LangGraph

LangGraph is a low-level orchestration framework for building stateful agents, released under the MIT license. It lets you coordinate multiple actors across many steps as a graph, with loops, branching and persistent state.

2026 update

It no longer needs LangChain: the README says LangGraph “can be used without LangChain”, although it integrates with LangChain’s products. It highlights durable execution (agents resume after failures), human-in-the-loop inspection and editing of state, and short- and long-term memory.

The framework is free. Hosting and observability go through LangSmith, which has a free Developer plan (one seat, 5k traces a month) and a Plus plan at $39 per seat per month, with deployments metered on top, per langchain.com/pricing.

See how it compares in CrewAI vs LangGraph vs AutoGen.

Why it matters

Standard RAG chains are linear (DAGs). Real-world agentic workflows often need loops (e.g. “Draft -> Critique -> Improve -> Repeat”). LangGraph makes these loops first-class citizens.

Key Features

• Cyclic graphs: Build workflows with loops and conditional branching
• State management: Maintain and update state across multiple steps
• Multi-actor coordination: Orchestrate multiple agents working together
• Persistence: Save and resume workflow state
• LangChain integration: Seamless compatibility with LangChain ecosystem

Use Cases

• Iterative refinement: Create workflows that draft, review, and improve content in cycles
• Multi-step reasoning: Build agents that reason through complex problems with backtracking
• Human-in-the-loop: Design workflows that pause for human input and resume
• Long-running processes: Handle workflows that span hours or days with state persistence
• Complex decision trees: Implement sophisticated branching logic based on intermediate results

Benefits

LangGraph provides unparalleled control over agentic workflows compared to simpler frameworks. The graph-based approach makes complex workflows visualizable and debuggable. State persistence enables long-running processes that can survive interruptions. While the learning curve is steeper than alternatives, the power and flexibility make it ideal for production systems where reliability and control are paramount. Built by the LangChain team, it works on its own and integrates with the broader LangChain ecosystem.