v3.4.0
v0.14.6
Features
- Inline image generation - Auto-generate and embed AI illustrations in reports using Google Gemini; configure via
IMAGE_GENERATION_ENABLED,GOOGLE_API_KEY,IMAGE_GENERATION_MODEL,IMAGE_GENERATION_STYLE - LangSmith integration - Native tracing and observability for research workflows; track LLM calls, token usage, sub-query generation, report steps, and costs via
LANGCHAIN_TRACING_V2andLANGCHAIN_API_KEY - Claude Code skills directory - Added
.claude/skills/with 1,500+ lineSKILL.md(architecture, workflows, method signatures, 8-step feature pattern) andREFERENCE.mdfor config/API/WebSocket lookups - Aggregated summary flag - New option for
quick_search
Improvements
- Image generation runs during research phase for seamless UX
- LLM analyzes research to identify visualization opportunities
- Updated README and documentation for new features
Such an exciting release with powerful new features.
🖼️ Inline Image Generation
GPT Researcher can now automatically generate and embed AI-created illustrations directly in your research reports using Google's Gemini models.
Key features:
- Pre-generation flow - Images are generated during research, not after, for seamless UX
- Context-aware - LLM analyzes your research to identify the best visualization opportunities
- Dark mode styling - Generated images match the app's aesthetic by default
- Fully configurable - Control max images, style (dark/light/auto), and model
Quick setup:
IMAGE_GENERATION_ENABLED=true
GOOGLE_API_KEY=your_key
IMAGE_GENERATION_MODEL=models/gemini-2.5-flash-image
IMAGE_GENERATION_STYLE=dark
📖 Image Generation Documentation
🔍 LangSmith Integration
Added native support for LangSmith tracing and observability. Monitor your research workflows, debug LLM calls, and analyze performance with full visibility into the research pipeline.
Quick setup:
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=your_langsmith_key
LANGCHAIN_PROJECT=gpt-researcher
What you can track:
- LLM calls and token usage
- Research planning and sub-query generation
- Report generation steps
- Cost analysis per research task
📖 LangSmith Logs Documentation
🤖 Claude Code Skills Integration
Added comprehensive .claude/skills/ directory that enables Claude Code to understand, use, and extend GPT Researcher effectively.
What's included:
SKILL.md(1,500+ lines) - Complete architecture, workflows, method signatures, data flow diagrams, and the 8-step feature pattern for adding new featuresREFERENCE.md- Quick lookup for all config vars, API endpoints, and WebSocket events- Real case studies including the Image Generation implementation as a reference
Benefits:
- Faster contributor onboarding with AI assistance
- Consistent code patterns across contributions
- End-to-end feature development following established patterns
📖 AI-Assisted Development Documentation
📚 Documentation Updates
- ✨ New: Image Generation guide
- ✨ New: AI-Assisted Development guide for Claude Code users
- 📝 Updated README with new features section
Installation
pip install --upgrade gpt-researcher
Or with Docker:
docker pull gptresearcher/gpt-researcher:latest
Full Changelog: v0.14.5...v0.14.6
What's Changed
- Add aggregated summary flag to quick_search by @TheSpaceGod in https://github.com/assafelovic/gpt-researcher/pull/1604
- feat: enable LangSmith tracing for enhanced observability by @tiandee in https://github.com/assafelovic/gpt-researcher/pull/1599
- added image generation with nano banana by @assafelovic in https://github.com/assafelovic/gpt-researcher/pull/1608
Full Changelog: https://github.com/assafelovic/gpt-researcher/compare/v3.3.9...v3.4.0