fastagency vs langgraph
Side-by-side comparison of two AI agent tools
fastagencyopen-source
The fastest way to bring multi-agent workflows to production.
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| fastagency | langgraph | |
|---|---|---|
| Stars | 532 | 28.0k |
| Star velocity /mo | 0 | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 1 | 10 |
| Overall score | 0.366807033196986 | 0.8081963872278098 |
Pros
- +Unified interface for deploying AG2 workflows to production with minimal code changes
- +Supports both web chat applications and REST API services from the same codebase
- +Built-in scaling capabilities with distributed architecture and message broker coordination
- +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
Cons
- -Dependent on AG2 framework, limiting flexibility to other agent frameworks
- -Relatively small community with 532 GitHub stars compared to major frameworks
- -Limited documentation available in the provided materials for advanced features
- -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
Use Cases
- •Deploying AG2 multi-agent chatbots as web applications for customer service or support
- •Creating REST API services that expose agent workflows for integration with existing systems
- •Building scalable distributed agent systems that coordinate across multiple servers or datacenters
- •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