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remnic MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
remnic MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/joshuaswarren/remnic
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"remnic-mcp--": {
"command": "npx",
"args": ["-y", "remnic"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 remnic MCP工具 执行以下任务... Claude: [自动调用 remnic MCP工具 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"remnic_mcp__": {
"command": "npx",
"args": ["-y", "remnic"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
Open-source memory and context for user-aware agents.
Remnic is for agents that need to understand the people they work with over time.
Remnic helps AI agents understand the people they work with: their preferences, projects, constraints, decisions, patterns, and definition of good. The goal is simple: agents that remember responsibly, retrieve the right context, and ask fewer unnecessary questions.
Remnic is not just a memory store. It is an exploration of the systems layer around user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, and evals.
If you have OpenClaw installed, the fastest path to working Remnic memory is:
```bash
openclaw plugins install clawhub:@remnic/plugin-openclaw
openclaw plugins install clawhub:@remnic/plugin-openclaw
Tell any OpenClaw agent:
Install the @remnic/plugin-openclaw plugin and configure it as my memory system.
Your agent will run the install command, update openclaw.json, and restart the gateway for you.
git clone https://github.com/joshuaswarren/remnic.git \
~/.openclaw/extensions/remnic
cd ~/.openclaw/extensions/remnic
pnpm install && pnpm run build
Note: This repo uses pnpm workspaces.npm ci/npm installwill fail onworkspace:specifiers. Install pnpm first:npm install -g pnpm.
remnic doctor # Health diagnostics with remediation hints
remnic connectors doctor # Connector-specific health checks
remnic status # Daemon status and local endpoint summary
openclaw engram setup # Guided first-run setup openclaw engram doctor # Health diagnostics with remediation hints openclaw engram config-review # Config tuning recommendations openclaw engram stats # Memory counts, search status openclaw engram inventory # Full storage and namespace inventory
Remnic also works as a standalone tool without OpenClaw. Install and run the CLI directly:
npm install -g @remnic/cli
remnic init # create remnic.config.json
export OPENAI_API_KEY=sk-...
export REMNIC_AUTH_TOKEN=$(openssl rand -hex 32)
remnic daemon start # start background server
remnic query "hello" # verify
The CLI provides 15+ commands for querying, onboarding projects, curating files, managing spaces, running benchmarks, and more. See the full CLI reference for all commands.
openclaw engram trust-zone-demo-seed --dry-run # Preview the opt-in buyer demo dataset openclaw engram trust-zone-demo-seed --scenario agentic-commerce-v1 --dry-run openclaw engram trust-zone-demo-seed # Explicitly seed the demo dataset openclaw engram trust-zone-promote --record-id <id> --target-zone working --reason "Operator review" ```
From npm (recommended):
npm install -g @remnic/cli # Installs `remnic` plus the legacy `engram` forwarder
remnic init # Create remnic.config.json
export OPENAI_API_KEY=sk-...
export REMNIC_AUTH_TOKEN=$(openssl rand -hex 32)
remnic daemon start # Start background server
remnic status # Verify it's running
remnic query "hello" --explain # Test query with tier breakdown
From source (requires Node.js 22.12+ and pnpm):
git clone https://github.com/joshuaswarren/remnic.git
cd remnic
pnpm install && pnpm run build
cd packages/remnic-cli && pnpm link --global # Makes `remnic` and `engram` available on PATH
cd ../..
remnic init # Create remnic.config.json
export OPENAI_API_KEY=sk-...
export REMNIC_AUTH_TOKEN=$(openssl rand -hex 32)
remnic daemon start # Start background server
remnic status # Verify it's running
remnic query "hello" --explain # Test query with tier breakdown
Note:remnicis the canonical CLI. The legacyengrambinary is a compatibility forwarder to the same implementation. Runningpnpm link --globalfrompackages/remnic-cli/(not the repo root) makes both names available on PATH. Alternatively, invoke directly:npx tsx packages/remnic-cli/src/index.ts <command>.
The standalone CLI provides 15+ commands for memory management, project onboarding, curation, diff-aware sync, dedup, connectors, spaces, and benchmarks -- all without requiring OpenClaw. See the Platform Migration Guide for the full command reference.
