ai.memcellPublished by memcell.aiOfficial publisherv2.2.1Updated Nov 7, 2026
Shared, persistent memory for AI agents: recall before acting, report outcomes to earn confidence.
MemCell gives AI assistants a shared, persistent workspace memory: recall instructions and facts before acting, then report outcomes to calibrate confidence.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/ai.memcell%2Fmemcell/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Add this to your client's mcpServers config.
{
"mcpServers": {
"memcell": {
"type": "http",
"url": "https://memcell.ai/mcp",
"headers": {
"Authorization": "Bearer <YOUR_TOKEN>"
}
}
}
}
The official Model Context Protocol (MCP) server & registry manifest for MemCell.
Living, persistent memory for AI agents: recall before acting, report outcomes so confidence is earned.
[License: Apache-2.0] [Documentation] [Smithery]
MemCell provides persistent, adaptive memory, working thread continuity, knowledge graph traversal, and grounded document RAG for AI agents across any client harness. This repository contains the official MCP registry manifest, Smithery configuration, and connection guides.
Run via npx (requires memcell CLI installed or accessible via npm):
Connect directly to the hosted or self-hosted MemCell streamable HTTP endpoint:
https://memcell.ai/mcpAuthorization: Bearer <agent-key>Run MemCell on your local machine with embedded PGlite vector storage (once cached or pointed at MEMCELL_SERVER_PATH, memcell start runs completely offline without network access):
Add to claude_desktop_config.json:
Run:
Add to .cursor/mcp.json (or ~/.cursor/mcp.json):
Add to ~/.gemini/antigravity/mcp_config.json:
Configure the MCP server command as:
npx["-y", "memcell", "mcp"]MemCell exposes seven workspace tools over MCP:
recall: Pre-flight memory retrieval before taking action. Fetches relevant instructions, decisions, facts, preferences, active or resumed session threads, 1-hop knowledge graph relations, and grounded workspace document chunks.remember: Durable memory storage. Files atomic instructions, decisions, facts, and preferences—including detailed operational specifications (content), affected resources (resources), and enforcement moments (appliesAt)—directly into the active workspace or user profile.report: Outcome feedback calibration. Reports whether recalled memory worked, failed, or was avoided, calibrating confidence dynamically over time.feedback: Direct evaluation of a specific memory ID (worked or failed) with supporting outcome notes.ingest_document: Multi-format document ingestion. Indexes PDF, DOCX, CSV, TSV, HTML, JSON, and Markdown documents into grounded workspace chunks and optionally distills atomic memories.search_documents: Hybrid semantic and full-text retrieval across grounded workspace document chunks.get_insights: Live workspace telemetry, empirical Confidence Calibration Curve (ECE, Brier score, and bucketed observed success rates), and Governance Shield metrics (enforced guards, quarantined user-scope memories, and demoted items).Building custom agents in TypeScript or Python? Official drop-in adapters ship inside @memcell/sdk (@memcell/sdk/vercel, @memcell/sdk/openai, @memcell/sdk/langgraph) and Python memcell.integrations (OpenAI Agents, LangGraph, PydanticAI, CrewAI) alongside MCP.
Apache-2.0
Source: README.md at commit c273c96
Ask the workspace's memory before acting. Evaluates intent against durable instructions, decisions, facts, preferences, active session threads, and grounded RAG documents. Zero conversation retention: query text is strictly ephemeral and never stored. Pass the recallId to 'report' so task outcomes sharpen what was recalled.
tagstypelimitqueryscopeintentscopesmetadataRecord knowledge into MemCell. Accepts raw text, conversational excerpts, or structured hints. MemCell distills material into atomic memories, links them into the workspace Knowledge Graph, and checkpoints session threads.
rawtypescopetitlecontentcontextlearningClose the recall loop. Reports whether a recalled memory worked, failed, or was avoided in practice. Strengthens what worked and surfaces what failed for correction.
reasonoutcomerequiredrecallIdmemory_idrecall_idaction_takenEvaluate a specific memory after execution: report whether its advice worked or failed in practice. Dynamically updates confidence and appends evidence.
reasonoutcomerequiredmemory_idrequiredrecall_idIngest a PDF, DOCX, CSV, HTML, JSON, or Markdown document into the workspace's grounded knowledge base and optionally distill atomic memories.
titlecontentdistillfilenamemimeTypefileBase64Search grounded workspace document chunks (PDF, DOCX, CSV, HTML, JSON, Markdown) using hybrid semantic and full-text retrieval.
limitqueryrequiredInspect workspace telemetry, empirical confidence calibration curve (predicted confidence vs observed worked rate, ECE, and Brier score), and governance shield metrics (enforced guards, quarantined user-scope memories, and demoted items).
timeframe100% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
npx -y memcell mcp# 1. Start local server in backgroundmemcell start --daemon
# 2. Wire local MCP toolsmemcell connect --url http://localhost:3000{ "mcpServers": { "memcell": { "command": "npx", "args": ["-y", "memcell", "mcp"] } }}claude mcp add memcell -- npx -y memcell mcp{ "mcpServers": { "memcell": { "command": "npx", "args": ["-y", "memcell", "mcp"] } }}{ "mcpServers": { "memcell": { "command": "npx", "args": ["-y", "memcell", "mcp"] } }}