oris-runtime 0.61.0

An agentic workflow runtime and programmable AI execution system in Rust: stateful graphs, agents, tools, and multi-step execution.
use std::sync::Arc;

use async_trait::async_trait;
use oris_runtime::{
    agent::{AgentExecutor, OpenAiToolAgentBuilder},
    chain::{options::ChainCallOptions, Chain},
    error::ToolError,
    llm::openai::OpenAI,
    memory::SimpleMemory,
    prompt_args,
    tools::{CommandExecutor, DuckDuckGoSearchResults, SerpApi, Tool},
};

use serde_json::Value;
struct Date {}

#[async_trait]
impl Tool for Date {
    fn name(&self) -> String {
        "Date".to_string()
    }
    fn description(&self) -> String {
        "Useful when you need to get the date,input is  a query".to_string()
    }
    async fn run(&self, _input: Value) -> Result<String, ToolError> {
        Ok("25  of november of 2025".to_string())
    }
}

#[tokio::main]
async fn main() {
    let llm = OpenAI::default();
    let memory = SimpleMemory::new();
    let serpapi_tool = SerpApi::default();
    let duckduckgo_tool = DuckDuckGoSearchResults::default();
    let tool_calc = Date {};
    let command_executor = CommandExecutor::default();
    let agent = OpenAiToolAgentBuilder::new()
        .tools(&[
            Arc::new(serpapi_tool),
            Arc::new(tool_calc),
            Arc::new(command_executor),
            Arc::new(duckduckgo_tool),
        ])
        .options(ChainCallOptions::new().with_max_tokens(1000))
        .build(llm)
        .unwrap();

    let executor = AgentExecutor::from_agent(agent).with_memory(memory.into());

    let input_variables = prompt_args! {
        "input" => "What the name of the current dir, And what date is today",
    };

    match executor.invoke(input_variables).await {
        Ok(result) => {
            println!("Result: {:?}", result.replace("\n", " "));
        }
        Err(e) => panic!("Error invoking LLMChain: {:?}", e),
    }
}