility. Resolves TODO.md item #2 (inconsistent parameter handling).	2026-03-24 14:35:02 +01:00