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DAIV

Open-source, self-hosted SWE agents for GitLab & GitHub

Turn issues into merge requests, answer review comments, and fix failing CI — with every agent running in a sandbox you control, the network egress you define, and the LLM provider you choose.

Python Version License CI

DAIV integrates directly with GitLab and GitHub through webhooks — no new tools to adopt, no context-switching. Beyond your Git workflow, DAIV plugs into your editor over MCP and ships with an optional self-hosted dashboard to chat with the agent, start and watch runs, schedule jobs, and review what changed. You host it, you pick the model, and every task executes in an isolated sandbox whose network access you define.

Three ways to put DAIV to work

In your Git platform — webhooks, zero setup

  • Issue Addressing — DAIV reads a labelled issue, proposes a plan, and — once you approve — opens a merge/pull request with the implementation.
  • Pull Request Assistant — answers reviewer comments, applies requested changes, and repairs failing CI/CD pipelines, all inside the merge/pull request thread.
  • Slash Commands & Skills — invoke /plan, /code-review, /help, and your own custom skills straight from issues and merge requests.

From your editor and pipelines

  • MCP Endpoint — connect Claude Code, Cursor, or Codex CLI over the Model Context Protocol and delegate tasks without leaving your editor.
  • Jobs API — trigger agents programmatically from CI, scripts, or other tools, then poll for the result.

From the dashboard

  • Sessions — a unified workspace and history: chat with the agent live, start background runs, and see every execution — webhook, API, MCP, scheduled, or manual — in one list, with retries.
  • Scheduled Jobs — run agents on any cron schedule: dependency audits, code-quality scans, stale-branch cleanup, and more.
  • Sandbox Environments — define a reusable runtime once: base image, CPU/memory, network egress policy, and encrypted secrets, scoped to the repositories you choose.
  • Per-run model & effort — pick the LLM and thinking effort for each run.
  • Notifications — know the moment work finishes, via the in-app bell, email, or Rocket Chat.
  • Merge Metrics — track code velocity with commit-level DAIV-vs-human attribution.

Quick example

  1. You create an issue: "Add rate limiting to the API endpoints"
  2. DAIV posts a plan: Analyzes the codebase and proposes implementation steps
  3. You approve: Comment @daiv proceed
  4. DAIV implements: Creates a merge request with the code changes
  5. Reviewer asks for changes: "@daiv use Redis instead of in-memory storage"
  6. DAIV updates the code: Modifies the implementation and pushes

Under the hood

DAIV is powered by Deep Agents, a general-purpose deep-agent framework built on LangGraph with sub-agent spawning, a middleware stack, and a virtual filesystem. On top of it, DAIV adds:

  • Subagents — specialized agents for fast codebase exploration and complex multi-step tasks.
  • Sandbox — secure command execution for tests, builds, linters, and package management inside an isolated Docker container.
  • MCP Tools — external integrations over the Model Context Protocol, such as Sentry for error tracking.
  • Monitoring — trace every agent execution with LangSmith to analyze performance and spot issues.
  • Scalable Workers — background workers scale horizontally by adding replicas, with a dedicated scheduler for recurring jobs.
  • LLM Providers — run on OpenRouter, Anthropic, OpenAI, or Google — your keys, your choice.

For how these pieces fit together — the middleware stack, skill loading, and how a single run is assembled — see Agent Architecture.

Supported platforms

  • GitLab

    GitLab.com and self-hosted instances. Full feature support.

  • GitHub

    GitHub.com and GitHub Enterprise. Full feature support.

Get started

  • Deploy DAIV

    Install and run DAIV with Docker Compose or Docker Swarm.

    Deployment

  • Connect your repository

    Link DAIV to your GitLab or GitHub repository.

    Platform Setup

  • Choose your LLM

    Configure OpenRouter, Anthropic, OpenAI, or Google as your provider.

    LLM Providers

  • Customize behavior

    Tailor DAIV to your team with .daiv.yml, skills, and MCP tools.

    Repository Config

  • Use from your editor

    Connect Claude Code, Cursor, or Codex CLI to DAIV via MCP.

    MCP Endpoint

  • Automate recurring tasks

    Run agents on a schedule — dependency audits, cleanup, reports, and more.

    Scheduled Jobs

Contributing

DAIV is open-source under Apache 2.0. See Community for how to contribute, report issues, and get support.