Introduction

10xGraph is an open-source Python framework for production multi-agent AI. You write the agent, and 10xGraph generates the production server around it: authentication, scoped access control, owner-only threads, rate limits, and Docker and Kubernetes deployment. Memory has two tiers, with hot data in a Redis cache and cold data in PostgreSQL.

What you write

A graph of agents and tools in plain Python. Use the prebuilt ReactAgent for the common tool-calling loop, or build your own StateGraph when you need routing, parallel branches or human approval steps. Tools are ordinary Python functions, such as lookup_order(order_id: str) or refund_order(order_id: str, amount: float). Models come from OpenAI, Google Gemini or Anthropic, and you can change the model without touching the graph.

When you are ready to serve it, you point 10xgraph.json at your compiled graph. You do not write routes, auth middleware or deployment files by hand.

What 10xGraph generates

Piece	What you get
API server	REST, SSE streaming, WebSocket and realtime audio endpoints on port 8000
Security	JWT or custom auth, owner-only threads, role scopes on every endpoint, rate limits
Reliability	Replay-safe tools, versioned state writes, node and tool timeouts
Deployment	Dockerfile, docker-compose.yml and a Kubernetes Deployment and Service

Where to go next

Build something real

Each use case page gives a reference architecture, the tools it needs and the guardrails to add before it ships.

Integrate with your stack

10xGraph fits into a service or frontend you already run. It does not ask you to replace it.

Ship to production

Start with the build guide, which takes a support agent from code to a secured API with tests and Docker files. Then use the how-tos for each concern.

How the docs are organized

Each section answers a different question. Use the list to jump to the one you need.

• Get started: install 10xGraph, build a first agent and see what the production template generates.
• Beginner path: a guided route from the mental model to a served agent and a TypeScript client.
• Concepts: how graphs, state, tools, memory and the production runtime work.
• Prebuilt: ready-made agents and tools to use as they are or extend.
• How-to guides: task recipes for the Python library, the CLI, the TypeScript client and production.
• Testing and QA: unit tests with mocked models, evaluation sets and simulated users.
• Tutorials: end-to-end builds based on the examples in the repository.
• Reference: exact details for the Python library, REST API, CLI and client.
• Troubleshooting: symptoms, causes and fixes, plus the error code reference.
• Learn more: use cases, integrations, providers and framework comparisons.
• Glossary: plain definitions of AI agent terms, from ReAct to durable execution.
• Project: roadmap, security policy, upgrade guides, support and contributing.

Pick a path

New to agents. Read the beginner mental model, then build your first agent and add a tool.

Already using LangGraph. Start with 10xGraph vs LangGraph for an honest side-by-side, including where LangGraph is stronger, such as its visual tooling. Then read Concepts to see how StateGraph maps across, and the Quickstart to run something. 10xGraph is pre-1.0, so pin versions and read the changelog.

Going to production. Begin with production how-to guides for auth and authorization, checkpointing and deployment. Then read Replay-safe tools before you give an agent tools that move money or send email, and keep Troubleshooting close when you deploy.