mirror of
https://github.com/anomalyco/opencode.git
synced 2026-05-15 17:13:12 +00:00
542 lines
20 KiB
TypeScript
542 lines
20 KiB
TypeScript
import { describe, expect } from "bun:test"
|
|
import { Effect, Schema, Stream } from "effect"
|
|
import { GenerationOptions, LLM, LLMEvent, LLMRequest, LLMResponse, ToolChoice } from "../src"
|
|
import { LLMClient } from "../src/route"
|
|
import * as AnthropicMessages from "../src/protocols/anthropic-messages"
|
|
import * as OpenAIChat from "../src/protocols/openai-chat"
|
|
import { tool, ToolFailure, type ToolExecuteContext } from "../src/tool"
|
|
import { ToolRuntime } from "../src/tool-runtime"
|
|
import { it } from "./lib/effect"
|
|
import * as TestToolRuntime from "./lib/tool-runtime"
|
|
import { dynamicResponse, scriptedResponses } from "./lib/http"
|
|
import { deltaChunk, finishChunk, toolCallChunk } from "./lib/openai-chunks"
|
|
import { sseEvents } from "./lib/sse"
|
|
|
|
const model = OpenAIChat.model({
|
|
id: "gpt-4o-mini",
|
|
baseURL: "https://api.openai.test/v1/",
|
|
headers: { authorization: "Bearer test" },
|
|
})
|
|
const Json = Schema.fromJsonString(Schema.Unknown)
|
|
const decodeJson = Schema.decodeUnknownSync(Json)
|
|
|
|
const baseRequest = LLM.request({
|
|
id: "req_1",
|
|
model,
|
|
prompt: "Use the tool.",
|
|
})
|
|
|
|
const get_weather = tool({
|
|
description: "Get current weather for a city.",
|
|
parameters: Schema.Struct({ city: Schema.String }),
|
|
success: Schema.Struct({ temperature: Schema.Number, condition: Schema.String }),
|
|
execute: ({ city }) =>
|
|
Effect.gen(function* () {
|
|
if (city === "FAIL") return yield* new ToolFailure({ message: `Weather lookup failed for ${city}` })
|
|
return { temperature: 22, condition: "sunny" }
|
|
}),
|
|
})
|
|
|
|
const schema_only_weather = tool({
|
|
description: "Get current weather for a city.",
|
|
parameters: Schema.Struct({ city: Schema.String }),
|
|
success: Schema.Struct({ temperature: Schema.Number, condition: Schema.String }),
|
|
})
|
|
|
|
describe("LLMClient tools", () => {
|
|
it.effect("uses the registered model route when adding runtime tools", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(deltaChunk({ role: "assistant", content: "Done." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
expect(LLMResponse.text({ events })).toBe("Done.")
|
|
}),
|
|
)
|
|
|
|
it.effect("sends tool-call history and request options on the follow-up request", () =>
|
|
Effect.gen(function* () {
|
|
const bodies: unknown[] = []
|
|
const responses = [
|
|
sseEvents(toolCallChunk("call_1", "get_weather", '{"city":"Paris"}'), finishChunk("tool_calls")),
|
|
sseEvents(deltaChunk({ role: "assistant", content: "It's sunny in Paris." }), finishChunk("stop")),
|
|
]
|
|
const layer = dynamicResponse((input) =>
|
|
Effect.sync(() => {
|
|
bodies.push(decodeJson(input.text))
|
|
return input.respond(responses[bodies.length - 1] ?? responses[responses.length - 1], {
|
|
headers: { "content-type": "text/event-stream" },
|
|
})
|
|
}),
|
|
)
|
|
|
|
yield* TestToolRuntime.runTools({
|
|
request: LLMRequest.update(baseRequest, {
|
|
generation: GenerationOptions.make({ maxTokens: 50 }),
|
|
toolChoice: ToolChoice.make("auto"),
|
|
}),
|
|
tools: { get_weather },
|
|
}).pipe(Stream.runCollect, Effect.provide(layer))
|
|
|
|
const second = bodies[1] as {
|
|
readonly messages?: ReadonlyArray<Record<string, unknown>>
|
|
readonly tools?: ReadonlyArray<unknown>
|
|
readonly tool_choice?: unknown
|
|
readonly max_tokens?: unknown
|
|
}
|
|
|
|
expect(second.max_tokens).toBe(50)
|
|
expect(second.tool_choice).toBe("auto")
|
|
expect(second.tools).toHaveLength(1)
|
|
expect(second.messages?.map((message) => message.role)).toEqual(["user", "assistant", "tool"])
|
|
expect(second.messages?.[1]).toMatchObject({
|
|
role: "assistant",
|
|
content: null,
|
|
tool_calls: [{ id: "call_1", type: "function", function: { name: "get_weather" } }],
|
|
})
|
|
expect(second.messages?.[2]).toMatchObject({
|
|
role: "tool",
|
|
tool_call_id: "call_1",
|
|
content: '{"temperature":22,"condition":"sunny"}',
|
|
})
|
|
}),
|
|
)
|
|
|
|
it.effect("dispatches a tool call, appends results, and resumes streaming", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(toolCallChunk("call_1", "get_weather", '{"city":"Paris"}'), finishChunk("tool_calls")),
|
|
sseEvents(deltaChunk({ role: "assistant", content: "It's sunny in Paris." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
const result = events.find(LLMEvent.is.toolResult)
|
|
expect(result).toMatchObject({
|
|
type: "tool-result",
|
|
id: "call_1",
|
|
name: "get_weather",
|
|
result: { type: "json", value: { temperature: 22, condition: "sunny" } },
|
|
})
|
|
expect(events.at(-1)?.type).toBe("finish")
|
|
expect(LLMResponse.text({ events })).toBe("It's sunny in Paris.")
