fix(cli): resume a checkpoint without duplicating messages or leaking the saver

- on resume, the CLI and propagate re-passed the initial state to a thread with
  an existing checkpoint; nodes do not re-run, but the message reducer appended
  the initial messages again, duplicating them in the resumed state
- feed None on resume (checkpoint_input) so LangGraph continues the interrupted
  run, and wrap the CLI stream in try/finally so the checkpointer tears down even
  if the stream raises
- correct the _fetch_returns docstring to the 4-tuple return #1249

cli/main.py

@@ -1129,16 +1129,19 @@ def run_analysis(checkpoint: bool | None = None):
 
         # Recompile with a checkpointer and inject the thread_id so --checkpoint
         # actually saves and resumes on the CLI path (#1249); a no-op when
-        # checkpointing is disabled. Paired with end_checkpoint after the stream.
+        # checkpointing is disabled. Torn down in the finally below.
         checkpoint_tid = graph.begin_checkpoint(
             selections["ticker"], selections["analysis_date"], selections["asset_type"]
         )
         if checkpoint_tid is not None:
             args.setdefault("config", {}).setdefault("configurable", {})["thread_id"] = checkpoint_tid
 
-        # Stream the analysis
+        # Stream the analysis. On resume, feed None so LangGraph continues the
+        # interrupted run instead of re-appending the initial state (#1249); the
+        # try/finally tears the checkpointer down even if the stream raises.
         trace = []
-        for chunk in graph.graph.stream(init_agent_state, **args):
+        try:
+            for chunk in graph.graph.stream(graph.checkpoint_input(init_agent_state), **args):
                 # Process all messages in chunk, deduplicating by message ID
                 for message in chunk.get("messages", []):
                     msg_id = getattr(message, "id", None)
@@ -1240,12 +1243,13 @@ def run_analysis(checkpoint: bool | None = None):
 
                 trace.append(chunk)
 
-        # The stream completed: drop this run's checkpoint and restore the plain
+            # Clean run: drop this run's checkpoint so a later run starts fresh.
-        # graph (#1249). A mid-stream failure skips this, leaving the checkpoint
+            # A mid-stream failure skips this, keeping the checkpoint for resume.
-        # in place so the next run resumes.
             graph.clear_checkpoint_on_success(
                 selections["ticker"], selections["analysis_date"], selections["asset_type"]
             )
+        finally:
+            # Always restore the plain uncheckpointed graph, even on failure.
             graph.end_checkpoint()
 
         # Streamed chunks are per-node deltas, not full state. Merge them

tests/test_checkpoint_lifecycle.py

@@ -84,6 +84,38 @@ def test_begin_returns_thread_id_and_recompiles():
         assert g._checkpointer_ctx is None  # restored
 
 
+@pytest.mark.unit
+def test_checkpoint_input_is_none_only_when_resuming():
+    global _should_crash
+    with tempfile.TemporaryDirectory() as tmp:
+        init = {"count": 0}
+        args = ("AAPL", "2026-05-08", "stock")
+        # Fresh run: no checkpoint yet -> stream the initial state, then crash.
+        g1 = _bare_graph(tmp)
+        tid = g1.begin_checkpoint(*args)
+        try:
+            assert g1._resuming is False
+            assert g1.checkpoint_input(init) is init  # not resuming -> initial state
+            _should_crash = True
+            with pytest.raises(RuntimeError):
+                for _ in g1.graph.stream(init, config={"configurable": {"thread_id": tid}}):
+                    pass
+        finally:
+            g1.end_checkpoint()
+        assert g1.checkpoint_input(init) is init  # reset after teardown
+
+        # A later run finds the checkpoint -> resume by feeding None, not the
+        # initial state (re-passing it would duplicate messages, #1249).
+        _should_crash = False
+        g2 = _bare_graph(tmp)
+        g2.begin_checkpoint(*args)
+        try:
+            assert g2._resuming is True
+            assert g2.checkpoint_input(init) is None
+        finally:
+            g2.end_checkpoint()
+
+
 @pytest.mark.unit
 def test_cli_style_usage_saves_then_resumes():
     global _should_crash

tradingagents/graph/trading_graph.py

@@ -163,6 +163,7 @@ class TradingAgentsGraph:
         self.workflow = self.graph_setup.setup_graph(selected_analysts)
         self.graph = self.workflow.compile()
         self._checkpointer_ctx = None
+        self._resuming = False
 
     def _get_provider_kwargs(self) -> dict[str, Any]:
         """Get provider-specific kwargs for LLM client creation."""
@@ -277,8 +278,10 @@ class TradingAgentsGraph:
 
         ``benchmark`` is the index used as the alpha baseline (resolved by the
         caller via ``_resolve_benchmark``). Returns ``(raw_return, alpha_return,
-        actual_holding_days)`` or ``(None, None, None)`` if price data is
+        actual_holding_days, resolution_date)`` — where ``resolution_date`` is
-        unavailable (too recent, delisted, or network error).
+        the date of the last price bar used, i.e. when the outcome became known
+        (#1251) — or ``(None, None, None, None)`` if price data is unavailable
+        (too recent, delisted, or network error).
         """
         from tradingagents.dataflows.symbol_utils import normalize_symbol
 
@@ -433,6 +436,7 @@ class TradingAgentsGraph:
         lived only inside ``propagate`` and the CLI streamed the checkpointer-less
         graph, making the flag a no-op.
         """
+        self._resuming = False
         if not self.config.get("checkpoint_enabled"):
             return None
         signature = self._run_signature(asset_type)
@@ -443,18 +447,30 @@ class TradingAgentsGraph:
         step = checkpoint_step(
             self.config["data_cache_dir"], company_name, str(trade_date), signature
         )
+        self._resuming = step is not None
         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)
         return thread_id(company_name, str(trade_date), signature)
 
+    def checkpoint_input(self, init_state):
+        """The value to stream/invoke: ``None`` to resume an existing checkpoint,
+        else the initial state for a fresh run.
+
+        LangGraph resumes an interrupted thread when invoked with ``None``;
+        re-passing the initial state instead appends it through the message
+        reducer, duplicating messages in the resumed state (#1249).
+        """
+        return None if self._resuming else init_state
+
     def end_checkpoint(self):
         """Restore the plain uncheckpointed graph after a checkpointed run."""
         if self._checkpointer_ctx is not None:
             self._checkpointer_ctx.__exit__(None, None, None)
             self._checkpointer_ctx = None
             self.graph = self.workflow.compile()
+        self._resuming = False
 
     @contextmanager
     def checkpoint_scope(self, company_name, trade_date, asset_type: str = "stock"):
@@ -512,10 +528,12 @@ class TradingAgentsGraph:
         if checkpoint_thread_id is not None:
             args.setdefault("config", {}).setdefault("configurable", {})["thread_id"] = checkpoint_thread_id
 
+        # None resumes an existing checkpoint; init_agent_state starts fresh (#1249).
+        graph_input = self.checkpoint_input(init_agent_state)
         if self.debug:
             trace = []
             last_printed = None
-            for chunk in self.graph.stream(init_agent_state, **args):
+            for chunk in self.graph.stream(graph_input, **args):
                 if chunk["messages"]:
                     msg = chunk["messages"][-1]
                     # Nodes after the trader don't append to messages, so the
@@ -532,7 +550,7 @@ class TradingAgentsGraph:
             for chunk in trace:
                 final_state.update(chunk)
         else:
-            final_state = self.graph.invoke(init_agent_state, **args)
+            final_state = self.graph.invoke(graph_input, **args)
 
         # Store current state for reflection.
         self.curr_state = final_state