Rp Engine

riceprompt-engine

YAML-native agent workflow execution engine, written in Rust.

You describe an agent workflow as a YAML file — nodes, edges, prompts, data sources, MCP tools — and the engine parses it, resolves dependencies, and executes the graph: making LLM calls, running scripts, querying databases, calling MCP tools, iterating over data, and orchestrating multi-agent plans.

This engine powers RicePrompt — the visual agent IDE where you build and run these workflows without writing YAML by hand.

[dependencies]
riceprompt-engine = "0.1"

Features

• YAML-native — entire workflow (graph, prompts, data sources, providers)

in a single declarative file. See [docs/FLOW_SPEC.md](docs/FLOW_SPEC.md) for the authoritative spec.

• Multi-provider LLM support — OpenAI, Anthropic, Gemini, DeepSeek, Qwen,

Zhipu, Moonshot, MiniMax, xAI, Huoshan, and any OpenAI-compatible endpoint.

• Streaming, tool calling, structured output — first-class across providers.
• Rich node types — generate, transform (Rhai scripting), iterator,

supervisor (multi-agent routing), subgraph, data_connector, skill_set (progressive-disclosure knowledge bundles), mcp / mcp_tools (Model Context Protocol).

• Built-in data connectors — PostgreSQL, MySQL, MongoDB, Redis, Qdrant,

S3-compatible object storage, REST APIs.

• Harness layer — workflow-level instructions (CLAUDE.md-style) injected

into every generate node, with persistent memory support.

• Self-describing results — ExecutionResult can include the source YAML

so downstream tooling renders the topology + per-node results from one file.

• Checkpoint / resume — pause and resume long-running workflows.

Quick start

A minimal three-node workflow:

version: "1.0"
name: "hello_world"

providers:
  openai:
    api_key: "${OPENAI_API_KEY}"

nodes:
  - id: start
    type: start

  - id: greet
    type: generate
    config:
      provider: openai
      model: gpt-4o-mini
      template: tpl_greet
      variables:
        name: "start.name"

  - id: response
    type: response
    config:
      output:
        greeting: "greet.output"

edges:
  - from: start
    to: greet
  - from: greet
    to: response

templates:
  tpl_greet:
    user_prompt: "Greet {{name}} warmly in one sentence."

Run it:

use riceprompt_engine::Engine;
use serde_json::json;

#[tokio::main]
async fn main() -> anyhow::Result {
    let yaml = std::fs::read_to_string("hello.yaml")?;
    let engine = Engine::builder().build()?;
    let result = engine.run_yaml(&yaml, json!({ "name": "Ada" })).await?;
    println!("{}", serde_json::to_string_pretty(&result)?);
    Ok(())
}

More runnable examples live under [examples/](examples/).

Documentation

• [docs/FLOW_SPEC.md](docs/FLOW_SPEC.md) — authoritative YAML workflow spec

(node types, fields, providers, data sources, harness, skills, MCP).

• A user-facing usage guide ("skill guide") will be published separately.

Related

• RicePrompt — visual agent IDE built on top

of this engine. Design workflows in a graph editor, run them in-browser, and export the same YAML this engine consumes.

Project status

0.1.x — the API may change between minor versions while the spec stabilizes. Pin an exact version if you need stability.

Contributing

Issues and PRs welcome. Please:

• Run cargo fmt and cargo clippy --all-targets before submitting.
• Add tests for new node types or provider behaviors.
• For changes that touch the YAML surface, update docs/FLOW_SPEC.md in

the same PR.

License

Licensed under either of

• Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or

)

• MIT license ([LICENSE-MIT](LICENSE-MIT) or

)

at your option.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: jieyefriic
• Source: jieyefriic/rp-engine
• License: Apache-2.0
• Homepage: https://crates.io/crates/riceprompt-engine

Install and usage instructions live in the source repository linked above.