Serve and CLI

AutoAgents includes a serving library (autoagents-serve) and a CLI (autoagents-cli) for running agent workflows defined in YAML.

To install the CLI, clone the AutoAgents repository and run:

cargo install --path ./crates/autoagents-cli.

CLI

Binary: autoagents

Subcommands:

• run --workflow <file> --input <text>
  • Loads a workflow YAML and executes it once with the given input.
• serve [--workflow <file> | --directory <dir>] [--name <name>] [--host <host>] [--port <port>]
  • Serves one or many workflows over HTTP.

Examples:

# Run a single workflow once
OPENAI_API_KEY=... cargo run -p autoagents-cli -- run --workflow examples/serve/workflows/direct.yaml --input "2 + 2"

# Serve a single workflow
OPENAI_API_KEY=... cargo run -p autoagents-cli -- serve --workflow examples/serve/workflows/direct.yaml --host 127.0.0.1 --port 8080

# Serve all workflows in a directory
OPENAI_API_KEY=... cargo run -p autoagents-cli -- serve --directory examples/serve/workflows --host 127.0.0.1 --port 8080

Workflow YAML

Top-level fields:

• kind: Direct | Sequential | Parallel | Routing
• name: Optional workflow name
• description: Optional description
• version: Optional version
• stream: true/false to enable streaming
• memory_persistence: Optional persistence policy { mode: "memory" | "file" }
• workflow: Workflow spec (see below)
• environment: Optional { working_directory, timeout_seconds }
• runtime: Optional { type: "single_threaded", max_concurrent }

Workflow spec variants:

• Direct:
  • workflow.agent: Single agent to run
  • workflow.output: Output type
• Sequential/Parallel:
  • workflow.agents: List of agents to run (in order or concurrently)
  • workflow.output: Output type
• Routing:
  • workflow.router: Router agent
  • workflow.handlers: [{ condition, agent }, ...] routing targets
  • workflow.output: Output type

Agent

agent:
  name: MathAgent
  description: "Helpful math agent"
  instructions: |
    You are a math expert. Solve the problem step by step.
  executor: ReAct   # or Basic
  memory:
    kind: sliding_window
    parameters:
      window_size: 10  # or `n_slide`
  model:
    kind: llm
    backend:
      kind: Cloud       # or Local
      base_url: ...     # optional
    provider: OpenAI    # e.g., OpenAI, Anthropic, Groq, OpenRouter, Ollama
    model_name: gpt-4o-mini
    parameters:
      temperature: 0.1
      max_tokens: 500
      top_p: 0.95
      top_k: 40
  tools: [ ]           # list of tool names (see Tools)
  output:
    type: text         # text | json | structured

Tools

Tool entries are resolved by the server’s registry; for built-in toolkits (e.g., search), enable features like search-tools on autoagents-serve.

tools:
  - name: brave_search
    options: { }
    config: { }

Output

Output config allows specifying the format:

• type: text — plain text
• type: json — JSON value
• type: structured — JSON Schema via schema

Examples

Study the sample workflows in examples/serve/workflows/:

• Direct: examples/serve/workflows/direct.yaml
• Sequential: examples/serve/workflows/sequential.yaml
• Parallel: examples/serve/workflows/parallel.yaml
• Routing: examples/serve/workflows/routing.yaml
• Research (with search tool): examples/serve/workflows/research.yaml

Library Usage (Serve)

For embedding in your app, use WorkflowBuilder and (optionally) HTTPServer from autoagents-serve:

#![allow(unused)]
fn main() {
use autoagents_serve::{WorkflowBuilder, HTTPServer, ServerConfig};

// Build and run in-process
let built = WorkflowBuilder::from_yaml_file("examples/serve/workflows/direct.yaml")?
    .build()?;
let result = built.run("2 + 2".to_string()).await?;

// Serve over HTTP
let server = HTTPServer::new(ServerConfig { host: "127.0.0.1".into(), port: 8080 },
                             std::collections::HashMap::from([
                               ("calc".into(), "examples/serve/workflows/direct.yaml".into())
                             ]));
server.serve().await?;
}

See crate docs for endpoint details and streaming support.