LangGraph vs Swarm
Side-by-side comparison of two AI agent tools
Short answer
- LangGraph is growing faster: +2,365 GitHub stars in the last 30 days vs +125 for Swarm.
- Pick LangGraph for: build resilient language agents as graphs. Pick Swarm for: educational framework exploring ergonomic, lightweight multi-agent orchestration.
From GitHub data refreshed daily.
LangGraphopen-source
Build resilient language agents as graphs.
Swarmopen-source
Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.
Metrics
| LangGraph | Swarm | |
|---|---|---|
| Stars | 42.7k | 22.0k |
| Star velocity /mo | 2.4k | 125.05263157894736 |
| Commits (90d) | 132 | 0 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 43.7M | — |
| Overall score | 0.8091319530692536 | 0.27558355927842015 |
Pros
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
- +Lightweight and highly controllable design that avoids steep learning curves while enabling complex multi-agent interactions
- +Highly customizable architecture allowing developers to build scalable, real-world solutions with flexible agent coordination patterns
- +Easily testable framework with simple primitives that make debugging and validation straightforward
Cons
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
- -Experimental and educational status means it's not intended for production use cases
- -Now officially replaced by OpenAI Agents SDK, making it a deprecated solution
- -Stateless design between calls requires external state management for persistent conversations
Use Cases
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
- •Learning and experimenting with multi-agent orchestration patterns in a controlled educational environment
- •Prototyping systems with large numbers of independent capabilities that are difficult to encode in single prompts
- •Building lightweight agent coordination systems where full state management isn't required
FAQ
- Which is more popular, LangGraph or Swarm?
- LangGraph has more GitHub stars (42,656 vs 22,034).
- Which is more actively developed, LangGraph or Swarm?
- LangGraph had more commits in the last 90 days (132 vs 0).
- Should I use LangGraph or Swarm?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.