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Quick Start

Dev Agents needs three things: an LLM API key, a local git checkout to analyze, and at least one entrypoint to talk to it through. Everything else is optional.

1. Configuration

Generate a .env with the Setup Wizard, or start from the example:

wget -O .env https://raw.githubusercontent.com/codeligence/dev-agents/main/.env.example

The minimum:

ANTHROPIC_API_KEY=sk-ant-...              # or OPENAI_API_KEY / AWS credentials
LLM_MODEL_LARGE=anthropic:claude-sonnet-5
LLM_MODEL_SMALL=anthropic:claude-haiku-4-5
GIT_REPO_PATH=/code                       # path to the checkout to analyze

Model strings follow Pydantic AI (provider:model-name). Add Slack tokens and provider credentials as needed — see environment variables.

2. Run

docker run --rm -it --env-file=.env \
  -v /path/to/your/repo:/code \
  codeligence/dev-agents

Add -v (after the image name) for verbose logs.

Requires Python 3.11+.

git clone https://github.com/codeligence/dev-agents.git
cd dev-agents
cp .env.example .env      # then edit
pip install -e ".[prod]"  # or ".[all]" for dev tooling
dev-agents

3. Talk to it

On startup, Dev Agents detects which services are configured and starts them all in parallel: Slack, platform services, NATS, the HTTP entrypoints (AG-UI and the OpenAI-compatible API), and — when stdin is a terminal — an interactive CLI chat.

dev-agents                      # interactive chat + every configured service
dev-agents --prompt "How does the auth flow work?"   # single prompt, then exit
dev-agents -v                   # verbose logging

In Slack:

@DevAgents how does the authentication flow work?

Next steps