Not affiliated with LangChain. See DISCLAIMER.md.

---

What it does

Four harnesses, one shared foundation:

• 🔍 MR Review — fires on webhook events, reads the diff, drafts comments, publishes them. No polling, no manual trigger.
• 🛠️ Work Item Resolve — assign a GitLab issue or task and get an agent that runs inside its own Kata Containers VM (a dedicated guest kernel per sandbox, not just syscall interception), opens a draft MR, and keeps responding to follow-up comments on the same branch.
• 📋 Task Resolve — the same sandboxed loop, started from a free-text instruction instead of a GitLab issue — on demand or on a recurring schedule.
• 💬 Chat — an interactive, GitLab-aware assistant with real tool access: GitLab data, a headless browser, its own review/chat history; sensitive tool calls pause for explicit human approval before running.

Shared foundation: every drafted comment is screened before it posts; a run that misbehaves (repeats a call, loops, ends on a question, skips a check) gets caught and corrected mid-run — backed by persistent checkpoints, so a run resumes instead of restarting from scratch.

Full breakdown: Features · Architecture

Screenshots

More in the screenshots gallery.

Roadmap

• Automatic MR reviews, issue-to-draft-MR, on-demand tasks, and a GitLab-aware chat
• Isolated sandbox for code-changing runs, with human approval before Chat takes sensitive actions
• Project memory that carries across reviews, and crashed runs that resume instead of restarting
• Recurring scheduled tasks
• GitHub support: assign an issue to the bot, get a draft PR (#35)
• Project memory that keeps itself up to date, with visibility into what it learned (#2)
• A self-hosted memory engine that links related facts and retrieves them by relevance (#1)
• Web search, so agents can look things up instead of only fetching a known page (#24)

Everything else is tracked in open issues.

Stack

• Frontend — React admin UI (Dashboard, Threads, Chat, Workflows) for monitoring runs and chatting directly with the agent
• Backend — Elysia API server running a LangGraph agent with persistent checkpoints
• Agent — Multi-provider LLM (OpenRouter, Gemini, Groq, Ollama, or sglang) with GitLab MCP tools and skill-based workflows
• Queue — BullMQ; debounced re-reviews, capped concurrency, live queue state in the UI
• Memory — context engineering for GitLab: durable, project-scoped facts learned across reviews; personal memory in Chat

Quick Start

Prerequisites: Node.js 22+, Docker, a GitLab PAT (api scope), a GitLab OAuth app, and an OpenRouter/Gemini/Groq key or a local Ollama/sglang instance.

npm install
cp backend/.env.example backend/.env
# fill in GITLAB_PAT, GITLAB_OAUTH_CLIENT_ID/SECRET, SESSION_SECRET, SECRETS_ENCRYPTION_KEY,
# ADMIN_GITLAB_USERNAMES, and one of OPENROUTER_API_KEY / GEMINI_API_KEY / GROQ_API_KEY / OLLAMA_BASE_URL / SGLANG_BASE_URL
npm run dev

Frontend at http://localhost:5173, API at http://localhost:3698.

Full walkthrough — GitLab OAuth app setup, webhook config, per-repo .harness.yml: Getting Started.

Deployment & CI/CD

Self-hosted via Docker Compose; production deploys are automated through GitLab CI on push. See Deployment for server setup, and sglang Deployment if self-hosting the LLM backend.

The Story

Curious how this got built? The Story So Far.

---

"LangGraph" is a trademark of LangChain, Inc., used here under nominative fair use to describe the framework this project is built on. This project is independent and not affiliated with LangChain, Inc. — see DISCLAIMER.md. Licensed under the MIT License.