Quickstart

import asyncio
from icrl import Agent, LiteLLMProvider

class SimpleEnv:
    def __init__(self):
        self._goal = ""

    def reset(self, goal: str) -> str:
        self._goal = goal
        return "Ready"

    def step(self, action: str) -> tuple[str, bool, bool]:
        if action.strip().lower() == "done":
            return "Completed", True, True
        return f"Observed action: {action}", False, False

agent = Agent(
    llm=LiteLLMProvider(model="gpt-4o-mini"),
    db_path="./trajectories",
    plan_prompt="Goal: {goal}\n\nExamples:\n{examples}\n\nCreate a short plan.",
    reason_prompt="Goal: {goal}\nPlan: {plan}\nObservation: {observation}\nHistory:\n{history}\nExamples:\n{examples}\n\nThink:",
    act_prompt="Goal: {goal}\nPlan: {plan}\nReasoning: {reasoning}\nObservation: {observation}\n\nRespond with one action.",
)

async def main() -> None:
    env = SimpleEnv()
    await agent.train(env, "finish this task")
    await agent.run(env, "finish this task again")

asyncio.run(main())

import OpenAI from "openai";
import { Agent, FileSystemAdapter, OpenAIEmbedder, OpenAIProvider, type Environment } from "icrl";

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

class SimpleEnv implements Environment {
  reset(goal: string): string {
    return `Goal: ${goal}`;
  }

  async step(action: string) {
    if (action.trim().toLowerCase() === "done") {
      return { observation: "Completed", done: true, success: true };
    }
    return { observation: `Observed action: ${action}`, done: false, success: false };
  }
}

const agent = new Agent({
  llm: new OpenAIProvider(openai, { model: "gpt-4o-mini" }),
  embedder: new OpenAIEmbedder(openai),
  storage: new FileSystemAdapter("./trajectories-ts"),
  planPrompt: "Goal: {goal}\nExamples:\n{examples}\nCreate a short plan.",
  reasonPrompt:
    "Goal: {goal}\nPlan: {plan}\nObservation: {observation}\nHistory:\n{history}\nExamples:\n{examples}\nThink:",
  actPrompt:
    "Goal: {goal}\nPlan: {plan}\nReasoning: {reasoning}\nObservation: {observation}\nRespond with one action.",
});

await agent.init();
await agent.train(new SimpleEnv(), "finish this task");
await agent.run(new SimpleEnv(), "finish this task again");