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/).

cli/main.py

@@ -926,7 +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()
 
@@ -943,6 +943,7 @@ def run_analysis():
     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()
@@ -1197,8 +1198,23 @@ def run_analysis():
 
 
 @app.command()
-def analyze():
+def analyze(
-    run_analysis()
+    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__":

pyproject.toml

@@ -16,6 +16,7 @@ dependencies = [
     "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",

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()

tests/test_memory_log.py

@@ -629,7 +629,9 @@ class TestLegacyRemoval:
             create_portfolio_manager(mock_llm, memory=MagicMock())
 
     def test_full_pipeline_no_regression(self, tmp_path):
-        """propagate() completes without AttributeError after legacy cleanup."""
+        """propagate() completes and stores the decision after the redesign."""
+        import functools
+
         fake_state = {
             "final_trade_decision": "Rating: Buy\nBuy NVDA.",
             "company_of_interest": "NVDA",
@@ -660,6 +662,11 @@ class TestLegacyRemoval:
         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

tradingagents/default_config.py

@@ -16,6 +16,9 @@ DEFAULT_CONFIG = {
     "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",

tradingagents/graph/checkpointer.py

@@ -0,0 +1,86 @@
+"""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
+
+
+def _db_path(data_dir: str | Path, ticker: str) -> Path:
+    """Return the SQLite checkpoint DB path for a ticker."""
+    p = Path(data_dir) / "checkpoints"
+    p.mkdir(parents=True, exist_ok=True)
+    return p / f"{ticker.upper()}.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()

tradingagents/graph/setup.py

@@ -179,5 +179,4 @@ class GraphSetup:
 
         workflow.add_edge("Portfolio Manager", END)
 
-        # Compile and return
+        return workflow
-        return workflow.compile()

tradingagents/graph/trading_graph.py

@@ -38,6 +38,7 @@ 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
@@ -123,8 +124,10 @@ class TradingAgentsGraph:
         self.ticker = None
         self.log_states_dict = {}  # date to full state dict
 
-        # Set up the graph
+        # Set up the graph: keep the workflow for recompilation with a checkpointer.
-        self.graph = self.graph_setup.setup_graph(selected_analysts)
+        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."""
@@ -259,23 +262,58 @@ class TradingAgentsGraph:
             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."""
+        """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
 
-        # Resolve any pending log entries for this ticker before the pipeline runs.
+        # Resolve any pending memory-log entries for this ticker before the pipeline runs.
-        # This adds the outcome + reflection from the previous run at zero latency cost.
         self._resolve_pending_entries(company_name)
 
-        # Initialize state — inject memory log context for PM
+        # 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, 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:
@@ -283,26 +321,29 @@ 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)
 
-        # Store decision for deferred reflection.
+        # 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"],
         )
 
-        # Return decision and processed signal
+        # 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):