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  3. Recall
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Health: Not checked yetWe have not completed a health check for this listing yet.Last checked 8/11/2026, 12:11:51 AM

Recall

Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository

Open-source MCP memory server. Persistent, searchable, tiered memory across sessions.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Install Config Generator

Choose your client
claude_desktop_config.json
{
  "mcpServers": {
    "recall-2": {
      "command": "npx",
      "args": [
        "-y",
        "recall-2"
      ]
    }
  }
}

πŸ’‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

Recallβ„’

A better memory server for AI agents β€” works for one, scales to many. Local, free, zero-config, MCP-native. Your data stays on your machine.

Tests Docker PyPI npm License: MIT Python 3.11+ MCP Container

Quickstart Β· vs. mem0/Letta/Zep Β· Multi-agent Β· Recall Pro β†’ Β· Book a demo

Without a memory server, every Claude / Copilot / Cursor conversation starts cold. You re-explain the codebase, the conventions, the decisions, the gotchas β€” every time. Recall fixes that.

Start here: what Recall does for one developer, one AI

Install it once, point your MCP client at it, and your AI now:

  • Remembers across sessions β€” "what did we decide about the auth flow last week?" returns the actual decision, not a hallucination
  • Indexes your code and docs β€” index_file + recall = local semantic RAG over your repo
  • Cites where the answer came from β€” answer returns text plus the chunks it pulled from
  • Builds project knowledge β€” every checkpoint, reflect, and anti_pattern becomes searchable later
  • Survives restarts β€” append-only artifacts on disk, vector store rebuildable from them

One pip install, one config block, done. No API key. No external service. No per-token bill. MIT license. This is what 95% of users will ever use Recall for.


How is this different from mem0 / Letta / Zep?

Recall does the same job they do β€” persistent memory across AI sessions, semantic recall, "remember what the user said last week." The difference is where and how:

mem0 / Letta / ZepRecall
Where memory livesTheir cloudYour ~/.recall/
API key requiredYesNo
CostPer-token / monthly SaaSFree
EmbeddingsTheir serviceLocal ONNX (offline)
Network callsEvery recallZero
Air-gappableNoYes
MCP-nativeWrapper or SDKBuilt on MCP
Multi-agent coordinationNone6 primitives

If you're happy paying a hosted memory provider per token, those are great products and you don't need Recall. If you'd rather your AI's memory live on your laptop or your own server, free and offline, that's what Recall is for.


Scaling up: coordination when you run more than one agent

The same install that gives one developer a personal AI memory also works as a shared brain when more than one agent talks to it. Two Copilot windows. A planner + executor pair. Three Claude instances dividing up a refactor. A pre-commit agent and a code-review agent on the same PR. They all remember and recall from the same store.

That introduces a new problem none of the hosted memory services have even tried to solve: agents stepping on each other. Agent A starts refactoring src/auth.py. Agent B, in another window, rewrites the same file with no idea A is mid-edit. Whoever saves last wins. The other agent's work is gone.

Recall ships six MCP primitives that turn parallel agents from a clobber-fest into a coordinated team:

ToolWhat it does
claim(resource, agent)Soft-lock a file/table/URL with an auto-expiring TTL
release(resource, agent)Drop the lock (soft-archive β€” audit trail survives)
who_has(resource)"Is anyone editing src/foo.py right now?"
claims()All active locks across all agents
handoff(to_agent, ...)Explicit work transfer with intent + files + context
pulse_others(self_agent)The N most recent checkpoints from agents other than you

Claims are advisory (like git locks) β€” Recall doesn't physically stop a second agent from writing, but every well-behaved client checks first. TTLs prevent a crashed agent from freezing a resource forever. Releases soft-archive (per the project-wide delete=archive rule) so the audit trail of who held what when survives.

