open-tag

An open-source, model-agnostic & platform-agnostic alternative to Anthropic's Claude Tag — a persistent AI teammate that lives in your team chat.

---

Why

Claude Tag is a great idea wrapped in two constraints: it's Slack-only and Opus-only. open-tag keeps the idea and drops both constraints:

• Platform-agnostic — Telegram and Discord today, Slack and anything with a bot API next.
• Model-agnostic — Ollama Cloud (default), Anthropic, OpenAI, or any provider, swappable with a single config string.
• Open source & self-hostable — Apache-2.0, runs on your own infra with just an LLM API key.

It's not a chatbot. It's the five things that make Claude Tag feel like a teammate:

1. One shared agent per channel (multiplayer) — everyone sees the same agent working; anyone continues the thread.
2. Ambient / proactive mode — it decides on its own when to jump in, flag things, and chase forgotten threads.
3. Long-running async + self-scheduling — runs a task over hours or days and schedules its own follow-ups.
4. Contextual memory — learns from channel history so you stop re-explaining.
5. Per-channel permissioned connectors — admins scope which tools, data, and codebases each channel can touch.

How

open-tag is deliberately not an agent framework. It's a thin product layer on two open building blocks, so we build only what's actually differentiated:

   Telegram / Discord / Slack channels        ← platform seam (we own; grammY + discord.js)
                  │
   open-tag product layer                      ← what we build:
   shared per-channel sessions · ambient          multiplayer state, ambient triage,
   mode · per-channel permission policy           per-channel policy
                  │
   Flue + Pi harness  (Apache-2.0)             ← adopt: agent loop, subagents, sandboxes,
                  │                                durable execution
   Ollama Cloud / Vercel AI Gateway            ← adopt: model seam — one string picks the
                                                  provider; gateway adds routing + fallback

Layer	Tech	Build / adopt
Agent core	Flue + Pi harness	adopt
Model seam	Ollama Cloud (default) · Vercel AI Gateway	adopt
Platform adapters	grammY (Telegram) · discord.js (Discord)	build
Connectors	MCP servers as Flue tools	adopt
State / memory	Postgres + pgvector	build (schema)
Runtime	Node 22.19+, TypeScript, pnpm	—

See DESIGN.md for the full architecture and SPIKE.md for the de-risking spike that validated the stack (Pi is fully self-hostable; AI Gateway wiring works; model-swap-by-one-string confirmed).

Status

All five roadmap steps land. @mention (or DM) the bot → it joins the shared per-channel session → the Flue teammate agent runs (tools, self-scheduling, memory) → the reply streams back, edited in place. Works on Telegram and Discord through one normalized adapter seam. Models default to Ollama Cloud and swap with one string. Per-channel ambient mode, model, and tool permissions are set with in-chat admin commands. Optional Postgres + pgvector adds semantic recall over channel history and facts (recall_context). Admin commands default to the platform's own roles (Telegram chat admins, Discord permissions). MCP connectors plug in as tools, opt-in per channel (@bot mcp allow <server>).

• Step 0 — spike: Flue + AI Gateway, validate self-hostability
• Step 1 — Telegram channel → shared session → agent → streamed reply
• Step 2 — second platform (Discord) on the same normalized adapter seam
• Step 3 — durable self-scheduling: the agent can schedule follow-ups for itself
• Step 4 — ambient mode (opt-in, triaged, rate-limited) + per-channel memory
• Step 5 — per-channel tool RBAC, model-picker, and admin commands

Step 2 proved the seam: adding Discord was one new adapter (src/platform/discord.ts) plus the launcher's env selection — the product layer did not change. A new platform is one file.

Step 3 made the agent proactive. Each channel session is mirrored to the channel by a persistent event tail (SessionMirror), so output isn't tied to a request/response — a durable schedule_task can fire hours later and the reply still lands in the channel.

