arcwright-ai 0.2.7

Deterministic orchestration shell for autonomous AI agent execution

Arcwright AI

Deterministic orchestration shell for autonomous AI agent execution.

Arcwright AI takes BMAD planning artifacts (PRD, Architecture, Epics, Stories) and autonomously executes them through Claude, enforcing validation gates, tracking decision provenance, and writing structured run artifacts after every execution.

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Table of Contents

• Prerequisites
• Installation
• Project Setup
• Running Stories
• Run Artifacts
• Understanding the Output
• LangGraph Studio
• Development
• Troubleshooting

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Prerequisites

• Python 3.11+ (3.14 recommended; see LangGraph Studio for the exception)
• Claude API key (Anthropic): ARCWRIGHT_API_CLAUDE_API_KEY
• BMAD 6.1+ — planning artifacts and dev-story workflow features require BMAD 6.1 or later
• A project initialised with BMAD (_spec/planning-artifacts/ containing PRD, architecture, epics, and story files)

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Installation

From PyPI (end users — install in your target project):

cd /path/to/your/project
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install arcwright-ai

To version-control the dependency, add a requirements.txt to your project:

arcwright-ai>=0.2.4

From source (contributors):

git clone https://github.com/ProductEngineerIO/arcwright-ai.git
cd arcwright-ai/arcwright-ai
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Set your API key (add to your shell profile or .env):

export ARCWRIGHT_API_CLAUDE_API_KEY="sk-ant-..."

Or copy the generated .env.example and fill in your values:

cp .env.example .env
# Edit .env — at minimum set ARCWRIGHT_API_CLAUDE_API_KEY

.env files are loaded automatically by arcwright-ai on startup. The .env file is git-ignored by init — secrets never enter version control. See .env.example for the full list of supported variables.

Tip — guaranteed local execution: Use python -m arcwright_ai instead of the bare arcwright-ai command. This always runs the copy installed in the active virtual environment, never a stale global install.

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Project Setup

Before dispatching stories, initialise Arcwright AI in your target project (the project whose stories you want to implement — not this repo):

# From inside the target project root (venv activated):
python -m arcwright_ai init

# Or point explicitly:
python -m arcwright_ai init --path /path/to/your/project

This creates .arcwright-ai/ with the following layout:

.arcwright-ai/
├── config.yaml       ← project-level configuration (committed)
├── runs/             ← execution artifacts (git-ignored)
├── worktrees/        ← git worktrees (git-ignored)
└── tmp/              ← transient scratch space (git-ignored)

It also places a .env.example in the project root. Copy it to get started:

cp .env.example .env
# Fill in at minimum: ARCWRIGHT_API_CLAUDE_API_KEY

config.yaml defaults (edit to suit your project):

model:
  version: "claude-opus-4-6"

limits:
  tokens_per_story: 200000
  cost_per_run: 10.0
  retry_budget: 3
  timeout_per_story: 300

methodology:
  artifacts_path: "_bmad-output"   # where your BMAD planning docs live
  type: "bmad"

scm:
  branch_template: "arcwright-ai/{story_slug}"

API key security: Never put your API key in config.yaml. Use the .env file (git-ignored), the ARCWRIGHT_API_CLAUDE_API_KEY environment variable, or the global ~/.arcwright-ai/config.yaml (user-level, outside any repo).

Verify your setup:

python -m arcwright_ai validate-setup

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Running Stories

Dispatch a single story by its epic.story identifier (e.g., story 4 of epic 2 is 2.4):

# From inside the target project root (venv activated):
python -m arcwright_ai dispatch --story 2.4

# Dashes and STORY- prefix also work:
python -m arcwright_ai dispatch --story 2-4
python -m arcwright_ai dispatch --story STORY-2.4

The pipeline runs:

preflight → budget_check → agent_dispatch → validate → commit → finalize

Each node writes artifacts to .arcwright-ai/runs/<run-id>/stories/<story-slug>/.

Exit codes:

Code	Meaning
0	Story completed successfully
1	Unexpected error (configuration, I/O, etc.)
2	Story escalated (validation failed, could not auto-fix)

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Run Artifacts

Every execution produces a run directory:

.arcwright-ai/runs/<run-id>/
├── run.yaml                          ← metadata: status, cost, story list
└── stories/<story-slug>/
    ├── context-bundle.md             ← assembled context injected into the agent
    ├── agent-output.md               ← raw output from Claude
    ├── validation.md                 ← V6 invariant + V3 reflexion results and decision log
    ├── halt-report.md                ← populated only on escalation
    └── summary.md                    ← produced by finalize node (success or halt)

Run ID format: YYYYMMDD-HHMMSS-<4-char-id> (e.g. 20260305-022632-4b90)

Reading a halt report

When a run escalates, check these files in order:

1. halt-report.md — escalation reason, retry history, suggested fix
2. validation.md — exact V6 invariant failures and V3 reflexion AC results
3. agent-output.md — what Claude produced (verify files actually exist on disk before trusting V6 failures)

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Understanding the Output

status: escalated vs. failure

escalated means the pipeline ran successfully but validation could not be satisfied within the retry budget. It does not mean the agent crashed. The agent's work (files, code) is still on disk in the target project.

