--- title: "eval-view vs imcodes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-im4codes-imcodes" tools: ["hidai25-eval-view", "im4codes-imcodes"] --- # eval-view vs imcodes *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 imcodes if imcodes provides shared context and memory for AI agents with supervised execution across multiple providers like Claude, Codex, Gemini, and OpenAI. [eval-view](https://evalview.com) reports 134 GitHub stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. [imcodes](https://im.codes) has 973 stars, 121 forks, and 2 open issues, last pushed Sep 20, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [imcodes's repository](https://github.com/im4codes/imcodes). | | [eval-view](/tools/hidai25-eval-view.md) | [imcodes](/tools/im4codes-imcodes.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | Shared Agent Context & Memory with Supervised Execution | | Stars | 134 | 973 | | Forks | 24 | 121 | | Open issues | 2 | 2 | | Language | Python | TypeScript | | 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 | imcodes provides shared context and memory for AI agents with supervised execution across multiple providers like Claude, Codex, Gemini, and OpenAI. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Available under the MIT License, permitting free use, modification, distribution, but not liable for any damages or problems derived from its usage. | | 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) | [imcodes](/tools/im4codes-imcodes.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 13d | 0d | | Stars delta | +8 (30d) | -43 (30d) | | Open issues delta | -1 (30d) | +2 (30d) | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/im4codes-imcodes/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: imcodes - **Requirements:** Developed using TypeScript, potentially requiring a development team familiar with TypeScript and node.js ecosystems. - **Adopt for:** imcodes provides shared context and memory for AI agents with supervised execution across multiple providers like Claude, Codex, Gemini, and OpenAI. - **License detail:** Available under the MIT License, permitting free use, modification, distribution, but not liable for any damages or problems derived from its usage. ## Choose when ### Choose eval-view if… - eval-view is primarily Python; imcodes is TypeScript. - License: eval-view is Apache-2.0, imcodes is MIT. - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, anthropic. - When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic. ### Choose imcodes if… - imcodes is primarily TypeScript; eval-view is Python. - License: imcodes is MIT, eval-view is Apache-2.0. - Requirements: Developed using TypeScript, potentially requiring a development team familiar with TypeScript and node.js ecosystems.. - Tags unique to imcodes: automation, claude, codex, cross-agent-audit. - imcodes ships an MCP server manifest. - When you need to manage unified memory and context across different AI agent providers for consistent performance evaluation and inter-agent cooperation. ## 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 imcodes - If your project strictly requires proprietary tooling that does not allow cross-provider usage or if you aim to avoid external memory management for security reasons. - When the integration complexity of supporting multiple providers is a barrier, and you prefer simpler, single-provider solutions with less overhead in terms of setup and maintenance. ## Common questions ### What is the difference between eval-view and imcodes? eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. imcodes: Shared Agent Context & Memory with Supervised Execution. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over imcodes? Choose eval-view over imcodes when eval-view is primarily Python; imcodes is TypeScript; License: eval-view is Apache-2.0, imcodes is MIT; Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, anthropic; 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 imcodes over eval-view? Choose imcodes over eval-view when imcodes is primarily TypeScript; eval-view is Python; License: imcodes is MIT, eval-view is Apache-2.0; Requirements: Developed using TypeScript, potentially requiring a development team familiar with TypeScript and node.js ecosystems.; Tags unique to imcodes: automation, claude, codex, cross-agent-audit; imcodes ships an MCP server manifest; When you need to manage unified memory and context across different AI agent providers for consistent performance evaluation and inter-agent cooperation. ### 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 imcodes? If your project strictly requires proprietary tooling that does not allow cross-provider usage or if you aim to avoid external memory management for security reasons. When the integration complexity of supporting multiple providers is a barrier, and you prefer simpler, single-provider solutions with less overhead in terms of setup and maintenance. ### Is eval-view or imcodes more popular on GitHub? imcodes has more GitHub stars (973 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and imcodes open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, imcodes: MIT). ### Where can I find alternatives to eval-view or imcodes? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [imcodes alternatives](/tools/im4codes-imcodes/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [imcodes markdown twin](/tools/im4codes-imcodes/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-im4codes-imcodes.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or imcodes? eval-view: Active. imcodes: 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 imcodes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [imcodes trust report](/tools/im4codes-imcodes/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/_