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The self-hosted AI workflow automation platform for Git repositories.
- 🔍 Review pull requests
- 🧪 Generate tests
- ✏️ Improve issues
- 🤖 Turn issues into pull requests
- 🎬 Create and run E2E tests
- 📝 Keep documentation in sync with the code
- 🌍 Keep translations in sync across locale files
- 💬 Answer questions inside code reviews
- 📊 Observe workflows via Prometheus metrics
Every engineering team has a list of things they know should happen:
- Pull requests should be reviewed carefully
- Bugs should get regression tests
- Issues should have acceptance criteria
- Documentation should stay up to date with the code
- Preview environments should be cleaned up
- Small maintenance tickets should eventually get implemented
Nobody disagrees with any of those ideas. The problem is that these tasks are:
- Uncomfortable
- Repetitive
- Difficult to prioritize
- Easy to postpone when deadlines get tight
AI-Git-Bot exists to turn these engineering chores into repeatable workflows that happen automatically inside your Git platform.
- No new development process.
- No migration project.
- No vendor lock-in.
Just better engineering hygiene through automation.
You may not fit neatly into a single category — many teams overlap across these concerns. Here are three illustrative examples:
Many teams choose Gitea because they want ownership of their source code and infrastructure.
Unfortunately, most AI products focus primarily on GitHub.
AI-Git-Bot brings:
- AI code reviews
- AI-generated tests
- AI issue authoring
- AI coding workflows
- AI documentation upkeep
- Interactive PR discussions
directly into Gitea.
Use OpenAI, Claude, Gemini or local Ollama models without forcing developers to leave their existing workflows.
👉 Think: "Copilot-style workflow automation for Gitea."
Many organizations cannot use cloud-only AI products due to compliance, privacy or contractual requirements.
AI-Git-Bot supports:
- Ollama
- llama.cpp
- Self-hosted Git platforms
- Private networks
- Provider-independent architectures
Source code, prompts, credentials and models remain under your control.
👉 Think: "AI workflow automation without handing your repositories to a SaaS vendor."
Every repository accumulates engineering chores:
- Reviews
- Tests
- Documentation
- Acceptance criteria
- Follow-up fixes
AI-Git-Bot turns those activities into repeatable workflows triggered by events your team is already producing:
- Pull request opened
- Pull request reopened
- New commits pushed (opt-in per bot)
- Reviewer requested
- Issue assigned
@botmentioned
👉 Think: "The AI teammate that never forgets the boring but important work."
💡 Already using GitHub Copilot?
Great.
Copilot helps developers write code faster. AI-Git-Bot helps teams automate reviews, tests, issues and pull-request workflows.
Many teams use both.
| AI providers | Git platforms |
|---|---|
| Anthropic (Claude) | Gitea (self-hosted) |
| OpenAI (+ OpenAI-compatible APIs) | GitHub / GitHub Enterprise |
| Google AI / Gemini | GitLab (gitlab.com & self-managed) |
| Ollama (local LLMs) | Bitbucket Cloud |
| llama.cpp (local GGUF models) |
Unlike most AI coding tools, AI-Git-Bot is not tied to a specific Git platform or AI provider.
Fully self-hostable. Your code can stay inside your infrastructure.
AI-Git-Bot lives where your developers already work:
- GitHub
- GitHub Enterprise
- Gitea
- GitLab
- Bitbucket Cloud
- Assign it a review.
- Assign it an issue.
- Mention it in a comment.
- The bot responds directly inside your Git platform. No extra dashboard.
No browser extension.
No Slack bot to babysit.
🎥 Watch the PR workflows in action:
AI-Git-Bot reviews pull requests and leaves actionable inline feedback directly on the diff.
📸 Screenshots: reviews, conversations, and coding agents across platforms
Gitea: 
GitHub: 
GitLab: 
Bitbucket: 
Coding agent (GitHub): 
Mention the bot anywhere in a pull request discussion.
@bot can you explain why this implementation might fail?
The bot answers directly in the thread and keeps conversation context.
📸 Screenshot: Inline comment in Gitea
Gitea: 
Assign an PR to a bot and it can generate a Playwright test suite for the changes, deploy a preview environment, run the tests against that preview, and post the results back to the PR.
📸 Screenshot: E2E test generation as part of a PR
Gitlab: 
Assign an issue to a bot with the Coding Agent issue workflow and it can create an implementation pull request on your behalf.
📸 Screenshot: Issue implementation agent
Gitlab: 
| Workflow | Trigger | Result |
|---|---|---|
| PR Review | PR opened or review re-requested | Review comments and findings |
| Interactive Q&A | @bot mention in PR comments |
Context-aware conversation |
| Issue → Code | Issue assigned to a bot with the Coding Agent issue workflow | Pull request |
| Issue → Better Issue | Issue assigned to a bot with the Writer Agent issue workflow | Structured issue with acceptance criteria |
| Issue Workflows | Configurable per bot | Pluggable issue-assigned behavior, independent from PR workflows |
| Unit Test Generation | PR opened or command triggered | Generated tests committed to branch |
| Full-Stack QA | PR opened | Playwright suite executed against preview environment |
| README Sync | PR opened or command triggered | Documentation updated to match code changes |
| i18n Coverage | PR opened or command triggered | Missing translations drafted across locale files |
| PR Re-Review | Force-push or review request | Updated analysis |
| Workflow Automation | Git events | Automated engineering chores |
| Prometheus Metrics | Built-in /actuator/prometheus endpoint |
Operational metrics for reviews, findings, AI usage, errors, and tool calls |
| Outgoing Webhooks | Signed event push to external systems | Durable, retried HMAC-signed notifications for workflow runs, findings, and issue assignments |
Many AI development tools focus on helping developers write code.
