AgentX is an AI operations layer for small and medium businesses, enabling seamless integration of AI agents across various communication channels.

AgentX

The AI operations layer for small & medium businesses. Plug in the channels your team already uses — Telegram, WhatsApp, Slack, Discord, GitLab — set schedules, and watch your agents work. Web wizard for non-technical operators, CLI for engineers. Self-hosted.

Who it's for

Small & medium businesses running AI agents on real channels. Support queues, devops squads, ops teams, internal automation. You want multiple agents handling different jobs, coordinating across machines, answering on the tools your people already use — without standing up a ML platform or hiring dedicated infra.

Non-technical operators add agents, connect channels, and schedule jobs from a browser wizard and /admin panel — no JSON editing.
Engineers get a full CLI, agentx.json, scoped API tokens, mesh federation, and a mutateConfig-safe write path.

Running AgentX solo for yourself? OpenClaw is built for single-user assistants and has a lighter install path. If you outgrow it, we import your config — see Migrate from OpenClaw.

What you get out of the box

Answer on Telegram, WhatsApp, Slack, Discord, GitLab — one config, all channels. Pair WhatsApp with a QR code in the browser.
Browser-based setup wizard + /admin panel — add agents, wire channels, mint scoped API tokens, schedule crons, all without editing JSON
Agents = folders, not code — each agent has its own persona, knowledge, and tools in plain Markdown
Multiple machines, one team — mesh federation across laptops + servers; manage any peer's config from a single dashboard via the mesh selector
Scheduled jobs in plain English — agentx schedule "every Monday at 9am" --agent sales
Live dashboard — a browser view of what every agent is doing right now, with full task history, streaming output, and replay
Scoped API tokens — let external apps message an agent with a time-bound, scope-limited token (see Tokens and Public agents)
agentx doctor — pre-flight health check that catches missing keys, unreachable daemons, and misconfigured channels
Bring your own AI — Claude Code (deep reasoning + tools), OpenAI / Anthropic API, Ollama, or anything in between
Wiki memory — conversations compound into a shared knowledge base each agent draws from

Install

One line:

curl -fsSL https://raw.githubusercontent.com/anis-marrouchi/agentx/master/install.sh | bash

Installs the CLI and launches the web setup wizard — no YAML, no JSON.

Docker:

git clone https://github.com/anis-marrouchi/agentx.git && cd agentx
cp agentx.example.json agentx-data/agentx.json    # or run `agentx setup` later
docker compose up -d

Manual:

npm install -g agentix-cli
agentx setup               # opens the web wizard

Open the dashboard at http://127.0.0.1:4202 — live agents, task history, Kanban boards, and the /admin panel for managing agents, channels, and tokens. A ?-Glossary link is in the topbar if anything looks unfamiliar.

See the full install guide for advanced setups.

Docs

Full documentation: https://agentx-docs.pages.dev (or pnpm docs:dev locally).

Start here:

Install — from zero to a running daemon in 5 minutes
Concepts — what an agent, channel, schedule, and team network are (glossary also lives at /glossary in the dashboard)

Worked examples, simple → advanced:

1. Telegram Q&A bot — one agent, one channel, one conversation
2. Scheduled reports with failure alerts
3. Multi-agent group chat
7. Run a team with AI agents — roles, KPIs, org chart
8. Two machines, one team — mesh federation

For configuration:

CLI reference · Config schema · Communication matrix
Scoped API tokens — mint / scope / revoke
Public agents — expose an agent over HTTP
agentx doctor — pre-flight health check
Slack channel — Socket Mode setup

Moving from another tool:

Migrate from OpenClaw — we import the bulk of your config in one shot

Contributing

Architecture

graph LR
  T[Telegram] --> R(Router)
  W[WhatsApp] --> R
  S[Slack] --> R
  D[Discord] --> R
  G[GitLab] --> R
  C[Cron] --> R
  H[Webhook] --> R
  M[Mesh peer] --> R
  R --> CTX[Context Engine<br/>10 layers, token-budgeted]
  CTX --> AG[Agent workspace]
  AG --> P1[claude -p]
  AG --> P2[OpenAI]
  AG --> P3[Ollama]
  AG -.-> MEM[(Wiki + Memory)]
  AG -.-> R

Each agent = a workspace directory with Claude Code configuration (.claude/, CLAUDE.md, skills, hooks, MCP servers). AgentX orchestrates when and where agents run.

License

MIT.