feat(opencode): plumb nativeTools through StreamInput (audit gap #4 phase 2 step 2a)
Adds opt-in `nativeTools?: ReadonlyArray<Tool.Def>` to `LLM.StreamInput` so callers that route through the native path can attach typed opencode tool definitions alongside the AI SDK `tools` record. The gate in `runNative` widens accordingly: a session can use the native path when it has zero tools (existing behavior) OR when it explicitly provides `nativeTools` matching its AI SDK `tools` (new opt-in). When `nativeTools` reaches `LLMNative.request`, the existing `toolDefinition` converter folds each `Tool.Def` into the request's `tools` array and the LLM core lowers it onto the wire. This commit deliberately does NOT include the dispatch loop. A session that opts in by setting `nativeTools` and that triggers a `tool-call` from the model will see the call event but no `tool-result` because the native path has no execute handler yet. That's why no production caller populates `nativeTools`: phase 2 step 2b will land the dispatch loop and only then will real production sessions route through here. What this lays in place: - `StreamInput.nativeTools` typed against `Tool.Def[]` from `@/tool`. Aliased to `OpenCodeTool` at the import to dodge a clash with the AI SDK `Tool` type that the same file already imports. - The `runNative` gate flips from "no tools allowed" to "either no tools, or `nativeTools` is supplied". An AI SDK tool count > 0 with `nativeTools` undefined still falls through, so existing production sessions are unaffected. - `LLMNative.request` already accepted `tools: ReadonlyArray<Tool.Def>` and converts via `toolDefinition`. We just forward the input through; no LLM-bridge change. Smoke coverage: a new test in `llm-native-stream.test.ts` builds a typed `Tool.Def` (Effect Schema parameters), routes it through `LLMNative.request` + `LLMClient.prepare`, and asserts the prepared Anthropic target carries the tool as an `input_schema` block with the expected JSON Schema shape. This validates the conversion path that phase 2 step 2b will exercise from inside `runNative`. Verification: opencode typecheck clean; 35/0/0 across the three bridge-area tests (`llm-native.test.ts`, `llm-native-stream.test.ts`, `llm-bridge.test.ts`).
This commit is contained in:
Kit Langton
@@ -22,6 +22,8 @@ import { Config } from "@/config/config"
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import { InstanceState } from "@/effect/instance-state"
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import type { Agent } from "@/agent/agent"
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import type { MessageV2 } from "./message-v2"
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// Aliased to avoid a name clash with the AI SDK `Tool` type imported above.
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import type { Tool as OpenCodeTool } from "@/tool"
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import { Plugin } from "@/plugin"
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import { SystemPrompt } from "./system"
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import { Flag } from "@opencode-ai/core/flag/flag"
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@@ -60,6 +62,13 @@ export type StreamInput = {
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retries?: number
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toolChoice?: "auto" | "required" | "none"
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nativeMessages?: ReadonlyArray<MessageV2.WithParts>
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// Opcode-native `Tool.Def[]` parallel to `tools` (AI SDK shape). When
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// populated alongside `tools`, the LLM-native path forwards definitions to
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// the model. Dispatch + multi-round tool loops land in Phase 2 step 2b; for
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// now the request can carry tools but the gate keeps real production tool
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// sessions on the AI SDK path because no production caller populates this
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// field yet.
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nativeTools?: ReadonlyArray<OpenCodeTool.Def>
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}
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export type StreamRequest = StreamInput & {
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@@ -478,7 +487,13 @@ const live: Layer.Layer<
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const runNative = Effect.fn("LLM.runNative")(function* (input: StreamRequest) {
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if (!Flag.OPENCODE_EXPERIMENTAL_LLM_NATIVE) return undefined
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if (!input.nativeMessages || input.nativeMessages.length === 0) return undefined
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if (Object.keys(input.tools).length > 0) return undefined
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// Tools without dispatch wiring would mean the model issues tool-call
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// events that never get a tool-result. The gate fall-through keeps
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// tool-using sessions on the AI SDK path until step 2b lands the
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// dispatch loop. Sessions with zero tools, OR sessions that explicitly
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// opt in by populating `nativeTools`, can route here.
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const hasAITools = Object.keys(input.tools).length > 0
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if (hasAITools && (input.nativeTools === undefined || input.nativeTools.length === 0)) return undefined
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const item = yield* provider.getProvider(input.model.providerID)
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const llmRequest = yield* LLMNative.request({
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@@ -487,6 +502,7 @@ const live: Layer.Layer<
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model: input.model,
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system: input.system,
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messages: input.nativeMessages,
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tools: input.nativeTools,
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})
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if (!NATIVE_PROTOCOLS.has(llmRequest.model.protocol)) return undefined
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@@ -10,7 +10,7 @@ import {
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ProviderPatch,
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RequestExecutor,
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} from "@opencode-ai/llm"
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import { Effect, Layer, Stream } from "effect"
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import { Effect, Layer, Schema, Stream } from "effect"
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import { HttpClient, HttpClientResponse } from "effect/unstable/http"
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import { ModelID, ProviderID } from "../../src/provider/schema"
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import { MessageID, PartID, SessionID } from "../../src/session/schema"
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@@ -20,6 +20,7 @@ import { ProviderTest } from "../fake/provider"
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import { testEffect } from "../lib/effect"
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import type { MessageV2 } from "../../src/session/message-v2"
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import type { Provider } from "../../src/provider"
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import type { Tool } from "../../src/tool"
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// Inline HTTP layer that returns a single fixed body. Mirrors the
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// `fixedResponse` helper in `packages/llm/test/lib/http.ts` — duplicated here
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@@ -148,4 +149,51 @@ describe("LLMNative stream wire-up (audit gap #4 phase 1)", () => {
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expect(collected.some((event) => event.type === "error")).toBe(false)
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}),
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)
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// Phase 2 step 2a: verifies a tool-bearing `nativeTools` array reaches the
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// wire as Anthropic `tools[]` blocks. The model in this fixture answers with
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// plain text instead of issuing a tool call (we don't yet have dispatch).
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// This proves tool definitions plumb through `LLMNative.request` →
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// `LLMRequest` → adapter `prepare` → wire body.
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it.effect("forwards nativeTools to the wire as Anthropic tools when the gate is open", () =>
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Effect.gen(function* () {
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const mdl = anthropicModel()
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const provider = ProviderTest.info({ id: ProviderID.make("anthropic"), key: "anthropic-key" }, mdl)
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const userID = MessageID.ascending()
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const lookupParameters = Schema.Struct({
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query: Schema.String.annotate({ description: "Search query" }),
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})
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const lookupTool: Tool.Def<typeof lookupParameters> = {
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id: "lookup",
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description: "Lookup project data",
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parameters: lookupParameters,
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execute: () => Effect.succeed({ title: "", metadata: {}, output: "" }),
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}
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const llmRequest = yield* LLMNative.request({
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id: "smoke-tools",
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provider,
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model: mdl,
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system: ["You are concise."],
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messages: [userMessage(mdl, userID, [userPart(userID, "Look something up.")])],
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tools: [lookupTool],
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})
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const prepared = yield* LLMClient.make({ adapters, patches: ProviderPatch.defaults }).prepare(llmRequest)
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expect(prepared.target).toMatchObject({
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tools: [
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{
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name: "lookup",
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description: "Lookup project data",
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input_schema: {
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type: "object",
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properties: { query: { type: "string", description: "Search query" } },
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required: ["query"],
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},
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},
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],
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})
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}),
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)
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})
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