Backstitch

Spec-to-code traceability and invariant checks for Python repositories.

$ uv tool install backstitch
$ cd your-project
$ mkdir -p docs/specs docs/plans src
$ backstitch check --spec-root docs/specs --code-root src

Backstitch verifies that spec requirements point to real implementation owners, code points back to the requirements it implements, and declared invariants are bound to real tests. Deterministic checks run without a model. An optional semantic lane reviews bounded packets through the llm ecosystem without giving a model open-ended repository access.

Backstitch is a standalone tool. Weft is a reference target and eventual consumer, not a package dependency.

Recommended For

• Spec-driven Python projects. Stable requirement IDs and reciprocal links make intended behavior navigable from docs to code and back.
• Repositories maintained by coding agents. Deterministic ownership, diagnostics, and acceptance gates replace prose-only compliance claims.
• Critical behavior that needs explicit test bindings. Required and draft invariants make missing, malformed, duplicate, or misplaced bindings visible.
• Teams that want bounded semantic review. Backstitch generates finite, reproducible packets before any model call and validates returned evidence.

Table of Contents

• Backstitch
  • Recommended For
  • Table of Contents
  • Features
  • Installation
  • Quick Start
  • Command Reference
    • Global Options
    • Commands
    • Exit Codes
  • Core Concepts
    • Spec and Code Traceability
    • Invariant Bindings
    • Diagnostic Policy
    • Configuration Discovery
  • Semantic Review
  • Development and Contributing
    • Runtime
    • Testing
    • Live LLM Tests
    • Releases
  • License

Features

• Bidirectional traceability - Resolves spec mappings and Python backlinks
• Invariant checking - Connects required or draft guarantees to real tests
• Stable diagnostics - Canonical names, short aliases, contexts, and levels
• Configurable policy - Ordered selectors, fail_on, and suppressibility
• Auditable suppression - Hidden findings remain available with reasons
• Deterministic reports - Stable JSON, ordering, locators, and content hashes
• Bounded semantic review - Section and invariant packets with evidence limits
• Runtime-independent parsing - CommonMark and modern Python syntax support
• Self-application - Backstitch's own repository is a strict acceptance corpus

Installation

# Install the CLI globally with uv (recommended)
uv tool install backstitch

# Or with pipx
pipx install backstitch

# Or add it to a project environment
uv add --dev backstitch
pip install backstitch

For a source checkout:

$ uv sync --extra dev
$ uv run backstitch --version
backstitch 0.3.0

Requirements:

• Python 3.11+
• llm, markdown-it-py, tree-sitter, and tree-sitter-python (installed automatically)

Quick Start

For a project with application code under src, create the scan roots and add the repository profile:

$ mkdir -p docs/specs docs/plans src tests

# pyproject.toml
[tool.backstitch.profile]
name = "backstitch-style-v1"
spec_roots = ["docs/specs"]
plan_roots = ["docs/plans"]
code_roots = ["src", "tests"]
test_roots = ["tests"]

Give a requirement a stable ID and map it to an exact repository-relative implementation path:

## Authentication boundary [AUTH-1]

The request path must reject unauthenticated callers.

_Implementation mapping_:
- `src/auth.py`

Add the reciprocal backlink in the owning Python docstring or a nearby comment:

def authenticate(request: Request) -> User:
    """Authenticate one request.

    Spec: docs/specs/auth.md [AUTH-1]
    """

Run the deterministic checker from the repository root:

$ backstitch check
$ backstitch check --format json --output spec-trace.json
$ backstitch check --show-suppressions

The built-in backstitch-style-v1 profile defaults to docs/specs for specs and backstitch plus tests for code. Override roots in configuration or with repeatable --spec-root, --code-root, and --test-root options.

Command Reference

Global Options

Global configuration options may appear before a command. Command-specific forms are also available where relevant.

• --config PATH - Use exactly one configuration file; skip discovery
• --no-config - Skip repository configuration; packaged defaults still load
• --version - Show the installed Backstitch version
• --help - Show help

Commands

Common deterministic examples:

$ backstitch check --repo-root . --warnings-as-errors
$ backstitch check --repo-root . --format json --output spec-trace.json
$ backstitch packets --repo-root . --kind invariant --output invariants.jsonl
$ backstitch packets --repo-root . --kind all --output packets.jsonl

Run backstitch <command> --help for the full option set.

Exit Codes

• 0 - Command completed and no effective fail_on level was present
• 1 - The target repository has a finding at an effective failing level
• 2 - Arguments, configuration, input artifacts, output, or the tool failed

Exit 1 describes the target repository. Exit 2 describes the invocation or tool. Backstitch contains per-file failures where possible and never exposes a Python traceback as normal CLI output.

Core Concepts

Spec and Code Traceability

Backstitch parses Markdown sections with IDs such as [AUTH-1], implementation mapping blocks, and Python references in module, class, function, and method docstrings or comments. Mappings use exact repository-relative paths. Bare IDs resolve only when unique; Backstitch reports ambiguity instead of guessing.

The deterministic report contains normalized sections, code references, mappings, resolved edges, invariants, binds, issues, and summary counts. Identical inputs produce stable JSON ordering.

Invariant Bindings

Declare behavior in an owning Python docstring or inside an ID-bearing spec section:

def resolve() -> Report:
    """Build the deterministic report.

