refactor: reorganize into monorepo with separate subprojects
Structure webber into three independent subprojects: - webber-api/: FastAPI backend server with all agent code - webber-cli/: Standalone CLI client (renamed from cli/ to webber_cli/) - webber-sandbox/: Test project for functional testing Key changes: - Each subproject has its own .venv (Python 3.12+) - Added sandbox.sh for managing test project templates - Created sandbox-templates/ with calculator-cli and empty starter - Updated CI/CD for prefixed tags (api/v*, cli/v*) - Added comprehensive AGENTS.md with operational instructions - Added gitignore filtering to glob and grep tools - Created pyproject.toml for each subproject Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Claude Opus 4.5
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# Webber
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# Webber - Multi-Agent AI Development System
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Multi-Agent AI Development System - a FastAPI-based service that orchestrates local LLM agents for code exploration, planning, and task execution.
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A Claude Code-inspired development assistant powered by local LLMs via Ollama.
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## Overview
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## Structure
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Webber provides autonomous AI agents similar to Claude Code but running locally with configurable models via Ollama. It's designed for:
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This is a monorepo containing three subprojects:
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- **Explore Agent** - Fast codebase navigation and code search
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- **Plan Agent** - Implementation design and step-by-step planning
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- **Task Agent** - Autonomous multi-step code generation and modification
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| Directory | Description |
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|-----------|-------------|
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| `webber-api/` | FastAPI backend server with agent orchestration |
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| `webber-cli/` | Command-line client for interacting with the API |
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| `webber-sandbox/` | Test project for functional testing |
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Built on [PydanticAI](https://ai.pydantic.dev/) for structured LLM interactions.
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### Additional Directories
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| Directory | Description |
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|-----------|-------------|
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| `sandbox-templates/` | Reusable project templates for the sandbox |
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| `.gitea/workflows/` | CI/CD workflows for releases |
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## Quick Start
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### Prerequisites
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### 1. Start the API Server
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```bash
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cd webber-api
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python3.12 -m venv .venv
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source .venv/bin/activate
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pip install -r requirements.txt -r requirements-dev.txt
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./wakeup.sh
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```
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### 2. Set Up the CLI
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```bash
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cd webber-cli
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python3.12 -m venv .venv
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source .venv/bin/activate
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pip install -r requirements.txt
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pip install -e .
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# Test connection
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webber-cli status
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```
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### 3. Load a Sandbox Project
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```bash
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# From repo root
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./sandbox.sh list
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./sandbox.sh load calculator-cli
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cd webber-sandbox
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python3.12 -m venv .venv
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source .venv/bin/activate
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pip install -r requirements.txt
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```
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### 4. Explore with Webber
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```bash
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cd webber-cli
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webber-cli explore "find all bugs in the code" -d ../webber-sandbox
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```
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## Versioning
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This project uses prefixed tags for independent release cycles:
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- `api/v0.3.0` - Triggers API Docker build and deployment
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- `cli/v0.1.0` - Triggers CLI installer build (future)
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## Requirements
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- Python 3.12+
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- [Ollama](https://ollama.ai/) with models installed
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- (Optional) Tatlock for multi-tenant authentication
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- Ollama running with `mistral-nemo:latest` model
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- Docker (for production deployment)
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### Installation
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## Documentation
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```bash
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# Clone the repository
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git clone https://git.schweitz.internal/jpmschweitzer/webber.git
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cd webber
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# Create virtual environment
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python -m venv .venv
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source .venv/bin/activate
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# Install dependencies
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pip install -r requirements.txt
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# For development (includes testing and linting tools)
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pip install -r requirements-dev.txt
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```
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### Configuration
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```bash
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# Copy example config
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cp .env.example .env
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# Edit .env with your settings
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# At minimum, configure OLLAMA_URL to point to your Ollama instance
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```
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### Running
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```bash
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# Development (with auto-reload)
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./wakeup.sh
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# Or manually
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uvicorn src.main:app --host 0.0.0.0 --port 8086 --reload
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```
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The service will be available at `http://localhost:8086`. API docs at `/docs`.
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## Configuration
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All settings via environment variables or `.env` file:
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `DEBUG` | `false` | Enable debug mode |
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| `LOG_LEVEL` | `INFO` | Logging level |
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| `PORT` | `8086` | Server port |
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| `OLLAMA_URL` | `http://192.168.86.149:11434` | Ollama API URL |
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| `OLLAMA_AGENT_MODEL` | `mistral-nemo-large:latest` | Model for agent reasoning |
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| `OLLAMA_EMBED_MODEL` | `nomic-embed-text:latest` | Model for embeddings |
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| `TOOL_TIMEOUT_SECONDS` | `120` | Tool execution timeout |
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| `SANDBOX_ENABLED` | `true` | Sandbox tool execution |
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| `ALLOWED_PATHS` | `[]` | Paths accessible to tools |
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See [.env.example](.env.example) for full configuration options.
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## Development
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### Code Quality
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```bash
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# Type checking
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mypy src/
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# Linting
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ruff check src/ tests/
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# Auto-fix lint issues
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ruff check src/ tests/ --fix
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# Format code
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ruff format src/ tests/
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```
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### Testing
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```bash
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# Run all tests
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pytest tests/ -v
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# With coverage
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pytest tests/ --cov=src --cov-report=html
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```
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### Security Audit
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```bash
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# Check dependencies for CVEs
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pip-audit
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```
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## Architecture
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Webber uses a domain-based architecture with clean separation of concerns:
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```
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src/
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├── main.py # FastAPI app entry point
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├── shared/ # Cross-cutting infrastructure
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│ ├── base.py # BaseController, BaseSchema
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│ ├── config.py # Settings from pyproject.toml + env
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│ ├── logging.py # @logged decorator with timing
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│ └── exceptions.py # Exception hierarchy
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└── domains/ # Feature domains
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├── health/ # Health check endpoints
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├── agents/ # Agent orchestration
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└── tools/ # Tool execution (file, shell, search)
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```
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See [docs/architecture.md](docs/architecture.md) for detailed patterns and conventions.
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## API Endpoints
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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| `/` | GET | Service information |
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| `/health` | GET | Health check for monitoring |
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| `/docs` | GET | Interactive API documentation |
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## Docker
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```bash
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# Build
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docker build -t webber .
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# Run
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docker run -p 8086:8086 --env-file .env webber
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```
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The container includes a healthcheck that pings `/health` every 30 seconds.
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## Deployment
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Deployed via Gitea Actions CI/CD:
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1. Tag a release (`git tag v0.x.x && git push --tags`)
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2. Workflow builds and pushes Docker image
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3. Watchtower auto-deploys to production
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Production runs in Portainer `agents` stack on the `docker-dataplane` network.
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## Status
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**Alpha** - Core infrastructure is complete. Agent and tool implementations are in progress.
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- `webber-api/AGENTS.md` - API development guidelines
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- `webber-api/docs/` - Architecture and coverage docs
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- `webber-cli/README.md` - CLI usage guide
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## License
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