aweteam: AI Coding Team Coordinator

Thin tmux handoff interface for local AI coding teams.

Start a leader, delegate to configured workers, keep conversations visible in tmux panes.

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Start a leader, delegate to configured workers, keep conversations visible in tmux panes.

aweteam starts a real leader CLI in leader/main, lets that leader create worker panes from configured profiles, and records explicit run artifacts under .aweteam/runs/<run-id>/.

It is intentionally small. It is not a scheduler, hosted agent platform, or replacement UI. The normal workflow stays inside tmux: you talk to the leader in plain language, the leader delegates to configured workers, and the worker conversations remain visible in their own panes.

Demo

Install

Requires Node.js 20 or later and tmux.

Install from npm:

npm install -g aweteam
aweteam --help

Or install from this repository for local development:

git clone https://github.com/Webioinfo01/aweteam.git
cd aweteam
npm install
npm link

Quick Start

Create a local config:

cp aweteam.example.json aweteam.json

If your config uses environment-backed profiles, export the required variables before starting a run. For example:

export GLM_ANTHROPIC_AUTH_TOKEN="your-token"

Start a team session:

aweteam --config aweteam.json

This creates a run, writes its artifacts under .aweteam/runs/<run-id>/, starts the dispatcher, and attaches you to a tmux session focused on leader/main.

To provide an explicit topic at startup:

aweteam run "Create three agents to review the login module" --config aweteam.json

Inside the leader pane, describe the work naturally:

Use the frontend profile to review the login UI, the backend profile to inspect
session handling, and the review profile to check security risks. Choose only
from the configured aweteam worker pool.

The leader should create workers without requiring you to mention JSON, outbox files, dispatcher internals, or command-line calls.

Config

aweteam reads JSON config only. A run freezes its resolved config into .aweteam/runs/<run-id>/config.resolved.json, so later edits to aweteam.json do not change an existing run.

Minimal shape:

{
  "leader": "claudecode-official",
  "workers": ["codex"],
  "profiles": {
    "codex": {
      "provider": "codex",
      "command": "codex",
      "model": "gpt-5.4-mini",
      "max_instances": 1
    },
    "claudecode-official": {
      "provider": "claude",
      "command": "claude",
      "env": {
        "ANTHROPIC_MODEL": "sonnet"
      }
    }
  }
}

For claude provider profiles, the model is controlled via env.ANTHROPIC_MODEL (passed to Claude Code through --settings). The model field is only used by codex provider profiles (passed as --model).

Optional Claude defaults such as ANTHROPIC_DEFAULT_HAIKU_MODEL, ANTHROPIC_DEFAULT_SONNET_MODEL, and ANTHROPIC_DEFAULT_OPUS_MODEL are not configured by default.

If you want Claude Code to use a lighter model for lightweight or background functionality, you can manually add ANTHROPIC_DEFAULT_HAIKU_MODEL to the profile env. Example:

{
  "provider": "claude",
  "command": "claude",
  "env": {
    "ANTHROPIC_BASE_URL": "https://token-plan-sgp.xiaomimimo.com/anthropic",
    "ANTHROPIC_AUTH_TOKEN": "${XIAOMI_ANTHROPIC_AUTH_TOKEN}",
    "ANTHROPIC_MODEL": "mimo-v2.5-pro",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "mimo-v2.5"
  }
}

This keeps the main model on mimo-v2.5-pro while allowing Claude Code to use mimo-v2.5 for lighter or background tasks.

Environment variables

Profiles can reference shell environment variables with ${VAR_NAME} syntax. Values are resolved at startup; missing variables cause an error.

For third-party API proxies, set the endpoint and token via env vars:

export GLM_ANTHROPIC_AUTH_TOKEN="your-token"

Then reference it in the profile config:

{
  "provider": "claude",
  "command": "claude",
  "max_instances": 2,
  "env": {
    "ANTHROPIC_BASE_URL": "https://your-glm-compatible-endpoint",
    "ANTHROPIC_AUTH_TOKEN": "${GLM_ANTHROPIC_AUTH_TOKEN}",
    "ANTHROPIC_MODEL": "glm-4.6"
  }
}

Key environment variables for Claude Code:

Variable	Purpose
ANTHROPIC_MODEL	Primary model selection
ANTHROPIC_AUTH_TOKEN	API authentication token (use ${VAR} for secrets)
ANTHROPIC_BASE_URL	API endpoint (for proxies, hardcoded is fine)

Only profiles listed in workers can be spawned as workers. max_instances limits how many workers can be created from a profile in one run.

Workflow

Each run is a tmux team console:

• prefix+1 selects the leader pane
• prefix+2 through prefix+9 select worker panes as they are spawned
• worker panes run the interactive agent UI and stay open after completion
• worker final answers are visible in tmux and persisted to result.md
• the dispatcher sends created, completed, and all-done notices back to the leader pane

The important run artifacts are:

.aweteam/runs/<run-id>/
  config.resolved.json
  run.json
  events.jsonl
  leader/
    instructions.md
    outbox/
    inbox/
    summary.md
  workers/
    worker-1/
      task.md
      result.md
      status.json

For Claude Code leaders, aweteam disables native Task delegation and injects instructions that "agent" means an aweteam tmux worker pane. This keeps Claude Code's internal agents from substituting for aweteam workers.

Commands

aweteam --config aweteam.json
aweteam run "task" --config aweteam.json
aweteam status <run-id>
aweteam focus <run-id> <leader|worker-name|profile>

The primary entrypoints are aweteam --config aweteam.json and aweteam run "task" --config aweteam.json.

Useful debugging commands from another terminal:

aweteam status <run-id>
aweteam focus <run-id> leader
aweteam focus <run-id> worker-1

Example status shape:

run_id: <run-id>
session: aweteam-<run-id>
leader: claudecode-official    %0
workers:
worker-1    codex    done    %1    result=/path/to/result.md

Documentation

• docs/CONTRIBUTING.md describes architecture, local development, testing, and contribution expectations.
• docs/CHANGELOG.md describes release history.

Development

npm test