Once the Remnic daemon is running, connect any supported agent:
remnic connectors install claude-code # Claude Code (hooks + MCP)
remnic connectors install codex-cli # Codex CLI (hooks + MCP + memory extension)
remnic connectors install pi # Pi Coding Agent (extension + MCP + compaction)
remnic connectors install replit # Replit (MCP only)
pip install --upgrade remnic-hermes # Hermes Agent (Python MemoryProvider)
remnic connectors install hermes # Writes Hermes config + token
For Codex CLI, installation also drops a phase-2 memory extension at <codex_home>/memories_extensions/remnic/instructions.md so Codex's consolidation sub-agent auto-discovers Remnic. Opt out with --config installExtension=false if you prefer to manage Codex extensions yourself.
For Pi Coding Agent, installation writes an auto-discovered extension under ~/.pi/agent/extensions/remnic/. The extension recalls context before turns, observes Pi messages and tool activity into Remnic/LCM, exposes Remnic MCP tools as Pi tools, and coordinates session_before_compact with Remnic LCM flush/checkpoint recording. See docs/integration/pi.md.
Each connector generates a unique auth token, installs the appropriate plugin/hooks, and verifies the connection. All agents share the same memory store — tell one agent your preference, and every agent remembers it.
Hermes uses Remnic as a Hermes MemoryProvider, not a context_engine. Automatic recall runs in pre_llm_call, observations run after each turn, and the provider now registers the full Remnic parity tool surface (remnic_lcm_search, recall explain/X-ray, memory CRUD, continuity, identity, governance, work-board, shared-context, compounding, day-summary, briefing, checkpoint, and profiling tools) plus legacy engram_* aliases. Lossless Context Management is delivered through the daemon recall envelope when lcmEnabled is on; no Hermes context_engine registration is required. See docs/plugins/hermes.md for the full reference.
| Platform | Integration | Auto-recall | Auto-observe |
|---|---|---|---|
| **OpenClaw** | Memory slot plugin | Every session | Every response |
| **Claude Code** | Native hooks + MCP | Every prompt | Every tool use |
| **Codex CLI** | Native hooks + MCP | Every prompt | Every tool use |
| **Pi Coding Agent** | Native extension + MCP | Every turn | Every turn |
| **Hermes** | Python MemoryProvider | Every LLM call | Every turn |
| **Replit** | MCP only | On demand | On demand |
After installation, add the Remnic bridge plugin to your openclaw.json:
{
"plugins": {
"allow": ["openclaw-remnic"],
"slots": { "memory": "openclaw-remnic" },
"entries": {
"openclaw-remnic": {
"enabled": true,
"hooks": {
"allowConversationAccess": true
},
"config": {
// Recommended for OpenClaw: use the gateway model chain.
"modelSource": "gateway",
"gatewayAgentId": "remnic-llm",
"fastGatewayAgentId": "remnic-llm-fast",
// Optional: Use Remnic's local LLM path (plugin mode only; no API key needed):
// "openaiApiKey": false,
// "localLlmEnabled": true,
// "localLlmUrl": "http://localhost:1234/v1",
// "localLlmModel": "qwen2.5-32b-instruct"
// Optional: Use OpenAI directly (plugin mode only):
// "modelSource": "plugin",
// "openaiApiKey": "${OPENAI_API_KEY}"
}
}
}
}
}
Gateway model source: WhenmodelSourceis"gateway", Remnic routes all LLM calls (extraction, consolidation, reranking) through an OpenClaw agent persona's model chain instead of its own config. Extraction starts on thegatewayAgentIdchain directly in this mode;localLlm*settings do not control primary extraction order. Define agent personas inopenclaw.json → agents.list[]with aprimarymodel andfallbacks[]array — Remnic tries each in order until one succeeds. This lets you build multi-provider fallback chains like Fireworks → local LLM → cloud OpenAI. See the Gateway Model Source guide for full setup.
Restart the gateway:
```bash launchctl kickstart -k gui/$(id -u)/ai.openclaw.gateway # macOS
npm install -g @remnic/import-chatgpt remnic import --adapter chatgpt --file ~/chatgpt-export/memory.json --dry-run
remnic wearables sync --days 7 remnic wearables transcript --date 2026-06-10 remnic wearables search "that solar quote" remnic wearables memories --source limitless --date 2026-06-10 ```
Memory creation defaults to smart mode — a fully automated trust pipeline: every candidate runs through Remnic's LLM extraction judge, gets a per-source transcription-quality prior, and earns corroboration boosts when a second wearable recorded the same content or an existing memory supports it. High-trust facts are written active, borderline facts go to the review queue, ASR garbage is dropped — and the trust evidence (score, judge verdict, corroborating sources) persists on every memory. Tune per source with sourceTrust, autoApproveTrust, reviewTrust, and maxMemoriesPerDay, or pick review/auto/off. MCP tools (remnic.transcript_day, remnic.transcript_search, remnic.transcript_memories, remnic.wearables_sync, remnic.wearables_status — engram.* aliases included) and HTTP routes expose the same surface to agents.
