Lean AI

Your codebase already has an architect. It just needs tools.

Lean AI is an agentic coding assistant that reads your project, plans changes, and executes them — all inside your editor. Give it a task in plain English, review the plan, and watch it work.

Run it fully local with Ollama, or connect to OpenAI and Anthropic when you need heavier reasoning. Switch between models mid-session from the UI. No cloud account required to get started.

Why Lean AI?

• Plan first, then execute — a 6-phase planning pipeline reads your codebase, traces data flow across files, and produces a structured plan before touching any code. You approve (or revise) before anything changes. Thinking and content tokens stream in real-time during every planning phase so you can follow the agent's reasoning as it works.
• Learns from your corrections — every time you reject a plan, every time a test fix works, every TDD dispute, Lean AI extracts a short lesson and offers it to you as an inline chip in the chat ("Remember this?"). Confirmed lessons are read back into future planning so the tool stops repeating the same mistakes. Curated Memory
• Training archive for future fine-tuning — every workflow decision is captured locally in an append-only archive behind a fail-closed secrets scrubber. Opt in to export via a bearer-token API when you're ready to ship a LoRA adapter for your expert model. Training Pipeline
• Multi-provider flexibility — Ollama for free local inference, OpenAI for GPT-4o, Anthropic for Claude. Switch from the dropdown without restarting. Use cheap local models for small fixes, cloud models for hard problems.
• Dual-model pipeline — run a fast local model for codebase exploration and code execution, then automatically hand off to a cloud model (Claude, GPT-4o) for reasoning-heavy planning phases and complex fix attempts. Save cloud tokens for the decisions that matter.
• Local Refiner — when using cloud providers, a local Ollama model pre-processes your prompts: enriches them with private reference library context, strips sensitive data, and structures vague requests into detailed specs. Your proprietary docs never leave your machine. Learn more
• Zero prompt engineering — chat mode helps you refine ideas into detailed tasks. Project context and framework guides teach the LLM your codebase conventions automatically.
• Reference library — drop your internal docs (PDF, EPUB, Word, Markdown) into .lean_ai/reference/ and the agent uses them for better plans without leaking content to cloud APIs.
• UI verification — the agent can screenshot web pages or desktop GUIs (Tkinter, Qt, Swing, JavaFX, Electron) and analyse layout with a local vision model. Useful for planning UI changes, discussing layout with the agent, or diagnosing visual regressions that pass a type-check but look broken. Learn more
• Built-in code quality — after every execution, Lean AI runs your project's linter and tests automatically. Failures are fed back to the LLM for self-correction. Lint, test, and format commands are auto-detected from your project files — zero configuration needed. When a test command is available, the agent writes tests alongside code changes.
• Git-native workflow — every task runs on its own branch. Approve to merge, reject to discard. Your main branch stays clean.
• 19 scaffold recipes — bootstrap new projects (FastAPI, Next.js, Laravel, Rails, and more) with a single command.

Quick Start

1. Install the extension

VS Code / VSCodium: Install from the VS Code Marketplace or OpenVSX.

JetBrains IDEs (IntelliJ, PyCharm, WebStorm, CLion, etc.): See the JetBrains Plugin guide for build and install instructions.

On first activation, the extension automatically creates a Python virtual environment and installs the backend server — no manual setup required.

2. Install Ollama and pull a model

Download Ollama and pull a model:

ollama pull qwen3-coder:30b

Cloud models: Ollama also supports cloud-hosted models. Pull one with ollama pull model-name:cloud, then run ollama run model-name:cloud and follow the link to connect your Ollama Cloud account. Once linked, select the cloud model from the Lean AI dropdown — it works the same as a local model.

3. Open a project and run /init

Type /init in the chat panel to index your workspace and generate project context. Then describe what you want built.

Building from Source

If you prefer to build from source instead of installing from the marketplace:

Backend

cd backend
pip install -e ".[dev]"

Need cloud providers or reference library support? See optional extras.

Start the server manually:

uvicorn lean_ai.main:app --reload --port 8422

Extension

cd extension && npm install && npm run build
npx vsce package --no-dependencies

Install the generated .vsix file: Extensions sidebar > ... menu > Install from VSIX...

When building from source, set lean-ai.backendDir or lean-ai.pythonPath in the extension settings — the automatic installer is skipped when either setting is explicitly configured.

How It Works

You: "Add user authentication with JWT tokens"
                    |
          [Chat refines the idea]
                    |
          [6-phase planning pipeline]
            scope -> files -> design -> risks -> plan
                    |
          [You review and approve]
                    |
          [Agent executes step-by-step]
            creates files, edits code, writes tests
                    |
          [Post-execution validation]
            auto-format -> lint fix -> lint check -> test
            failures fed back to LLM for self-correction
                    |
          [Changes committed on a branch]
            /approve to merge, /reject to discard

Two modes:

• /agent — full planning pipeline for features and refactors
• /fix — skip planning, let the agent explore and fix directly

See Architecture for the full breakdown.

