Chidori vs Agno
Side-by-side comparison of two AI agent tools
Short answer
- Agno is growing faster: +558 GitHub stars in the last 30 days vs +4 for Chidori.
- Pick Chidori for: a reactive runtime for building durable AI agents. Pick Agno for: build, run, manage agentic software at scale.
From GitHub data refreshed daily.
Chidoriopen-source
A reactive runtime for building durable AI agents
Agnoopen-source
Build, run, manage agentic software at scale.
Metrics
| Chidori | Agno | |
|---|---|---|
| Stars | 1.4k | 42.5k |
| Star velocity /mo | 4.1269841269841265 | 558.2539682539682 |
| Commits (90d) | 75 | 349 |
| Releases (6m) | 5 | 10 |
| Overall score | 0.4497982600371453 | 0.814447863455959 |
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
- +Production-ready runtime with built-in scalability, session isolation, and native tracing capabilities
- +Comprehensive monitoring and management through AgentOS UI for testing, debugging, and production oversight
- +Simple development experience - build sophisticated agents with memory and tools in approximately 20 lines of Python code
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
- -Python-focused platform with limited examples for other programming languages
- -Requires multiple dependencies and proper configuration of API keys and database connections
- -May have a learning curve for implementing complex multi-agent workflows and team coordination
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
- •Building production AI agents with persistent state, memory, and custom tool integrations for customer service or automation
- •Creating multi-agent teams and workflows for complex business processes that require coordination between specialized agents
- •Enterprise deployment of AI agents with comprehensive monitoring, user session management, and production-grade reliability requirements
FAQ
- Which is more popular, Chidori or Agno?
- Agno has more GitHub stars (42,494 vs 1,365).
- Which is more actively developed, Chidori or Agno?
- Agno had more commits in the last 90 days (349 vs 75).
- Should I use Chidori or Agno?
- 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.