Open Claude Tag — The open-source Claude Tag alternative

🔥 Claude Tag launched June 23, 2026 — Anthropic's always-on AI teammate that lives in Slack, learns your company, and works autonomously. It's closed, paid, locked to Anthropic, and cloud-only. This is the open-source alternative: self-hostable, LLM-agnostic, and channel-native.

Quickstart · How it works · Channel config · LLMs · Roadmap · Discord

Open Claude Tag is a free, self-hostable AI teammate for Slack that works the way Claude Tag does — one shared agent per channel, persistent memory, skill auto-creation, ambient monitoring — without Anthropic's paywall, without cloud lock-in, without the single-vendor constraint.

Most Slack AI bots are personal assistants — one context per user, isolated DMs. Open Claude Tag flips this: one agent per channel, shared by the whole team. Everyone sees the same context, picks up mid-thread, and the agent knows who said what.

Community: Join Reddit & Discord for discussions and support. Follow the creator for updates.

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Why Open Claude Tag

On June 23, 2026, Anthropic released Claude Tag — the first AI that joins Slack as a shared channel teammate rather than a personal DM bot. It went viral. But it stayed closed-source, paid-only, cloud-only, locked to Claude models, and locked to Anthropic's access control model. No self-host, no BYOK for other providers, no custom tool integrations without Anthropic's approval.

Open Claude Tag is the open-source alternative. Same channel-native mental model, none of the lock-in:

• 🏢 Channel-scoped, not user-scoped. One agent per channel, shared by the whole team. All users see the same context, pick up mid-thread.
• 🤖 LLM-agnostic. Use Claude, GPT-4o, Gemini, Groq, or local Ollama. Swap with one env var. Different channels can use different models.
• 💾 Agent-curated memory. After each conversation, the agent decides what's worth keeping in MEMORY.md. No noisy append-only logs.
• 🧠 Skill auto-creation. After complex multi-step tasks, the agent writes a SKILL.md capturing what it learned. Institutional knowledge accumulates automatically.
• 🔔 Ambient monitoring. Configurable heartbeat: the agent proactively surfaces stale threads, approaching deadlines, and forgotten questions.
• 🔌 MCP-native tools. Plug in any MCP server per channel. Admins control exactly what each channel's agent can access.
• 📁 File-based config. Each channel is a directory of Markdown files. Version-controllable, auditable, no UI required.
• 🔒 Self-hostable. Your Slack data stays on your infrastructure. No round-trips to Anthropic's cloud.

Comparison

How it Works

The core inversion

Every other Slack bot keys sessions on user_id. Open Claude Tag keys sessions on (workspace_id, channel_id). That one change is what makes it feel like a teammate rather than a chatbot.

[#engineering channel]

@alice  Can you review the PR for the auth refactor?
@agent  Sure. I pulled the PR — looks good overall, one concern:
        the session expiry logic on line 42 doesn't handle clock skew.
        @bob you mentioned this pattern in the DB migration last week —
        does the same fix apply here?
@bob    Yeah, add a 5s leeway. Same as auth/session.py:L88
@agent  Got it. Adding to MEMORY.md: "session expiry: always add 5s
        leeway for clock skew (pattern from auth/session.py:L88)"

Every user in the channel sees the same thread. The agent knows who said what, follows up with the right person, and decides what's worth remembering.

Agent loop

Slack @mention
       │
       ▼
  Channel Router ──── (workspace_id + channel_id) → AgentSession
       │                  ↑ serialized lock: no parallel writes to context
       ▼
  Context Assembler
  ├── CHANNEL.md       (identity, purpose, tone)
  ├── MEMORY.md        (agent-curated facts, always in context)
  ├── skills/*.md      (auto-created playbooks, loaded on semantic match)
  └── Last 50 messages (with @username attribution)
       │
       ▼
  Agent Loop  (ReAct + tool-use via LiteLLM)
  ├── Tool Registry  ← MCP servers defined in tools.toml
  ├── Built-in tools ← web search, Python runner, channel search
  └── Stream reply → Slack thread
       │
       ├── Memory curation turn  ← agent decides what to write to MEMORY.md
       │   (Letta inner-loop: model gets one extra turn to curate)
       │
       └── Skill evaluator  ← ≥5 tool calls? write SKILL.md
           (Hermes pattern: agent authors its own playbooks)
       │
       ▼
  SQLite + FTS5  (per-workspace DB, channel-isolated, WAL mode)
       │
       ▼
  Ambient Engine  (background — Phase 3)
  ├── Per-channel APScheduler crons
  ├── Heartbeat evaluator: "anything worth surfacing?"
  └── Proactive Slack post if yes, SILENT if no

Memory architecture

Layer 1 — Context window (always loaded)
  CHANNEL.md + MEMORY.md + active SKILL.md files + last 50 messages

Layer 2 — Session store (SQLite + FTS5, per workspace)
  Full message history with user_id, timestamps, thread_ts
  Full-text search: "what did we decide about X last month?"

