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Agentic Workflow (awf)

license: MIT Python 3.10+ tests PyPI

Multi-agent pipeline orchestrator for opencode. Plan → build → verify → commit — through typed MCP tools, not bash.

The problem it solves

AI coding agents are powerful but chaotic. They jump straight to code without planning, skip review, leave bugs.

awf adds structure: a supervisor agent plans the work, worker agents execute through a pipeline you design (any roles, any depth — from a single worker to a multi-stage chain), and you approve each result before it commits. All through natural language — "start working on the backlog", "verify and approve", "reject — the DOMParser fix is missing".

You stay in control. The agent stays on rails.

Quick Start

# 1. Install
pip install awf agent-workflow-ui
# After install the `awf` console command is available (or `python -m awf`)

# 2. Configure opencode — add to ~/.config/opencode/opencode.json:
# {
#   "mcp": {
#     "agent-workflow-ui": {
#       "type": "local",
#       "command": ["python3", "-m", "agent_workflow_ui"],
#       "enabled": true,
#       "timeout": 600000
#     }
#   }
# }

# 3. Run — just talk to opencode:
# "Initialize awf in my project"
# "Develop the MVP based on the backlog"

Requirements

Component Requirement
Python 3.10+
opencode any recent version with MCP support
OS Linux (tested), macOS (should work), Windows (untested)
Models Model-agnostic. Tested with Qwen vLLM. Should work with Claude, GPT, or any opencode-supported provider.
Git Required (commit gate, baselines, rollback)

Timeouts: agent stages time out after 1 hour. Extend with AWF_SUPERVISOR_TIMEOUT=7200 (env) or awf start --timeout 7200 (USAGE.md → Troubleshooting).

Features

  • 🎯 State-Machine Orchestration (SMO) — awf guides the supervisor through phases: init → goal → form → normalize → brief → run → verify → done. Every tool returns a next_action hint — even weak models follow the full flow without getting lost.
  • 🔧 53 MCP tools — typed pipeline control: init, dispatch, start, approve, reject, rollback, dashboard, model validation, run-queue revision, service run, commit-workflow. No bash, no manual file editing.
  • 📊 Live Dashboard — HTTP server with real-time polling. Chat-style agent handoffs, TODO content, TODO timeline, worker status, browser notifications. No page reloads.
  • 🧱 Custom pipelines — any roles, any depth. 1 stage or 10. You choose in the setup form.
  • ✅ Approve / Reject — symmetric verify tools. Approve commits and archives. Reject kills the pipeline and asks for fixes.
  • 🔍 Pre-dispatch check — before launching a pipeline, awf greps your codebase for keywords from the TODO. Warning if the task might already be done.
  • 🔄 Crash recovery — salvage path when workers don't signal, orphan TODO cleanup, state reconciliation on startup.
  • 📋 Increment planning — decomposition variants (vertical, horizontal, risk-first) presented as an HTML form for user choice.

Usage

Talk in natural language — the supervisor agent calls the right tools:

You say What happens
"Initialize awf in my project" Creates .agentic/, detects stack, asks for your goal
"Develop the MVP" Opens setup form → configures pipeline → plans first TODO
"Verify" Supervisor reads handoffs, checks git diff, approves or rejects
"Reject — the cache is missing" Pipeline killed, new TODO dispatched with fix instructions

See USAGE.md for full scenarios and tool reference.

SMO Flow

graph LR
    init --> goal --> form --> normalize --> brief --> run --> verify --> done

Phase-by-phase: USAGE.md → SMO phases.

Architecture

graph TD
    A[opencode supervisor LLM] -->|MCP stdio - 53 tools| B[agent-workflow-ui plugin]
    B -->|Python import| C[awf orchestrator]
    C -->|subprocess| D[opencode run - worker agents]
    C -->|HTTP daemon| E[Dashboard - live /api/state]

Two packages:

  • awf — Python core. Pipeline engine, phase state machine, signals, commit gate, dashboard server.
  • agent_workflow_ui — MCP plugin. Thin async wrappers + next_action guidance + HTML forms.

Comparison

awf Aider Claude Code Devin
Planning before code ✅ ❌ ⚠️ ✅
Human approve/reject ✅ ❌ ❌ ✅
Custom pipelines ✅ ❌ ❌ ❌
MCP-native ✅ ❌ ❌ ❌
Self-hosted / free ✅ ✅ ❌ ❌
Any LLM provider ✅ ✅ ❌ ❌

Real-world results

This repository is built on awf: a 31-unit stabilization program and the follow-up verification and documentation waves (13 more units) — plan, implement, review, verify, commit, with the supervisor approving every unit and the review stage catching defects before merge.

Roadmap

  • SMO escape hatches — manual phase jumps and interruptions (from real session edge cases)
  • PyPI — pip install awf agent-workflow-ui
  • Dashboard v3 — stage timing bars, session summary, sound notifications
  • Standalone mode — awf without opencode (API-only)

Full backlog: BACKLOG.md

Documentation

License

MIT — see LICENSE.

Metadata

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