Step 4 lets it watch and remember. Ambient mode is off until a channel runs @bot ambient on; then a conservative, rate-limited triage decides when to chime in on messages it wasn't addressed in. remember_fact keeps durable per-channel facts (injected into the prompt) on top of the rolling memory Flue's continuing session already provides.

Step 5 puts channels in control. @bot help lists the admin commands: model <provider/model> picks the channel's model, tools deny <name> enforces per-channel RBAC (denied tools are filtered out before the model ever sees them), and OPEN_TAG_ADMINS gates who may change settings.

Quick start

Requires Node 22.19+, pnpm, a Telegram bot token from @BotFather, and an Ollama Cloud API key (or a Vercel AI Gateway key).

git clone https://github.com/Lulzx/open-tag.git
cd open-tag
pnpm install
cp .env.example .env          # add OLLAMA_API_KEY + TELEGRAM_BOT_TOKEN and/or DISCORD_BOT_TOKEN
pnpm dev                      # runs the Flue agent server + the bot (every platform with a token)

Then @mention your bot in a Telegram or Discord channel (or DM it) and it replies in the shared channel session.

pnpm dev runs two processes — start them separately if you prefer:

pnpm dev:server   # the Flue agent server (hosts the teammate agent)
pnpm dev:bot       # the Telegram bot (long-polls; talks to the server over the Flue SDK)
pnpm run agent:hello   # optional: smoke-test the model wiring with the step-0 agent

Swap the model by changing a single value in .env — the prefix picks the provider:

OPEN_TAG_MODEL=ollama/gpt-oss:120b           # Ollama Cloud (default)
OPEN_TAG_MODEL=ollama/qwen3-coder:480b       # any Ollama Cloud model
OPEN_TAG_MODEL=anthropic/claude-sonnet-4-6   # via Vercel AI Gateway (needs AI_GATEWAY_API_KEY)

Extending

Two seams, both additive — neither touches the product layer.

A new platform is one adapter file plus one line. Implement PlatformAdapter (src/platform/types.ts), export an AdapterFactory, and add it to the manifest:

// src/platform/msteams.ts
export class MsTeamsAdapter implements PlatformAdapter { /* … */ }
export const msteamsFactory: AdapterFactory = {
  platform: 'msteams',
  envHint: 'MSTEAMS_BOT_TOKEN',
  fromEnv: () => (process.env.MSTEAMS_BOT_TOKEN ? new MsTeamsAdapter(process.env.MSTEAMS_BOT_TOKEN) : null),
};

// src/platform/adapters.ts  ← the only edit
export const adapterFactories = [telegramFactory, discordFactory, msteamsFactory];

Platform is an open string, so nothing else changes — session keys, the admin allowlist, and the channel registry all keep working.

A new feature is a plugin. Every command, tool, ambient behavior, and startup hook is a Plugin (src/plugins/types.ts) registered in src/plugins/index.ts — the built-ins (help, model, tools, mcp, ambient triage, recall ingestion, the 👀 ack, the self-scheduling and memory tools) are just plugins themselves. A plugin can contribute any of:

export const myPlugin: Plugin = {
  name: 'my-feature',
  commands: [{ name: 'ping', help: '• `ping` — pong', parse: (t) =>
    /^ping$/i.test(t.trim()) ? { mutating: false, run: () => 'pong' } : null }],
  tools: ({ sessionId }) => [/* defineTool(...) */],   // exposed to the agent
  hooks: { onMessage(ctx) { /* observe every message */ } },
  lifecycle: { onServerStart() { /* re-arm timers, warm caches */ } },
};

Manifest order is resolution order. Commands and hooks run in the bot process; tools and onServerStart run in the agent server — so a plugin module must not import a platform SDK at the top level. See DESIGN.md for the contracts.

Contributing

Early and moving fast — issues and ideas welcome. Start with DESIGN.md to understand the architecture before opening a PR.

License

Apache-2.0.