AutoAgents

A production-grade multi-agent framework in Rust

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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.

• 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

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

Provider support is actively expanding based on community needs.

More info at GitHub

• Rust (latest stable recommended)
• Cargo package manager
• LeftHook for Git hooks management

macOS (Homebrew):

brew install lefthook

Linux/Windows (npm):

npm install -g lefthook

git clone https://github.com/liquidos-ai/AutoAgents.git
cd AutoAgents
lefthook install
cargo build --workspace --all-features

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

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 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.

Explore the examples to get started quickly:

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

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

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

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

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

A simple agent which can run tools in WASM runtime.

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

Run AutoAgents Speech Example with realtime TTS and STT.

Example App that runs AutoAgents with Local models in Android using AutoAgents-llamacpp backend

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

• 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)

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

cargo bench -p autoagents-core --bench agent_runtime

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

We welcome contributions. Please see our Contributing Guidelines and Code of Conduct for details.

• API Documentation: Complete framework docs
• Examples: Practical implementation examples

• GitHub Issues: Bug reports and feature requests
• Discussions: Community Q&A and ideas
• Discord: Join our Discord Community using https://discord.gg/zfAF9MkEtK

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

AutoAgents is dual-licensed under:

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

You may choose either license for your use case.

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