<div align="center">
<img src="assets/logo.png" alt="AutoAgents Logo" width="200" height="200">
# AutoAgents
**A production-grade multi-agent framework in Rust**
[](https://crates.io/crates/autoagents)
[](https://liquidos-ai.github.io/AutoAgents)
[](https://github.com/liquidos-ai/AutoAgents#license)
[](https://github.com/liquidos-ai/AutoAgents/actions)
[](https://codecov.io/gh/liquidos-ai/AutoAgents)
[](https://deepwiki.com/liquidos-ai/AutoAgents)
<sub>Translations may lag behind the English README.</sub>
<br />
<strong>Like this project?</strong> <a href="https://github.com/liquidos-ai/AutoAgents">Star us on GitHub</a>
</div>
---
## Overview
AutoAgents is a modular, multi-agent framework for building intelligent systems in Rust. It combines a type-safe agent
model with structured tool calling, configurable memory, and pluggable LLM backends. The architecture is designed for
performance, safety, and composability across server, edge.
---
## Key Features
- **Agent execution**: ReAct and basic executors, streaming responses, and structured outputs
- **Tooling**: Derive macros for tools and outputs, plus a sandboxed WASM runtime for tool execution
- **Memory**: Sliding window memory with extensible backends
- **LLM providers**: Cloud and local backends behind a unified interface
- **Multi-agent orchestration**: Typed pub/sub communication and environment management
- **Speech-Processing**: Local TTS and STT support
- **Observability**: OpenTelemetry tracing and metrics with pluggable exporters
---
## Supported LLM Providers
### Cloud Providers
| **OpenAI** | ✅ |
| **OpenRouter** | ✅ |
| **Anthropic** | ✅ |
| **DeepSeek** | ✅ |
| **xAI** | ✅ |
| **Phind** | ✅ |
| **Groq** | ✅ |
| **Google** | ✅ |
| **Azure OpenAI** | ✅ |
| **MiniMax** | ✅ |
### Local Providers
| **Ollama** | ✅ |
| **Mistral-rs** | ✅ |
| **Llama-Cpp** | ✅ |
### Experimental Providers
See https://github.com/liquidos-ai/AutoAgents-Experimental-Backends
| **Burn** | ⚠️ Experimental |
| **Onnx** | ⚠️ Experimental |
Provider support is actively expanding based on community needs.
---
## Benchmarks
More info at [GitHub](https://github.com/liquidos-ai/autoagents-bench)
---
## Installation
### Prerequisites
- **Rust** (latest stable recommended)
- **Cargo** package manager
- **LeftHook** for Git hooks management
### Prerequisite
```bash
sudo apt update
sudo apt install build-essential libasound2-dev alsa-utils pkg-config libssl-dev -y
```
### Install LeftHook
macOS (Homebrew):
```bash
brew install lefthook
```
Linux/Windows (npm):
```bash
npm install -g lefthook
```
### Clone and Build
```bash
git clone https://github.com/liquidos-ai/AutoAgents.git
cd AutoAgents
lefthook install
cargo build --workspace --all-features
```
### Run Tests
```bash
cargo test --workspace --features default --exclude autoagents-burn --exclude autoagents-mistral-rs --exclude wasm_agent
```
---
## Quick Start
```rust
use autoagents::core::agent::memory::SlidingWindowMemory;
use autoagents::core::agent::prebuilt::executor::{ReActAgent, ReActAgentOutput};
use autoagents::core::agent::task::Task;
use autoagents::core::agent::{AgentBuilder, AgentDeriveT, AgentOutputT, DirectAgent};
use autoagents::core::error::Error;
use autoagents::core::tool::{ToolCallError, ToolInputT, ToolRuntime, ToolT};
use autoagents::llm::LLMProvider;
use autoagents::llm::backends::openai::OpenAI;
use autoagents::llm::builder::LLMBuilder;
use autoagents_derive::{agent, tool, AgentHooks, AgentOutput, ToolInput};
use serde::{Deserialize, Serialize};
use serde_json::Value;
use std::sync::Arc;
#[derive(Serialize, Deserialize, ToolInput, Debug)]
pub struct AdditionArgs {
#[input(description = "Left Operand for addition")]
left: i64,
#[input(description = "Right Operand for addition")]
right: i64,
}
#[tool(
name = "Addition",
description = "Use this tool to Add two numbers",
input = AdditionArgs,
)]
struct Addition {}
#[async_trait]
impl ToolRuntime for Addition {
async fn execute(&self, args: Value) -> Result<Value, ToolCallError> {
println!("execute tool: {:?}", args);
let typed_args: AdditionArgs = serde_json::from_value(args)?;
let result = typed_args.left + typed_args.right;
Ok(result.into())
}
}
#[derive(Debug, Serialize, Deserialize, AgentOutput)]
pub struct MathAgentOutput {
#[output(description = "The addition result")]
value: i64,
#[output(description = "Explanation of the logic")]
explanation: String,
#[output(description = "If user asks other than math questions, use this to answer them.")]