    Invariant: [INV.RES.1] Identical inputs produce byte-identical JSON.
    """

Bind the invariant inside a real test definition:

def test_report_is_stable() -> None:
    """Tests-invariant: [INV.RES.1]"""

Use Invariant (draft): for an advisory declaration. Under packaged defaults, an untested required invariant is an error and an untested draft invariant is a warning. packets --kind invariant includes bounded target and binding-test snippets for advisory semantic review.

Diagnostic Policy

Diagnostics have canonical names such as CODE_REF_UNMAPPED_FROM_SPEC and stable short aliases such as BSC008. Rules are ordered and the last matching rule wins:

[tool.backstitch.diagnostics]
fail_on = ["error", "warning"]
suppressible_levels = ["warning", "info"]

[[tool.backstitch.diagnostics.levels]]
select = ["BSC*", "INVARIANT_UNTESTED:draft"]
level = "warning"

Selectors accept canonical codes, short codes, *, family prefixes, and supported contexts. A diagnostic set to off is omitted from normal findings but remains available in suppressed_issues under --show-suppressions.

Configuration Discovery

Backstitch always starts with packaged defaults, then applies repository configuration, supported environment values, and explicit CLI options. It searches upward for the nearest .backstitch.toml or pyproject.toml with a [tool.backstitch] table. A standalone file uses the same key layout without the tool.backstitch prefix.

[tool.backstitch.profile]
name = "backstitch-style-v1"
spec_roots = ["docs/specs"]
code_roots = ["src", "tests"]
test_roots = ["tests"]

[tool.backstitch.check]
format = "text"
warnings_as_errors = false

Test roots classify paths within code roots. Replacing code_roots without also supplying test_roots resets test roots for that configuration layer; every nonempty final test root must be contained by a final code root.

Semantic Review

Deterministic checks never call a model. Generate packets explicitly, then analyze and summarize them:

$ backstitch check --format json --output spec-trace.json
$ backstitch packets --kind all --output packets.jsonl
$ backstitch analyze --packets packets.jsonl --output analysis.jsonl
$ backstitch summarize-analysis \
    --deterministic-report spec-trace.json \
    --analysis-results analysis.jsonl

Packets bound the spec text, code snippets, tests, deterministic findings, and evidence ranges shown to the model. Model output is untrusted: Backstitch owns packet identity, validates structured rows and evidence locality, and contains malformed output per packet.

Use backstitch doctor --probe before semantic analysis to check model registration, credentials, constrained decoding, and endpoint reachability.

Development and Contributing

Runtime

Backstitch uses markdown-it-py for CommonMark block structure and tree-sitter-python for Python ownership, comments, and docstrings. Running on Python 3.11 can therefore analyze newer target syntax such as PEP 695 generics and PEP 701 f-strings without relying on the host interpreter's ast grammar.

Testing

The repository pytest configuration runs the live cloud-provider contract test in the default local suite. A normal local run needs a working llm model and credential:

$ uv run pytest -q

For an intentionally hermetic run:

$ uv run pytest -q -m "not live_llm"
$ uv run pytest tests/live/test_live_llm.py -q -o run_live_llm=false

The completion gate also includes:

$ uv run pytest tests/acceptance -q
$ uv run ruff check backstitch tests bin
$ uv run ruff format --check backstitch bin .github/scripts tests
$ uv run mypy backstitch bin/release.py tests
$ uv run backstitch check --repo-root . --show-suppressions

Live LLM Tests

tests/live/test_live_llm.py drives the real packets to analyze to check to summarize-analysis path. It asserts structured contracts, transport, and model success for cloud runs, not exact wording or classification.

# Store a provider key once, then run the local-default live test
$ uv run llm keys set openai
$ LLM_MODEL=gpt-5.4-mini uv run pytest tests/live/test_live_llm.py -q

# Or use a provider environment variable
$ OPENAI_API_KEY=... LLM_MODEL=gpt-5.4-mini uv run pytest -m live_llm -q

The same test supports a loopback OpenAI-compatible endpoint such as Ollama:

$ docker run -d --name backstitch-llm \
    -p 127.0.0.1:11434:11434 \
    -v "$PWD/.ollama-cache:/root/.ollama" \
    ollama/ollama
$ docker exec backstitch-llm ollama pull llama3.2:3b
$ BACKSTITCH_LOCAL_LLM_UPSTREAM=http://127.0.0.1:11434/v1 \
    BACKSTITCH_LOCAL_LLM_SERVED_MODEL=llama3.2:3b \
    BACKSTITCH_LIVE_LLM_KIND=local \
    uv run pytest -m live_llm -q

Cloud tests cost money and can fail because of provider outages, rate limits, or model retirement. Local endpoints avoid provider credentials but add model startup and output-quality variance. See docs/implementation/06-choosing-a-local-model.md for measured local-model guidance.

The hermetic CI matrix always deselects live tests. The cloud CI job requires BACKSTITCH_CI_LIVE_LLM=1 plus a main-branch push or manual run on main, and uses read-only repository permissions. It runs the provider probe only when the OPENAI_API_KEY repository secret is configured; otherwise that job exits successfully with a skip notice. The separate local-llm workflow owns the Ollama canary.

Releases

The local release helper runs regular, cloud-live, and local-live checks before creating and pushing a version tag. The tag-triggered workflow publishes to PyPI through Trusted Publishing and creates the GitHub Release.

$ bin/release.py --version X.Y.Z --dry-run
$ bin/release.py --version X.Y.Z

# When version files and CHANGELOG.md are already prepared
$ bin/release.py all --dry-run
$ bin/release.py all

See docs/implementation/05-release-publishing.md for release setup, rollback, and verification.

License

Backstitch is released under the MIT License. See LICENSE.