AgentRecall-MCP
Agents retain context and learnings across sessions, reducing redundant analysis and improving decision quality on iterative tasks.
An MCP server that gives Claude Code persistent memory built around a corrections ledger: each time you correct the agent, the correction is stored with severity, evidence, and outcome tracking, and later sessions record whether it was heeded or recurred. Ships as an MCP server, SDK, and CLI (npm: agent-recall-mcp), local-first with no cloud by default. Unusually, the project publishes its own measured recall and heed-rate numbers, including unflattering ones.
- Code review agent learning from previous PR feedback patterns
- Research agent building knowledge across multiple document analysis sessions
- Debugging assistant refining hypotheses from failed test runs
Agents retain context and learnings across sessions, reducing redundant analysis and improving decision quality on iterative tasks. Think-Execute-Reflect loops let Claude refine its approach based on previous outcomes.
Teams running multi-turn agent workflows where consistency and accumulated context reduce rework—code review agents, research synthesis, or debugging loops.
https://github.com/Goldentrii/AgentRecall-MCP-MCP
By Goldentrii
How to Get It
claude mcp add --scope user agent-recall -- npx -y agent-recall-mcp
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Once it’s connected, paste this into Claude:
Help me code review agent learning from previous PR feedback patterns
Trust Signals Auto-scanned
Data & Access
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 307 GitHub stars; 1 contributors; last commit 44d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- Single maintainer. Consider the risk if this person stops maintaining the project.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 307 GitHub stars; 1 contributors; last commit 44d ago; license MIT.