If you're a single user, these tools just sit there unused. If you ever scale up to multiple agents, they're already there.

text
   β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”                           β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
   β”‚  Agent a3f7  β”‚      claim(file, ttl)     β”‚  Agent b1c4  β”‚
   β”‚  Claude #1   β”‚ ───────────┐  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β–Ί β”‚  Claude #2   β”‚
   β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”˜            β–Ό  β”‚           β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”˜
          β”‚              β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”          β”‚
          β”‚   remember   β”‚     Recall     β”‚   pulse  β”‚
          β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Ί β”‚ β€’ shared memoryβ”‚ ◄─────────
          β”‚              β”‚ β€’ claims/locks β”‚          β”‚
          β”‚   handoff    β”‚ β€’ handoffs     β”‚  handoff β”‚
          β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Ί β”‚ β€’ who_has      β”‚ ◄─────────
          β”‚              β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜          β”‚
          β–Ό                                          β–Ό
       22 MCP tools β€” Copilot, Claude, Cursor, custom

22 MCP tools total β€” 16 memory tools every user gets, plus the 6 coordination primitives that activate when you scale up.


One-line install (Claude Desktop, VS Code, Cursor)

Recall ships as a stdio MCP server. Zero config β€” no API keys, no Docker, no ports. Memory lives in ~/.recall/.

Terminal
pip install "ai-recallworks[mcp]"

Then add Recall to your MCP client config:

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):

config.json
{
  "mcpServers": {
    "recall": {
      "command": "recall-mcp"
    }
  }
}

VS Code (mcp.json in your workspace or user settings):

config.json
{
  "servers": {
    "recall": {
      "command": "recall-mcp"
    }
  }
}

Restart the client. Your agent now has persistent memory across sessions. Embeddings run fully offline (Chroma's bundled all-MiniLM-L6-v2). Upgrade to Ollama / OpenAI / Voyage embeddings via env vars when you want.


Five-minute install (HTTP / multi-user / team)

1. Run the server:

Terminal
docker run -d --name recall \
  -p 8787:8787 \
  -e API_KEY=changeme \
  -v recall-data:/data \
  ghcr.io/recallworks/recall:latest

2. Talk to it β€” pick your stack:

bash
# Raw HTTP (any language)
curl -H "X-API-Key: changeme" \
     -H "Content-Type: application/json" \
     -d '{"content":"first memory","tags":"hello"}' \
     http://localhost:8787/tool/remember
server.ts
# Python (use requests/httpx β€” no SDK pkg needed)
import requests
h = {"X-API-Key": "changeme", "Content-Type": "application/json"}
requests.post("http://localhost:8787/tool/remember", headers=h,
              json={"content": "first memory", "tags": "hello"})
print(requests.post("http://localhost:8787/tool/recall", headers=h,
                    json={"query": "memory"}).json()["result"])
server.ts
// TypeScript / JavaScript (Node 18+, Bun, Deno, browser)
npm install @recallworks/recall-client

import { RecallClient } from "@recallworks/recall-client";
const c = new RecallClient({ baseUrl: "http://localhost:8787", apiKey: "changeme" });
await c.remember("first memory", { tags: "hello" });
console.log((await c.recall("memory")).result);

Full walkthrough: docs/quickstart.md.


What you get

  • 13 tools β€” remember, recall, reflect, anti_pattern, checkpoint, pulse, session_close, index_file, reindex, snapshot_index, memory_stats, forget, maintenance.
  • Two transports β€” plain HTTP (POST /tool/{name}) and MCP over SSE. Drop into Copilot, Claude Code, Cursor, or any MCP client.
  • Bring your own models β€” pluggable embedder (default / OpenAI / Ollama) and summarizer (noop / OpenAI / Ollama). Run fully offline, fully on-prem, or against your own Azure-OpenAI tenant. See docs/byo-models.md.
  • Durable by default β€” ephemeral live store with auto-snapshot to disk; container restarts come up whole.
  • Append-only artifacts β€” every write also lands as a .md file. If the vector store ever burns down, reindex rebuilds it from the artifacts.
  • forget is soft-archive β€” guardrail wired into the OSS code itself, not bolted on as policy. Memory you delete can be recovered.