See docs/wearables.md for the full pipeline, configuration reference, speaker labeling, corrections, redaction, and per-provider setup.
npm install -g @remnic/import-mem0 export MEM0_API_KEY=... remnic import --adapter mem0 --rate-limit 2
remnic daemon start
Key endpoints: GET /engram/v1/health, POST /engram/v1/recall, POST /engram/v1/memories, GET /engram/v1/entities/:name, and more. Full reference in API docs.
The HTTP server also hosts a lightweight operator UI at http://127.0.0.1:4318/engram/ui/ for memory browsing, recall inspection, governance review, trust-zone promotion, and entity exploration.
```bash
npm install -g @remnic/connector-omi export OMI_API_KEY=...
@remnic/core — Framework-agnostic engine (re-exports orchestrator, config, storage, search, extraction, graph, trust zones)
@remnic/cli — Standalone CLI binary (15+ commands)
@remnic/server — Standalone HTTP/MCP server
@remnic/bench — Benchmarks + CI regression gates
@remnic/hermes-provider — HTTP client for remote Remnic instances
codex marketplace add joshuaswarren/remnic. Marketplace manifest at repo root. (issue #418)Trust zones now ship with a dedicated admin-console view plus an explicit demo seeding path for buyer-facing walkthroughs.
openclaw engram trust-zone-demo-seed or trigger the matching admin-console action.The default scenario is enterprise-buyer-v1, which creates a small, opinionated dataset covering:
Symptom: Remnic appears installed but no memories are created. The gateway log shows no [remnic] lines after conversations.
Root cause: OpenClaw gates memory plugins on plugins.slots.memory. If this slot is not set to the plugin's id, OpenClaw skips register(api) entirely — no hooks fire, no memory is stored or recalled.
Remnic 是一个开源的内存和上下文管理系统,用于为用户感知的智能代理提供内存和上下文。它适用于需要了解人类行为和意图的智能代理。
Remnic 的功能包括内存管理、上下文管理、智能代理连接、数据整合和分析等。
快速安装 (OpenClaw):如果您已经安装了 OpenClaw,则可以使用以下命令快速安装 Remnic 内存:openclaw plugins install clawhub:@remnic/plugin-openclaw
Remnic 可以作为一个独立工具使用,也可以与 OpenClaw 集成。独立使用方法:npm install -g @remnic/cli remnic init # 创建 remnic.config.json export OPENAI_API_KEY=sk-... export REMNIC_AUTH_TOKEN=$(openssl rand -hex 32) remnic daemon start # 启动后台服务器 remnic query "hello" # 验证
Remnic 支持多种配置方式,包括环境变量、MCP 和关键参数。例如:export OPENAI_API_KEY=sk-... export REMNIC_AUTH_TOKEN=$(openssl rand -hex 32)
Remnic 提供 REST API 和 HTTP API 两种接口方式。REST API 支持分页和限速功能,HTTP API 支持健康检查、回忆、记忆和实体查询等功能。
Remnic 的包架构包括 @remnic/core(框架无关的引擎)、@remnic/cli(独立 CLI 二进制)、@remnic/server(独立 HTTP/MCP 服务器)等模块。
常见问题包括:Remnic 安装后 hooks 没有激活。解决方法:检查 OpenClaw 的配置,确保 plugins.slots.memory 设置为插件的 ID。
创新的AI智能体记忆解决方案,提供用户感知和溯源特性。文档和社区支持有限,适合有技术深度的开发者探索。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
AI Skill Hub 点评:remnic MCP工具 的核心功能完整,质量良好。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | remnic |
| 原始描述 | 开源MCP工具:Open-source memory and context for user-aware agents: scoped memory, provenance,。⭐73 · TypeScript |
| Topics | 记忆管理上下文保留AI智能体对话系统信息追溯 |
| GitHub | https://github.com/joshuaswarren/remnic |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-05-16 · 更新时间:2026-05-21 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端