Agent Runtime

Crates.io License: MIT OR Apache-2.0

A Rust framework for building AI agent workflows with tools, streaming LLM responses, event tracking, and intelligent tool-loop prevention.

Features

• Agents backed by pluggable LLM providers (OpenAI, llama.cpp / LM Studio)
• Tools — native Rust functions or external MCP servers
• Workflows — sequential, conditional, transform, and nested sub-workflow steps
• Streaming — token-by-token LLM output via channels
• Events — unified scope × type × status event stream for full observability
• Context management — pluggable history pruning (token budget, sliding window, summarization)
• Tool loop prevention — detects and short-circuits repeat tool calls
• Config — load runtime config from YAML or TOML

Install

[dependencies]
agent-runtime = "0.4"
tokio = { version = "1", features = ["full"] }

Quick start

Agent + llama.cpp / LM Studio

use agent_runtime::llm::LlamaClient;
use agent_runtime::types::AgentInput;
use agent_runtime::{Agent, AgentConfig};
use std::sync::Arc;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Arc::new(LlamaClient::new("http://localhost:8080", "llama"));

    let agent = Agent::new(
        AgentConfig::builder("assistant")
            .system_prompt("You are a helpful assistant.")
            .build(),
    )
    .with_client(client);

    let output = agent
        .execute(&AgentInput::from_text("What is 42 * 137?"))
        .await?;

    println!("{}", output.data);
    Ok(())
}

Agent with native tools

use agent_runtime::tools::{CalculatorTool, ToolRegistry};
use agent_runtime::{Agent, AgentConfig};
use std::sync::Arc;

let mut registry = ToolRegistry::new();
registry.register(CalculatorTool);

let agent = Agent::new(
    AgentConfig::builder("math-bot")
        .system_prompt("Use tools to compute answers.")
        .tools(Arc::new(registry))
        .build(),
)
.with_client(client);

Workflow with multiple steps

use agent_runtime::workflow::steps::{AgentStep, TransformStep};
use agent_runtime::{Runtime, Workflow};

let workflow = Workflow::builder()
    .add_step(Box::new(AgentStep::new(researcher_config)))
    .add_step(Box::new(TransformStep::new(
        "summarize-prompt".into(),
        |data| serde_json::json!({ "text": format!("Summarize: {}", data) }),
    )))
    .add_step(Box::new(AgentStep::new(summarizer_config)))
    .build();

let runtime = Runtime::new();
let run = runtime.execute(workflow).await;

Event streaming

use agent_runtime::{EventScope, EventType, Runtime};

let runtime = Runtime::new();
let mut rx = runtime.event_stream().subscribe();

tokio::spawn(async move {
    while let Ok(event) = rx.recv().await {
        match (event.scope, event.event_type) {
            (EventScope::LlmRequest, EventType::Progress) => {
                if let Some(chunk) = event.data.get("chunk").and_then(|c| c.as_str()) {
                    print!("{}", chunk);
                }
            }
            (EventScope::Tool, EventType::Completed) => {
                println!("✓ {}", event.component_id);
            }
            _ => {}
        }
    }
});

runtime.execute(workflow).await;

MCP external tools

use agent_runtime::tools::McpClient;

let mcp = McpClient::new_stdio(
    "npx",
    vec!["-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
).await?;

let tools = mcp.list_tools().await?;

Configuration file

# agent-runtime.yaml
llm:
  base_url: "http://localhost:8080"
  model: "llama"

agents:
  - name: researcher
    system_prompt: "You are a research assistant."
    max_tool_iterations: 10

use agent_runtime::RuntimeConfig;

let config = RuntimeConfig::from_file("agent-runtime.yaml")?;

Module layout

src/
├── agent/         Agent + AgentConfig + execution loop
├── config.rs      YAML/TOML configuration
├── context/       WorkflowContext + pruning strategies/
├── error.rs       Error types
├── event/         Event, EventStream, EventScope/Type/Status
├── llm/           LlmClient trait + provider/{llama, openai}
├── runtime/       Runtime + retry + timeout
├── tools/         Tool trait, registry, native, mcp, loop_detection, builtin
├── types.rs       AgentInput/Output, ToolResult, shared types
└── workflow/      Workflow + step + steps/{agent, transform, conditional, subworkflow}

Event model

Every event has a scope (Workflow, WorkflowStep, Agent, LlmRequest, Tool, System), a type (Started, Progress, Completed, Failed, Canceled), and a status.

Component IDs follow predictable formats: workflow_name, workflow:step:N, agent_name, agent:llm:N, tool_name:N, system:subsystem.

Documentation

• docs/ — full guides for events, tools, workflows, MCP, configuration
• crates/agent-discourse/ — multi-agent demo

Testing

cargo test
cargo clippy --workspace --all-targets -- -D warnings

License

Dual-licensed under MIT or Apache-2.0 at your option.