LangGraph vs Trigger.dev

Side-by-side comparison of two AI agent tools

LangGraphopen-source

Build resilient language agents as graphs.

T
Trigger.devopen-source

Trigger.dev – build and deploy durable AI agents and workflows

Metrics

LangGraphTrigger.dev
Stars42.5k16.4k
Star velocity /mo2.4k1.4k
Commits (90d)129710
Releases (6m)1010
Overall score0.79728441091962780.8124194788720216

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

    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

      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

        FAQ

        Which is more popular, LangGraph or Trigger.dev?
        LangGraph has more GitHub stars (42,525 vs 16,445).
        Which is more actively developed, LangGraph or Trigger.dev?
        Trigger.dev had more commits in the last 90 days (710 vs 129).
        Should I use LangGraph or Trigger.dev?
        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.