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ORBIT: Enterprise AI Gateway

Open Retrieval-Based Inference Toolkit

Connect 20+ LLM providers and enterprise data through one governed API.

ORBIT is a self-hosted gateway that eliminates vendor lock-in and integration glue code. It unifies LLMs, databases, APIs, and voice engines behind one OpenAI-compatible interface and MCP endpoint, letting teams standardize on one integration surface while keeping security, compliance, and operational controls consistent.

Try the Sandbox | API Reference | Docker Guide

Try It Yourself!

orbit-demo.mp4

Star ORBIT on GitHub to follow new adapters, releases, and production features.

Officially backed by Schmitech, the ORBIT service provider for enterprise deployment and support.


Real-world example: PoliceStats.ca uses ORBIT to power a public chat over Canadian municipal police open data. Users ask in plain language about auto theft, break-ins, crime by neighbourhood, and cross-city comparisons.


⚑ Get Value in 60 Seconds

A) Try hosted API now

curl -X POST https://orbit.schmitech.ai/v1/chat \
  -H 'Content-Type: application/json' \
  -H 'X-API-Key: default-key' \
  -H 'X-Session-ID: test-session' \
  -d '{
    "messages": [{"role": "user", "content": "What is ORBIT?"}],
    "stream": false
  }'

B) Run ORBIT locally with Docker Compose (recommended β€” includes Ollama)

git clone https://github.com/schmitech/orbit.git && cd orbit/docker
docker compose up -d

# Wait for services to start, then test
curl -X POST http://localhost:3000/v1/chat \
  -H 'Content-Type: application/json' \
  -H 'X-API-Key: default-key' \
  -H 'X-Session-ID: local-test' \
  -d '{
    "messages": [{"role": "user", "content": "Summarize ORBIT in one sentence."}],
    "stream": false
  }'

For GPU acceleration (NVIDIA): docker compose -f docker-compose.yml -f docker-compose.gpu.yml up -d

C) Run ORBIT from the pre-built image (server only; point it at your own Ollama)

docker pull schmitech/orbit:basic
docker run -d --name orbit-basic -p 3000:3000 schmitech/orbit:basic

If Ollama runs on your host (e.g. port 11434), add -e OLLAMA_HOST=host.docker.internal:11434 so the container can reach it. The image includes simple-chat only; for the full stack (Ollama + models), use option B or the Docker Guide.


πŸ–₯️ Admin Panel

ORBIT ships with a built-in admin panel for managing users, API keys, personas, and live server monitoring β€” all from your browser at /admin.

orbit-admin.mp4


πŸš€ Key Capabilities

  • Unified API: Switch OpenAI, Anthropic, Gemini, Groq, or local models (Ollama/vLLM) by config.
  • Agentic AI & MCP: Compatible with Model Context Protocol (MCP) for tool-enabled agent workflows.
  • Native RAG: Connect Postgres, MongoDB, Elasticsearch, or Pinecone for natural-language data access.
  • Voice-First: Real-time, full-duplex speech-to-speech with interruption handling via PersonaPlex.
  • Governance Built In: RBAC, rate limiting, audit logging, and circuit breakers.
  • Privacy First: Self-host on your own infrastructure for full data control.

πŸ†š Why Enterprise Teams Choose ORBIT

If you use... You often get... ORBIT gives you...
Single-provider SDKs Vendor lock-in and provider-specific rewrites One OpenAI-compatible API across providers
Basic LLM proxy only Model routing, but no data connectivity Unified model + retrieval + tooling gateway
RAG-only framework Strong retrieval, weak multi-provider inference control Native RAG with multi-provider and policy controls
In-house glue scripts Fragile integrations and high ops cost A production-ready gateway with RBAC, limits, and logs

🏒 Enterprise Readiness

  • Deployment Flexibility: Run ORBIT in your own environment for strict data-boundary requirements.
  • Operational Control: Standardize access, traffic policies, and audit trails behind one gateway.
  • Architecture Fit: Integrates with existing data systems, identity patterns, and model providers.
  • Service Backing: Schmitech provides enterprise onboarding, deployment support, and ongoing operations guidance.

🎯 Common Use Cases

  • Enterprise RAG: Query SQL, NoSQL, and vector stores with one natural-language API.
  • Provider Failover: Route between Ollama, vLLM, OpenAI, Anthropic, Gemini, Groq, etc. without rewrites.
  • Voice Agents: Build full-duplex speech-to-speech experiences with interruption handling.
  • MCP Tooling Layer: Expose data and actions to agentic apps through MCP compatibility.

πŸ› οΈ One Gateway, Many Clients

Client Link Description
Web Chat ORBIT Chat React UI.
CLI pip install schmitech-orbit-client Chat directly from your terminal.
Mobile ORBIT Mobile iOS & Android app built with Expo.
SDKs Node SDK Or use any standard OpenAI-compatible SDK.

πŸ“¦ Deployment

Docker Compose (Fastest Path)

git clone https://github.com/schmitech/orbit.git && cd orbit/docker
docker compose up -d

This starts ORBIT + Ollama with SmolLM2, auto-pulls models, and exposes the API on port 3000. Connect orbitchat from your host: ORBIT_ADAPTER_KEYS='{"simple-chat":"default-key"}' npx orbitchat

Pre-built image only (server + your own Ollama): docker pull schmitech/orbit:basic then docker run -d --name orbit-basic -p 3000:3000 -e OLLAMA_HOST=host.docker.internal:11434 schmitech/orbit:basic if Ollama runs on the host.

See the full Docker Guide for GPU mode, volumes, single-container run, and configuration.

Stable Release (Recommended for Production)

curl -L https://github.com/schmitech/orbit/releases/download/v2.6.3/orbit-2.6.3.tar.gz -o orbit-2.6.3.tar.gz
tar -xzf orbit-2.6.3.tar.gz && cd orbit-2.6.3

cp env.example .env && ./install/setup.sh
source venv/bin/activate
./bin/orbit.sh start && cat ./logs/orbit.log

πŸ“ˆ Project Momentum


🧩 Supported Integrations

Inference: OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, Mistral, AWS Bedrock, Azure, Together, Ollama, vLLM, llama.cpp.

Data Adapters: PostgreSQL, MySQL, MongoDB, Elasticsearch, DuckDB, Chroma, Qdrant, Pinecone, Milvus, Weaviate.


πŸ“š Resources & Support

⭐ Help ORBIT grow: Star the repo to support the project and get notified of new adapters!

πŸ“„ License

Apache 2.0 – see LICENSE.

About

An adaptable, open-source context-aware inference engine designed for privacy, control, and independence from proprietary models.

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