Recall

by recallworks

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Open-source MCP memory server for AI coding agents — durable cross-session memory, per-agent namespaces, ChromaDB-backed, self-hosted.

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Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Recall

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

From the repository

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

pip install "ai-recallworks](https://github.com/RecallWorks/Recall/blob/HEAD/mailto:[email protected]?subject=Recall%20demo)[mcp]"

Then add Recall to your MCP client config:

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

{ "mcpServers": { "recall": { "command": "recall-mcp" } } }

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

{ "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.

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

# 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

# Python (use requests/httpx — 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"])

// 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);

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

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

These are thepracticesthat make the tools pay off. Pick what fits.

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

Alpha. The code insrc/recall/isextracted 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.

Yes — please read CONTRIBUTING.mdfirst. We accept bug fixes, newStorebackends, doc improvements, and anti-pattern entries. We don't accept architectural rewrites without prior discussion.

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

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "recall": {
            "server": {
                "command": "uvx",
                "args": [
                    "ai-recallworks"
                ]
            }
        }
    }
}

McpServers

{
    "server": {
        "command": "uvx",
        "args": [
            "ai-recallworks"
        ]
    }
}

Transport

"stdio"

Package

"ai-recallworks"

Registry

"pypi"

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

Onepip 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.

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Transport

Package

Registry

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

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