--- title: "every_eval_ever vs claude-mem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evaleval-every-eval-ever-vs-thedotmack-claude-mem" tools: ["evaleval-every-eval-ever", "thedotmack-claude-mem"] --- # every_eval_ever vs claude-mem *GraphCanon updated Jul 15, 2026* ## Verdict Pick every_eval_ever when every_eval_ever is primarily Python; claude-mem is JavaScript; pick claude-mem when claude-mem is primarily JavaScript; every_eval_ever is Python. [every_eval_ever](https://evalevalai.com/projects/every-eval-ever/) reports 93 GitHub stars, 42 forks, and 48 open issues, last pushed Jul 4, 2026. [claude-mem](https://claude-mem.ai) has 87k stars, 7.5k forks, and 252 open issues, last pushed Jul 9, 2026. Figures are from public GitHub metadata via [every_eval_ever's repository](https://github.com/evaleval/every_eval_ever) and [claude-mem's repository](https://github.com/thedotmack/claude-mem). | | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [claude-mem](/tools/thedotmack-claude-mem.md) | | --- | --- | --- | | Tagline | Every Eval Ever is a shared schema and crowdsourced eval database. It defines a standardized metadata format for storing AI evaluation results, from leaderboard scrapes and research papers to local ev | Persistent Context Across Sessions for Every Agent | | Stars | 93 | 86,816 | | Forks | 42 | 7,500 | | Open issues | 48 | 252 | | Language | Python | JavaScript | | Adopt for | - | claude-mem provides persistent context across sessions, leveraging AI to capture and compress agent activity. | | Persona | - | - | | Runtime | - | - | | License | MIT | Claude-Mem is distributed under the Apache License 2.0, allowing it to be freely used in a variety of development and production environments while maintaining clear licensing terms. | | Categories | AI Agents, Inference & Serving, LLM Frameworks | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [claude-mem](/tools/thedotmack-claude-mem.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 10d | 1d | | Open issues (now) | 48 | 252 | | Owner type | Organization | User | | Full report | [trust report](/tools/evaleval-every-eval-ever/trust.md) | [trust report](/tools/thedotmack-claude-mem/trust.md) | ## Shared compatibility - **Python**: [every_eval_ever](/tools/evaleval-every-eval-ever.md) - Python runtime; [claude-mem](/tools/thedotmack-claude-mem.md) - Python runtime ## Decision facts: claude-mem - **Requirements:** Requires Node.js v20.0.0 or higher.; Must have the latest version of Claude Code with plugin support.; Auto-installs Bun (JavaScript runtime and process manager) if not already present.; Requires uv (Python package for vector search), auto-installation supported. - **Adopt for:** claude-mem provides persistent context across sessions, leveraging AI to capture and compress agent activity. - **License detail:** Claude-Mem is distributed under the Apache License 2.0, allowing it to be freely used in a variety of development and production environments while maintaining clear licensing terms. ## Choose when ### Choose every_eval_ever if… - every_eval_ever is primarily Python; claude-mem is JavaScript. - License: every_eval_ever is MIT, claude-mem is Apache-2.0. - Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra. - Also covers LLM Frameworks. ### Choose claude-mem if… - claude-mem is primarily JavaScript; every_eval_ever is Python. - License: claude-mem is Apache-2.0, every_eval_ever is MIT. - Requirements: Requires Node.js v20.0.0 or higher.; Must have the latest version of Claude Code with plugin support.; Auto-installs Bun (JavaScript runtime and process manager) if not already present.; Requires uv (Python package for vector search), auto-installation supported.. - Tags unique to claude-mem: ai-agents, ai-memory, chromadb, claude. - When you require seamless and persistent context continuity between different session interactions with Claude Code or other supported agents. ## When NOT to use every_eval_ever - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use claude-mem - When working in environments where Node.js v20.0.0 or higher cannot be installed, as claude-mem requires this version to operate. - If your specific AI agent or tool does not integrate with the supported frameworks listed (e.g., Claude Code, OpenClaw), making context persistence ineffective for your workflow. ## Common questions ### What is the difference between every_eval_ever and claude-mem? every_eval_ever: Every Eval Ever is a shared schema and crowdsourced eval database. It defines a standardized metadata format for storing AI evaluation results, from leaderboard scrapes and research papers to local ev. claude-mem: Persistent Context Across Sessions for Every Agent. See the comparison table for live GitHub stats and shared categories. ### When should I choose every_eval_ever over claude-mem? Choose every_eval_ever over claude-mem when every_eval_ever is primarily Python; claude-mem is JavaScript; License: every_eval_ever is MIT, claude-mem is Apache-2.0; Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra; Also covers LLM Frameworks. ### When should I choose claude-mem over every_eval_ever? Choose claude-mem over every_eval_ever when claude-mem is primarily JavaScript; every_eval_ever is Python; License: claude-mem is Apache-2.0, every_eval_ever is MIT; Requirements: Requires Node.js v20.0.0 or higher.; Must have the latest version of Claude Code with plugin support.; Auto-installs Bun (JavaScript runtime and process manager) if not already present.; Requires uv (Python package for vector search), auto-installation supported.; Tags unique to claude-mem: ai-agents, ai-memory, chromadb, claude; When you require seamless and persistent context continuity between different session interactions with Claude Code or other supported agents. ### When should I avoid every_eval_ever? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid claude-mem? When working in environments where Node.js v20.0.0 or higher cannot be installed, as claude-mem requires this version to operate. If your specific AI agent or tool does not integrate with the supported frameworks listed (e.g., Claude Code, OpenClaw), making context persistence ineffective for your workflow. ### Is every_eval_ever or claude-mem more popular on GitHub? claude-mem has more GitHub stars (86,816 vs 93). Stars measure visibility, not whether either tool fits your constraints. ### Are every_eval_ever and claude-mem open source? Yes - both are open-source projects on GitHub (every_eval_ever: MIT, claude-mem: Apache-2.0). ### Where can I find alternatives to every_eval_ever or claude-mem? GraphCanon lists graph-backed alternatives at [every_eval_ever alternatives](/tools/evaleval-every-eval-ever/alternatives) and [claude-mem alternatives](/tools/thedotmack-claude-mem/alternatives) ([every_eval_ever markdown twin](/tools/evaleval-every-eval-ever/alternatives.md), [claude-mem markdown twin](/tools/thedotmack-claude-mem/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/evaleval-every-eval-ever-vs-thedotmack-claude-mem.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, every_eval_ever or claude-mem? every_eval_ever: Active. claude-mem: 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 every_eval_ever and claude-mem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [every_eval_ever trust report](/tools/evaleval-every-eval-ever/trust); [claude-mem trust report](/tools/thedotmack-claude-mem/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evaleval-every-eval-ever`](/api/graphcanon/graph?tool=evaleval-every-eval-ever) - 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/_