AutoBot is a self-hosted AI platform you own — not a service you rent.
Feed it your docs, your codebase, your business knowledge. Plug in any LLM you trust — local or hosted. Your data stays on your machines. Your AI stays yours.
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AutoBot is infrastructure you own, not a subscription. A small, solid core. A management layer that runs the hard infrastructure for you. Modules you install on top. Deploy it once, feed it your knowledge, connect whatever LLM you trust, and run it forever — on your terms.
| What you get | What stays yours |
|---|---|
| Chat interface | Your prompts |
| RAG knowledge base | Your documents |
| Fleet management | Your infrastructure |
| Module ecosystem | Your data |
AutoBot is three layers, bottom to top:
- Platform Core — small, solid, and yours. Chat and streaming, a RAG knowledge base backed by a knowledge graph (your institutional memory), an LLM gateway with provider fallback, local inference, hooks, and governance (auth, RBAC, review gates, budgets). It changes slowly so everything above can depend on it.
- Management Layer — Service Lifecycle Manager (SLM). Private AI has a lot of moving infrastructure: vector DB, cache, database, inference, and background workers, often across several machines. The Service Lifecycle Manager (SLM) owns the full lifecycle — deploy → operate → scale — so you can run it in production without becoming a full-time operator. (SLM means Service Lifecycle Manager, never "small language model.")
- Modules — installable capabilities built on the core's bones. A module inherits institutional memory, local inference at zero marginal cost, hooks, and governance, so it stays small relative to what it delivers. Modules include AutoBot LLC (an autonomous agent-company), Codebase Analytics, and the Transcriber.
→ Full picture: The AutoBot Platform Model
git clone https://github.com/mrveiss/AutoBot-AI.git
cd AutoBot-AIcp .env.example .env
docker compose up -dVisit http://localhost in your browser. AutoBot is ready to use.
AutoBot is a self-hosted AI platform: conversational AI, a private knowledge base, and the machinery to deploy and operate it all on your own hardware. You keep complete control:
- Unified Self-Hosted Dashboard — manage AI, infrastructure, fleet operations, and analytics from one place on your servers
- Natural Language Control — issue commands in plain English; AutoBot handles the complexity locally
- Self-Hosted Knowledge Bases — build knowledge bases from your docs, code, runbooks, and workflows — RAG-indexed and stored locally
- Vision Processing — analyze screenshots and diagrams locally to guide decisions
- Self-Hosted Fleet Management — deploy, operate, and scale multi-server stacks with the Service Lifecycle Manager (SLM)
- Installable Modules — extend the platform with AutoBot LLC (an autonomous agent-company), Codebase Analytics, the Transcriber, and more
- Complete Data Privacy — full data control, no external dependencies, runs entirely on your hardware, no vendor lock-in
→ Browse everything in the Capability Catalog.
| Component | Minimum | Recommended |
|---|---|---|
| CPU | 4 cores | 8+ cores |
| RAM | 8 GB | 16+ GB |
| Storage | 20 GB SSD | 50+ GB SSD |
| GPU | None (CPU-only mode) | NVIDIA GPU for faster inference |
| OS | Ubuntu 20.04+ / Debian 11+ | Ubuntu 22.04 LTS |
| Docker | 24.0+ | 25.0+ |
| Feature | Capability |
|---|---|
| Chat | Multi-turn conversations with function calling, streaming responses |
| Knowledge Bases | RAG-powered retrieval from documents, code, infrastructure docs |
| Knowledge Graph | Institutional memory shared across sessions and modules |
| LLM Gateway | One place to plug in any model, with provider routing and fallback |
| Workflow Builder | Visual and code-based workflow creation |
| Vision | Image/screenshot analysis for troubleshooting |
The Service Lifecycle Manager (SLM) is how AutoBot runs the infrastructure behind private AI for you. It owns the full lifecycle of the stack — vector DB, cache, database, inference, and workers — across one or more machines:
| Stage | What the SLM does |
|---|---|
| Deploy | Stand up the full stack from a blank host or across a fleet (Ansible) |
| Operate | Upgrades, health monitoring, recovery, certificate rotation, configuration |
| Scale | Add fleet nodes, add NPU workers, assign roles, provision new capacity |
The SLM dashboard lives at http://localhost/slm. See SLM deployment.
