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AgentRecall

AgentRecall

Active·★ 258·MIT·Updated 2026-05-26

★ Trending★ Memory & Context

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

AgentRecall is a learning system that bridges the gap between human thinking and AI agent behavior. It provides persistent, compounding memory with automatic correction capture via MCP server, SDK, and CLI. Every mistake is recorded once and never repeated, and token savings compound over sessions.

#agent-memory#ai-agents#claude-code#intelligent-distance#mcp#mcp-server#mcp-servers#quality-loop

$ Install

$ npm install -g agent-recall-cli

↗ Visit site ★ GitHub

01

Features

01Persistent, compounding memory with 200-line awareness cap

02Automatic correction capture and recall

03Cross-project insight recall via keyword matching

04MCP, SDK, and CLI for universal compatibility

05Zero cloud, all local markdown storage

02

Compatibility

Claude Code

Claude Code

Verified via docs

Cursor

Cursor

Verified via docs

VS Code (Copilot)

VS Code

Verified via docs

Windsurf

Windsurf

Verified via docs

OpenAI Codex

Codex

Verified via docs

Claude Desktop

Claude Desktop

Verified via docs

03

Quick start

1

$ npm install -g agent-recall-cli

04

Use cases

↳Scattered human: structurize non-linear instructions across sessions

↳Cross-project lesson transfer: rate limiting insight from Project A to Project B

↳Correction that sticks: agent never repeats a correction again

05

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Related searches

AgentRecall Alternatives Best Memory & Context Tools 2026 Open Source Memory & Context AgentRecall Tutorial AgentRecall Vs Competitors agent-memory ai-agents claude-code

Comments

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R
Robin JohnsonMay 12, 2026
The reflect phase catches errors the execute phase misses.
H
Hayden ZhangApr 16, 2026
Good for agents that need to maintain context and quality standards over extended use.
C
Corey WhiteMar 20, 2026
Session memory that persists across restarts is the baseline for useful long-running agents.
R
Robin BrownMar 3, 2026
Think-Execute-Reflect quality loops give agents systematic self-improvement.

On this page

01Features 02Compatibility 03Quick start 04Use cases 05Alternatives

Stats

GitHub Stars★ 258

Last commit4d ago

StatusActive

LicenseMIT

CategoryMemory & Context

Trend (30d)

+10.3↑ 0.9%

Links

Documentation↗Discussion↗Issues↗Releases↗

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