AgentRecall — MCP Server by Goldentrii

by Goldentrii · MCP Server · ★ 250

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

About AgentRecall

AgentRecall Your agent doesn't just remember. It learns how you think. Your agent remembers yesterday, learns from corrections, and gets better at working with you over time. Persistent, compounding memory + Intelligent Distance Protocol. MCP server + SDK + CLI. EN:&nbsp; Why · <a href="#three-ways-to-use

agent-memoryai-agentsclaude-codeintelligent-distancemcpmcp-serverquality-loopsession-memory

Quick Facts

Stars250
Forks21
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score53.988/100
Open Issues1
Last Updated2026-04-26
Created2026-03-24
Platformsclaude-code, mcp, node
Est. Tokens~84k

AgentRecall alternative? Top 6 similar tools

Looking for a AgentRecall alternative? If you're comparing AgentRecall with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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Frequently Asked Questions

What is AgentRecall?

AgentRecall is AI Session Memory with Think-Execute-Reflect Quality Loops — give your agent a brain that survives every session. Built on the Intelligent Distance principle.. It is categorized as a MCP Server with 250 GitHub stars.

What programming language is AgentRecall written in?

AgentRecall is primarily written in TypeScript. It covers topics such as agent-memory, ai-agents, claude-code.

How do I install or use AgentRecall?

You can find installation instructions and usage details in the AgentRecall GitHub repository at github.com/Goldentrii/AgentRecall. The project has 250 stars and 21 forks, indicating an active community.

What license does AgentRecall use?

AgentRecall is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to AgentRecall?

The top alternatives to AgentRecall on Agent Skills Hub include swarmclaw, memorix, daymon. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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