278 lines
10 KiB
TypeScript
278 lines
10 KiB
TypeScript
import { NodeFileSystem } from "@effect/platform-node"
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import { HttpRecorder, Redactor } from "@opencode-ai/http-recorder"
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import { describe, expect } from "bun:test"
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import { tool, type ModelMessage, type JSONValue } from "ai"
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import { Effect, Layer, Stream } from "effect"
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import { FetchHttpClient } from "effect/unstable/http"
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import path from "node:path"
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import z from "zod"
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import { Auth } from "@/auth"
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import { Config } from "@/config/config"
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import { Plugin } from "@/plugin"
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import { Provider } from "@/provider/provider"
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import { ModelID, ProviderID } from "@/provider/schema"
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import { Filesystem } from "@/util/filesystem"
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import { LLMEvent, LLMResponse } from "@opencode-ai/llm"
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import { LLMClient, RequestExecutor } from "@opencode-ai/llm/route"
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import { RuntimeFlags } from "@/effect/runtime-flags"
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import type { Agent } from "../../src/agent/agent"
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import { LLM } from "../../src/session/llm"
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import { MessageV2 } from "../../src/session/message-v2"
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import { MessageID, SessionID } from "../../src/session/schema"
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import type { ModelsDev } from "@opencode-ai/core/models-dev"
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import { TestInstance } from "../fixture/fixture"
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import { testEffect } from "../lib/effect"
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const FIXTURES_DIR = path.join(import.meta.dir, "../fixtures/recordings")
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const zenURL = (connection: string) => `https://console.opencode.ai/proxy/connections/${connection}/v1`
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type ProviderSpec = {
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readonly providerID: ProviderID
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readonly modelID: string
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readonly cassette: string
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readonly protocol: string
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readonly tags: ReadonlyArray<string>
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readonly canRecord: boolean
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readonly config: (model: ModelsDev.Provider["models"][string]) => Partial<Config.Info>
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}
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const cloneModel = (model: ModelsDev.Provider["models"][string]) =>
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structuredClone(model) as NonNullable<NonNullable<Config.Info["provider"]>[string]["models"]>[string]
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const PROVIDERS = {
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openai: {
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providerID: ProviderID.openai,
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modelID: "gpt-4.1-mini",
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cassette: "session/native-openai-tool-loop",
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protocol: "openai-responses",
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tags: ["opencode", "native", "tool-loop"],
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canRecord: Boolean(process.env.OPENCODE_RECORD_OPENAI_API_KEY ?? process.env.OPENAI_API_KEY),
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config: (model) => ({
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enabled_providers: ["openai"],
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provider: {
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openai: {
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name: "OpenAI",
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env: ["OPENAI_API_KEY"],
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npm: "@ai-sdk/openai",
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api: "https://api.openai.com/v1",
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models: { [model.id]: cloneModel(model) },
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options: {
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apiKey: process.env.OPENCODE_RECORD_OPENAI_API_KEY ?? process.env.OPENAI_API_KEY ?? "fixture-openai-key",
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baseURL: "https://api.openai.com/v1",
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},
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},
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},
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}),
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},
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opencode: {
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providerID: ProviderID.opencode,
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modelID: "gpt-5.2-codex",
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cassette: "session/native-zen-tool-loop",
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protocol: "openai-responses",
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tags: ["opencode", "zen", "native", "tool-loop"],
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canRecord: Boolean(process.env.OPENCODE_RECORD_CONSOLE_TOKEN && process.env.OPENCODE_RECORD_ZEN_ORG_ID),
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config: (model) => ({
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enabled_providers: ["opencode"],
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provider: {
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opencode: {
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name: "OpenCode Zen",
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env: ["OPENCODE_CONSOLE_TOKEN"],
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npm: "@ai-sdk/openai-compatible",
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// The connection slug is account-specific; the cassette redactor
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// normalizes it to {connection} for replay. Set during recording.
