Goldentrii/AgentRecall
by Various
Persistent, correction-driven memory for AI agents. Cross-session, cross-platform (Claude Code, Codex, Gemini — any MCP client). Learns from mistakes, compresses context to save to
MCP
Goldentrii/AgentRecall
Added 18 June 2026
Overview
AgentRecall is a persistent memory layer for AI agents that works across sessions and platforms via the Model Context Protocol (MCP). It stores corrections and learnings from agent mistakes, compresses context to reduce token usage, and consolidates knowledge overnight. The tool is available as an npm package (agent-recall-mcp) and is written in TypeScript.
Best for
Best for
Developers running multiple AI agent sessions who want persistent, cost-efficient memory that improves from errors.
Use cases
- Give an agent long-term memory across Claude Code, Codex, and Gemini sessions
- Automatically capture and apply corrections from agent mistakes
- Reduce token costs by compressing and consolidating context overnight
How to use
Install
npx -y agent-recall-mcpTools exposed
agent-recall-mcpagent-recall-sdkagent-recall-cli
Tested with
Claude Code, Cursor, VS Code, Windsurf, Codex
Example client config
[object Object]Notes
AgentRecall is a persistent memory layer for AI agents that works across sessions and platforms via the Model Context Protocol (MCP). It stores corrections and learnings from agent mistakes, compresses context to reduce token usage, and consolidates knowledge overnight. The tool is available as an npm package (agent-recall-mcp) and is written in TypeScript.
303 stars on GitHub. Last updated 2026-06-18. Licensed MIT.
Use cases
- Give an agent long-term memory across Claude Code, Codex, and Gemini sessions
- Automatically capture and apply corrections from agent mistakes
- Reduce token costs by compressing and consolidating context overnight
Pros
- Cross-platform support via MCP works with multiple agent tools
- Reduces token usage through automatic context compression
- Learns from mistakes without manual intervention
Cons
- Requires MCP-compatible client to function
- Overnight consolidation may delay knowledge updates
- Limited to correction-driven memory, not general-purpose storage
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Cross-platform support via MCP works with multiple agent tools
- Reduces token usage through automatic context compression
- Learns from mistakes without manual intervention
Cons
- Requires MCP-compatible client to function
- Overnight consolidation may delay knowledge updates
- Limited to correction-driven memory, not general-purpose storage
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