OwA — Ollama Workspace Agent

A local, privacy‑first coding agent that understands your codebase and edits files on your machine.
Runs entirely on your own hardware via Ollama — no cloud, no telemetry, no external API keys.

• Chat with your project using a local LLM
• Semantic search across your codebase
• Safe, review‑before‑run shell commands
• Git‑aware workflow (status, diff, log)
• Optional HTTP API for integration with other tools

Quick Start

1. Install Ollama and models

   # Install Ollama: https://ollama.com
   ollama pull llama3.1:8b
   ollama pull nomic-embed-text
   

2. Install OwA

   pip install ollama-workspace-agent
   # or, for an isolated install:
   pipx install ollama-workspace-agent
   

3. Run in your project

   cd your-project
   owa
   

4. Configure once

   On first run, type /setup and follow the prompts.
   Config is saved to ~/.config/owa/.env.

5. Start coding

   Try prompts like:

   • “Where is the auth logic implemented?”
   • “Add input validation to app/api.py and show me the diff.”
   • “Run the tests and summarize failures.”

Requirements

• Python 3.11+
• Ollama running on a reachable machine
• A chat model installed on Ollama, e.g. llama3.1:8b
• An embedding model installed on Ollama, e.g. nomic-embed-text

Configuration

Run /setup inside OwA, or manually create ~/.config/owa/.env:

# ~/.config/owa/.env

# Ollama on the same machine
LLM_BASE_URL=http://127.0.0.1:11434/v1
EMBEDDING_BASE_URL=http://127.0.0.1:11434

# Or Ollama on another machine in your LAN
# LLM_BASE_URL=http://192.168.1.50:11434/v1
# EMBEDDING_BASE_URL=http://192.168.1.50:11434

LLM_MODEL=llama3.1:8b
EMBEDDING_MODEL=nomic-embed-text

# Optional: protect the HTTP API
API_KEY=choose-a-private-api-key

You can also place a .env in your project root to override the global config for that project.

• LLM_BASE_URL uses Ollama’s OpenAI‑compatible /v1 API.
• EMBEDDING_BASE_URL uses Ollama’s native /api/embed API.
• If Ollama runs on the same machine, use 127.0.0.1 as the host.

Troubleshooting config

• Verify Ollama is running:

  curl http://127.0.0.1:11434/api/tags
  

  A successful response contains a models list.

• Ensure the models in your config are installed:

  ollama pull llama3.1:8b
  ollama pull nomic-embed-text
  

• For remote Ollama, make sure the host’s firewall allows port 11434.

CLI Commands

Indexing

OwA auto‑indexes your project on first run. Common directories are excluded automatically (node_modules, dist, build, .github, .git, .venv, etc.).

To exclude additional files or directories, create a .owaignore in your project root:

# .owaignore
secrets.json
fixtures/
*.min.js

The local index is stored in .owa/ and is automatically added to your .gitignore on first index.

Tools

The agent can call these workspace tools:

Example prompts:

• “Search for where we handle JWT expiration and suggest improvements.”
• “Patch app/api.py to add request validation using Pydantic.”
• “Run pytest -q and summarize failing tests.”
• “Show me the current git diff and explain what changed.”

API Mode

Run the HTTP API:

uvicorn app.api:app --host 127.0.0.1 --port 8000

Use the CLI as an API client:

owa --api-url http://127.0.0.1:8000

Or call directly with curl:

curl -X POST http://127.0.0.1:8000/chat \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key" \
  -d '{"message": "Explain the main entry point in this project"}'

Available endpoints: GET /health, GET /status, POST /chat, POST /chat/stream, POST /clear, POST /index.

When API_KEY is configured, include it in the X-API-Key header for every endpoint except /health.

Privacy & Safety

• All inference and embeddings run on your own Ollama server.
• No code or conversation leaves your machine unless you explicitly call a remote API.
• Shell commands require explicit user confirmation before execution.
• Sensitive paths and common noise directories are excluded from indexing by default.
• The project index lives in .owa/ and is automatically added to .gitignore.

Why OwA?

OwA is designed for developers who want:

• Fully local inference: No cloud dependencies; just Ollama + your models.
• Codebase awareness: Automatic indexing and semantic search over your repo.
• Safe editing: Patch/write files and run commands only after your approval.
• Simple deployment: Single Python package, minimal config, works on laptop or server.

Compared to heavier frameworks, OwA aims to be:

• Easy to install and run (pip install + owa)
• Transparent and hackable (clear project layout, simple agent loop)
• Focused on day‑to‑day coding tasks rather than complex orchestration

Project Layout

.
├── app/
│   ├── agent/       Agent orchestration and system prompt
│   ├── api.py       FastAPI endpoints and request models
│   ├── cli.py       CLI entry point
│   ├── config.py    Global config path resolution
│   ├── service.py   Shared chat, indexing, and status service
│   ├── indexer/     Code indexing and semantic search
│   ├── llm/         OpenAI-compatible Ollama client
│   └── tools/       Workspace tool implementations and registry
├── demo/            Small demo code
├── main.py          Thin shim for `python main.py`
├── CHANGELOG.md     Project change history
└── README.md        Project documentation

Development

Clone and install in editable mode:

git clone https://github.com/ZakaCoding/ollama-workspace-agent
cd ollama-workspace-agent
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Run tests:

pytest tests/ -v

Compile check:

python -m py_compile main.py app/agent/*.py app/indexer/*.py app/llm/*.py app/tools/*.py

Contributing

Contributions are welcome! Feel free to:

• Open issues for bugs, ideas, or questions
• Submit pull requests for improvements or new tools
• Share workflows or prompts that work well with OwA

License

Licensed under the MIT License. See LICENSE for details.