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.

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
-        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."""
@@ -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.
-        # This adds the outcome + reflection from the previous run at zero latency cost.
+        # Resolve any pending memory-log entries for this ticker before the pipeline runs.
         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):