Build resilient language agents as stateful graphs with cycles, branching, and persistence
Key Features
Multi-Agent Single Agent Human-in-the-Loop Streaming Async Support Short-Term Memory Long-Term Memory Shared Memory Plugin System Custom Tools MCP Protocol A2A Protocol DAG Workflows CLI Self-Hosted Cloud Hosted
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Strengths
- Controllable multi-agent flows
- Durable execution surviving failures
- Human-in-the-loop support
- Graph-based fine-grained control
Weaknesses
- Requires understanding graph concepts
- Tightly coupled to LangChain ecosystem
- Newer project with evolving API
LangGraph Details
| Organization | LangChain Inc |
| Organization Type | Company |
| Funding | Series b+ |
| Category | Framework |
| Subcategory | Orchestration |
| Deployment | Hybrid |
| Primary Language | Python |
| Runtime | Python 3.9+ |
| License | MIT |
| Commercial Use | Unrestricted |
| Install Command | pip install langgraph |
| GitHub Stars | 27,370 |
| GitHub Forks | 4,706 |
| Release Cadence | Weekly |
| Maturity | Stable |
| Pricing Model | Open core |
| Free Tier | MIT-licensed core is free |
| Self-Hosted Free | Yes |
| Cost Model | free + LLM costs |
| Community Size | Large |
| Community Activity | Very active |
| Sentiment | Positive |
| GPU Required | No |
| Research Date | 2026-03-24 |
| LLM Providers | All via LangChain |
| API Keys Required | LLM provider API key |
Use Cases
- Complex multi-agent workflows
- Human-in-the-loop approval workflows
- Long-running agent processes with persistence
- Branching and cyclical agent logic
When to Use
Best for: Complex multi-agent workflows needing durable execution and fine-grained control
Avoid when: Simple single-agent use cases or when LangChain dependency is unwanted
Original data from HuggingFace, OpenCompass and various public git repos.
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Release v20260328a