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.

tradingagents/llm_clients/openai_client.py

@@ -18,6 +18,22 @@ class NormalizedChatOpenAI(ChatOpenAI):
     def invoke(self, input, config=None, **kwargs):
         return normalize_content(super().invoke(input, config, **kwargs))
 
+    def with_structured_output(self, schema, *, method=None, **kwargs):
+        """Wrap with structured output, defaulting to function_calling for OpenAI.
+
+        langchain-openai's Responses-API-parse path (the default for json_schema
+        when use_responses_api=True) calls response.model_dump(...) on the OpenAI
+        SDK's union-typed parsed response, which makes Pydantic emit ~20
+        PydanticSerializationUnexpectedValue warnings per call. The function-calling
+        path returns a plain tool-call shape that does not trigger that
+        serialization, so it is the cleaner choice for our combination of
+        use_responses_api=True + with_structured_output. Both paths use OpenAI's
+        strict mode and produce the same typed Pydantic instance.
+        """
+        if method is None:
+            method = "function_calling"
+        return super().with_structured_output(schema, method=method, **kwargs)
+
 # Kwargs forwarded from user config to ChatOpenAI
 _PASSTHROUGH_KWARGS = (
     "timeout", "max_retries", "reasoning_effort",