codeledger 0.1.1

Auto-generated code comprehension for AI-assisted development — documentation as a development cadence.

📄 CodeLedger

Auto-generated code comprehension for AI-assisted development

Documentation as a development cadence — not an afterthought.

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Why CodeLedger?

AI-assisted ("vibe") coding moves fast. Code evolves through rapid iteration with LLMs, and traditional documentation can't keep up. Six months later, you (or a new team member) opens the project and has no idea why anything was built the way it was.

CodeLedger generates structured documentation at configurable intervals during development — capturing architecture decisions, component logic, and integration patterns while your project evolves, not after.

Who is this for?

• Solo devs who vibe-code with AI and want to remember what they built
• Teams onboarding new members to AI-iterated codebases
• Anyone tired of writing docs after the fact (so... everyone?)

How It Works

Your Code → Scan → Parse → Classify → Compress → Generate → Doc
                                                     ↓
                              Multiple Docs → Merge → Final Documentation

1. Scan — Walks your project, respects .gitignore, builds a file manifest
2. Snapshot — Hash-based change detection (no git required)
3. Parse — AST analysis (Python) or regex extraction (JS/TS, Java, Go, Rust)
4. Classify — Determines session scope: trivial → minor → standard → major → refactor
5. Compress — Token-efficient representation within your model's budget
6. Generate — Sends structured prompt to Anthropic, OpenAI, or Ollama
7. Merge — Combines multiple doc snapshots into a single conceptualized document

Quick Start

Install

pip install codeledger

Initialize

cd your-project
codeledger init --preset python_api

This creates .codeledger/config.yaml with sensible defaults for your project type.

Generate Documentation

codeledger generate

CodeLedger scans your code, detects what changed, classifies the session, and generates a structured doc snapshot.

Merge Into Final Docs

codeledger merge

Combines all generated snapshots into a single DOCUMENTATION.md.

Other Commands

codeledger status       # Show project status and doc history
codeledger diff         # Show changes since last snapshot
codeledger explain pd_001  # Display a specific doc by ID
codeledger version      # Show version

Configuration

After codeledger init, edit .codeledger/config.yaml:

project:
  name: my-project
  language: python
  type: api

cadence:
  n_value: 5          # Generate every N interactions
  trigger: manual     # manual | file_watch | time_interval

model:
  tier: api
  provider: anthropic
  model_name: claude-sonnet-4-20250514
  api_key_env: ANTHROPIC_API_KEY
  max_input_tokens: 3000
  max_output_tokens: 5000

focus:
  include_patterns:
    - "**/*.py"
  exclude_patterns:
    - "tests/**"
    - "__pycache__/**"
  highlight:
    - "src/core/engine.py"   # Pay extra attention to these files

Presets

Start fast with a preset that matches your project:

Preset	Use Case
python_api	Python REST/GraphQL APIs
react_frontend	React/Next.js frontends
fullstack	Full-stack applications
data_pipeline	ETL and data processing
ml_research	ML/AI research projects
cli_tool	Command-line tools
minimal	Bare minimum setup

codeledger init --preset fullstack --name my-app

Model Support

Provider	Tier	Setup
Anthropic	Cloud	Set ANTHROPIC_API_KEY env var
OpenAI	Cloud	Set OPENAI_API_KEY env var
Ollama	Local (free)	Install Ollama, pull a model

Using Ollama (free, runs locally)

model:
  tier: local
  provider: ollama
  model_name: llama3.1
  max_output_tokens: 5000

No API key needed — runs entirely on your machine.

Smart Session Classification

CodeLedger doesn't waste tokens on tiny changes. It classifies each session to calibrate documentation depth:

Type	When	Token Budget	What Happens
Trivial	<2 files, <30 lines	0	Deferred and batched
Minor	<5 files, <150 lines	~1.5K	Micro-doc generated
Standard	<15 files, <500 lines	~5K	Full documentation
Major	15+ files, 500+ lines	~8K	Comprehensive deep-dive
Refactor	Many deletes + creates	~3K	Refactor-focused analysis

Trivial sessions are automatically deferred and batched until they accumulate enough significance — so you only pay for docs when they matter.

No Git Required

CodeLedger uses its own Snapshot Engine — SHA-256 hashing of file contents for change detection. Git is completely optional.

This means it works for:

• Projects without version control
• Quick prototypes and experiments
• Environments where git isn't available
• Vibe coding sessions where you just want to build

What Gets Documented

Each generated doc includes up to 9 configurable sections:

Section	What It Captures
Phase Execution Summary	What was built and current status
Code Architecture	File tree and structural overview
Decision Rationale	Why things were built this way
Component Logic	How non-obvious parts work
Integration & Data Flow	How components connect
Edge Cases & Error Handling	Boundary conditions and failure modes
Interview & Learning Notes	Q&A format insights
Technical Debt	Known issues and future work
Quick Reference	Common commands and entry points

Sections are prioritized (P1/P2/P3) and automatically trimmed to fit your token budget.

Architecture

src/codeledger/
├── config/          # Pydantic schema, YAML loader, 7 presets
├── scanner/         # File scanner, snapshot engine, dependency resolver, Change DAG
├── parser/          # Python AST parser + regex fallback for JS/TS, Java, Go, Rust
├── classifier/      # Rule-based session classification with deferred batching
├── compressor/      # Token compression and budget-aware scope trimming
├── generator/       # Prompt builder, model router, API + local clients
├── postprocess/     # Output validation, formatting, file management
├── merge/           # Multi-doc extraction, deduplication, merge engine
├── templates/       # 4 prompt templates + 2 Jinja2 output templates
└── cli.py           # Typer CLI entry point

Key design decisions:

• No git dependency — Snapshot engine uses SHA-256 hashing
• Change DAG — Dependency graph propagation for token-efficient scoping
• Budget-aware pipeline — Every stage respects the configured token limit
• Validation layer — Catches hallucinated file paths, checks section coverage

Development

git clone https://github.com/codeledger/codeledger.git
cd codeledger
pip install -e ".[dev]"

pytest                          # Run tests
ruff check src/ tests/          # Lint
ruff format src/ tests/         # Format
mypy src/codeledger/             # Type check

Roadmap

• Core pipeline (scan → parse → classify → compress → generate)
• Snapshot engine (git-free change detection)
• Change DAG with dependency propagation
• Session classifier with deferred batching
• Multi-model support (Anthropic, OpenAI, Ollama)
• Merge engine with deduplication
• File watcher mode for automatic triggers
• Tree-sitter parsers for deeper JS/TS and Java analysis
• MkDocs documentation site
• VS Code extension

License

MIT — Use it however you want.