generic: Option<String>,
}
#[agent(
name = "math_agent",
description = "You are a Math agent",
tools = [Addition],
output = MathAgentOutput,
)]
#[derive(Default, Clone, AgentHooks)]
pub struct MathAgent {}
impl From<ReActAgentOutput> for MathAgentOutput {
fn from(output: ReActAgentOutput) -> Self {
let resp = output.response;
if output.done && !resp.trim().is_empty() {
if let Ok(value) = serde_json::from_str::<MathAgentOutput>(&resp) {
return value;
}
}
MathAgentOutput {
value: 0,
explanation: resp,
generic: None,
}
}
}
pub async fn simple_agent(llm: Arc<dyn LLMProvider>) -> Result<(), Error> {
let sliding_window_memory = Box::new(SlidingWindowMemory::new(10));
let agent_handle = AgentBuilder::<_, DirectAgent>::new(ReActAgent::new(MathAgent {}))
.llm(llm)
.memory(sliding_window_memory)
.build()
.await?;
let result = agent_handle.agent.run(Task::new("What is 1 + 1?")).await?;
println!("Result: {:?}", result);
Ok(())
}
#[tokio::main]
async fn main() -> Result<(), Error> {
let api_key = std::env::var("OPENAI_API_KEY").unwrap_or("".into());
let llm: Arc<OpenAI> = LLMBuilder::<OpenAI>::new()
.api_key(api_key)
.model("gpt-4o")
.max_tokens(512)
.temperature(0.2)
.build()
.expect("Failed to build LLM");
let _ = simple_agent(llm).await?;
Ok(())
}
```
### AutoAgents CLI
AutoAgents CLI helps in running Agentic Workflows from YAML configurations and serves them over HTTP. You can check it out at https://github.com/liquidos-ai/AutoAgents-CLI.
---
## Examples
Explore the examples to get started quickly:
### [Basic](examples/basic/)
Demonstrates various examples like Simple Agent with Tools, Very Basic Agent, Edge Agent, Chaining, Actor Based Model,
Streaming and Adding Agent Hooks.
### [MCP Integration](examples/mcp/)
Demonstrates how to integrate AutoAgents with the Model Context Protocol (MCP).
### [Local Models](examples/mistral_rs)
Demonstrates how to integrate AutoAgents with the Mistral-rs for Local Models.
### [Design Patterns](examples/design_patterns/)
Demonstrates various design patterns like Chaining, Planning, Routing, Parallel and Reflection.
### [Providers](examples/providers/)
Contains examples demonstrating how to use different LLM providers with AutoAgents.
### [WASM Tool Execution](examples/wasm_runner/)
A simple agent which can run tools in WASM runtime.
### [Coding Agent](examples/coding_agent/)
A sophisticated ReAct-based coding agent with file manipulation capabilities.
