sembl 0.1.9

Graph-first Work Order generator for AI coding agents

Sembl

Graph-first Work Order generator for AI coding agents.

Sembl turns vague repo tasks into scoped execution contracts before an AI coding agent starts editing. Use it with Cursor, Claude Code, Codex, Aider, OpenCode, or any executor that can follow a structured prompt.

repo + task -> Work Order -> agent executes with tighter scope

A Work Order tells an agent:

• what the goal is, and what it is not
• which files it can touch
• which files it should inspect but not modify
• what must be true when it finishes
• how to prove it succeeded
• when to stop and ask a human

Website | PyPI | Issues

Why Sembl

AI coding agents are strong executors, but loose prompts still create avoidable failure modes: broad edits, fake validation commands, missed test context, and unclear stop conditions.

Sembl sits before execution:

1. It probes the repo.
2. It uses Graphify and code-review-graph when available.
3. It asks an LLM to produce a strict Work Order.
4. It writes Markdown and JSON outputs that another agent can execute.

The result is not "more autonomy." It is a narrower, clearer packet of work.

Quickstart

pip install "sembl[graph-pipeline]"

$env:NVIDIA_API_KEY="..."
sembl doctor --repo C:\path\to\repo
sembl generate --repo C:\path\to\repo --task "fix the failing login redirect test" --provider nvidia --graph-mode required --refresh-graph

For a core install without Graphify/code-review-graph:

pip install sembl

Example Output

Work Order generated

Goal: Fix the failing login redirect test by correcting redirect behavior after successful authentication
Outcome: Users are correctly redirected to the intended destination after logging in
Task type: bugfix | Risk: MEDIUM

Acceptance criteria: 5 items
Validation commands: 4 commands
Stop conditions: 5 triggers

Output: .sembl/work-orders/wo-project-.../

The generated folder contains work-order.md, executor-prompt.md, validation-plan.md, work-order.json, and graph impact notes when graph context is available.

Status

Sembl is early but usable for testing. The current CLI supports:

• repo probing for language/framework/branch/dirty state
• optional Graphify context
• optional code-review-graph context
• graph diagnostics via sembl doctor, and --graph-mode auto|required|off
• LLM graph-impact synthesis over code-review-graph output (--no-graph-enrichment to skip)
• OpenAI, Anthropic, Gemini, NVIDIA NIM, OpenRouter, and local Ollama providers
• work-order output as Markdown, JSON, executor prompt, validation plan, and graph-impact analysis

Install

Sembl is published on PyPI: https://pypi.org/project/sembl/

# Core CLI
pip install sembl

# With the graph pipeline (Graphify + code-review-graph)
pip install "sembl[graph-pipeline]"

# As an isolated tool
uv tool install sembl

Pre-release channels

For the latest unreleased commits, install from GitHub:

uv pip install "sembl[graph-pipeline] @ git+https://github.com/speedvibecode/sembl.git"

TestPyPI mirrors each release (the --extra-index-url lets dependencies resolve from the real PyPI):

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ sembl

Install From Source

git clone https://github.com/speedvibecode/sembl
cd sembl
uv pip install -e ".[graph-pipeline]"

Plain pip also works:

pip install -e ".[graph-pipeline]"

Provider Keys

Set one provider key before generation:

$env:OPENAI_API_KEY="..."
$env:ANTHROPIC_API_KEY="..."
$env:GEMINI_API_KEY="..."
$env:NVIDIA_API_KEY="..."
$env:OPENROUTER_API_KEY="..."
# Ollama needs no key (local); set OLLAMA_HOST only to point at a non-default server.

Then choose the provider (openai, anthropic, gemini, nvidia, openrouter, or ollama):

sembl generate --repo C:\path\to\repo --task "replace starter screen text" --provider nvidia

# OpenRouter (OpenAI-compatible) routes to any model in its catalog:
sembl generate --repo C:\path\to\repo --task "replace starter screen text" --provider openrouter --model moonshotai/kimi-k2

# Ollama runs locally — no key, no rate limits, offline (slower on CPU-only machines):
#   ollama pull qwen2.5-coder:7b
sembl generate --repo C:\path\to\repo --task "replace starter screen text" --provider ollama --model qwen2.5-coder:7b

Optional Graph Context

Sembl can run without graph tools, but the strongest results come from Graphify plus code-review-graph.

graphify update C:\path\to\repo --no-cluster
code-review-graph build --repo C:\path\to\repo --data-dir C:\path\to\repo-specific-crg-data --skip-flows

$env:CRG_DATA_DIR="C:\path\to\repo-specific-crg-data"
sembl generate --repo C:\path\to\repo --task "fix the failing login redirect test" --provider nvidia --graph-mode required

Sembl guards against stale generic CRG_DATA_DIR values by deriving a repo-specific graph data directory when the env var does not look like it belongs to the target repo.

