Skip to main content

AI-driven software factory: CrewAI + Codex CLI + Claude Code CLI with multi-CLI router, A2A roundtable, MCP server, scale-adaptive delivery from brief to release-ready project.

Project description

autodev-ai

License: MIT Python ≥3.10 PyPI

A multi-CLI software factory powered by CrewAI agents, Codex CLI, and Claude Code CLI.

From a one-paragraph project brief to milestone-driven, gate-protected delivery — with a full audit trail, A2A agent networking, MCP server support, and sprint-mode planning.



Install

Status: alpha — v0.1.0-alpha on GitHub Releases. PyPI publish gated on user feedback; install from wheel or source for now.

From the GitHub Release wheel:

pip install --pre autodev-ai           # once published on PyPI
# OR install directly from the GitHub Release wheel:
pip install https://github.com/merchloubna70-dot/autodev-ai/releases/download/v0.1.0a1/autodev_ai-0.1.0a1-py3-none-any.whl

From source:

git clone https://github.com/merchloubna70-dot/autodev-ai.git
cd autodev-ai
pip install -e ".[dev]"

Docker (codex + claude pre-installed, ~2 GB):

docker pull ghcr.io/merchloubna70-dot/autodev-ai:0.1.0-alpha
docker run --rm ghcr.io/merchloubna70-dot/autodev-ai:0.1.0-alpha --help

Optional — real CrewAI runtime:

pip install -e ".[crewai]"

5-minute quickstart

No API key required — the mock executor generates realistic artifacts instantly.

# Deliver a full project from a brief (dry-run, mock mode)
autodev deliver-project \
  --project-brief examples/01-mdlines/brief.md \
  --from-scratch true \
  --mode dry-run \
  --executor auto \
  --allow-mock-executor true \
  --repo-path /tmp/mdlines-demo

# Explore what was produced
ls /tmp/mdlines-demo/.dev-factory/runs/*/delivery/
cat /tmp/mdlines-demo/.dev-factory/runs/*/delivery/final_report.md

Expected output:

[autodev] --scale not given; will auto-infer from PRD/brief
run_id=20240514-143012-a1b2c3 mode=dry-run mock=True release=NotReleaseReady

See the full 5-minute quickstart guide.


Features

CrewAI agents — full pipeline coverage

A structured agent graph converts any input (brief / PRD / GitHub issue / bug description) into structured delivery artifacts:

InputClassifier → ProductManager → PRDWriter → SystemArchitect
    → MilestonePlanner → TaskDecomposer → ExecutorRouter
    → QualityGate → SecurityReviewer → Verifier → DocWriter
    → ReleaseManager → audit artifacts on disk

Multi-CLI executor routing

All CLI traffic passes through a single ExecutorRouter. It automatically selects between Codex CLI (mechanical writes, small patches, tests, scaffolds) and Claude Code CLI (architecture, long-context refactors, security reviews, release roll-ups).

autodev deliver-project ... --executor auto    # smart routing (default)
autodev deliver-project ... --executor codex   # force Codex for everything
autodev deliver-project ... --executor claude  # force Claude Code for everything
Task type Default backend
Scaffold, test generation, small patch Codex CLI
Architecture, refactor, security, docs, release Claude Code CLI
Either CLI missing + --allow-mock-executor true Mock (deterministic)

4-stage bug-fix flow

autodev fix-bug \
  --bug "p99 latency is wrong: off-by-one index in aggregate.py" \
  --repo-path /tmp/log-analyzer \
  --mode dry-run \
  --allow-mock-executor true

Stages: Reproduce → Locate → Patch → Verify. Every step produces a structured JSON artifact. See Tutorial 01 — Bug-fix flow.

BMAD-derived sprint mode

Plan and track multi-week sprints with course-correction support:

autodev sprint-start  --goal "Deliver MVP slug library" --duration-days 10
autodev sprint-status
autodev sprint-retro  --sprint-id sprint-001
autodev sprint-correct --sprint-id sprint-001 --change "Add JSON output mode"

See Tutorial 04 — Sprint mode.

Roundtable party-mode (A2A)

Recruit N specialist agents by skill, get independent analysis, synthesize:

autodev roundtable \
  --topic "SQLite vs PostgreSQL for the kanban board" \
  --skills security,arch,perf \
  --repo-path /tmp/my-project

Set FACTORY_FORCE_MOCK=1 for CI / no-API-key usage. See Tutorial 05 — Roundtable.

MCP server — use autodev from Claude Desktop

autodev mcp-serve   # JSON-RPC 2.0 over stdio

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "autodev": {
      "command": "autodev",
      "args": ["mcp-serve"]
    }
  }
}

See Tutorial 06 — MCP server.

A2A server — accept tasks from external agents

autodev a2a-serve --port 8421
autodev a2a-call --endpoint http://127.0.0.1:8421 --skill fix-bug \
  --task-json '{"text": "Fix the percentile bug"}'

See Tutorial 07 — A2A server.

Full audit trail

Every run writes a structured artifact tree under <repo>/.dev-factory/runs/<run_id>/:

input/ product/ architecture/ planning/ execution/
quality/ verification/ delivery/ run_state.json

Runs are resumable (autodev continue-run), replayable (autodev replay), and support milestone-by-milestone execution (autodev execute-milestone).

