jmdl-ai-workflows 0.1.3

Composable AI workflow framework built on LangGraph + MCP.

ai-workflows

A framework for building multi-step AI workflows that can plan, execute, validate, and recover from failures. Supports multiple models (Claude, Gemini, Ollama), human approval steps, and resumable runs with persistent state.

A LangGraph-native workflow framework for solo developers. Orchestrates multi-step AI workflows with durable state, multi-provider routing, and deterministic cost accounting across Gemini (via LiteLLM), Qwen (via Ollama), and Claude Code (via OAuth CLI subprocess).

Status

Milestone	State
M1 — Reconciliation & cleanup	Complete (2026-04-19)
M2 — Graph-layer adapters + provider drivers	Complete (2026-04-19)
M3 — First workflow (planner, single tier)	Complete (2026-04-20)
M4 — MCP server (FastMCP)	Complete (2026-04-20)
M5 — Multi-tier planner	Complete (2026-04-20)
M6 — slice_refactor DAG	Complete (2026-04-20)
M7 — Eval harness	Complete (2026-04-21)
M8 — Ollama infrastructure	Complete (2026-04-21)
M9 — Claude Code skill packaging	Complete (2026-04-21)
M10 — Ollama fault-tolerance hardening	Planned
M11 — MCP gate-review surface	Complete (2026-04-22)
M12 — Tiered audit cascade	Planned
M13 — v0.1.0 release + PyPI packaging	Complete (2026-04-22)
M14 — MCP HTTP transport	Complete (2026-04-22)

What it is

ai-workflows exposes two surfaces over the same workflow registry: an aiw CLI for interactive and scripted use, and an aiw-mcp MCP server for Claude Code, Cursor, Zed, and browser-origin consumers (via streamable-HTTP). A workflow is a Python module that builds a LangGraph StateGraph composed of graph primitives (TieredNode, ValidatorNode, HumanGate, RetryingEdge) and registered by name. There is no hosted control plane and no Anthropic API dependency — Claude access is OAuth-only through the claude CLI subprocess.

Architecture at a glance

Four layers with a one-way dependency direction enforced by import-linter:

surfaces        (ai_workflows.cli, ai_workflows.mcp)
    ↓
workflows       (ai_workflows.workflows.*)        — concrete LangGraph StateGraphs
    ↓
graph           (ai_workflows.graph.*)            — LangGraph adapters over primitives
    ↓
primitives      (ai_workflows.primitives.*)       — storage, cost, tiers, providers, retry, logging

Full overview in docs/architecture.md. Tutorials for authoring a new workflow or extending the graph layer live at docs/writing-a-workflow.md and docs/writing-a-graph-primitive.md.

Install

Requires Python ≥ 3.12 and uv.

One-shot via uvx — no persistent install; every invocation fetches the wheel into a cache:

uvx --from jmdl-ai-workflows aiw run planner --goal 'Write a release checklist' --run-id demo

Persistent tool install — puts aiw + aiw-mcp on PATH:

uv tool install jmdl-ai-workflows
aiw run planner --goal 'Write a release checklist' --run-id demo

Setup

aiw and aiw-mcp read configuration from environment variables. As of 0.1.1 both binaries auto-load a .env file from your current working directory at startup (shell-exported values always win over .env values).

Required for most workflows

• GEMINI_API_KEY — Google Gemini key, used by the LiteLLM adapter. Get one at https://aistudio.google.com/apikey. Required for any workflow that routes through a Gemini tier (the default planner-explorer / planner-synth paths cover this).

Optional

• OLLAMA_BASE_URL — default http://localhost:11434. Set this if your Ollama daemon listens elsewhere. Needed only when a workflow routes to the local Qwen tier.
• AIW_STORAGE_DB — path override for the run registry database. Defaults to ~/.ai-workflows/storage.sqlite3.
• AIW_CHECKPOINT_DB — path override for the LangGraph checkpoint database. Defaults to ~/.ai-workflows/checkpoint.sqlite3.

Claude Code tier (no API key needed)

Some workflows route to the claude CLI via OAuth — install and authenticate it separately per Anthropic's setup docs. aiw never reads ANTHROPIC_API_KEY and never imports the anthropic SDK; Claude access is OAuth-only through the CLI subprocess.

.env auto-load

Create a .env in the directory you run aiw / aiw-mcp from:

# .env
GEMINI_API_KEY=your-key-here
# OLLAMA_BASE_URL=http://localhost:11434  # uncomment if non-default

A shell-exported value wins over the .env value — right precedence for CI pipelines, direnv setups, and one-off inline overrides (GEMINI_API_KEY=x aiw run ...).

Troubleshooting

Seeing a 401 Unauthorized from LiteLLM? Your GEMINI_API_KEY is missing, invalid, or out-of-quota. Confirm echo $GEMINI_API_KEY in the shell you're running aiw from, or verify the value in your .env.

Getting started

After Setup, drive a planner run end-to-end:

aiw run planner --goal 'Write a release checklist' --run-id demo
aiw resume demo --approve
aiw list-runs

The planner workflow composes two LLM tiers (Qwen explorer via Ollama + Claude Code Opus synth). If you want to swap either tier, pass --tier-override planner-synth=planner-explorer (route both steps through Qwen for a fully-local smoke) or edit the workflow's tier registry in ai_workflows/workflows/planner.py on a clone.

MCP server

Register aiw-mcp with any MCP host — Claude Code, Cursor, Zed, or an HTTP client via the streamable-HTTP transport — to drive the same workflows inside-out:

claude mcp add ai-workflows --scope user -- uvx --from jmdl-ai-workflows aiw-mcp

The HTTP transport is opt-in for browser-origin consumers: aiw-mcp --transport http --port 8080 --cors-origin http://localhost:3000. Full skill-install walkthrough (builder-only, on design branch).

Security notes

• Loopback default — aiw-mcp --transport http binds to 127.0.0.1; unreachable from other machines. --host 0.0.0.0 exposes the server to every process on the host and to the LAN. aiw-mcp has no built-in auth; the bind address is the only access boundary. Only pass 0.0.0.0 on a machine you own every process on, and put a reverse proxy in front if you need TLS.
• CORS is opt-in, exact-match — --cors-origin <url> adds one origin; without any flags the server emits no Access-Control-Allow-Origin header (same-origin only).

Contributing / from source

Clone the repo for development or to modify the framework itself:

git clone https://github.com/yeevon/ai-workflows.git
cd ai-workflows
uv sync              # install runtime + dev dependencies
uv run aiw version   # prints the installed package version

For the full builder/auditor workflow — task specs, audit issue files, Builder / Auditor mode conventions — switch to the design_branch (builder-only, on design branch).

Development

Three gates guard every change:

uv run pytest         # unit + scaffolding tests (hermetic; skips e2e unless AIW_E2E=1)
uv run lint-imports   # four-layer import contract
uv run ruff check     # style + basic correctness

Next

Roadmap + per-milestone task files live at design_docs/roadmap.md (builder-only, on design branch).