---

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 high level agent harness.

---

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

Provider	Status
OpenAI	✅
OpenRouter	✅
Anthropic	✅
DeepSeek	✅
xAI	✅
Phind	✅
Groq	✅
Google	✅
Azure OpenAI	✅
MiniMax	✅

Local Providers

Provider	Status
Ollama	✅
Mistral-rs	✅
Llama-Cpp	✅

Experimental Providers

See https://github.com/liquidos-ai/AutoAgents-Experimental-Backends

Provider	Status
Burn	⚠️ Experimental
Onnx	⚠️ Experimental

Provider support is actively expanding based on community needs.

---

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

sudo apt update
sudo apt install build-essential libasound2-dev alsa-utils pkg-config libssl-dev -y

Install LeftHook

macOS (Homebrew):

brew install lefthook

Linux/Windows (npm):

npm install -g lefthook

Clone and Build

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:

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:

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

cargo test --features "full" --workspace

---

Quick Start

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

Demonstrates various examples like Simple Agent with Tools, Very Basic Agent, Edge Agent, Chaining, Actor Based Model, Streaming and Adding Agent Hooks.

LLM Pipelines

Demonstrates LLM pipelines with optimization passes such as cache and retry to improve performance and reliability.

Guardrails

Demonstrates configurable input and output guardrails with Block, Sanitize, and Audit policies using an LLMLayer in the pipeline.

MCP Integration

Demonstrates how to integrate AutoAgents with the Model Context Protocol (MCP).

Local Models

Demonstrates how to integrate AutoAgents with the Mistral-rs for Local Models.

Design Patterns

Demonstrates various design patterns like Chaining, Planning, Routing, Parallel and Reflection.

Providers

Contains examples demonstrating how to use different LLM providers with AutoAgents.

WASM Tool Execution

A simple agent which can run tools in WASM runtime.

Coding Agent

A sophisticated ReAct-based coding agent with file manipulation capabilities.

Speech

Run AutoAgents Speech Example with realtime TTS and STT.

Android Local Agent

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

sudo apt update
sudo apt install build-essential libasound2-dev alsa-utils pkg-config libssl-dev -y

Running Tests

cargo test --workspace --features default --exclude autoagents-burn --exclude autoagents-mistral-rs --exclude wasm_agent

# Coverage (requires cargo-tarpaulin)
cargo install cargo-tarpaulin
rustup component add llvm-tools-preview
make coverage-rust

Running Benchmarks

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 and Code of Conduct for details.

---

Documentation

• API Documentation: Complete framework docs
• 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

---

FAQ

General

What is AutoAgents? AutoAgents is a production-grade, multi-agent framework written in Rust. It provides a modular architecture for building intelligent systems with type-safe agent models, structured tool calling, configurable memory, and pluggable LLM backends — designed for performance, safety, and composability across server and edge environments.

How does AutoAgents differ from other agent frameworks? AutoAgents is Rust-first, offering memory safety, zero-cost abstractions, and high performance. It provides a unified interface for cloud and local LLM providers, built-in guardrails, optimization passes (cache/retry), and a WASM sandbox for tool execution — all in a single framework.

Is there a Python version? Yes. AutoAgents provides Python bindings via autoagents-py on PyPI, enabling Python developers to leverage the Rust core with a familiar API.

Setup & Configuration

How do I install AutoAgents? Install via Cargo: cargo add autoagents, or via PyPI for Python: pip install autoagents-py. See the documentation for detailed setup guides.

Which LLM providers are supported? AutoAgents supports OpenAI, OpenRouter, Anthropic, DeepSeek, xAI, and local models via a unified interface. Configure your API keys in the environment or configuration file.

Can I use local models? Yes. AutoAgents supports local LLM backends through its unified provider interface, enabling fully offline agent operation.

Agent Development

What is the ReAct executor? The ReAct (Reasoning + Acting) executor is AutoAgents' primary agent execution model. It alternates between reasoning steps and tool calls, enabling agents to plan, execute, and observe results in a loop until the task is complete.

How does the tool system work? Tools are defined using derive macros (#[derive(Tool)]) for type-safe input/output. AutoAgents also provides a sandboxed WASM runtime for executing untrusted tools securely.

What memory backends are available? AutoAgents uses a sliding window memory model by default, with extensible backends for custom memory strategies — enabling fine-grained control over context management.

Multi-Agent Orchestration

How do agents communicate? AutoAgents provides typed pub/sub communication between agents, enabling structured message passing with compile-time type safety. Agents can publish events and subscribe to topics in a decoupled architecture.

What is the environment system? The environment system manages shared state and resources across multiple agents. It provides a controlled space where agents can interact, share observations, and coordinate actions.

Troubleshooting

Build fails with Rust version errors. What should I do? AutoAgents requires Rust 1.75+. Run rustup update to get the latest stable version. Check the documentation for minimum version requirements.

Where can I get help?

• Documentation: https://liquidos-ai.github.io/AutoAgents/
• Examples: examples/ directory in the repository
• DeepWiki: https://deepwiki.com/liquidos-ai/AutoAgents
• GitHub Issues: https://github.com/liquidos-ai/AutoAgents/issues

License

AutoAgents is dual-licensed under:

• MIT License (MIT_LICENSE)
• Apache License 2.0 (APACHE_LICENSE)

You may choose either license for your use case.

---

Acknowledgments

Built by the Liquidos AI team and wonderful community of researchers and engineers.

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