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api: zenURL(process.env.OPENCODE_RECORD_ZEN_CONNECTION ?? "fixture"),
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models: { [model.id]: cloneModel(model) },
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options: {
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apiKey: process.env.OPENCODE_RECORD_CONSOLE_TOKEN ?? "fixture-console-token",
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headers: { "x-org-id": process.env.OPENCODE_RECORD_ZEN_ORG_ID ?? "fixture-org" },
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},
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},
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},
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}),
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},
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anthropic: {
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providerID: ProviderID.anthropic,
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modelID: "claude-haiku-4-5-20251001",
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cassette: "session/native-anthropic-tool-loop",
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protocol: "anthropic-messages",
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tags: ["opencode", "native", "tool-loop"],
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canRecord: Boolean(process.env.OPENCODE_RECORD_ANTHROPIC_API_KEY ?? process.env.ANTHROPIC_API_KEY),
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config: (model) => ({
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enabled_providers: ["anthropic"],
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provider: {
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anthropic: {
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name: "Anthropic",
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env: ["ANTHROPIC_API_KEY"],
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npm: "@ai-sdk/anthropic",
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api: "https://api.anthropic.com/v1",
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models: { [model.id]: cloneModel(model) },
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options: {
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apiKey:
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process.env.OPENCODE_RECORD_ANTHROPIC_API_KEY ?? process.env.ANTHROPIC_API_KEY ?? "fixture-anthropic-key",
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baseURL: "https://api.anthropic.com/v1",
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},
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},
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},
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}),
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},
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} satisfies Record<string, ProviderSpec>
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const shouldRecord = process.env.RECORD === "true"
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const canRun = (spec: ProviderSpec) =>
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shouldRecord ? spec.canRecord : HttpRecorder.hasCassetteSync(spec.cassette, { directory: FIXTURES_DIR })
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async function loadFixture(providerID: string, modelID: string) {
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const data = await Filesystem.readJson<Record<string, ModelsDev.Provider>>(
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path.join(import.meta.dir, "../tool/fixtures/models-api.json"),
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)
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const provider = data[providerID]
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if (!provider) throw new Error(`Missing provider in fixture: ${providerID}`)
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const model = provider.models[modelID]
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if (!model) throw new Error(`Missing model in fixture: ${modelID}`)
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return model
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}
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function recordedNativeLLMLayer(spec: ProviderSpec) {
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// Only the HTTP client is recorded; RequestExecutor and the opencode LLM stack remain real.
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const recordedClient = LLMClient.layer.pipe(
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Layer.provide(RequestExecutor.layer),
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Layer.provide(
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HttpRecorder.recordingLayer(spec.cassette, {
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mode: shouldRecord ? "record" : "replay",
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metadata: { provider: spec.providerID, protocol: spec.protocol, route: spec.protocol, tags: spec.tags },
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redactor: Redactor.compose(
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Redactor.defaults({
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url: {
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transform: (url) => url.replace(/\/proxy\/connections\/[^/]+\/v1/, "/proxy/connections/{connection}/v1"),
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},
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}),
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{
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response: (snapshot) => ({ ...snapshot, body: snapshot.body.replace(/wrk_[A-Z0-9]+/g, "wrk_redacted") }),
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},
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),
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}).pipe(Layer.provide(FetchHttpClient.layer)),
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),
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)
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return Layer.mergeAll(
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Provider.defaultLayer.pipe(
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Layer.provide(Auth.defaultLayer),
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Layer.provide(Config.defaultLayer),
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Layer.provide(Plugin.defaultLayer),
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),
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LLM.layer.pipe(
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Layer.provide(Auth.defaultLayer),
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Layer.provide(Config.defaultLayer),
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Layer.provide(Provider.defaultLayer),
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Layer.provide(Plugin.defaultLayer),
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Layer.provide(recordedClient),
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Layer.provide(
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HttpRecorder.Cassette.fileSystem({ directory: FIXTURES_DIR }).pipe(Layer.provide(NodeFileSystem.layer)),
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),
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Layer.provide(RuntimeFlags.layer({ experimentalNativeLlm: true })),
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),
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)
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}
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const writeConfig = (directory: string, spec: ProviderSpec, model: ModelsDev.Provider["models"][string]) =>
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Effect.promise(() =>
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Bun.write(
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path.join(directory, "opencode.json"),
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JSON.stringify({ $schema: "https://opencode.ai/config.json", ...spec.config(model) }),
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),
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)
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const collect = (input: LLM.StreamInput) =>
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Effect.gen(function* () {
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const llm = yield* LLM.Service
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return Array.from(yield* llm.stream(input).pipe(Stream.runCollect))
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})
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const WEATHER_RESULT = { temperature: 22, condition: "sunny" } as const
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const WEATHER_SYSTEM =
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"Use the get_weather tool exactly once to look up Paris, then reply with exactly: Paris is sunny."
