Ares

Agentic Retrieval Enhanced Server. Rust. Multi-provider LLM. Tool calling. RAG. MCP.
Extensible via ContextProvider trait. Run standalone or embed as a library.

A.R.E.S is a production-grade agentic AI server built in Rust. Multi-provider LLM routing, structured tool calling, RAG, MCP integration, multi-tenant auth, and workflow orchestration. Extensible via ContextProvider trait and base_router() for building managed platforms on top.

Built by DIRMACS. Documentation

Features

• Multi-provider LLM: Ollama, OpenAI, Anthropic Claude, LlamaCpp (direct GGUF loading)
• TOML configuration: declarative, hot-reloading
• Configurable agents: define via TOON with custom models, tools, and prompts
• Workflow engine: declarative execution with agent routing
• Tool calling: type-safe function calling with automatic schema generation
• ToolCoordinator: provider-agnostic multi-turn tool calling for all LLM clients
• Per-agent tool filtering: restrict which tools each agent can access
• Streaming: real-time responses from all providers
• Auth: JWT with Argon2 password hashing
• Database: PostgreSQL with multi-tenant isolation, optional vector stores (ares-vector, Qdrant, LanceDB)
• MCP: pluggable Model Context Protocol server integration
• Multi-agent orchestration: specialized agent routing
• RAG: pure-Rust vector store, multi-strategy search (semantic, BM25, fuzzy, hybrid), reranking
• Memory: user personalization and context management
• Deep research: multi-step research with parallel subagents
• Web search: built-in via daedra
• OpenAPI: automatic documentation generation
• Config validation: circular reference detection and unused config warnings
• Loop detection: 3-tier escalation (warn → force alternative → halt) for repetitive outputs
• Crash recovery: checkpoint serialization — save agent state at each step, restore on restart

Installation

A.R.E.S can be used as a standalone server or as a library in your Rust project.

As a Library

Add to your project:

cargo add ares-server

Basic usage:

use ares::{Provider, LLMClient};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create an Ollama provider
    let provider = Provider::Ollama {
        base_url: "http://localhost:11434".to_string(),
        model: "llama3.2:3b".to_string(),
    };

    // Create a client and generate a response
    let client = provider.create_client().await?;
    let response = client.generate("Hello, world!").await?;
    println!("{}", response);

    Ok(())
}

As a Binary

# Install from crates.io (basic installation)
cargo install ares-server

# Install with embedded Web UI
cargo install ares-server --features ui

# Initialize a new project (creates ares.toml and config files)
ares-server init

# Run the server
ares-server

CLI Commands

A.R.E.S provides a full-featured CLI with colored output:

# Initialize a new project with all configuration files
ares-server init

# Initialize with custom options
ares-server init --provider openai --port 8080 --host 0.0.0.0

# Initialize with minimal configuration
ares-server init --minimal

# View configuration summary
ares-server config

# Validate configuration
ares-server config --validate

# List a

... [View full README on GitHub](https://github.com/dirmacs/ares#readme)