AgentRecall-MCP
Agents retain context and learnings across sessions, reducing redundant analysis and improving decision quality on iterative tasks.
AI Session Memory with Think-Execute-Reflect Quality Loops — give your agent a brain that survives every session. Built on the Intelligent Distance principle.
- 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
By Goldentrii
How to Get It
claude mcp add AgentRecall-MCP -- npx -y AgentRecall-MCP
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Trust Signals Auto-scanned
Data & Access
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
Scored from trust signals (evidence-eval-v1): 252 GitHub stars; 1 contributors; last commit 14d ago; license MIT.