Slash Commands

Command	What it does
/init	Index workspace and generate project context
/agent	Send a task to the planning pipeline
/fix	Skip planning, fix directly with full tool access
/approve	Merge the agent's branch
/reject	Discard the agent's branch
/style	Generate style guide from CSS/templates
/skill <name> <task>	Load a local skill from .lean_ai/skills/<name>/instructions.md and apply it to the task
/request <task>	Skip planning, open-ended task with full tool access
/resume [session_id]	Resume a previous session
/help	Show this help
/interview-prep	Convert a .docx resume and tailor it for a specific role
/batch-prep	Tailor resumes + cover letters for many roles in one run
/ats-check [slug]	Keyword gap report comparing resume to the job description
/thank-you [slug]	Draft a post-interview thank-you note
/recruiter-reply	Draft a reply to a recruiter's cold outreach
/negotiate [slug]	Research market comp and build a negotiation brief
/analyse-rejection [slug]	Post-mortem a rejection with concrete takeaways
/log-applied [slug]	Append a tracker row and commit the application folder to git
/mock-interview [slug]	Interactive Q&A practice with rubric scoring
/note	Save a note (auto-categorized by project)
/scaffold	Bootstrap a new project
/memories	Manually trigger memory extraction from the last completed workflow session
/reboot	Restart the backend server

Configuration

Create a backend/config.yaml file (or use the extension's settings panel):

# Provider: ollama, openai, or anthropic
llm_provider: ollama

# Local model (default)
ollama_model: "qwen3-coder:30b"
ollama_context_window: 128  # 128k — shorthand for 131072

# Cloud API keys — encrypt with: python -m lean_ai encrypt-key <key>
openai_api_key: "enc:gAAAAABf..."
anthropic_api_key: "enc:gAAAAABf..."

Environment variables (LEAN_AI_*) and legacy .env files also work. See configuration priority.

Dual-model setup (save cloud tokens)

Use a local model for the bulk of the work and a cloud model only for planning and complex fixes:

llm_provider: ollama
ollama_model: "qwen3-coder:30b"

# Expert: cloud model for planning phases 3-5 and final fix retry
expert_llm_provider: anthropic
anthropic_api_key: "enc:gAAAAABf..."
anthropic_expert_model: "claude-opus-4-6"

The expert model only runs for planning phases 3–5 (design + risk synthesis, plan assembly, verification) and the final validation fix retry. All codebase exploration, implementation, and routine tool calls use the primary local model.

Note: the cloud provider's Python SDK must be installed even when the primary provider is Ollama — run pip install -e ".[dev,anthropic]" or pip install -e ".[dev,openai]" as appropriate, then restart the server.

All-local three-model setup

Use three different local models matched to each role:

ollama_model: "qwen3-coder:30b"

# Expert: larger model for planning and complex reasoning
ollama_model_expert: "qwen3-coder-next:80b"

# Request: smaller model for chat conversation and prompt building
request_llm_provider: openai
openai_base_url: "http://localhost:11434/v1"
openai_request_model: "gpt-oss:20b"

See the full configuration reference for all options.

Documentation

Guide	Description
Configuration	All environment variables, extension settings, and model setup
Architecture	Planning pipeline, workflow modes, tools, and internals
Example Flow	End-to-end walkthrough of a real session with every guardrail called out
Reference Library & Refiner	Private docs, RAG enrichment, and cloud privacy
UI Verification	Vision-backed screenshot analysis for web pages and desktop GUIs
API Reference	REST endpoints and WebSocket protocol
Extension Guide	VSCode/VSCodium setup, commands, and settings
JetBrains Plugin	IntelliJ, PyCharm, WebStorm, CLion, and all JetBrains IDEs
Prompt Customization	Customizing LLM prompts per project
Skills Guide	How /skill works and how to author high-quality skills
Modelfile Guide	Customizing Ollama models with persistent rules
llama-server Guide	Using llama.cpp as an alternative to Ollama
Regulated Environments	Self-hosted deployment for HIPAA, SOX, GDPR, ITAR, and air-gapped networks

Requirements

• Python 3.10+
• Node.js 18+ (for the extension)
• At least one LLM provider:
  • Ollama with a capable model (e.g., qwen3-coder:30b) — free, local, no account needed
  • OpenAI API key (GPT-4o, etc.)
  • Anthropic API key (Claude, etc.)

Ollama is always required for inline predictions and embeddings, even when using cloud providers.

Technology Stack

Layer	Technology
Backend	Python, FastAPI, aiosqlite
LLM providers	Ollama, OpenAI, Anthropic
Code analysis	tree-sitter (13 languages)
Search	Whoosh BM25F + embedding RRF
VS Code Extension	TypeScript, VSCode API
JetBrains Plugin	Kotlin, IntelliJ Platform SDK, JCEF

License

MIT