# Hanzo Agent SDK — Multi-agent orchestration in Rust, Python, Go & TypeScript — Hanzo AI

> Build, orchestrate, and deploy autonomous AI coding agents and multi-agent networks on a pure-Rust runtime with MCP tool execution, memory, and sandboxed VMs.

Hanzo Agent · SDK

# An Agent SDK for networks that work together

An agent is a model, a set of instructions, and the tools it may call. A network is several of them behind a router that decides which one gets the turn — backed by a pure-Rust runtime (hanzoai/dev) and the Hanzo AI gateway (hanzoai/ai).

[Read the docs](https://docs.hanzo.ai/docs/openapi/agents/)[View on GitHub](https://github.com/hanzoai/agent)

Open source (hanzoai/agent). pip install hanzo-agent or cargo add hanzo-agent. Works against api.hanzo.ai or any endpoint that speaks Chat Completions and MCP.

Available throughRustPythonGoTypeScriptMCP toolsHanzo Cloud

Agents — Hanzo

[Models](https://hanzo.ai/models)[Agents](https://hanzo.ai/agents)[Inference](https://hanzo.ai/engine)[Embeddings](https://hanzo.ai/cloud/embeddings)[Providers](https://hanzo.ai/cloud/providers)[Prompts](https://hanzo.ai/cloud/prompts)

- Add rate limiting to the payments API.
- Read 14 files. The limiter belongs in the gateway, not the handler — one place, every route.
- Wrote gateway/limit.go and a test at 100 req/min per key. Tests pass.

`api.hanzo.ai/v1/agent`[Open in the console](https://hanzo.ai/home)

Example project. Every name in it is one you would give your own.

What is Hanzo Agent

## One agent, or several with a router between them

The hard part of multi-agent work is not the prompting. It is deciding who answers, keeping what they learn where the next one can read it, and running tools inside a fast, sandboxed Rust harness.

01

### Pure-Rust Core & Multi-Language SDKs

Built on hanzo-agent (Rust) and the hanzoai/dev Rust harness for sandboxed execution, with first-class SDKs in Rust, Python, Go, and TypeScript (hanzoai/agent).

02

### Networks

create_network(agents=[researcher, writer], router=SemanticRouter()) puts specialists behind one call. Route on meaning, on rules you write, or by load — and swap the router without touching the agents.

03

### Tools & MCP

Every Hanzo MCP tool (260+ tools across files, AST, shell, browser, Kai, and 117 Cloud services) mounts over stdio or streamable HTTP without custom wrappers.

What comes with it

## The parts you would otherwise write yourself

### Routing you can choose

SemanticRouter reads the request and picks. Rule-based routing does what you say. Load-balanced routing spreads turns across equals. Same network object either way.

### Workflows, not just chat

Steps that run in parallel, branch on a condition, or loop until something holds — for the work where the order matters and a prompt cannot express it.

### Shared state

Agents in a network read and write one state object, so the writer sees what the researcher found without you pasting it into the next prompt.

### Memory with retrieval

A store, a retriever, and vector search over both, so an agent recalls the relevant part of a long history instead of replaying all of it.

### Tracing

Spans and traces around every model call, tool call and handoff, through a processor interface you can point at your own collector.

### Extras you opt into

pip install "hanzo-agent[tee]" adds attestation for confidential runs. [web3] adds wallets and on-chain identity. [cli] adds the command line. Nothing you skip is loaded.

Get started

## Two agents and a router

python

```
# Rust (cargo add hanzo-agent hanzo-agents) · Python (pip install hanzo-agent) from agents import Agent from agents.network import create_network, SemanticRouter researcher = Agent( name="Researcher", instructions="You find and analyze information.", tools=[search_tool, analyze_tool], ) writer = Agent( name="Writer", instructions="You create content based on research.", tools=[format_tool], ) network = create_network(agents=[researcher, writer], router=SemanticRouter()) result = await network.run("Research and write about quantum computing")
```

[Read the docs](https://docs.hanzo.ai/docs/openapi/agents/)[View on GitHub](https://github.com/hanzoai/agent)

## Write the first one

pip install hanzo-agent, give it instructions and a tool, and run it.

[Deploy on Hanzo Cloud](https://platform.hanzo.ai)[GitHub](https://github.com/hanzoai/agent)

## Get Agents

Multi-agent SDK + runtime + tool harness

License: Apache-2.0[hanzoai](https://github.com/hanzoai)

[Deploy to Cloud](https://platform.hanzo.ai/deploy)