LangGraph vs Temporal
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 +672 for Temporal.
- Pick LangGraph for: build resilient language agents as graphs. Pick Temporal for: temporal service.
From GitHub data refreshed daily.
Metrics
| LangGraph | Temporal | |
|---|---|---|
| Stars | 42.7k | 23.4k |
| Star velocity /mo | 2.4k | 672.3157894736843 |
| Commits (90d) | 132 | 574 |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | 43.7M | — |
| Overall score | 0.8091319530692536 | 0.8247299329543042 |
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
- +Automatic failure handling and retry logic eliminates complex error recovery code
- +Mature, battle-tested technology originally developed at Uber with strong reliability track record
- +Comprehensive tooling ecosystem including CLI, Web UI, and multi-language SDK support
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
- -Requires learning workflow-based programming paradigms which can have a steep learning curve
- -Additional infrastructure complexity requiring Temporal server deployment and maintenance
- -Overhead for simple applications that don't require durable execution guarantees
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
- •Long-running business processes with multiple steps that need guaranteed completion
- •Microservice orchestration and coordination across distributed systems
- •Data processing pipelines requiring automatic retry and failure recovery mechanisms
FAQ
- Which is more popular, LangGraph or Temporal?
- LangGraph has more GitHub stars (42,656 vs 23,436).
- Which is more actively developed, LangGraph or Temporal?
- Temporal had more commits in the last 90 days (574 vs 132).
- Should I use LangGraph or Temporal?
- 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.