Chidori vs Langfuse
Side-by-side comparison of two AI agent tools
Short answer
- Langfuse is growing faster: +1,812 GitHub stars in the last 30 days vs +4 for Chidori.
- Pick Chidori for: a reactive runtime for building durable AI agents. Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management.
From GitHub data refreshed daily.
Chidoriopen-source
A reactive runtime for building durable AI agents
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
Metrics
| Chidori | Langfuse | |
|---|---|---|
| Stars | 1.4k | 35.3k |
| Star velocity /mo | 4.1269841269841265 | 1.8k |
| Commits (90d) | 75 | 2.0k |
| Releases (6m) | 5 | 10 |
| Overall score | 0.4497982600371453 | 0.9067292616632036 |
Pros
- +Time travel debugging allows reverting to previous execution states for better understanding of agent behavior and decision paths
- +Multi-language support (Python and JavaScript) with familiar programming patterns, avoiding the need to learn new DSLs or frameworks
- +Visual debugging environment with monitoring and observability features for understanding complex AI workflow execution
- +Open source with MIT license allowing full customization and transparency, plus active community support
- +Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
- +Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK
Cons
- -Being in v2 suggests it may still be evolving with potential breaking changes and incomplete features
- -Rust-based runtime may introduce complexity for teams without Rust expertise when customization or debugging runtime issues is needed
- -Limited documentation in the provided materials suggests the learning curve and setup process may require additional research
- -May require significant setup and configuration for self-hosted deployments
- -Could be overwhelming for simple use cases that only need basic LLM monitoring
- -Self-hosting requires technical expertise and infrastructure resources
Use Cases
- •Building long-running AI agents that need to pause execution for human approval or input before proceeding with critical decisions
- •Debugging complex AI workflows by stepping through execution history and understanding how agents reached specific states or decisions
- •Developing AI agents with branching logic where you need to explore different execution paths and revert to optimal decision points
- •Production LLM application monitoring to track performance, costs, and identify issues in real-time
- •Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
- •LLM evaluation and testing to measure model performance across different datasets and use cases
FAQ
- Which is more popular, Chidori or Langfuse?
- Langfuse has more GitHub stars (35,301 vs 1,365).
- Which is more actively developed, Chidori or Langfuse?
- Langfuse had more commits in the last 90 days (2,007 vs 75).
- Should I use Chidori or Langfuse?
- 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.