How it's different

RecallMem0 / Letta / Zep
License (core)MITmixed; SaaS-first
Self-hostone docker runvaries, often non-trivial
BYO embedderdefault / OpenAI / Ollama (env var)usually fixed
BYO LLMnoop / OpenAI / Ollama (env var)usually fixed
Storage modelappend-only artifacts + vector index, rebuildablelive DB only
deletesoft-archive by designhard delete
Tool surface13 opinionated tools (memory + workflow)embedding + retrieval primitives
MCP-nativeyes, plus plain HTTPpartial / via wrapper
Ops modelsingle binary, single containermulti-service stack

If you want a managed service, see Recall Cloud below. If you want a brain you fully own, this OSS core is enough.


Repo layout

PathWhat
src/recall/OSS server (MIT)
src/recall/tools/One module per tool
src/recall/transport/HTTP + MCP/SSE adapters
docker/single-tenant/Reference Dockerfile + compose
tests/pytest suite (no Docker required)
docs/Quickstart, conventions, architecture
enterprise/Multi-tenant, SSO, control plane (BSL)

Conventions

These are the practices that make the tools pay off. Pick what fits.

  • Cold-start ritual β€” opening protocol every session should run.
  • Branding β€” signed-edit headers so you can trace which agent touched which file when.

Status

Alpha. The code in src/recall/ is extracted from a hosted production brain that has served thousands of sessions, then sanitized of org-specific paths, extensions, and tenant data. Expect breaking changes before 1.0; pin the image tag.


Contributing

Yes β€” please read CONTRIBUTING.md first. We accept bug fixes, new Store backends, doc improvements, and anti-pattern entries. We don't accept architectural rewrites without prior discussion.

Security issues: see SECURITY.md.


License

  • src/recall/, clients/, docker/single-tenant/, docs/, examples/ β€” MIT (LICENSE)
  • enterprise/ β€” BSL 1.1, 5-seat additional-use grant, converts to MIT after 3 years (LICENSE-COMMERCIAL.md)

Recall Open Source vs. Recall Pro vs. Hosted

CapabilityOSS (this repo)Recall ProRecall Cloud
Single-tenant Docker imageβœ…βœ…n/a (hosted)
13 memory tools, MCP + HTTPβœ…βœ…βœ…
BYO embedder + LLMβœ…βœ…βœ…
Append-only artifacts + auto-snapshotβœ…βœ…βœ…
Multi-tenant, SSO, RBACβ€”βœ…βœ…
Audit log + retention policyβ€”βœ…βœ…
Cross-session entity graphβ€”βœ…βœ…
PII sanitization pipelineβ€”βœ…βœ…
Snapshot replication / DRβ€”βœ…βœ…
Vendor support + SLAcommunitybusiness hours24Γ—7
Hosted on our infraβ€”β€”βœ…
Pricingfreefrom $99/mo per nodefrom $0.10 per 1k tools

Recall Pro ships from the enterprise/ tree under a Business Source License β€” source-available, 5-seat free Additional Use Grant, converts to MIT after 3 years. Buy a license and the enterprise/ modules light up alongside your OSS install.

Recall Cloud is the hosted multi-tenant version. Same tools, no infra. Reach out for early-access pricing.

➑️ Talk to sales: sales@recall.works · Book a 20-min walkthrough: https://recall.works/demo


Vertical builds powered by Recall

Recall is the engine. We ship turn-key vertical brains on top of it:

  • IceWhisperer β€” the memory + workflow brain for ICE Mortgage Technology / Encompass shops. Pre-loaded SDK index, settings recipes, plugin audits, drift detection. Pilots from $250/mo.

If you want a vertical brain for your industry, we'll build it. Email partners@recall.works.


Maintainers

Reach the maintainers at maintainers@recall.works. Issues and PRs welcome on GitHub.

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Frequently Asked Questions about Recall

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "recall": { "command": "npx", "args": ["-y", "recall"] } }

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