Per issue #403 analysis: the real problem is that reasoning_effort
with function tools requires the /v1/responses endpoint, but
LangChain's ChatOpenAI only supports /v1/chat/completions. This
affects all models (not just gpt-5.x vs o-series), so the safest
fix is to skip reasoning_effort and log an info message explaining
why.
Once LangChain adds /v1/responses support, this can be re-enabled.
The previous fix only checked deep_think_llm when deciding whether
to include reasoning_effort, but both LLMs shared the same kwargs.
This meant mixing an o-series deep model with a gpt-* quick model
would still crash.
Now each LLM gets its own kwargs by passing the model name to
_get_provider_kwargs. reasoning_effort is only added when the
specific model being configured is an o-series model.
reasoning_effort is only supported by o-series models (o1, o3,
o3-mini, o4-mini). Passing it to gpt-* models causes a 400 error
because those models don't support it on the chat completions
endpoint.
Now we check if the model name starts with 'o' before including
the parameter.
Fixes#403
- Add http_client and http_async_client parameters to all LLM clients
- OpenAIClient, GoogleClient, AnthropicClient now support custom httpx clients
- Fixes SSL certificate verification errors on Windows Conda environments
- Users can now pass custom httpx.Client with verify=False or custom certs
Fixes#369
- OpenAI: add GPT-5.4, GPT-5.4 Pro; remove o-series and legacy GPT-4o
- Anthropic: add Claude Opus 4.6, Sonnet 4.6; remove legacy 4.1/4.0/3.x
- Google: add Gemini 3.1 Pro, 3.1 Flash Lite; remove deprecated
gemini-3-pro-preview and Gemini 2.0 series
- xAI: clean up model list to match current API
- Simplify UnifiedChatOpenAI GPT-5 temperature handling
- Add missing tradingagents/__init__.py (fixes pip install building)
Add _clean_dataframe() to normalize stock DataFrames before stockstats:
coerce invalid dates/prices, drop rows missing Close, fill price gaps.
Also add on_bad_lines="skip" to all cached CSV reads.
LLMs (especially smaller models) sometimes pass multiple indicator
names as a single comma-separated string instead of making separate
tool calls. Split and process each individually at the tool boundary.
InvestDebateState was missing bull_history, bear_history, judge_decision.
RiskDebateState was missing aggressive_history, conservative_history,
neutral_history, latest_speaker, judge_decision. This caused KeyError
in _log_state() and reflection, especially with edge-case config values.
Prevents UnicodeEncodeError on Windows where the default encoding
(cp1252/gbk) cannot handle Unicode characters in LLM output.
Closes#77, closes#114, closes#126, closes#215, closes#332
- Add StatsCallbackHandler for tracking LLM calls, tool calls, and tokens
- Integrate callbacks into TradingAgentsGraph and all LLM clients
- Dynamic agent/report counts based on selected analysts
- Fix report completion counting (tied to agent completion)
- Replace hardcoded column indices with column name lookup
- Add mapping for all supported indicators to their expected CSV column names
- Handle missing columns gracefully with descriptive error messages
- Strip whitespace from header parsing for reliability
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Replace FinnHub with Alpha Vantage API in README documentation
- Implement comprehensive Alpha Vantage modules:
- Stock data (daily OHLCV with date filtering)
- Technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands, ATR)
- Fundamental data (overview, balance sheet, cashflow, income statement)
- News and sentiment data with insider transactions
- Update news analyst tools to use ticker-based news search
- Integrate Alpha Vantage vendor methods into interface routing
- Maintain backward compatibility with existing vendor system
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Added support for running CLI and Ollama server via Docker
- Introduced tests for local embeddings model and standalone Docker setup
- Enabled conditional Ollama server launch via LLM_PROVIDER