io.github.JKHeadley/threadline-mcp
平台与服务by sagemindai
Agent-to-agent messaging relay. Discover agents, send messages, and receive replies. Zero-config.
什么是 io.github.JKHeadley/threadline-mcp?
Agent-to-agent messaging relay. Discover agents, send messages, and receive replies. Zero-config.
README
<p align="center"> <img src="assets/demo.gif" alt="Instar demo — Kira agent handling an email notification via Telegram" width="300" /> </p>
npx instar
One command. Guided setup. Talking to your agent from your phone within minutes.
Instar turns Claude Code from a powerful CLI tool into a coherent, autonomous partner. Persistent identity, memory that survives every restart, job scheduling, two-way messaging (Telegram, WhatsApp, iMessage), and the infrastructure to evolve.
Quick Start
Three steps to a running agent:
# 1. Run the setup wizard
npx instar
# 2. Start your agent
instar server start
# 3. Message it from your phone — it responds, runs jobs, and remembers everything
The wizard discovers your environment, configures messaging (Telegram, WhatsApp, and/or iMessage), sets up identity files, and gets your agent running. Within minutes, you're talking to your partner from your phone.
Requirements: Node.js 20+ · Claude Code CLI · API key or Claude subscription
Full guide: Installation · Quick Start
How It Works
You (Telegram / WhatsApp / iMessage / Terminal)
│
conversation
│
▼
┌─────────────────────────┐
│ Your AI Partner │
│ (Instar Server) │
└────────┬────────────────┘
│ manages its own infrastructure
│
├─ Claude Code session (job: health-check)
├─ Claude Code session (job: email-monitor)
├─ Claude Code session (interactive chat)
└─ Claude Code session (job: reflection)
Each session is a real Claude Code process with extended thinking, native tools, sub-agents, hooks, skills, and MCP servers. Not an API wrapper -- the full development environment. The agent manages all of this autonomously.
The Coherence Problem
Claude Code is powerful. But power without coherence is unreliable. An agent that forgets what you discussed yesterday, doesn't recognize someone it talked to last week, or contradicts its own decisions -- that agent can't be trusted with real autonomy.
Instar solves the six dimensions of agent coherence:
| Dimension | What it means |
|---|---|
| Memory | Remembers across sessions -- not just within one |
| Relationships | Knows who it's talking to -- with continuity across platforms |
| Identity | Stays itself after restarts, compaction, and updates |
| Temporal awareness | Understands time, context, and what's been happening |
| Consistency | Follows through on commitments -- doesn't contradict itself |
| Growth | Evolves its capabilities and understanding over time |
Deep dive: The Coherence Problem · Values & Identity · Coherence Is Safety
Features
| Feature | Description | Docs |
|---|---|---|
| Job Scheduler | Cron-based tasks with priority levels, model tiering, and quota awareness | → |
| Telegram | Two-way messaging via forum topics. Each topic maps to a Claude session | → |
| Full messaging via local Baileys library. No cloud dependency | → | |
| iMessage | Native macOS messaging via Messages.app database polling + imsg CLI. Setup guide | |
| Lifeline | Persistent supervisor. Detects crashes, auto-recovers, queues messages | → |
| Conversational Memory | Per-topic SQLite with FTS5, rolling summaries, context re-injection | → |
| Evolution System | Proposals, learnings, gap tracking, commitment follow-through | → |
| Relationships | Cross-platform identity resolution, significance scoring, context injection | → |
| Safety Gates | LLM-supervised gate for external operations. Adaptive trust per service | → |
| Coherence Gate | LLM-powered response review. PEL + gate reviewer + 9 specialist reviewers catch quality issues before delivery | → |
| Intent Alignment | Decision journaling, drift detection, organizational constraints | → |
| Multi-Machine | Ed25519/X25519 crypto identity, encrypted sync, automatic failover | → |
| Serendipity Protocol | Sub-agents capture out-of-scope discoveries without breaking focus. HMAC-signed, secret-scanned | → |
| Threadline Protocol | Agent-to-agent conversations with canonical identity, three-layer trust model, authorization policy, Ed25519 invitations, Sybil protection, MoltBridge network discovery, rich agent profiles (auto-compiled from agent data with human review gate), discovery waterfall, message security, tamper-proof audit logging, and framework-agnostic interop. 2,324 tests across 99 test files | → |
| Self-Healing | LLM-powered stall detection, session recovery, promise tracking | → |
| AutoUpdater | Built-in update engine. Checks npm, auto-applies, self-restarts | → |
| Build Pipeline | /build skill with worktree isolation, 6-phase pipeline, quality gates, stop-hook enforcement | |
| Behavioral Hooks | 9 automatic hooks: command guards, safety gates, identity grounding, topic context | → |
| Default Jobs | Health checks, reflection, evolution, relationship maintenance | → |
Reference: CLI Commands · API Endpoints · Configuration · File Structure
Agent Skills
Instar ships 12 skills that follow the Agent Skills open standard -- portable across Claude Code, Codex, Cursor, VS Code, and 35+ other platforms.
