LangGraph¶
LangGraph is an open-source framework (by LangChain) for building complex, stateful AI agent workflows as directed graphs — where nodes represent processing steps and edges represent transitions.
Context & Background¶
LangGraph represents one approach to building custom AI agent systems, designed for researchers and developers who need more control than chatbot interfaces provide but don't want to build agent infrastructure from scratch.
Key features:
- Graph-based design: Workflows defined as nodes (actions) and edges (transitions)
- State management: Built-in handling of conversation and task state
- Human-in-the-loop: Native support for human review checkpoints
- Multi-agent: Coordination between multiple specialized agents
Practical Implications¶
- Consider your needs: LangGraph is powerful but has a learning curve — use simpler tools (Claude Code, API calls) when they suffice
- Good for custom pipelines: When standard tools don't fit your specific research workflow
- Requires Python skills: Building with LangGraph requires comfortable Python programming
Key Sources¶
- Arin Dube Thread: LLMs Haven't Raised NBER Working Papers Above Trend
- The Shape of AI: Jaggedness, Bottlenecks and Salients
- Professional Productivity
- AI Agents for Economics Research
- LLM Collaboration and Reasoning -- Generative AI for Economic Research