### [Speech](examples/speech/)
Run AutoAgents Speech Example with realtime TTS and STT.
### [Android Local Agent](https://github.com/liquidos-ai/AutoAgents-Android-Example)
Example App that runs AutoAgents with Local models in Android using AutoAgents-llamacpp backend
---
## Components
AutoAgents is built with a modular architecture:
```
AutoAgents/
├── crates/
│ ├── autoagents/ # Main library entry point
│ ├── autoagents-core/ # Core agent framework
│ ├── autoagents-protocol/ # Shared protocol/event types
│ ├── autoagents-llm/ # LLM provider implementations
│ ├── autoagents-telemetry/ # OpenTelemetry integration
│ ├── autoagents-toolkit/ # Collection of ready-to-use tools
│ ├── autoagents-mistral-rs/ # LLM provider implementations using Mistral-rs
│ ├── autoagents-llamacpp/ # LLM provider implementation using LlamaCpp
│ ├── autoagents-speech/ # Speech model support for TTS and STT
│ ├── autoagents-qdrant/ # Qdrant vector store
│ └── autoagents-derive/ # Procedural macros
├── examples/ # Example implementations
```
### Core Components
- **Agent**: The fundamental unit of intelligence
- **Environment**: Manages agent lifecycle and communication
- **Memory**: Configurable memory systems
- **Tools**: External capability integration
- **Executors**: Different reasoning patterns (ReAct, Chain-of-Thought)
---
## Development
### Prerequisite
```bash
sudo apt update
sudo apt install build-essential libasound2-dev alsa-utils pkg-config libssl-dev -y
```
### Running Tests
```bash
cargo test --workspace --features default --exclude autoagents-burn --exclude autoagents-mistral-rs --exclude wasm_agent
# Coverage (requires cargo-tarpaulin)
cargo install cargo-tarpaulin
cargo tarpaulin --all-features --out html
```
### Running Benchmarks
```bash
cargo bench -p autoagents-core --bench agent_runtime
```
### Git Hooks
This project uses LeftHook for Git hooks management. The hooks will automatically:
- Format code with `cargo fmt --check`
- Run linting with `cargo clippy -- -D warnings`
- Execute tests with `cargo test --all-features --workspace --exclude autoagents-burn`
### Contributing
We welcome contributions. Please see our [Contributing Guidelines](CONTRIBUTING.md)
and [Code of Conduct](CODE_OF_CONDUCT.md) for details.
---
## Documentation
- **[API Documentation](https://liquidos-ai.github.io/AutoAgents)**: Complete framework docs
- **[Examples](examples/)**: Practical implementation examples
---
## Community
- **GitHub Issues**: Bug reports and feature requests
- **Discussions**: Community Q&A and ideas
- **Discord**: Join our Discord Community using https://discord.gg/zfAF9MkEtK
---
## Performance
AutoAgents is designed for high performance:
- **Memory Efficient**: Optimized memory usage with configurable backends
- **Concurrent**: Full async/await support with tokio
- **Scalable**: Horizontal scaling with multi-agent coordination
- **Type Safe**: Compile-time guarantees with Rust's type system
---
## License
AutoAgents is dual-licensed under:
- **MIT License** ([MIT_LICENSE](MIT_LICENSE))
- **Apache License 2.0** ([APACHE_LICENSE](APACHE_LICENSE))
You may choose either license for your use case.
---
## Acknowledgments
Built by the [Liquidos AI](https://liquidos.ai) team and wonderful community of researchers and engineers.
<a href="https://github.com/liquidos-ai/AutoAgents/graphs/contributors">
<img src="https://contrib.rocks/image?repo=liquidos-ai/AutoAgents" />
</a>
Special thanks to:
- The Rust community for the excellent ecosystem
- LLM providers for enabling high-quality model APIs
- All contributors who help improve AutoAgents
---
## Star History
[](https://www.star-history.com/#liquidos-ai/AutoAgents&Date)