--- title: "LLMDebugger vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/floridsleeves-llmdebugger-vs-hidai25-eval-view" tools: ["floridsleeves-llmdebugger", "hidai25-eval-view"] --- # LLMDebugger vs eval-view *GraphCanon updated Aug 5, 2026* ## Verdict Pick LLMDebugger if lLMDebugger offers step-by-step verification of runtime execution for large language models; pick eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time. [LLMDebugger](https://github.com/FloridSleeves/LLMDebugger) reports 587 GitHub stars, 56 forks, and 5 open issues, last pushed Sep 10, 2024. [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 [LLMDebugger's repository](https://github.com/FloridSleeves/LLMDebugger) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [LLMDebugger](/tools/floridsleeves-llmdebugger.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | A Large Language Model Debugger verifying runtime execution step by step | Regression testing for AI agents | | Stars | 587 | 126 | | Forks | 56 | 21 | | Open issues | 5 | 3 | | Language | Python | Python | | Adopt for | LLMDebugger offers step-by-step verification of runtime execution for large language models. | Regression testing for AI agents to detect behavioral changes and output quality regressions over time. | | Persona | - | - | | Runtime | - | - | | License | The LLMDebugger is distributed under the Apache-2.0 license. | The software uses the Apache-2.0 license, offering permissive terms for use and distribution. | | Categories | Developer Tools, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMDebugger](/tools/floridsleeves-llmdebugger.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 693d | 6d | | Open issues (now) | 5 | 3 | | Full report | [trust report](/tools/floridsleeves-llmdebugger/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Shared compatibility - **Python**: [LLMDebugger](/tools/floridsleeves-llmdebugger.md) - Python runtime; [eval-view](/tools/hidai25-eval-view.md) - Python runtime ## Decision facts: LLMDebugger - **Pricing:** freemium - Free for use, based on its open-source nature with an Apache-2.0 license. - **Adopt for:** LLMDebugger offers step-by-step verification of runtime execution for large language models. - **License detail:** The LLMDebugger is distributed under the Apache-2.0 license. ## 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 LLMDebugger if… - Pricing: Free for use, based on its open-source nature with an Apache-2.0 license.. - Tags unique to LLMDebugger: acl'24, llm debugging, python debugger for ai, runtime verification. - Also covers Developer Tools. - When detailed step-by-step inspection of the runtime behavior of large language models is required, LLMDebugger can provide precise insights into each execution phase. ### Choose eval-view if… - 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, ai-agents, regression-testing. - Also covers AI Agents. - 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 LLMDebugger - Avoid using if you are only interested in higher-level performance metrics rather than the intricate details of runtime behavior, as LLMDebugger emphasizes step-by-step execution. - Not recommended for teams lacking experience with Python or specific to this tool's installation and usage workflow that involves setting up a Conda environment. ## 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 LLMDebugger and eval-view? LLMDebugger: A Large Language Model Debugger verifying runtime execution step by step. eval-view: Regression testing for AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMDebugger over eval-view? Choose LLMDebugger over eval-view when Pricing: Free for use, based on its open-source nature with an Apache-2.0 license.; Tags unique to LLMDebugger: acl'24, llm debugging, python debugger for ai, runtime verification; Also covers Developer Tools; When detailed step-by-step inspection of the runtime behavior of large language models is required, LLMDebugger can provide precise insights into each execution phase. ### When should I choose eval-view over LLMDebugger? Choose eval-view over LLMDebugger when 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, ai-agents, regression-testing; Also covers AI Agents; 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 LLMDebugger? Avoid using if you are only interested in higher-level performance metrics rather than the intricate details of runtime behavior, as LLMDebugger emphasizes step-by-step execution. Not recommended for teams lacking experience with Python or specific to this tool's installation and usage workflow that involves setting up a Conda environment. ### 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 LLMDebugger or eval-view more popular on GitHub? LLMDebugger has more GitHub stars (587 vs 126). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMDebugger and eval-view open source? Yes - both are open-source projects on GitHub (LLMDebugger: Apache-2.0, eval-view: Apache-2.0). ### Where can I find alternatives to LLMDebugger or eval-view? GraphCanon lists graph-backed alternatives at [LLMDebugger alternatives](/tools/floridsleeves-llmdebugger/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([LLMDebugger markdown twin](/tools/floridsleeves-llmdebugger/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/floridsleeves-llmdebugger-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, LLMDebugger or eval-view? LLMDebugger: Dormant. 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 LLMDebugger and eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMDebugger trust report](/tools/floridsleeves-llmdebugger/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=floridsleeves-llmdebugger`](/api/graphcanon/graph?tool=floridsleeves-llmdebugger) - 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/_