Layer 3 — Semantic recall (Mem0, Phase 2)
  Embeddings over key decisions and facts
  Namespace = channel_id (fully isolated per channel)

Layer 4 — Skill library (per channel)
  Auto-created after complex tasks (≥5 tool calls)
  Loaded into context when task description matches
  Curated weekly: stale after 30d, archived after 90d

Ambient heartbeat

The heartbeat evaluator runs on a configurable cron per channel. It dumps recent activity to the LLM and asks: "anything worth surfacing?" It only posts if there's genuine value — stale threads, approaching deadlines, forgotten questions, spotted risks. Otherwise: SILENT.

The agent can also create its own monitoring tasks via schedule_task(cron, description) — it decides what's worth checking and when.

Quickstart

Prerequisites

• Python 3.11+
• A Slack app with Socket Mode enabled (create one here)
• An API key for your preferred LLM provider (Anthropic, OpenAI, Gemini, or Groq)

1. Create the Slack app

1. Go to api.slack.com/apps → Create New App → From scratch
2. Settings → Socket Mode: enable it and generate an App-Level Token (xapp-...) with connections:write scope
3. Event Subscriptions: enable and subscribe to app_mention and message.channels
4. OAuth & Permissions → Bot Token Scopes: add app_mentions:read, channels:history, channels:read, chat:write, reactions:write, users:read
5. Install to workspace → copy the Bot Token (xoxb-...)

2. Install and configure

# Clone
git clone https://github.com/Anil-matcha/open-claude-tag
cd open-claude-tag

# Install
pip install -e .

# Configure
cp .env.example .env

Edit .env:

SLACK_BOT_TOKEN=xoxb-...
SLACK_APP_TOKEN=xapp-...

# Pick one LLM provider:
LLM_MODEL=claude-sonnet-4-6
ANTHROPIC_API_KEY=sk-ant-...

# or: LLM_MODEL=gpt-4o  +  OPENAI_API_KEY=sk-...
# or: LLM_MODEL=gemini/gemini-2.0-flash  +  GEMINI_API_KEY=...
# or: LLM_MODEL=ollama/llama3  (no key needed)

3. Configure your first channel

Get your channel ID: in Slack, right-click channel name → View channel details → scroll to the bottom.

mkdir -p data/channels/C01234ABC
cp channels/example/CHANNEL.md data/channels/C01234ABC/CHANNEL.md
# Edit CHANNEL.md to describe your channel's purpose and team

4. Run

tagopen

Then @open-claude-tag in your Slack channel.

Channel Configuration

Each channel gets a directory of plain Markdown files under data/channels/<channel_id>/. Version-controllable, human-readable, no database required.

data/channels/C01234ABC/
  CHANNEL.md      ← identity, purpose, tone
  MEMORY.md       ← agent-maintained facts (auto-updated, don't edit manually)
  tools.toml      ← MCP servers and per-channel LLM override
  skills/         ← auto-created playbooks
    deploy-to-staging.md
    oncall-handoff.md
    pr-review-checklist.md

CHANNEL.md

# Engineering Channel

You are the engineering team's AI teammate in #engineering.

## Purpose
Help with deployments, code reviews, incident response, and architecture decisions.

## Tone
Technical, direct, concise. Use code blocks. Ask before triggering deploys.

## Team context
- Stack: Python backend, React frontend, PostgreSQL, AWS
- CI/CD via GitHub Actions
- We do not deploy on Fridays

MEMORY.md — agent-curated facts

The agent writes this automatically. After each conversation it gets one internal LLM turn to decide what's worth persisting — using memory_append and memory_replace tools. Memory stays clean because the agent curates it, not a dumb append-only log.

Example of what accumulates over time:

# Channel Memory

- Session expiry: always add 5s leeway for clock skew (auth/session.py:L88)
- We use squash-merge for all PRs — rebase main before merging
- Alice: infra questions. Bob: auth layer.
- Never restart worker pods on Fridays — cron runs at 11pm PT

tools.toml — MCP servers and model override

# Per-channel LLM override (optional)
[llm]
model = "gpt-4o"

# MCP servers allowed in this channel
[[mcp_server]]
name = "github"
url = "mcp://localhost:3001"
allowed_tools = ["list_prs", "get_file", "create_comment", "trigger_workflow"]

[[mcp_server]]
name = "linear"
url = "mcp://localhost:3002"
allowed_tools = ["list_issues", "create_issue", "update_status"]

Skills — auto-created institutional knowledge

After any task requiring 5+ tool calls, the agent writes a SKILL.md. Next time a similar task comes up, the skill loads into context automatically.