Modules are large capabilities you install on the core. Because they inherit the core's institutional memory, local inference, hooks, and governance, they stay small relative to what they deliver.
- AutoBot LLC — an autonomous agent-company: define a company of AI agents (and human co-workers), give them goals and a backlog, schedule them to work autonomously, and govern their spend. See AutoBot LLC.
- Codebase Analytics — understand codebases, extract insights, and identify risks entirely on your own hardware: code structure analysis, risk detection, and dependency insights, with no source ever leaving your machines.
- Transcriber — a general-purpose audio transcription module: organize projects and recordings, transcribe locally, and export transcripts.
→ Browse all capabilities in the Capability Catalog.
AutoBot keeps your AI and your data on infrastructure you control:
- Data privacy & compliance — your data never leaves your hardware; supports HIPAA, GDPR, and SOC2 requirements
- Predictable cost — local inference runs at zero marginal cost per request; no per-request fees
- Full control — run custom models, modify the code, no vendor lock-in
- Low latency — local inference and command execution, no network round-trip
- Scale on your terms — grow within your own infrastructure with the SLM
📚 Learn more: Why Self-Hosted Infrastructure Automation?
Full diagrams (data flows, deployment topologies, sequence diagrams): docs/architecture/system-diagram.md
The conceptual model (core → SLM → modules): docs/architecture/PLATFORM_MODEL.md
Feature walkthroughs and demo recording scripts: docs/DEMOS.md
graph TB
User["👤 User<br/>(Browser)"]
Frontend["🎨 Frontend<br/>(Vue.js)"]
Backend["⚡ Backend<br/>(FastAPI)"]
Redis["🔴 Redis<br/>(Cache/Queue)"]
PostgreSQL["🐘 PostgreSQL<br/>(Data)"]
ChromaDB["🔍 ChromaDB<br/>(Vectors)"]
Inference["🧠 Local LLM<br/>(Ollama inference)"]
SLM["🛠️ Service Lifecycle Manager<br/>(deploy · operate · scale)"]
Ansible["🔧 Ansible<br/>(Fleet Ops)"]
Browser["🌐 Browser Automation<br/>(Chromium)"]
User -->|HTTP/WS| Frontend
Frontend -->|API| Backend
Backend -->|Read/Write| Redis
Backend -->|Query| PostgreSQL
Backend -->|Vector Search| ChromaDB
Backend -->|Inference| Inference
Backend -->|Control| Browser
SLM -->|Manages lifecycle of| Backend
SLM -->|Executes| Ansible
style User fill:#e1f5ff
style Frontend fill:#f3e5f5
style Backend fill:#fff3e0
style Redis fill:#ffebee
style PostgreSQL fill:#e8f5e9
style ChromaDB fill:#fce4ec
style Inference fill:#f1f8e9
style SLM fill:#ede7f6
style Ansible fill:#ede7f6
style Browser fill:#e0f2f1
Fastest way to get started. Includes all services pre-configured.
docker compose up -dFor development or custom setups. See INSTALL.md.
For contributing to AutoBot:
docker compose -f docker-compose.dev.yml up -dAutoBot runs as a coordinated set of services:
| Service | Role | Port |
|---|---|---|
| Frontend | Vue.js UI, TLS termination | 80, 443 |
| Backend | FastAPI API server | 8001 |
| SLM | Service Lifecycle Manager — fleet deploy/operate/scale | (via dashboard /slm) |
| Redis | Cache, message queue | 6379 |
| PostgreSQL | Relational database | 5432 |
| ChromaDB | Vector embeddings store | 8100 |
| Inference (Ollama) | Local LLM inference | 11434 (optional) |
| Prometheus | Metrics collection | 9090 (optional) |
| Grafana | Monitoring dashboards | 3000 (optional) |
Once running, navigate to http://localhost to access:
- Chat Interface — start conversing with AutoBot about your knowledge and infrastructure
- Knowledge Bases — upload and manage documents, codebases, runbooks
- Workflows — create automated tasks and operations
- Fleet Management — view and orchestrate multiple servers
- Analytics — monitor system health, performance, and activity
The Service Lifecycle Manager (SLM) — AutoBot's management layer for deploying, operating, and scaling your AI infrastructure (vector DB, cache, database, inference, and workers) — lives at http://localhost/slm.
