--- title: "agentops vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-hidai25-eval-view" tools: ["agentops-ai-agentops", "hidai25-eval-view"] --- # agentops vs eval-view *GraphCanon updated Aug 14, 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 eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 612 forks, and 176 open issues, last pushed Jun 25, 2026. [eval-view](https://evalview.com) has 126 stars, 21 forks, and 3 open issues, last pushed Jul 26, 2026. Figures are from public GitHub metadata via [agentops's repository](https://github.com/AgentOps-AI/agentops) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [agentops](/tools/agentops-ai-agentops.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | Regression testing for AI agents | | Stars | 5,771 | 126 | | Forks | 612 | 21 | | Open issues | 176 | 3 | | Language | Python | Python | | Adopt for | AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. | Regression testing for AI agents to detect behavioral changes and output quality regressions over time. | | Persona | - | - | | Runtime | - | - | | License | MIT | The software uses the Apache-2.0 license, offering permissive terms for use and distribution. | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 49d | 6d | | Open issues (now) | 176 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Shared compatibility - **Python**: [agentops](/tools/agentops-ai-agentops.md) - Python runtime; [eval-view](/tools/hidai25-eval-view.md) - Python runtime ## 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: eval-view - **Pricing:** freemium - Free to use under the terms of the Apache License, Version 2.0. - **Requirements:** Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality. - **Adopt for:** Regression testing for AI agents to detect behavioral changes and output quality regressions over time. - **License detail:** The software uses the Apache-2.0 license, offering permissive terms for use and distribution. ## Choose when ### Choose agentops if… - License: agentops is MIT, eval-view is Apache-2.0. - Tags unique to agentops: benchmarking, cost-tracking. - Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK ### Choose eval-view if… - License: eval-view is Apache-2.0, agentops is MIT. - Pricing: Free to use under the terms of the Apache License, Version 2.0.. - Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality.. - Tags unique to eval-view: agent-benchmark, agent-evaluation, regression-testing. - eval-view ships Docker support for self-hosted deployment. - When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. ## 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 eval-view - If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. - When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge. ## Common questions ### What is the difference between agentops and eval-view? agentops: Python SDK for AI agent monitoring and LLM cost tracking. eval-view: Regression testing for AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over eval-view? Choose agentops over eval-view when License: agentops is MIT, eval-view is Apache-2.0; Tags unique to agentops: benchmarking, cost-tracking; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK. ### When should I choose eval-view over agentops? Choose eval-view over agentops when License: eval-view is Apache-2.0, agentops is MIT; Pricing: Free to use under the terms of the Apache License, Version 2.0.; Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality.; Tags unique to eval-view: agent-benchmark, agent-evaluation, regression-testing; eval-view ships Docker support for self-hosted deployment; When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. ### 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 eval-view? If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge. ### Is agentops or eval-view more popular on GitHub? agentops has more GitHub stars (5,771 vs 126). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and eval-view open source? Yes - both are open-source projects on GitHub (agentops: MIT, eval-view: Apache-2.0). ### Where can I find alternatives to agentops or eval-view? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/alternatives.md), [eval-view markdown twin](/tools/hidai25-eval-view/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-hidai25-eval-view.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentops or eval-view? agentops: Steady. eval-view: 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 eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [eval-view trust report](/tools/hidai25-eval-view/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/_