--- title: "agentops vs plano" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-katanemo-plano" tools: ["agentops-ai-agentops", "katanemo-plano"] --- # agentops vs plano *GraphCanon updated Aug 20, 2026* ## Verdict Pick agentops if agentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage; pick plano if plano is an AI-native proxy and data plane for agentic applications built with Rust under the Apache-2.0 license, focusing on smart LLM routing capabilities alongside orchestration and observability features. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 612 forks, and 176 open issues, last pushed Jun 25, 2026. [plano](https://planoai.dev) has 7.0k stars, 482 forks, and 138 open issues, last pushed Aug 19, 2026. Figures are from public GitHub metadata via [agentops's repository](https://github.com/AgentOps-AI/agentops) and [plano's repository](https://github.com/katanemo/plano). | | [agentops](/tools/agentops-ai-agentops.md) | [plano](/tools/katanemo-plano.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | An AI-native proxy and data plane for agentic apps | | Stars | 5,771 | 7,004 | | Forks | 612 | 482 | | Open issues | 176 | 138 | | Language | Python | Rust | | Adopt for | AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. | Plano is an AI-native proxy and data plane for agentic applications built with Rust under the Apache-2.0 license, focusing on smart LLM routing capabilities alongside orchestration and observability features. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [plano](/tools/katanemo-plano.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 49d | 0d | | Open issues (now) | 176 | 138 | | Stars delta | Unknown | +127 (30d) | | Open issues delta | Unknown | +7 (30d) | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/katanemo-plano/trust.md) | ## Decision facts: agentops - **Adopt for:** AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. ## Decision facts: plano - **Pricing:** freemium - Freely available under Apache-2.0 license but potential for premium services around support and advanced features. - **Requirements:** Min 1 GB RAM - **Adopt for:** Plano is an AI-native proxy and data plane for agentic applications built with Rust under the Apache-2.0 license, focusing on smart LLM routing capabilities alongside orchestration and observability features. ## Choose when ### Choose agentops if… - agentops is primarily Python; plano is Rust. - License: agentops is MIT, plano is Apache-2.0. - Tags unique to agentops: ai-agents, benchmarking, cost-tracking. - Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK ### Choose plano if… - plano is primarily Rust; agentops is Python. - License: plano is Apache-2.0, agentops is MIT. - Pricing: Freely available under Apache-2.0 license but potential for premium services around support and advanced features.. - Requirements: Min 1 GB RAM. - Tags unique to plano: agency-apps, ai-gateway, llm-proxy, llm-routing. - Also covers Inference & Serving. - plano ships Docker support for self-hosted deployment. - You are working on building complex multi-agent workflows where intelligent LLM (Language Model) routing is required. ## When NOT to use agentops - If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI - In case self-hosting of components is impractical due to resource constraints ## When NOT to use plano - If your application does not require deep orchestration or smart routing between multiple models, choosing Plano might introduce unnecessary complexity. - For scenarios where minimal intervention routing is preferred without advanced observability and guardrail features, this tool may over-deliver on certain functionalities, possibly increasing overhead ## Common questions ### What is the difference between agentops and plano? agentops: Python SDK for AI agent monitoring and LLM cost tracking. plano: An AI-native proxy and data plane for agentic apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over plano? Choose agentops over plano when agentops is primarily Python; plano is Rust; License: agentops is MIT, plano is Apache-2.0; Tags unique to agentops: ai-agents, benchmarking, cost-tracking; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK. ### When should I choose plano over agentops? Choose plano over agentops when plano is primarily Rust; agentops is Python; License: plano is Apache-2.0, agentops is MIT; Pricing: Freely available under Apache-2.0 license but potential for premium services around support and advanced features.; Requirements: Min 1 GB RAM; Tags unique to plano: agency-apps, ai-gateway, llm-proxy, llm-routing; Also covers Inference & Serving; plano ships Docker support for self-hosted deployment; You are working on building complex multi-agent workflows where intelligent LLM (Language Model) routing is required. ### When should I avoid agentops? If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI In case self-hosting of components is impractical due to resource constraints ### When should I avoid plano? If your application does not require deep orchestration or smart routing between multiple models, choosing Plano might introduce unnecessary complexity. For scenarios where minimal intervention routing is preferred without advanced observability and guardrail features, this tool may over-deliver on certain functionalities, possibly increasing overhead ### Is agentops or plano more popular on GitHub? plano has more GitHub stars (7,004 vs 5,771). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and plano open source? Yes - both are open-source projects on GitHub (agentops: MIT, plano: Apache-2.0). ### Where can I find alternatives to agentops or plano? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [plano alternatives](/tools/katanemo-plano/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/alternatives.md), [plano markdown twin](/tools/katanemo-plano/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/agentops-ai-agentops-vs-katanemo-plano.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentops or plano? agentops: Steady. plano: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for agentops and plano? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [plano trust report](/tools/katanemo-plano/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentops-ai-agentops`](/api/graphcanon/graph?tool=agentops-ai-agentops) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_