Safety baked in

  • No business code calls codex or claude directly — only ExecutorRouter
  • Shell executor blocks rm -rf, sudo, cat .env, curl | bash, etc. in all modes
  • --commit, --push, --tag are off by default — opt in explicitly
  • release_check returns NotReleaseReady when mock execution or dry-run was used
  • final_report.md never relabels a failed gate as passed

Multi-CLI routing table

Task type Default backend Why
scaffold Codex Small mechanical writes
test Codex Targeted unit-test additions
feature (≤5 files, low/medium risk) Codex Fast, deterministic
feature (>5 files or high risk) Claude Code Long-context reasoning
refactor Claude Code Multi-file coherence
architecture Claude Code System design
integration (single-language) Codex Targeted API changes
integration (cross-language) Claude Code Contract reasoning
security Claude Code Deeper threat review
docs Claude Code Tone and cohesion
release Claude Code Evidence roll-up

All CLI commands

autodev deliver-project    — Brief/PRD → full project delivery
autodev run-issue          — GitHub issue → structured change
autodev fix-bug            — 4-stage bug-fix: reproduce/locate/patch/verify
autodev multi-patch-fix-bug — Generate N patch candidates, vote for best
autodev execute-milestone  — Run a single milestone from a completed plan
autodev continue-run       — Resume a failed run from last checkpoint
autodev replay             — Re-run a stage from a checkpoint
autodev scan               — Scan repo for context
autodev verify             — Run verification on a completed run
autodev release-check      — Evaluate release readiness
autodev report             — Print run summary
autodev export-delivery    — Export delivery artifacts to a directory
autodev push               — Push committed changes to remote
autodev create-pr          — Open a GitHub PR for a completed run
autodev sprint-start       — Open a new sprint
autodev sprint-status      — Check sprint health metrics
autodev sprint-retro       — Run a retrospective
autodev sprint-correct     — Analyse mid-sprint change impact
autodev roundtable         — Party-mode A2A discussion by skill
autodev mcp-serve          — Start MCP server over stdio
autodev a2a-serve          — Start A2A HTTP server
autodev a2a-register       — Register a remote A2A agent
autodev a2a-call           — Send a task to a remote A2A agent
autodev next               — Suggest next action from run state
autodev design-ux          — BMAD-Sally UX design workflow
autodev investigate        — Open a structured investigation case
autodev generate-context   — Generate project-context.md from repo
autodev document-project   — Generate brownfield AI-onboarding docs
autodev classify-input     — Classify a brief/issue into mode + metadata
autodev create-prd         — Generate a PRD from a brief
autodev plan-project       — Generate a project plan from PRD
autodev plan-milestones    — Generate milestones from a project plan
autodev plan-tasks         — Generate tasks from milestones
autodev review             — Approve/reject a paused human-review gate

Documentation

Doc Contents
5-minute quickstart Install → credentials → first run → explore artifacts
Tutorial 01 — Bug-fix flow 4-stage fix-bug on log-analyzer
Tutorial 02 — Rust project Deliver slug-rs from brief
Tutorial 03 — Multi-CLI routing ExecutorRouter internals
Tutorial 04 — Sprint mode sprint-start / status / retro / correct
Tutorial 05 — Roundtable Party-mode A2A discussion
Tutorial 06 — MCP server Claude Desktop integration
Tutorial 07 — A2A server HTTP agent-to-agent
Architecture reference Layers, flows, audit trail, failure policy
Configuration ConfigStack 4-layer TOML, env vars, Codex/Claude CLI auth, MCP, A2A
Troubleshooting 12 common problems and fixes
FAQ Top 15 questions
CHANGELOG Release history
Contributing How to contribute

License

MIT.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autodev_ai-0.1.0a3.tar.gz (850.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

autodev_ai-0.1.0a3-py3-none-any.whl (327.3 kB view details)

Uploaded Python 3

File details

Details for the file autodev_ai-0.1.0a3.tar.gz.

File metadata

  • Download URL: autodev_ai-0.1.0a3.tar.gz
  • Upload date:
  • Size: 850.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.13

File hashes

Hashes for autodev_ai-0.1.0a3.tar.gz
Algorithm Hash digest
SHA256 a137ba2c7f94727506f8d250f675ef5e6d4d4dd60718ef3e185d330c4d17d063
MD5 a984e5ddacc15c77c036857f4e0d2240
BLAKE2b-256 d3ac9886ff77ddf66571a989a8438566871e45b65e2b8550c04a63855a45893b

See more details on using hashes here.

File details

Details for the file autodev_ai-0.1.0a3-py3-none-any.whl.

File metadata

  • Download URL: autodev_ai-0.1.0a3-py3-none-any.whl
  • Upload date:
  • Size: 327.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.13

File hashes

Hashes for autodev_ai-0.1.0a3-py3-none-any.whl
Algorithm Hash digest
SHA256 9d180900edc2b9ee0277365f09907f3ed78e520520f1f721de0fa79a01efcae5
MD5 d726b966d7e413afdd054b6624bcf5e1
BLAKE2b-256 b72e91d5c5778c095ec0309d50dc72bda367edd3069c3fe3976231400b000c97

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page