toolFrameworksOrchestrationPython
LangGraph
A stateful, graph-based orchestration framework for building controllable, multi-actor LLM agent workflows. Part of the LangChain ecosystem.
AgentEngineering Editorial2 min read
Overview
LangGraph is an open-source library (Apache 2.0) from LangChain, Inc. that models agent and multi-agent workflows as directed graphs. Each node in the graph is a Python function (or a LangChain runnable); edges define the flow of execution, including conditional branching.
The graph metaphor gives developers explicit, visual control over orchestration logic — in contrast to implicit, prompt-driven control flow.
Key Concepts
- State — a typed dictionary that flows through the graph, accumulating data from each node.
- Nodes — functions that read state, perform computation (including LLM calls), and return updated state.
- Edges — connections between nodes; can be unconditional or conditional (routing based on state).
- Checkpointing — built-in persistence of graph state, enabling pause, resume, and human-in-the-loop patterns.
When to Use LangGraph
LangGraph is well-suited to scenarios where you need:
- Explicit, inspectable control flow (regulatory compliance, safety-critical applications).
- Long-running workflows that may span multiple sessions or require human approval.
- Multi-agent coordination with defined handoff points.
- Fine-grained observability into each step of a complex workflow.
It is less suited to simple single-shot agent tasks where a direct LLM call with tool use is sufficient.
Resources
- GitHub:
langchain-ai/langgraph - Documentation:
langchain-ai.github.io/langgraph - LangGraph Cloud: managed deployment for production LangGraph applications.