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const WEATHER_USER = "What is the weather in Paris?"
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const weatherTool = tool({
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description: "Get the current weather for a city.",
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inputSchema: z.object({ city: z.string() }),
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execute: async () => WEATHER_RESULT,
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})
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const toolRoundtrip = (
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call: { readonly id: string; readonly name: string; readonly input: unknown },
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result: JSONValue,
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): ModelMessage[] => [
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{ role: "assistant", content: [{ type: "tool-call", toolCallId: call.id, toolName: call.name, input: call.input }] },
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{
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role: "tool",
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content: [
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{ type: "tool-result", toolCallId: call.id, toolName: call.name, output: { type: "json", value: result } },
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],
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},
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]
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const driveToolLoop = (spec: ProviderSpec) =>
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Effect.gen(function* () {
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const test = yield* TestInstance
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const model = yield* Effect.promise(() => loadFixture(spec.providerID, spec.modelID))
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yield* writeConfig(test.directory, spec, model)
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const sessionID = SessionID.make(`session-recorded-${spec.providerID}-loop`)
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const modelID = ModelID.make(model.id)
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const agent = {
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name: "test",
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mode: "primary",
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prompt: "Answer using tools when appropriate.",
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options: {},
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permission: [{ permission: "*", pattern: "*", action: "allow" }],
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temperature: 0,
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} satisfies Agent.Info
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const provider = yield* Provider.Service
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const resolved = yield* provider.getModel(spec.providerID, modelID)
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const userMessage = { role: "user", content: WEATHER_USER } satisfies ModelMessage
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const base = {
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user: {
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id: MessageID.make(`msg_user-recorded-${spec.providerID}-loop`),
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sessionID,
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role: "user",
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time: { created: 0 },
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agent: agent.name,
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model: { providerID: spec.providerID, modelID },
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} satisfies MessageV2.User,
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sessionID,
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model: resolved,
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agent,
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system: [WEATHER_SYSTEM],
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tools: { get_weather: weatherTool },
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}
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const turn1 = yield* collect({ ...base, messages: [userMessage] })
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const toolCall = turn1.find(LLMEvent.is.toolCall)
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expect(toolCall).toBeDefined()
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expect(turn1.find(LLMEvent.is.toolResult)).toBeDefined()
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expect(toolCall!.name).toBe("get_weather")
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expect(toolCall!.input).toMatchObject({ city: expect.stringMatching(/Paris/i) })
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expect(turn1.filter(LLMEvent.is.stepFinish)).toHaveLength(1)
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const turn2 = yield* collect({
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...base,
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messages: [userMessage, ...toolRoundtrip(toolCall!, WEATHER_RESULT)],
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})
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expect(LLMResponse.text({ events: turn2 })).toMatch(/Paris is sunny/i)
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expect(turn2.filter(LLMEvent.is.finish)).toHaveLength(1)
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expect(turn2.filter(LLMEvent.is.toolCall)).toHaveLength(0)
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})
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describe("session.llm native recorded", () => {
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for (const [name, spec] of Object.entries(PROVIDERS)) {
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const it = testEffect(recordedNativeLLMLayer(spec))
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const instance = canRun(spec) ? it.instance : it.instance.skip
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instance(`${name}: drives a tool loop to a final text answer`, () => driveToolLoop(spec))
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}
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})
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