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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 it.

Here is what our own instrument found on our own live corpus (2026-07-03):

The heed instrument defaulted to "heeded" absent evidence before 2026-07-03; the reset default is "unknown" — the honest 0/3 is the correct starting point, not a regression. Transfer recall cannot support a point-estimate claim below 39 classes (claim-gate ledger, benchmark spec §2.6).

Verify it yourself: every number above regenerates from the committed artifacts — see docs/eval/REPRODUCE.md.

What this means: we captured 35% of real corrections in our own live use. The heed instrument was biased and we reset it. The offline transfer benchmark scores 0 on our own corpus — which is a density problem (32 active corrections across 19 projects is too sparse to front-run mistakes), not a retrieval architecture problem (confirmed 5× by internal experiments).

The learning loop framing is correct — the system is designed to track whether corrections change behavior — but the data we have so far is insufficient to quantify the uplift. We are publishing the measurement harness and running the experiment.

Why this is different from every other memory tool

In mid-2026, the agent-memory field is crowded (Mem0 ~60K stars, Graphiti/Zep ~28K, Supermemory ~28K, Letta ~24K). Most published benchmark numbers in this space are self-reported on the same 2–3 retrieval benchmarks and are hard to reproduce independently.

The confirmed gap (from our research report docs/research/agent-memory-landscape-2026-07.md §2): no public benchmark measures whether a captured correction changes what a fresh agent does in a new session. LongMemEval, LoCoMo, MemoryAgentBench, Letta Leaderboard — all test retrieval or within-session updates.

AgentRecall owns two pieces of the unclaimed ground:

• The corrections ledger — a governed data model (corrections-export/v1, scrubbed egress, retraction, severity, proof-confidence) that any engine can integrate against.
• The measurement harness — predict-loo (leave-one-out, anti-self-confirming, dual denominators) and the correction-transfer benchmark spec (HeedBench v1 — provisional name), which implements the missing pipeline: capture → persist → fresh session → measure recurrence.

Benchmark numbers in agent memory are typically self-reported and hard to reproduce. Ours regenerate from a fixed, hash-locked corpus with one command (npm run bench) — including the scores that make us look bad.

Quick Start

Visual setup guide — all 13 clients, copy-paste prompts: open warroom/install.html from the repo (or after unzipping the War Room release) in any browser. No server needed.

MCP Server — for AI agents

# Claude Code
claude mcp add --scope user agent-recall -- npx -y agent-recall-mcp

# Cursor — .cursor/mcp.json
{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }

# VS Code — .vscode/mcp.json
{ "servers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }

# Windsurf — ~/.codeium/windsurf/mcp_config.json
{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }

# Codex
codex mcp add agent-recall -- npx -y agent-recall-mcp

Skill (Claude Code only):

mkdir -p ~/.claude/skills/agent-recall
curl -o ~/.claude/skills/agent-recall/SKILL.md \
  https://raw.githubusercontent.com/Goldentrii/AgentRecall-X/main/SKILL.md

SDK & CLI

npm install agent-recall-sdk        # JS/TS apps
npx agent-recall-cli recall "topic" # terminal & CI

import { AgentRecall } from "agent-recall-sdk";
const memory = new AgentRecall({ project: "my-app" });
await memory.capture("What stack?", "Next.js + Postgres");
const ctx = await memory.recall("rate limiting");

5 Memory Layers

The canonical cognitive-psychology taxonomy mapped to your agent's filesystem: