Move config loading and validation functions from openai_client.py and factory.py into a new shared config_loader.py module. This centralizes configuration handling, reduces code duplication, and improves maintainability. The factory now gracefully falls back to default provider types if config loading fails.
- Add LM Studio as a new provider option in config.json
- Introduce LLM_PROVIDER_TYPES configuration for provider-to-client mapping
- Refactor factory.py to use centralized provider type configuration
- Add results and reports directories to .gitignore
The refactor centralizes provider configuration, making it easier to add new providers in the future without modifying the factory logic. LM Studio support enables local model hosting integration.
- Change _exit_with_config_error return type to NoReturn for clarity
- Replace exit() with raise SystemExit() for better testability
- Remove unreachable return statement after error exit
Remove .env.example and move LLM provider settings, base URLs, and model options to a centralized config.json file. Update default_config.py, openai_client.py, and cli/utils.py to load configuration from this file, improving maintainability and reducing hardcoded values across the codebase.
Add effort parameter (high/medium/low) for Claude 4.5+ and 4.6 models,
consistent with OpenAI reasoning_effort and Google thinking_level.
Also add content normalization for Anthropic responses.
- Point requirements.txt to pyproject.toml as single source of truth
- Resolve welcome.txt path relative to module for CLI portability
- Include cli/static files in package build
- Extract shared normalize_content for OpenAI Responses API and
Gemini 3 list-format responses into base_client.py
- Update README install and CLI usage instructions
Enable use_responses_api for native OpenAI provider, which supports
reasoning_effort with function tools across all model families.
Removes the UnifiedChatOpenAI subclass workaround.
Closes#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 save prompt after analysis with organized subfolder structure
- Fix report truncation by using sequential panels instead of Columns
- Add optional full report display prompt
- Add update_analyst_statuses() for unified status logic (pending/in_progress/completed)
- Normalize analyst selection to predefined ANALYST_ORDER for consistent execution
- Add message deduplication to prevent duplicates from stream_mode=values
- Restructure streaming loop so state handlers run on every chunk
- 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)
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