LangGraph vs SWE-agent

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	LangGraph Agents	SWE-agent Agents
Tagline	Stateful, graph-based agent orchestration from LangChain.	Open-source autonomous agent framework that lets LLMs fix GitHub issues and find security vulnerabilities by using a custom agent-computer interface.
Category	Agents	Agents
Pricing	Freemium· Free open-source; LangGraph Platform paid	Free· Free and open-source; you pay your own LLM API costs
Model	BYO (Claude / GPT / open)	Multi-model (GPT-4o, Claude Sonnet, DeepSeek, local via LiteLLM)
Editorial score	8.8 / 10	—
Use cases	stateful agentshuman-in-loopproduction	github-issue-fixingautonomous-codingswe-benchctf-securityagent-research
Pros	Reliable, debuggable agent graphs Built-in persistence + HITL Production-grade Tight LangSmith integration	Open-source under MIT with a strong research pedigree (Princeton/Stanford) Model-agnostic via LiteLLM - swap GPT-4o, Claude, or local models freely Reproducible SWE-bench harness makes it a credible baseline for agent research EnIGMA mode extends the same loop to CTF-style security tasks
Cons	Steeper learning curve than CrewAI Verbose to set up	Officially in maintenance mode; team now recommends mini-swe-agent No hosted product, GUI, or managed service - CLI and YAML only LLM API costs on long-horizon tasks can be significant Setup requires Docker and comfort with Python tooling
Website	www.langchain.com	swe-agent.com

Pick LangGraph if

Pick SWE-agent if

✅ Open-source under MIT with a strong research pedigree (Princeton/Stanford)
✅ Model-agnostic via LiteLLM - swap GPT-4o, Claude, or local models freely
✅ Reproducible SWE-bench harness makes it a credible baseline for agent research
✅ EnIGMA mode extends the same loop to CTF-style security tasks