--- title: "eval-view vs tma1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-tma1-ai-tma1" tools: ["hidai25-eval-view", "tma1-ai-tma1"] --- # eval-view vs tma1 *GraphCanon updated Sep 20, 2026* ## Verdict Pick eval-view if eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and; pick tma1 if tMA1 is specialized in local-first observability by tracking every LLM call and routing this information to the next agent turn via hooks and. [eval-view](https://evalview.com) reports 134 GitHub stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. [tma1](https://tma1.ai/) has 117 stars, 14 forks, and 5 open issues, last pushed Sep 10, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [tma1's repository](https://github.com/tma1-ai/tma1). | | [eval-view](/tools/hidai25-eval-view.md) | [tma1](/tools/tma1-ai-tma1.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | Local-first observability for AI agents with LLM call tracking | | Stars | 134 | 117 | | Forks | 24 | 14 | | Open issues | 2 | 5 | | Language | Python | Go | | Adopt for | Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and | TMA1 is specialized in local-first observability by tracking every LLM call and routing this information to the next agent turn via hooks and MCP. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | TMA1 is available under the Apache-2.0 license, allowing for broad usage with attribution. | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [eval-view](/tools/hidai25-eval-view.md) | [tma1](/tools/tma1-ai-tma1.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 13d | 0d | | Open issues (now) | 2 | 5 | | Stars delta | +8 (30d) | +2 (30d) | | Open issues delta | -1 (30d) | -10 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/tma1-ai-tma1/trust.md) | ## Decision facts: eval-view - **Adopt for:** Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and ## Decision facts: tma1 - **Pricing:** freemium - Free open-source tool with no initial cost to use or modify according to its Apache-2.0 license. - **Requirements:** TMA1 requires an installation script that auto-configures and sets up GreptimeDB.; Operational requirements are platform-independent, as it supports macOS/Linux install scripts and a PowerShell one for Windows. - **Adopt for:** TMA1 is specialized in local-first observability by tracking every LLM call and routing this information to the next agent turn via hooks and MCP. - **License detail:** TMA1 is available under the Apache-2.0 license, allowing for broad usage with attribution. ## Choose when ### Choose eval-view if… - eval-view is primarily Python; tma1 is Go. - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents. - eval-view ships Docker support for self-hosted deployment. - When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic. ### Choose tma1 if… - tma1 is primarily Go; eval-view is Python. - Pricing: Free open-source tool with no initial cost to use or modify according to its Apache-2.0 license.. - Requirements: TMA1 requires an installation script that auto-configures and sets up GreptimeDB.; Operational requirements are platform-independent, as it supports macOS/Linux install scripts and a PowerShell one for Windows.. - Tags unique to tma1: agent-observability, claude-code, codex, greptimedb. - Use TMA1 if you are operating self-hosted AI agents and require detailed observability over LLM calls that can be routed into subsequent turns for further action. ## When NOT to use eval-view - If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. - When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. - If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. - If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this. ## When NOT to use tma1 - Avoid TMA1 if you prefer cloud-based solutions or require real-time collaboration features that it does not inherently support due to its self-hosted nature. - Do not use TMA1 in environments sensitive to open ports, since it runs a local server and dashboard accessible at default localhost settings which may not suit all security policies. ## Common questions ### What is the difference between eval-view and tma1? eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. tma1: Local-first observability for AI agents with LLM call tracking. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over tma1? Choose eval-view over tma1 when eval-view is primarily Python; tma1 is Go; Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents; eval-view ships Docker support for self-hosted deployment; When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic. ### When should I choose tma1 over eval-view? Choose tma1 over eval-view when tma1 is primarily Go; eval-view is Python; Pricing: Free open-source tool with no initial cost to use or modify according to its Apache-2.0 license.; Requirements: TMA1 requires an installation script that auto-configures and sets up GreptimeDB.; Operational requirements are platform-independent, as it supports macOS/Linux install scripts and a PowerShell one for Windows.; Tags unique to tma1: agent-observability, claude-code, codex, greptimedb; Use TMA1 if you are operating self-hosted AI agents and require detailed observability over LLM calls that can be routed into subsequent turns for further action. ### When should I avoid eval-view? If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this. ### When should I avoid tma1? Avoid TMA1 if you prefer cloud-based solutions or require real-time collaboration features that it does not inherently support due to its self-hosted nature. Do not use TMA1 in environments sensitive to open ports, since it runs a local server and dashboard accessible at default localhost settings which may not suit all security policies. ### Is eval-view or tma1 more popular on GitHub? eval-view has more GitHub stars (134 vs 117). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and tma1 open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, tma1: Apache-2.0). ### Where can I find alternatives to eval-view or tma1? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [tma1 alternatives](/tools/tma1-ai-tma1/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [tma1 markdown twin](/tools/tma1-ai-tma1/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/hidai25-eval-view-vs-tma1-ai-tma1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or tma1? eval-view: Active. tma1: 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 eval-view and tma1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [tma1 trust report](/tools/tma1-ai-tma1/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hidai25-eval-view`](/api/graphcanon/graph?tool=hidai25-eval-view) - 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/_