|
|
}),
|
|
)
|
|
|
|
it.effect("executes tool calls for one step without looping by default", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(toolCallChunk("call_1", "get_weather", '{"city":"Paris"}'), finishChunk("tool_calls")),
|
|
sseEvents(deltaChunk({ role: "assistant", content: "Should not run." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* LLMClient.stream({ request: baseRequest, tools: { get_weather } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
expect(events.filter(LLMEvent.is.finish)).toHaveLength(1)
|
|
expect(events.find(LLMEvent.is.toolResult)).toMatchObject({ type: "tool-result", id: "call_1" })
|
|
}),
|
|
)
|
|
|
|
it.effect("passes tool call context to execute", () =>
|
|
Effect.gen(function* () {
|
|
let context: ToolExecuteContext | undefined
|
|
const contextual = tool({
|
|
description: "Capture tool context.",
|
|
parameters: Schema.Struct({ value: Schema.String }),
|
|
success: Schema.Struct({ ok: Schema.Boolean }),
|
|
execute: (_params, ctx) =>
|
|
Effect.sync(() => {
|
|
context = ctx
|
|
return { ok: true }
|
|
}),
|
|
})
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { contextual } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(
|
|
scriptedResponses([
|
|
sseEvents(toolCallChunk("call_ctx", "contextual", '{"value":"x"}'), finishChunk("tool_calls")),
|
|
]),
|
|
),
|
|
),
|
|
)
|
|
|
|
expect(events.some(LLMEvent.is.toolResult)).toBe(true)
|
|
expect(context).toEqual({ id: "call_ctx", name: "contextual" })
|
|
}),
|
|
)
|
|
|
|
it.effect("can expose tool schemas without executing tool calls", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(toolCallChunk("call_1", "get_weather", '{"city":"Paris"}'), finishChunk("tool_calls")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* LLMClient.stream({
|
|
request: baseRequest,
|
|
tools: { get_weather: schema_only_weather },
|
|
toolExecution: "none",
|
|
}).pipe(Stream.runCollect, Effect.provide(layer)),
|
|
)
|
|
|
|
expect(events.find(LLMEvent.is.toolCall)).toMatchObject({ type: "tool-call", id: "call_1" })
|
|
expect(events.find(LLMEvent.is.toolResult)).toBeUndefined()
|
|
}),
|
|
)
|
|
|
|
it.effect("preserves provider metadata when folding streamed assistant content into follow-up history", () =>
|
|
Effect.gen(function* () {
|
|
const bodies: unknown[] = []
|
|
const layer = dynamicResponse((input) =>
|
|
Effect.sync(() => {
|
|
bodies.push(decodeJson(input.text))
|
|
return input.respond(
|
|
bodies.length === 1
|
|
? sseEvents(
|
|
{ type: "message_start", message: { usage: { input_tokens: 5 } } },
|
|
{ type: "content_block_start", index: 0, content_block: { type: "thinking", thinking: "" } },
|
|
{ type: "content_block_delta", index: 0, delta: { type: "thinking_delta", thinking: "thinking" } },
|
|
{ type: "content_block_delta", index: 0, delta: { type: "signature_delta", signature: "sig_1" } },
|
|
{ type: "content_block_stop", index: 0 },
|
|
{
|
|
type: "content_block_start",
|
|
index: 1,
|
|
content_block: { type: "tool_use", id: "call_1", name: "get_weather" },
|
|
},
|
|
{
|
|
type: "content_block_delta",
|
|
index: 1,
|
|
delta: { type: "input_json_delta", partial_json: '{"city":"Paris"}' },
|
|
},
|
|
{ type: "content_block_stop", index: 1 },
|
|
{ type: "message_delta", delta: { stop_reason: "tool_use" }, usage: { output_tokens: 5 } },
|
|
)
|
|
: sseEvents(
|
|
{ type: "message_start", message: { usage: { input_tokens: 5 } } },
|
|
{ type: "content_block_start", index: 0, content_block: { type: "text", text: "" } },
|
|
{ type: "content_block_delta", index: 0, delta: { type: "text_delta", text: "Done." } },
|
|
{ type: "content_block_stop", index: 0 },
|
|
{ type: "message_delta", delta: { stop_reason: "end_turn" }, usage: { output_tokens: 1 } },
|
|
),
|
|
{ headers: { "content-type": "text/event-stream" } },
|
|
)
|
|
}),
|
|
)
|
|
|
|
yield* TestToolRuntime.runTools({
|
|
request: LLM.updateRequest(baseRequest, {
|
|
model: AnthropicMessages.model({ id: "claude-sonnet-4-5", apiKey: "test" }),
|
|
}),
|
|
tools: { get_weather },
|
|
}).pipe(Stream.runCollect, Effect.provide(layer))
|
|
|
|
expect(bodies[1]).toMatchObject({
|
|
messages: [
|
|
{ role: "user" },
