langgraph vs llm_agents
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
llm_agentsopen-source
Build agents which are controlled by LLMs
Metrics
| langgraph | llm_agents | |
|---|---|---|
| Stars | 28.0k | 1.0k |
| Star velocity /mo | 2.5k | 0 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8081963872278098 | 0.2903146293133927 |
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
- +Educational transparency with minimal abstraction layers for understanding agent mechanics
- +Easy customization and extension with simple tool integration API
- +Lightweight codebase that's easy to modify and debug
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
- -Limited built-in tools compared to comprehensive frameworks like LangChain
- -Requires manual setup of API keys for OpenAI and optional SERPAPI services
- -Lacks advanced features like memory management, conversation history, or production optimizations
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 how LLM agents work by studying and modifying a simple implementation
- •Rapid prototyping of custom agent workflows with specific tool combinations
- •Building educational demos or simple automation tasks where transparency matters more than features