Preview native LLM runtime stack (#27114)
This commit is contained in:
Kit Langton
283
packages/opencode/test/session/llm-native-recorded.test.ts
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283
packages/opencode/test/session/llm-native-recorded.test.ts
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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 } 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 { 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 OPENAI_CASSETTE = "session/native-openai-tool-call"
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const ZEN_CASSETTE = "session/native-zen-tool-call"
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const FIXTURES_DIR = path.join(import.meta.dir, "../fixtures/recordings")
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const OPENAI_API_KEY = process.env.OPENCODE_RECORD_OPENAI_API_KEY ?? process.env.OPENAI_API_KEY
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const CONSOLE_TOKEN = process.env.OPENCODE_RECORD_CONSOLE_TOKEN
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const ZEN_ORG_ID = process.env.OPENCODE_RECORD_ZEN_ORG_ID
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const ZEN_API_URL =
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process.env.OPENCODE_RECORD_ZEN_API_URL ?? "https://console.opencode.ai/proxy/connections/fixture/v1"
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const shouldRecord = process.env.RECORD === "true"
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const canRunOpenAI = shouldRecord
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? Boolean(OPENAI_API_KEY)
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: HttpRecorder.hasCassetteSync(OPENAI_CASSETTE, { directory: FIXTURES_DIR })
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const canRunZen = shouldRecord
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? Boolean(CONSOLE_TOKEN && ZEN_ORG_ID)
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: HttpRecorder.hasCassetteSync(ZEN_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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const openAIConfig = (model: ModelsDev.Provider["models"][string]): Partial<Config.Info> => ({
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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: {
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[model.id]: JSON.parse(JSON.stringify(model)) as NonNullable<
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NonNullable<Config.Info["provider"]>[string]["models"]
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>[string],
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},
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options: {
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apiKey: 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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const zenConfig = (model: ModelsDev.Provider["models"][string]): Partial<Config.Info> => ({
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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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api: ZEN_API_URL,
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models: {
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[model.id]: JSON.parse(JSON.stringify(model)) as NonNullable<
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NonNullable<Config.Info["provider"]>[string]["models"]
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>[string],
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},
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options: {
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apiKey: CONSOLE_TOKEN ?? "fixture-console-token",
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headers: {
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"x-org-id": 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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function recordedNativeLLMLayer(cassette: string, metadata: Record<string, unknown>) {
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const cassetteService = HttpRecorder.Cassette.fileSystem({ directory: FIXTURES_DIR }).pipe(
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Layer.provide(NodeFileSystem.layer),
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)
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// Only the HTTP client is recorded; RequestExecutor and the opencode LLM stack remain real.
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const recorder = HttpRecorder.recordingLayer(cassette, {
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mode: shouldRecord ? "record" : "replay",
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metadata,
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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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const executor = RequestExecutor.layer.pipe(Layer.provide(recorder))
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const client = LLMClient.layer.pipe(Layer.provide(executor))
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const providerLayer = 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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const llmLayer = 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(client),
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Layer.provide(cassetteService),
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Layer.provide(RuntimeFlags.layer({ experimentalNativeLlm: true })),
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)
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return Layer.mergeAll(providerLayer, llmLayer)
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}
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const openAIIt = testEffect(
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recordedNativeLLMLayer(OPENAI_CASSETTE, {
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provider: "openai",
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protocol: "openai-responses",
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route: "openai-responses",
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tags: ["opencode", "native", "tool-call"],
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}),
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)
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const zenIt = testEffect(
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recordedNativeLLMLayer(ZEN_CASSETTE, {
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provider: "opencode",
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protocol: "openai-responses",
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route: "openai-responses",
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tags: ["opencode", "zen", "native", "tool-call"],
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}),
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)
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const recordedOpenAIInstance = canRunOpenAI ? openAIIt.instance : openAIIt.instance.skip
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const recordedZenInstance = canRunZen ? zenIt.instance : zenIt.instance.skip
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const writeConfig = (
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directory: string,
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model: ModelsDev.Provider["models"][string],
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config: (model: ModelsDev.Provider["models"][string]) => Partial<Config.Info> = openAIConfig,
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) =>
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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", ...config(model) }),
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),
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)
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const getModel = (providerID: ProviderID, modelID: ModelID) =>
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Effect.gen(function* () {
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const provider = yield* Provider.Service
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return yield* provider.getModel(providerID, modelID)
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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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describe("session.llm native recorded", () => {
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recordedOpenAIInstance("uses real RequestExecutor with HTTP recorder for native OpenAI tools", () =>
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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("openai", "gpt-4.1-mini"))
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yield* writeConfig(test.directory, model)
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const sessionID = SessionID.make("session-recorded-native-tool")
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const agent = {
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name: "test",
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mode: "primary",
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prompt: "Call tools exactly as instructed.",
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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 resolved = yield* getModel(ProviderID.openai, ModelID.make(model.id))
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let executed: unknown
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const events = yield* collect({
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user: {
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id: MessageID.make("msg_user-recorded-native-tool"),
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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: ProviderID.make("openai"), modelID: ModelID.make(model.id) },
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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: ["You must call the lookup tool exactly once with query weather. Do not answer in text."],
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messages: [{ role: "user", content: "Use lookup." }],
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toolChoice: "required",
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tools: {
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lookup: tool({
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description: "Lookup data.",
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inputSchema: z.object({ query: z.string() }),
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execute: async (args, options) => {
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executed = { args, toolCallId: options.toolCallId }
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return { output: "looked up" }
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},
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}),
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},
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})
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expect(events.filter((event) => event.type === "step-finish")).toHaveLength(1)
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expect(events.filter((event) => event.type === "finish")).toHaveLength(1)
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expect(events.some((event) => event.type === "tool-result")).toBe(true)
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expect(executed).toMatchObject({ args: { query: "weather" }, toolCallId: expect.any(String) })
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}),
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)
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recordedZenInstance("uses console-managed Zen config with native OpenAI-compatible tools", () =>
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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("opencode", "gpt-5.2-codex"))
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yield* writeConfig(test.directory, model, zenConfig)
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const sessionID = SessionID.make("session-recorded-native-zen-tool")
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const agent = {
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name: "test",
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mode: "primary",
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prompt: "Call tools exactly as instructed.",
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options: {},
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permission: [{ permission: "*", pattern: "*", action: "allow" }],
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} satisfies Agent.Info
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const resolved = yield* getModel(ProviderID.opencode, ModelID.make(model.id))
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let executed: unknown
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const events = yield* collect({
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user: {
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id: MessageID.make("msg_user-recorded-native-zen-tool"),
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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: ProviderID.opencode, modelID: ModelID.make(model.id) },
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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: ["You must call the lookup tool exactly once with query weather. Do not answer in text."],
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messages: [{ role: "user", content: "Use lookup." }],
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toolChoice: "required",
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tools: {
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lookup: tool({
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description: "Lookup data.",
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inputSchema: z.object({ query: z.string() }),
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execute: async (args, options) => {
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executed = { args, toolCallId: options.toolCallId }
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return { output: "looked up" }
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},
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}),
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},
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})
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expect(events.filter((event) => event.type === "step-finish")).toHaveLength(1)
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expect(events.filter((event) => event.type === "finish")).toHaveLength(1)
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expect(events.some((event) => event.type === "tool-result")).toBe(true)
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expect(executed).toMatchObject({ args: { query: "weather" }, toolCallId: expect.any(String) })
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}),
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)
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})
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