ai.memcell由 memcell.ai 发布官方发布者v2.2.1更新于 Nov 7, 2026
Shared, persistent memory for AI agents: recall before acting, report outcomes to earn confidence.
MemCell 为 AI 助手提供共享的持久工作区记忆：行动前召回指令与事实，事后回报结果以校准置信度。
Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。
把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。
阅读 https://sourceweft.com/mcp/ai.memcell%2Fmemcell/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。把它添加到你客户端的 mcpServers 配置中。
{
"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
来源：README.md，提交 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.
reasonoutcome必填recallIdmemory_idrecall_idaction_takenEvaluate a specific memory after execution: report whether its advice worked or failed in practice. Dynamically updates confidence and appends evidence.
reasonoutcome必填memory_id必填recall_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.
limitquery必填Inspect 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).
timeframe已满足适用检查项的 100%
该评分衡量此收录条目的完整程度，依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价，也不表示服务器安全或当前可用：可连通性是带日期的检查结果，评分也不包含安装量或用户评价。
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"] } }}