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<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>
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---
## 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 and edge, and serves as the foundation for higher-level systems like [Odyssey](https://github.com/liquidos-ai/Odyssey).
---
## 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
- **LLM Guardrails**: Guardrail implementation for safeguarding LLM inference
- **LLM Optimization**: Build LLM pipelines with optimization passes like cache and retry for faster, more reliable inference
- **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
- **Python 3.9+** (required for Python bindings)
- **uv** for Python environment and package management
- **maturin** (required to build/install local Python bindings from source)
### 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
```
### Python Bindings
AutoAgents ships Python bindings to PyPI. Install the base package and add
backends via extras:
```bash
pip install autoagents-py # core + cloud LLM providers
pip install "autoagents-py[llamacpp]" # + llama.cpp CPU
pip install "autoagents-py[llamacpp-cuda]" # + llama.cpp CUDA
pip install "autoagents-py[llamacpp-metal]" # + llama.cpp Metal (macOS)
pip install "autoagents-py[llamacpp-vulkan]" # + llama.cpp Vulkan
pip install "autoagents-py[mistralrs]" # + mistral-rs CPU
pip install "autoagents-py[mistralrs-cuda]" # + mistral-rs CUDA
pip install "autoagents-py[mistralrs-metal]" # + mistral-rs Metal (macOS)
pip install "autoagents-py[guardrails]" # + Guardrails
pip install "autoagents-py[llamacpp-cuda,guardrails]" # combine extras
```
Development install from this repo:
```bash
uv venv --python=3.12
source .venv/bin/activate # Windows: .venv\Scripts\activate
uv pip install -U pip maturin pytest pytest-asyncio pytest-cov
# Clean, build, and install all CPU bindings into the active venv
make python-bindings-build
# Clean, build, and install CPU + CUDA bindings
make python-bindings-build-cuda
```
The Make targets remove stale editable-install extension artifacts before
rebuilding, which avoids loading out-of-date `.abi3.so` files from the source
tree.
Example scripts:
- Core cloud example: `bindings/python/autoagents/examples/openai_agent.py`
- llama.cpp example: `bindings/python/autoagents-llamacpp/examples/llamacpp_agent.py`
- mistral-rs example: `bindings/python/autoagents-mistralrs/examples/mistral_rs_agent.py`
### Run Tests
```bash
cargo test --features "full" --workspace
```
---
## 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.
### [LLM Pipelines](examples/pipeline/)
Demonstrates LLM pipelines with optimization passes such as cache and retry to improve performance and reliability.
### [Guardrails](examples/guardrails/)
Demonstrates configurable input and output guardrails with Block, Sanitize, and Audit policies using an LLMLayer in the pipeline.
### [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-guardrails/ # LLM Guardrails implementation
│ ├── autoagents-qdrant/ # Qdrant vector store
│ └── autoagents-derive/ # Procedural macros
├── examples/ # Example implementations
├── bindings/ # Bindings for different languages
```
### 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)