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AgentRecall

Claude Code memory that learns from corrections. The only learning loop that measures whether your agent actually stops repeating a mistake.

Corrections ledger + session lifecycle + honest measurement. MCP · SDK · CLI · Skill.

claude mcp add --scope user agent-recall -- npx -y agent-recall-mcp

{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }

At the start of a session, call session_start to load context.
When the human corrects you, call remember with type "correction".
At the end of a session, call session_end to compound what you learned.

What it does

AgentRecall is two things:

1. A governed corrections ledger — every time you correct your agent ("no, not that version", "put this section first", "ask me before you assume"), that correction is stored as a structured record with severity, evidence, and outcome tracking. It persists across sessions, projects, and agent restarts.

2. A measurement instrument — the only open-source system that tracks whether a correction actually changed what the agent does in a later session. Every correction accumulates retrieved_count, and every time the agent encounters the same situation, the outcome is recorded (heeded or recurred).

No other agent memory tool measures that second step. Every benchmark in the field tests retrieval; none tests behavioral change across sessions. We built the measurement harness first — and we publish what we found, including the unflattering numbers.

Measured, not promised

Most agent memory tools claim "never repeats the same mistake." None of them publish a number for

... View full README on GitHub