Diagnose and control graph context

Run sembl doctor first to see exactly what is installed, what is built, and the copy-paste command to fix each gap (add --fix to install missing graph tools, or --json for machine-readable output):

sembl doctor --repo C:\path\to\repo

Then choose how generation treats graph context with --graph-mode:

• auto (default): use graph context if available, otherwise explain what is missing and fall back to direct repo probing.
• required: fail before any LLM call if graph context is unavailable, so no tokens are wasted. (--require-graph-context is a backward-compatible alias.)
• off: skip Graphify and code-review-graph entirely.

Add --refresh-graph to rebuild the graphs (Graphify update + code-review-graph build) before generating.

Usage

# Generate a Work Order for the current repo
sembl generate --task "add recurring expenses to this tracker" --provider nvidia

# Generate for an explicit repo
sembl generate --repo C:\path\to\repo --task "fix the login redirect bug" --provider nvidia

# Check the graph subsystem (tools, graphs, keys) and how to fix gaps
sembl doctor --repo C:\path\to\repo

# Require graph context (fails before any LLM call if it is unavailable)
sembl generate --repo C:\path\to\repo --task "fix the login redirect bug" --provider nvidia --graph-mode required

# Rebuild the graphs first, then generate on fresh context
sembl generate --repo C:\path\to\repo --task "fix the login redirect bug" --provider nvidia --refresh-graph

# List Work Orders
sembl list

# Show latest Work Order
sembl show

# Show the executor prompt
sembl show --file executor-prompt

# Show the graph-impact analysis (when graph context was available)
sembl show --file graph-impact

Output

.sembl/work-orders/wo-myproject-{timestamp}-{slug}/
  work-order.md       - read this
  executor-prompt.md  - paste into your agent
  validation-plan.md  - run this after
  work-order.json     - machine-readable
  graph-impact.md     - LLM synthesis of code-review-graph blast radius (graph context only)

Graph Impact Synthesis

When code-review-graph context is available, Sembl runs a focused LLM pre-pass that turns the graph's terse structural output (blast radius, node/edge counts) into a concise, grounded impact analysis: likely edit targets, hidden coupling, and files to keep read-only. That synthesis grounds the main Work Order and is also written to graph-impact.md. It is best-effort - if the provider call fails it is skipped silently. Disable it with --no-graph-enrichment.

The 8 Locks

Lock	Purpose
Intent	Goal, outcome, task type
Boundary	Non-goals, forbidden areas
Scope	Editable paths, read-only context
Context	Files to inspect, architecture notes
Success	Acceptance criteria, regressions
Proof	Validation commands, tests to add
Safety	Stop conditions, risk level
Executor	Agent-ready prompt, patch expectations

Local Test

python -m unittest discover -s tests -v
python -m compileall -q sembl tests

Testing Notes

If you test Sembl on a real repo, the best feedback is:

• the exact command you ran
• whether graph context was available
• the generated work-order.md
• whether the executor agent could complete the task without scope confusion
• any hallucinated files, missing validation commands, or false stop conditions

Releasing

Publishing uses GitHub Actions and Trusted Publishing (OIDC). Stable releases publish from GitHub Releases; TestPyPI dev builds are manual. No API tokens are stored.

Stable releases -> PyPI

.github/workflows/release.yml publishes to PyPI when you publish a GitHub Release.

1. Bump the version in pyproject.toml and sembl/__init__.py (sembl --version is derived from sembl/__init__.py).
2. Commit and push.
3. On GitHub: Releases -> Draft a new release -> Create a new tag named v<version> (for example, v0.1.3, matching the bumped version) -> Publish.

The workflow builds, runs twine check, and publishes to PyPI. The tag must equal the pyproject.toml version or the build fails with a clear error.

Dev builds -> TestPyPI (manual, optional)

While the project is this early, stable releases go straight to PyPI and nothing sits in TestPyPI by default. .github/workflows/testpypi.yml is manual only (workflow_dispatch): trigger it from the Actions tab when you deliberately want a pre-release dev build (<version>.devN, stamped in CI). When you do, testers install it with pip:

pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ sembl

Or with uv (needs --prerelease allow and a best-match index strategy so the dev build is preferred over the last stable release):

uv pip install --prerelease allow --index-strategy unsafe-best-match --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ sembl

Before cutting TestPyPI dev builds after a stable release, bump master to the next patch or minor version (for example, after releasing 0.1.3, bump to 0.1.4) so dev builds sort above the last stable release.

Models write code. Sembl makes the work governable.