Example auto-created skill:

---
name: deploy-to-staging
description: Deploy a service to staging via GitHub Actions
created: 2026-06-25
uses: 3
status: active
---

## When to use this
When someone asks to deploy a service to staging.

## Steps
1. Check CI is passing on the branch (github:list_prs)
2. Confirm with the requester before triggering
3. Trigger `deploy-staging` workflow (github:trigger_workflow)
4. Monitor the run for 2 minutes, post the staging URL

## Known gotchas
- No deploys on Fridays — check day of week first
- `worker` service uses a separate `deploy-worker` workflow

Skills lifecycle: active → stale (30d unused) → archived (90d). A weekly curator pass merges overlapping skills and patches outdated ones.

Supported LLMs

Uses LiteLLM — one interface for every provider. Set LLM_MODEL and the matching key:

Per-channel model override — run a lighter model in #general, a more powerful one in #engineering. Add to data/channels/<id>/tools.toml:

[llm]
model = "claude-opus-4-8"

Built-in Tools

Always available in every channel — no configuration needed:

Add any other tool by listing an MCP server in tools.toml. Any MCP-compatible server works — GitHub, Linear, Notion, Jira, Datadog, PagerDuty, Sentry, etc.

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check .

# Type check
mypy tagopen/

Project structure

tagopen/
  gateway/
    app.py       ← Slack Bolt async app, @mention handler
    router.py    ← channel router: (workspace_id, channel_id) → AgentSession
  agent/
    loop.py      ← ReAct agent loop, tool dispatch, memory + skill hooks
    context.py   ← system prompt assembler (CHANNEL.md + MEMORY.md + skills)
    skills.py    ← skill auto-creation after complex tasks
  memory/
    store.py     ← SQLite + FTS5 message store, channel-isolated
    writer.py    ← inner loop: agent curates MEMORY.md
  tools/
    registry.py  ← per-channel tool registry, reads tools.toml
    builtins.py  ← web search, Python runner, channel history search
  ambient/
    heartbeat.py ← proactive monitoring (Phase 3)
  llm.py         ← LiteLLM wrapper: key injection, per-channel model resolve
  config.py      ← settings from .env via pydantic-settings
  cli.py         ← entry point: tagopen
channels/
  example/       ← copy these to data/channels/<id>/ to get started
tests/
  unit/          ← channel isolation, SQLite store, router tests
PLAN.md          ← full architecture and design decisions

Roadmap

• Phase 1 — Channel-native reactive teammate
  • Slack Bolt async app, Socket Mode
  • Channel router: (workspace_id, channel_id) → shared AgentSession
  • Multi-user attribution in context window
  • ReAct agent loop via LiteLLM
  • SQLite + FTS5 per-channel message store
  • File-based channel config (CHANNEL.md, MEMORY.md, tools.toml)
  • Built-in tools: web search, Python runner, channel history search
  • Per-channel model override
  • Multi-provider: Anthropic, OpenAI, Gemini, Groq, Ollama
• Phase 2 — Memory + Skills
  • Letta inner-loop memory curation (agent writes MEMORY.md)
  • Skill auto-creation (≥5 tool calls → SKILL.md)
  • Skill loader: semantic match to incoming task
  • Skill curator: weekly prune, stale/archived lifecycle
  • Mem0 semantic recall layer
• Phase 3 — Ambient mode
  • Per-channel APScheduler heartbeat crons
  • LLM heartbeat evaluator (SILENT or post)
  • Stale thread detection
  • schedule_task tool: agent creates its own monitoring crons
  • Temporal for durable task orchestration
• Phase 4 — Governance + Admin UI
  • Per-channel audit log (tokens spent, tools invoked)
  • Hard token budget enforcement via BUDGET.md
  • Next.js admin UI: channel config, tool access, budget view
• Phase 5 — Multi-platform
  • Discord adapter
  • Microsoft Teams adapter

See PLAN.md for full architecture decisions and research notes.

Community

• 💬 Discord — questions, feature requests, show-and-tell → discord.gg/s7KW4fsqXK
• 🐦 X / Twitter — updates and releases → @matchaman11
• 🐛 GitHub Issues — bug reports, feature requests → Issues

Contributing

Contributions welcome — especially:

git clone https://github.com/Anil-matcha/open-claude-tag
cd open-claude-tag
pip install -e ".[dev]"
pytest && ruff check .

Star history

References

License

MIT — free to use, modify, and self-host.

This project is independent and not affiliated with Anthropic or Slack. References to third-party platforms are for interoperability and educational purposes. All trademarks are the property of their respective owners.