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.

README.md

@@ -183,7 +183,7 @@ 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
 
@@ -207,7 +207,7 @@ 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["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
@@ -219,6 +219,34 @@ 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/).