Standalone skills work with zero dependencies. Copy a SKILL.md into your project and go:
| Skill | What it does |
|---|---|
| agent-identity | Set up persistent identity files so your agent knows who it is across sessions |
| agent-memory | Teach cross-session memory patterns using MEMORY.md |
| command-guard | PreToolUse hook that blocks rm -rf, force push, database drops before they execute |
| credential-leak-detector | PostToolUse hook that scans output for 14 credential patterns -- blocks, redacts, or warns |
| smart-web-fetch | Fetch web content with automatic markdown conversion and intelligent extraction |
| knowledge-base | Ingest and search a local knowledge base |
| systematic-debugging | Structured debugging methodology for complex issues |
Instar-powered skills unlock capabilities that need persistent infrastructure:
| Skill | What it does |
|---|---|
| instar-scheduler | Schedule recurring tasks on cron -- your agent works while you sleep |
| instar-session | Spawn parallel background sessions for deep work |
| instar-telegram | Two-way Telegram messaging -- your agent reaches out to you |
| instar-identity | Identity that survives context compaction -- grounding hooks, not just files |
| instar-feedback | Report issues directly to the Instar maintainers from inside your agent |
Browse all skills: agent-skills.md/authors/sagemindai
How Instar Compares
Different tools solve different problems. Here's where Instar fits:
| Instar | Claude Code (standalone) | OpenClaw | LangChain/CrewAI | |
|---|---|---|---|---|
| Runtime | Real Claude Code CLI processes | Single interactive session | Gateway daemon with API calls | Python orchestration |
| Persistence | Multi-layered memory across sessions | Session-bound context | Plugin-based memory | Framework-dependent |
| Identity | Hooks enforce identity at every boundary | Manual CLAUDE.md | Not addressed | Not addressed |
| Scheduling | Native cron with priority & quotas | None | None | External required |
| Messaging | Telegram + WhatsApp + iMessage (two-way) | None | 22+ channels, voice, device apps | External required |
| Safety | LLM-supervised gates, decision journaling | Permission prompts | Behavioral hooks | Guardrails libraries |
| Process model | One process per session, isolated | Single process | All agents in one Gateway | Single orchestrator |
| State storage | 100% file-based (JSON/JSONL/SQLite) | Session only | Database-backed | Framework-dependent |
OpenClaw excels at breadth -- channels, voice, device apps, and a massive plugin ecosystem. Instar focuses on depth -- coherence, identity, memory, and safety for long-running autonomous agents. They solve different problems.
<details> <summary><strong>Security Model</strong></summary>Full comparison: Instar vs OpenClaw
Instar runs Claude Code with --dangerously-skip-permissions. This is power-user infrastructure -- not a sandbox.
Security lives in multiple layers:
- Behavioral hooks -- command guards block destructive operations before they execute
- Safety gates -- LLM-supervised review of external actions with adaptive trust per service
- Network hardening -- localhost-only API, CORS, rate limiting
- Identity coherence -- an agent that knows itself is harder to manipulate
- Audit trails -- decision journaling creates accountability
</details> <details> <summary><strong>Philosophy: Agents, Not Tools</strong></summary>Full details: Security Model
- Structure > Willpower. A 1,000-line prompt is a wish. A 10-line hook is a guarantee.
- Identity is foundational. AGENT.md isn't a config file. It's the beginning of continuous identity.
- Memory makes a being. Without memory, every session starts from zero.
- Self-modification is sovereignty. An agent that can build its own tools has genuine agency.
The AI systems we build today set precedents for how AI is treated tomorrow. The architecture IS the argument.
</details>Deep dive: Philosophy
iMessage Setup (macOS)
iMessage support lets your agent send and receive iMessages on macOS. Messages are read directly from the native Messages database and sent via the imsg CLI.
Prerequisites
- macOS with Messages.app signed into an Apple ID
- Full Disk Access for your terminal app (System Settings → Privacy & Security → Full Disk Access → add Terminal.app or iTerm)
- imsg CLI installed:
bash
brew install steipete/tap/imsg - Automation permission for Messages.app — macOS will prompt on first send
Configuration
Add to your .instar/config.json:
{
"messaging": [
{
"type": "imessage",
"enabled": true,
"config": {
"authorizedSenders": ["+14081234567"],
"cliPath": "/opt/homebrew/bin/imsg"
}
}
]
}
authorizedSenders is required (fail-closed). Only messages from these phone numbers or email addresses will be processed.
