Getting started

Build an agent that classifies a bug report and returns a typed result.

Install

In a TypeScript project with Bun:

bun add @yielded/agent@beta effect

Use an Effect AI provider for model access. See the package map for compatibility.

Create an agent

Save as agent.ts:

import { InMemory, Agent, AgentRuntime } from "@yielded/agent";
import { OpenAiClient, OpenAiLanguageModel } from "@effect/ai-openai";
import { BunRuntime } from "@effect/platform-bun";
import { Config, Console, Effect, Schema } from "effect";
import { Toolkit } from "effect/ai";
import { FetchHttpClient } from "effect/http";

const triage = Agent.make("triage", {
  input: Schema.String,
  output: Schema.Struct({
    severity: Schema.Literals(["low", "medium", "high", "critical"]),
    explanation: Schema.String,
  }),
  instructions: "Classify the bug report by severity. Explain your reasoning in one sentence.",
  toolkit: Toolkit.empty,
  policy: {
    maxTurns: 2,
    maxToolCalls: 1,
    maxDuration: "30 seconds",
  },
});

const program = AgentRuntime.run(triage, "All users get a 500 error when signing in.").pipe(
  Effect.tap((result) => Console.log(result.output)),
  Effect.provide(OpenAiLanguageModel.model("gpt-6-luna")),
  Effect.provide(OpenAiClient.layerConfig({ apiKey: Config.Redacted("OPENAI_API_KEY") })),
  Effect.provide(FetchHttpClient.layer),
  Effect.provide(InMemory.layer),
);

BunRuntime.runMain(program);

The output schema validates the model’s answer. The policy limits the run. InMemory.layer keeps conversation history in memory for the application Scope. To continue a conversation, share that Layer and reuse the returned Thread ID; see in-memory conversations.

Run it

export OPENAI_API_KEY="your-api-key"
bun agent.ts

Example output:

{ "severity": "critical", "explanation": "All users are blocked from signing in." }

Next

Add tools, stream responses, or save thread history.