AI-Git-Bot focuses on helping teams ship software more consistently.
Instead of only answering:
"How do we write code faster?"
AI-Git-Bot tries to answer:
"How do we make sure important engineering work doesn't get skipped?"
Examples include:
- Reviewing every pull request
- Adding regression tests
- Maintaining E2E coverage
- Keeping documentation in sync with the code
- Keeping translations in sync across locale files
- Improving issue quality
- Validating preview deployments
- Automating recurring engineering tasks
GitHub Copilot is excellent.
In fact, many teams will use both tools together.
A realistic workflow looks like this:
Developer writes code with Copilot
↓
Pull Request opens
↓
AI-Git-Bot reviews it
↓
AI-Git-Bot generates tests
↓
AI-Git-Bot updates the docs
↓
AI-Git-Bot validates deployment
↓
Findings posted
Copilot helps developers write code faster.
AI-Git-Bot helps teams automate the work surrounding the code.
These goals complement each other.
Automatically review pull requests and provide:
- Summary findings
- Inline comments
- Suggested improvements
- Follow-up discussions
Assign a bot with the Writer Agent issue workflow to an issue.
The bot transforms rough requirements into structured engineering work items containing:
- Background
- Requirements
- Acceptance criteria
- Implementation notes
Generate white-box unit tests automatically based on pull request changes.
Tests can be validated using your project's own tooling before being committed.
The Full-Stack QA workflow can:
- Generate Playwright tests
- Deploy a preview environment
- Execute the suite
- Publish the results back to the pull request
- Clean up resources when the PR is closed
Keep project documentation in step with the code a pull request changes.
The workflow detects when a PR makes the README or other Markdown docs
inaccurate or outdated, then updates, adds, or removes the affected
documentation files within a configured scope and posts a short summary.
Markdown-only; every changed file stays inside the documentation patterns
you configure. Runs on PR open or on @bot regenerate-readme <instruction>.
Keep translations in sync across locale files when a pull request changes user-facing strings.
The workflow compares every locale file against a configurable baseline locale
and, when a translation is missing keys the baseline defines (added or changed
strings) or still carries keys the baseline deleted, drafts the missing
translations per locale and removes the stale keys. Supports both
messages_*.properties and i18n/*.json files; every changed file stays inside
the patterns you configure. Runs on PR open or on
@bot regenerate-i18n <instruction>.
Assign a bot with the Coding Agent issue workflow to an issue.
The bot:
- Reads the issue
- Clones the repository
- Implements the change
- Runs project validation
- Opens a pull request
For a quick local test, run AI-Git-Bot with a single Docker command using the built-in H2 database and default settings:
docker run -p 8080:8080 tmseidel/ai-git-bot:latestThe image is published as a multi-arch manifest for linux/amd64 and
linux/arm64, so the same command works on x86-64 servers, Apple Silicon,
AWS Graviton and 64-bit Raspberry Pi. See
Architectures for details.
Then:
- Open
http://localhost:8080 - Create your administrator account
- Create an AI Integration
- Create a Git Integration
- Create a Bot
- Configure the webhook
- You're done
For a configured production or long-term deployment, we recommend the Docker Compose approach, which gives you full control over configuration:
git clone https://github.com/tmseidel/ai-git-bot.git
cd ai-git-bot
docker compose up --build -dSee Deployment Guide for detailed configuration instructions.
Start with:
Start with:
Start with:
- Deployment Guide
- Ollama Integration Guide (or vLLM with OpenAI-compatible API)
Start with:
Start with:
The documentation is organized by audience in the Documentation Hub:
| You are a… | Start here |
|---|---|
| 👤 User — a bot is already set up, you just use the Git platform | Using the Bot |
| 🛠️ Administrator — you set up the software, bots, and workflows | Deployment · Admin Guide |
| 🧪 Tester — you want to try out features safely | Testing Guide |
| 💻 Developer — you work with the code | Local Development · Architecture |
- GitHub
- GitHub Enterprise
- Gitea
- GitLab
- Bitbucket Cloud
The project ships extensive system tests and sample environments to make validation and troubleshooting easier.
Bug reports are always welcome.
- 🔒 AES-256-GCM secret encryption
- 🤖 Multi-provider AI support
- 🏢 Multi-platform Git support
- 🧠 Local LLM support
- 🔌 MCP integration
- 🧪 System-tested workflows
- 🐳 Docker-first deployment
- 🌍 Self-hostable end-to-end
- ⭐ >100 GitHub stars
- 🚀 >15 releases
- 🐳 Docker image available
- 🌍 Users across GitHub, Gitea, GitLab and Bitbucket
docker pull tmseidel/ai-git-bot:latestAI-Git-Bot is not another coding assistant.
It's a self-hosted automation layer for software delivery workflows.
If your team already knows what good engineering practices look like—but struggles to do them consistently—AI-Git-Bot was built for exactly that problem.
Wire one bot.
Let the chores take care of themselves.
🚀 Happy shipping.