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](https://riceprompt.app)** — the visual agent IDE where you build and run these workflows without writing YAML by hand. ```toml [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: ```yaml 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: ```rust 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](https://riceprompt.app)** — 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 <http://www.apache.org/licenses/LICENSE-2.0>) - MIT license ([LICENSE-MIT](LICENSE-MIT) or <http://opensource.org/licenses/MIT>) 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.