# In the AutoBot chat:
# "Deploy the latest application version to all production servers"
# AutoBot handles the Ansible orchestration automatically# Ask AutoBot to analyze your codebase:
# "What are the critical dependencies in the auth module?"All configuration uses environment variables in .env. See .env.example for all options.
Key settings:
AUTOBOT_DEPLOYMENT_MODE—hybridordistributedAUTOBOT_LLM_PROVIDER—ollama(default) or othersAUTOBOT_SINGLE_USER_MODE—true(development) orfalse(multi-user)
AutoBot's LLM gateway supports local and hosted providers (Ollama, OpenAI, Anthropic, Groq, vLLM, HuggingFace, and OpenRouter). When a model hits a rate limit or quota cap, requests automatically route to a backup model via configurable fallback chains.
- LLM Fallback Configuration — configure fallback chains, tune rate limits, and troubleshoot quota issues
- Environment Variables Reference — full variable listing including
AUTOBOT_FALLBACK_CHAIN_*andAUTOBOT_LLM_RL_*
We welcome contributions! Whether you're fixing bugs, adding features, or improving documentation:
- Check out CONTRIBUTING.md for guidelines
- Look for issues tagged
good-first-issueif you're new - Join the community discussion in GitHub Discussions
- 💬 Questions? Start a discussion in GitHub Discussions
- 🐛 Found a bug? Open an issue
- 💡 Have an idea? Share it in Discussions → Ideas
AutoBot is open source and we welcome contributions from the community! Whether you're fixing bugs, adding features, or improving documentation, your contributions help build a better self-hosted AI platform for everyone.
Start with good-first-issue labeled tasks. These are:
- Self-contained and beginner-friendly
- Expected to take less than 2 hours
- Perfect for learning the codebase
- Great introduction to our contribution process
Find issues matching your skill area:
-
🎨 Frontend (Vue.js, TypeScript, CSS, Vite)
-
🔧 Backend (FastAPI, Python, APIs, Databases)
-
🚀 Infrastructure & DevOps (Docker, Ansible, CI/CD, Deployment)
-
📚 Documentation (Guides, Examples, README, API Docs)
-
🧪 Testing & QA (Unit Tests, Integration Tests, Test Coverage)
Detailed contribution process, code style guidelines, and development setup: → Read CONTRIBUTORS.md
Support AutoBot's development in multiple ways:
- GitHub Sponsors — Recurring sponsorship with direct support and updates
- Ko-fi — One-time or recurring donations for maintenance and features
Your support helps us:
- Maintain and improve the codebase
- Add new features and capabilities
- Expand documentation and examples
- Grow the community
- Enable contributors to participate
AutoBot is open source under the Apache License 2.0. Free forever for everyone — use it, modify it, distribute it, and profit from it, no permission or fees required. Attribution is required (see NOTICE), and the "AutoBot" / "mrveiss" names may not be used to endorse derived products without permission.
Free forever for individuals and hobbyists. Nothing you can do with AutoBot today becomes paid tomorrow. Production support is how we keep it funded — see FUNDING.md.
AutoBot is free to run forever. Paid production support (Community / Production Support / Compliance Pack) is how the project stays funded — tier definitions, sponsorship links, and the support contact path live in FUNDING.md.
- Multi-user authentication and RBAC
- Kubernetes orchestration support
- Advanced analytics dashboards
- Module ecosystem and registry
- Mobile companion app
- Cloud deployment templates
- Managed hosting option
- Enterprise features (SAML, audit logs)
- Frontend: Vue.js, TypeScript, Vite
- Backend: FastAPI, Python, AsyncIO
- Database: PostgreSQL, Redis, ChromaDB
- Inference: Ollama (local), LangChain
- Orchestration: Ansible, Docker, Kubernetes (coming)
- Infrastructure: Docker Compose, systemd
- Documentation Index — the full documentation home
- The AutoBot Platform Model — core → SLM → modules
- Capability Catalog — everything AutoBot can do
- INSTALL.md — detailed setup instructions
- CONTRIBUTING.md — get involved
Current Version: v1.5.0 (Active Development)
AutoBot is actively developed and used for self-hosted AI and infrastructure automation. It's suitable for:
- ✅ Self-hosted deployments on your infrastructure
- ✅ Development and testing environments
- ✅ Learning AI-driven automation
- 🚀 Production use (with monitoring and backups)
Made with ❤️ by the AutoBot community
