Lib.rs

Agents Runtime SDK

Rust SDK for building autonomous AI agents that operate over the MXP (mxp://) protocol. The focus is low-latency planning, secure execution, and predictable behaviour—this SDK is what agents use before they are deployed onto the MXP Nexus mesh.

Install once via the bundled facade crate:

cargo add mxp-agents

Why it exists

• Provide a unified runtime that wraps LLMs, tools, memory, and governance without depending on QUIC or third-party transports.
• Ensure every agent built for MXP Nexus speaks MXP natively and adheres to platform security, observability, and performance rules.
• Offer a developer-friendly path to compose agents locally, then promote them into the MXP Nexus platform when ready.

Scope

• In scope: agent lifecycle management, LLM connectors, tool registration, policy hooks, MXP message handling, memory integration (including the upcoming MXP Vector Store).
• Out of scope: MXP Nexus deployment tooling, mesh scheduling, or any "deep agents" research-oriented SDK—handled by separate projects.

Supported LLM stacks

• OpenAI, Anthropic, Gemini, Ollama, and future MXP-hosted models via a shared ModelAdapter trait.

MXP integration

• MXP crate (e.g. mxp = "0.2.0") provides the transport primitives. We no longer rely on QUIC; all messaging assumes the custom MXP stack and UDP carrier.
• Helpers for AgentRegister, AgentHeartbeat, Call, Response, Event, and Stream* payloads are part of the SDK surface.
• MxpRegistryClient handles registry registration, heartbeats (including needs_register responses), and graceful deregistration over MXP so agents can bootstrap themselves inside the mesh.

Key concepts

• Tools are pure Rust functions annotated with #[tool]; the SDK converts them into schemas consumable by LLMs and enforces capability scopes at runtime.
• Agents can share external state (memory bus, MXP Vector Store) or remain fully isolated.
• Governance and policy enforcement are first-class: hooks exist for allow/deny decisions and human-in-the-loop steps.

Quick Start

use agent_adapters::ollama::{OllamaAdapter, OllamaConfig};
use agent_adapters::traits::{InferenceRequest, MessageRole, ModelAdapter, PromptMessage};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create an adapter (works with OpenAI, Anthropic, Gemini, or Ollama)
    let adapter = OllamaAdapter::new(OllamaConfig::new("gemma2:2b"))?;

    // Build a request with system prompt
    let request = InferenceRequest::new(vec![
        PromptMessage::new(MessageRole::User, "What is MXP?"),
    ])?
    .with_system_prompt("You are an expert on MXP protocol")
    .with_temperature(0.7);

    // Get streaming response
    let mut stream = adapter.infer(request).await?;
    
    // Process chunks
    while let Some(chunk) = stream.next().await {
        let chunk = chunk?;
        print!("{}", chunk.delta);
    }
    
    Ok(())
}

System Prompts

All adapters support system prompts with provider-native optimizations:

// OpenAI/Ollama: Prepends as first message
let openai = OpenAiAdapter::new(OpenAiConfig::from_env("gpt-4"))?;

// Anthropic: Uses dedicated 'system' parameter
let anthropic = AnthropicAdapter::new(AnthropicConfig::from_env("claude-3-5-sonnet-20241022"))?;

// Gemini: Uses 'systemInstruction' field
let gemini = GeminiAdapter::new(GeminiConfig::from_env("gemini-1.5-pro"))?;

// Same API works across all providers
let request = InferenceRequest::new(messages)?
    .with_system_prompt("You are a helpful assistant");

Context Window Management (Optional)

For long conversations, enable automatic context management:

use agent_prompts::ContextWindowConfig;

let adapter = OllamaAdapter::new(config)?
    .with_context_config(ContextWindowConfig {
        max_tokens: 4096,
        recent_window_size: 10,
        ..Default::default()
    });

// SDK automatically manages conversation history within token budget

Getting started

1. Model your agent using the runtime primitives (AgentKernel, adapters, tool registry).
2. Wire MXP endpoints for discovery and message handling.
3. Configure memory providers (in-memory ring buffer today, pluggable MXP Vector Store soon).
4. Instrument with tracing spans and policy hooks.

See docs/overview.md for architectural detail and docs/usage.md for comprehensive examples.

Documentation Map

• docs/architecture.md — crate layout, component contracts, roadmap.
• docs/features.md — current feature set and facade feature flags.
• docs/usage.md — end-to-end setup guide for building an agent, including tooling examples.
• docs/errors.md — error surfaces and troubleshooting tips.

Future

// Move Memories to external Github projects, like embeddings, vectors ...etc each as a repo, then treat them as external deps where the run time can pull what is required // by default the agents are note require a memory to run tho, then can be stateless. for now we will keep them in this project for the sake of simplicity.