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AgentRecall-MCP

Connector Development Usable

AI Session Memory with Think-Execute-Reflect Quality Loops — give your agent a brain that survives every session. Built on the Intelligent Distance principle.

252 starsMIT (commercial OK)FreeNo code needed
Usable rating — This tool is functional but has notable gaps. Review the evaluation notes below before deploying.

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.

Claude Code Claude Cowork Claude Chat

https://github.com/Goldentrii/AgentRecall-MCP

By Goldentrii

How to Get It

Option 1: Claude Desktop AppOpen the Customize panel in the sidebar → browse connectors → search and add. Works in Claude Code, Claude Cowork, and Claude Chat.
Option 2: Paste into Claude CodeCopy the command below and paste it into a Claude Code conversation. Claude will run it for you.
Command
claude mcp add AgentRecall-MCP -- npx -y AgentRecall-MCP

Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.

CostFree

Trust Signals Auto-scanned

Stars252Contributors1Last updated2026-05-06LicenseMIT (OK for commercial use)Known CVEsNone foundSources: GitHub Advisory Database + OSV.dev · Scanned 2026-05-06 · scanner v1

Data & Access

Data processingPrompts sent to Anthropic API. Enterprise/Team plans exclude training.

Community Pulse Emerging

Discussed on Reddit

4 mentions across 1 sources

Reviewer notes

Auto-scanned review. These are observations, not a security certification.

Scored from trust signals (evidence-eval-v1): 252 GitHub stars; 1 contributors; last commit 14d ago; license MIT.

Things to check

  • 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

Ease of Use
3/5
Versatility
3/5
Reliability
3/5
Security
3/5
Overall score3.00 / 5.00 UsableEvaluatedMay 2026
Scored from trust signals (evidence-eval-v1): 252 GitHub stars; 1 contributors; last commit 14d ago; license MIT.

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