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Self-hosted infrastructure that provisions isolated workspaces, clones GitHub repos, and launches external coding agents (Claude Code CLI) — streaming their stdout as agent.output SSE events. Not an orchestrator: no tool-call parsing, no conversation loop.

Project description

Mad

That's mad!

Multi Agent Develop — a self-hosted infrastructure layer that provisions isolated workspaces, clones a GitHub repository, and launches an external coding agent (Claude Code CLI today) against it. Each agent's stdout is streamed as agent.output Server-Sent Events on a per-session log, and a final session.status_idle (or session.error) event signals completion.

Mad is infrastructure, not an orchestrator. It does NOT parse tool calls, NOT execute tools, and NOT manage a conversation loop — those concerns belong to the external agent's own harness. Multiple sessions can run in parallel, each with its own agent process and its own event stream; what Mad does not do is coordinate them into a single autonomous "team."

The full scope contract lives in CLAUDE.md ("What this project is" + hard rule 1).

Status

Early days — 0.x. Single launcher provider (claude_cli); HTTP + SSE surface stable enough to build clients against; multi-tenancy deferred (ADR-0006).

Requirements

  • Linux host (see Operating System :: POSIX :: Linux classifier)
  • Python ≥ 3.11
  • The claude CLI installed and on PATH (override the binary with MAD_CLAUDE_CLI_BIN; per-run timeout via MAD_CLAUDE_CLI_TIMEOUT_S)
  • Optionally: the opencode CLI for the opencode provider (override the binary with MAD_OPENCODE_BIN; per-run timeout via MAD_OPENCODE_TIMEOUT_S, default 600 s)
  • A GitHub token with repo scope for cloning private repos (passed per-request, never persisted — see hard rule 2)
  • Session workspaces are created under ~/mad by default. Override the base directory with MAD_WORKSPACE_DIR (used verbatim — no ~/$VAR expansion) when you need a larger or persistent disk; resolution is MAD_WORKSPACE_DIR~/mad → the system temp dir (last resort, only if the home directory cannot be resolved). The base is created on first use.

Install

The distribution is published as mad-bros; the import package and console script are both mad:

pip install mad-bros
mad serve            # uvicorn factory on 0.0.0.0:8000 by default

From a checkout (development):

make install   # create venv + `pip install -e '.[dev]'`
make test      # pytest -q
make serve     # uvicorn mad.adapters.inbound.http.app:create_app --factory
make help      # full target list

With Docker (one or more isolated instances on a single host):

cp .env.example .env
docker compose -f compose.example.yml up -d --build

See docs/docker.md for per-instance credential setup, the workspace bind-mount model, and running multiple instances.

Quickstart

A session has two parts: an agent spec (which launcher to run) and a list of resources to mount into the isolated workspace. Resources can be github_repository (cloned into mount_path) or file (literal content written at mount_path). The prompt is sent as a separate message after creation; that's what kicks the agent off.

# 1. Create the session — provisions a workspace and clones the repo.
curl -sS -X POST http://localhost:8000/v1/sessions \
  -H 'Content-Type: application/json' \
  -d '{
        "agent": {
          "name": "my-agent",
          "provider": "claude_cli"
        },
        "resources": [
          {
            "type": "github_repository",
            "url": "https://github.com/octocat/Hello-World.git",
            "mount_path": "/workspace/repo",
            "authorization_token": "ghp_xxx",
            "checkout": {"type": "branch", "name": "main"}
          }
        ]
      }'
# → { "session_id": "sesn_…", "status": "created", "workspace": "…", "resources_mounted": […] }

# 2. Send the first user message — this launches the external agent.
curl -sS -X POST http://localhost:8000/v1/sessions/sesn_XXX/messages \
  -H 'Content-Type: application/json' \
  -d '{"content": "Summarize the README in one sentence."}'

# 3. Stream the cross-session event log (Last-Event-ID resumable per ADR-0005).
curl -N http://localhost:8000/v1/events/stream
# Optional filters: ?session_id=sesn_XXX&kind=agent.output

Each frame on the stream is id: <uuidv7>\ndata: {…}\n\n where the JSON object carries event_id, session_id, type, data, and timestamp. Representative types Mad emits:

Type Emitted when
session.created Session row written and workspace provisioned
agent.output One line of stdout from the external agent
session.status_idle Agent exited 0
session.error Agent exited non-zero or timed out

For private repos, set authorization_token on the github_repository resource. Mad uses it once for git clone and immediately strips it from the remote URL (hard rule 2). For historical replay outside SSE, GET /v1/events?after_event_id=…&limit=… returns the same shape with a next_cursor.

Project structure

The package follows a hexagonal / ports-and-adapters layout — see ADR-0003 for the rationale.

mad/
├── pyproject.toml                     # package metadata, deps, `mad` console script
├── src/mad/
│   ├── core/                          # framework-free domain (no FastAPI, no subprocess)
│   │   ├── sessions/                  # sessions bounded context (domain, ports, use_cases)
│   │   └── events/                    # cross-session events (domain, ports, use_cases, emitter)
│   ├── adapters/
│   │   ├── inbound/http/              # FastAPI app factory + routes (sessions, events stream)
│   │   └── outbound/                  # agents (claude_cli launcher), persistence (JSONL), events
│   └── entry_points/cli.py            # `mad` console script (uvicorn launcher)
└── tests/
    ├── unit/                          # core + adapters in isolation
    ├── integration/                   # HTTP + SSE end-to-end
    └── support/                       # test-only doubles (e.g. ScriptedLauncher)

The architectural boundary (mad.core is framework-free and adapter-free) is enforced by import-linter — see hard rule 4 in CLAUDE.md.

Vision

Today Mad runs one external agent per session. The longer-term direction is to use this same infrastructure as the substrate for multi-agent workflows — multiple coordinated sessions collaborating on a goal, each one an isolated workspace with its own event stream. Mad itself stays an infrastructure layer; orchestration, when it exists, will live in a separate module on top.

The "Multi Agent Develop — takes an idea and ships it end-to-end" framing belongs to that future. The package today is the substrate, not the orchestrator.

Documentation

License

See LICENSE.

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