Escalation reasons:

Reason	Meaning
v6_invariant_failure	Hard rule violation (missing file, bad name, syntax error) — retries won't help without a fix
max_retries_exhausted	V3 reflexion (AC review) kept failing after N retries
budget_exceeded	Token/cost ceiling hit before validation passed

False-positive V6 failures

If validation.md shows a file_existence failure for a file that does exist on disk, check whether the path in the error has a leading backtick (e.g., `backend/app/routers/admin.py). This is a known pattern when the agent uses inline code formatting in markdown headers. The V6 checker strips backticks as of the current version. If you see this after upgrading from an older run, the files are fine — re-run to get a clean pass.

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LangGraph Studio

Arcwright AI ships a langgraph.json config so you can visualise and inspect the execution graph in LangGraph Studio.

Why a separate venv?

The main .venv uses Python 3.14. The langgraph-api package (required for langgraph dev) depends on pyo3-based Rust extensions that do not yet publish wheels for Python 3.14 and cannot be compiled without matching support. A separate Python 3.13 venv is used exclusively for Studio.

One-time setup

Ensure Python 3.13 is available (via Homebrew or pyenv), then:

cd arcwright-ai/

# Create Studio venv with Python 3.13
python3.13 -m venv .venv-studio

# Install project + LangGraph Studio deps
.venv-studio/bin/pip install -e ".[dev]" "langgraph-cli[inmem]"

Starting Studio

cd arcwright-ai/
.venv-studio/bin/langgraph dev

The server starts at http://127.0.0.1:2024. Open the Studio UI at:

https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

You'll see the story_graph with all nodes and conditional edges:

START → preflight → budget_check ──(ok)──→ agent_dispatch → validate ──(success)──→ commit → finalize → END
                          │                                       │
                     (exceeded)                               (escalated)
                          └──────────────────────────────────────┴──→ finalize → END
                                                    ↑(retry)
                                          validate ─┘ → budget_check

A free LangSmith account is required to use the Studio UI. The local API server itself runs without one.

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LangSmith Tracing

LangGraph has built-in support for LangSmith — LangChain's cloud observability platform. When enabled, every graph invocation is recorded as a trace you can inspect in the LangSmith web UI: node inputs/outputs, state transitions, timing, and token usage.

Setup

1. Create a free account at smith.langchain.com
2. Go to Settings → API Keys and create an API key
3. Set environment variables (add to your .env file or shell profile):

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY="lsv2_pt_..."
export LANGCHAIN_PROJECT="arcwright-ai"  # optional — names your project in the UI

The next python -m arcwright_ai dispatch will send traces automatically — no code changes required.

To disable, unset LANGCHAIN_TRACING_V2 or set it to false. Tracing is off by default.

Note: LangSmith tracing is independent of the local .arcwright-ai/runs/ artifacts, which are always written regardless.

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Development

All development commands use the main .venv (Python 3.14):

# Install dev dependencies
pip install -e ".[dev]"
# Prefer explicit venv invocation to avoid interpreter mismatch:
.venv/bin/pip install -e ".[dev]"

# Run tests
.venv/bin/python -m pytest -q

# Lint
.venv/bin/ruff check .
.venv/bin/ruff format --check .

# Type check
.venv/bin/python -m mypy --strict src/

# All quality gates in one pass
.venv/bin/ruff check . && .venv/bin/ruff format --check . && .venv/bin/python -m mypy --strict src/ && .venv/bin/python -m pytest -q

Python version note

The project targets Python 3.11+ and is developed against 3.14. The .venv-studio venv (Python 3.13) is only for running langgraph dev. Do not use it for tests or type checking — results may differ.

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Troubleshooting

ModuleNotFoundError: No module named 'arcwright_ai'

The venv's editable install link may be stale or was not processed correctly on Python 3.14. Re-install:

cd arcwright-ai/
.venv/bin/pip install -e .

This rewrites the .pth file. Verify with:

.venv/bin/python -c "import arcwright_ai; print(arcwright_ai.__file__)"

langgraph dev fails with Required package 'langgraph-api' is not installed

You're using the main .venv (Python 3.14). Use .venv-studio instead:

.venv-studio/bin/langgraph dev

Story dispatched but files don't match what validation expected

Check .arcwright-ai/config.yaml in the target project. The methodology.artifacts_path must point to the directory containing your BMAD planning artifacts (PRD, architecture, epics). Default is _bmad-output; adjust if your project uses _spec/planning-artifacts or another path.

Dev agent File List is consistently incomplete or doesn't match git diff output after a BMAD update

The dev-story workflow in this project includes a custom enhancement to workflow.md — the Step 9 git diff File List reconciliation audit. This customization lives in _bmad/bmm/workflows/4-implementation/dev-story/ — a directory that is gitignored and gets overwritten by BMAD framework updates. (Other features that were previously custom — review-continuation detection, [AI-Review] follow-up handling, enhanced checklist — are now stock in BMAD 6.1.)

If you have recently run a BMAD update and agent File Lists are again going unaudited, the Step 9 customization was likely overwritten. Re-apply it manually — see the BMAD Workflow Customizations section in the root README.md for details.