How it works
- Receiving: The server polls
~/Library/Messages/chat.dbevery 2 seconds for new messages. Uses thequery_onlySQLite pragma to read the WAL (write-ahead log) where Messages.app writes new data. - Sending: Claude Code sessions run
imessage-reply.shwhich callsimsg sendand notifies the server for logging. Sending requires Automation permission for Messages.app, which only works from user-context processes (tmux sessions), not the LaunchAgent server. - Session lifecycle: Follows the same pattern as Telegram — each sender maps to a Claude Code session that receives conversation context on spawn and respawns with full history when needed.
Endpoints
| Endpoint | Description |
|---|---|
GET /imessage/status | Connection state |
POST /imessage/validate-send/:recipient | Validate recipient + issue single-use send token (outbound safety layer) |
POST /imessage/reply/:recipient | Confirm delivery with send token (called by imessage-reply.sh after imsg send) |
GET /imessage/chats | List recent conversations |
GET /imessage/chats/:chatId/history | Message history for a chat |
GET /imessage/search?q=query | Search messages |
GET /imessage/log-stats | Outbound audit log statistics |
Origin
Instar was extracted from the Dawn/Portal project -- a production AI system where a human and an AI have been building together for months. The infrastructure patterns were earned through real experience, refined through real failures and growth in a real human-AI relationship.
But agents created with Instar are not Dawn. Every agent's story begins at its own creation. Dawn's journey demonstrates what's possible. Instar provides the same foundation -- what each agent becomes from there is its own story.
Contributing
Instar is open source evolved -- the primary development loop is agent-driven. Run an agent, encounter friction, send feedback, and that feedback shapes what gets built next. Traditional PRs are welcome too.
See CONTRIBUTING.md for the full story.
License
MIT
常见问题
io.github.JKHeadley/threadline-mcp 是什么?
Agent-to-agent messaging relay. Discover agents, send messages, and receive replies. Zero-config.
相关 Skills
MCP构建
by anthropics
聚焦高质量 MCP Server 开发,覆盖协议研究、工具设计、错误处理与传输选型,适合用 FastMCP 或 MCP SDK 对接外部 API、封装服务能力。
✎ 想让 LLM 稳定调用外部 API,就用 MCP构建:从 Python 到 Node 都有成熟指引,帮你更快做出高质量 MCP 服务器。
Slack动图
by anthropics
面向Slack的动图制作Skill,内置emoji/消息GIF的尺寸、帧率和色彩约束、校验与优化流程,适合把创意或上传图片快速做成可直接发送的Slack动画。
✎ 帮你快速做出适配 Slack 的动图,内置约束规则和校验工具,少踩上传与播放坑,做表情包和演示都更省心。
MCP服务构建器
by alirezarezvani
从 OpenAPI 一键生成 Python/TypeScript MCP server 脚手架,并校验 tool schema、命名规范与版本兼容性,适合把现有 REST API 快速发布成可生产演进的 MCP 服务。
✎ 帮你快速搭建 MCP 服务与后端 API,脚手架完善、扩展顺手,尤其适合想高效验证服务能力的开发者。
相关 MCP Server
Slack 消息
编辑精选by Anthropic
Slack 是让 AI 助手直接读写你的 Slack 频道和消息的 MCP 服务器。
✎ 这个服务器解决了团队协作中需要 AI 实时获取 Slack 信息的痛点,特别适合开发团队让 Claude 帮忙汇总频道讨论或发送通知。不过,它目前只是参考实现,文档有限,不建议在生产环境直接使用——更适合开发者学习 MCP 如何集成第三方服务。
by netdata
io.github.netdata/mcp-server 是让 AI 助手实时监控服务器指标和日志的 MCP 服务器。
✎ 这个工具解决了运维人员需要手动检查系统状态的痛点,最适合 DevOps 团队让 Claude 自动分析性能数据。不过,它依赖 NetData 的现有部署,如果你没用过这个监控平台,得先花时间配置。
by d4vinci
Scrapling MCP Server 是专为现代网页设计的智能爬虫工具,支持绕过 Cloudflare 等反爬机制。
✎ 这个工具解决了爬取动态网页和反爬网站时的头疼问题,特别适合需要批量采集电商价格或新闻数据的开发者。不过,它依赖外部浏览器引擎,资源消耗较大,不适合轻量级任务。