|
|
{
|
|
role: "assistant",
|
|
content: [
|
|
{ type: "thinking", thinking: "thinking", signature: "sig_1" },
|
|
{ type: "tool_use", id: "call_1", name: "get_weather", input: { city: "Paris" } },
|
|
],
|
|
},
|
|
{ role: "user", content: [{ type: "tool_result", tool_use_id: "call_1" }] },
|
|
],
|
|
})
|
|
}),
|
|
)
|
|
|
|
it.effect("emits tool-error for unknown tools so the model can self-correct", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(toolCallChunk("call_1", "missing_tool", "{}"), finishChunk("tool_calls")),
|
|
sseEvents(deltaChunk({ role: "assistant", content: "Sorry." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
const toolError = events.find(LLMEvent.is.toolError)
|
|
expect(toolError).toMatchObject({ type: "tool-error", id: "call_1", name: "missing_tool" })
|
|
expect(toolError?.message).toContain("Unknown tool")
|
|
expect(events.find(LLMEvent.is.toolResult)).toMatchObject({
|
|
type: "tool-result",
|
|
id: "call_1",
|
|
name: "missing_tool",
|
|
result: { type: "error", value: "Unknown tool: missing_tool" },
|
|
})
|
|
}),
|
|
)
|
|
|
|
it.effect("emits tool-error when the LLM input fails the parameters schema", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(toolCallChunk("call_1", "get_weather", '{"city":42}'), finishChunk("tool_calls")),
|
|
sseEvents(deltaChunk({ role: "assistant", content: "Done." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
const toolError = events.find(LLMEvent.is.toolError)
|
|
expect(toolError).toMatchObject({ type: "tool-error", id: "call_1", name: "get_weather" })
|
|
expect(toolError?.message).toContain("Invalid tool input")
|
|
}),
|
|
)
|
|
|
|
it.effect("emits tool-error when the handler returns a ToolFailure", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(toolCallChunk("call_1", "get_weather", '{"city":"FAIL"}'), finishChunk("tool_calls")),
|
|
sseEvents(deltaChunk({ role: "assistant", content: "Sorry." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
const toolError = events.find(LLMEvent.is.toolError)
|
|
expect(toolError).toMatchObject({ type: "tool-error", id: "call_1", name: "get_weather" })
|
|
expect(toolError?.message).toBe("Weather lookup failed for FAIL")
|
|
}),
|
|
)
|
|
|
|
it.effect("stops when the model finishes without requesting more tools", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(deltaChunk({ role: "assistant", content: "Done." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
expect(events.map((event) => event.type)).toEqual([
|
|
"step-start",
|
|
"text-start",
|
|
"text-delta",
|
|
"text-end",
|
|
"step-finish",
|
|
"finish",
|
|
])
|
|
expect(LLMResponse.text({ events })).toBe("Done.")
|
|
}),
|
|
)
|
|
|
|
it.effect("respects maxSteps and stops the loop", () =>
|
|
Effect.gen(function* () {
|
|
// Every script entry asks for another tool call. With maxSteps: 2 the
|
|
// runtime should run at most two model rounds and then exit even though
|
|
// the model still wants to keep going.
|
|
const toolCallStep = sseEvents(
|
|
toolCallChunk("call_x", "get_weather", '{"city":"Paris"}'),
|
|
finishChunk("tool_calls"),
|
|
)
|
|
const layer = scriptedResponses([toolCallStep, toolCallStep, toolCallStep])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather }, maxSteps: 2 }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
expect(events.filter(LLMEvent.is.finish)).toHaveLength(1)
|
|
expect(events.filter(LLMEvent.is.stepStart).map((event) => event.index)).toEqual([0, 1])
|
|
expect(events.filter(LLMEvent.is.stepFinish).map((event) => event.index)).toEqual([0, 1])
|
|
}),
|
|
)
|
|
|
|
it.effect("emits one final finish with aggregate usage", () =>
|
|
Effect.gen(function* () {
|
|
let calls = 0
|
|
const events = Array.from(
|
|
yield* ToolRuntime.stream({
|
|
request: baseRequest,
|
|
tools: { get_weather },
|
|
stopWhen: ToolRuntime.stepCountIs(2),
|
|
stream: () =>
|
|
Stream.fromIterable<LLMEvent>(
|
|
calls++ === 0
|
|
? [
|
|
LLMEvent.stepStart({ index: 0 }),
|
|
LLMEvent.toolCall({ id: "call_1", name: "get_weather", input: { city: "Paris" } }),
|
|
LLMEvent.stepFinish({
|
|
index: 0,
|
|
reason: "tool-calls",
|
|
usage: { inputTokens: 1, outputTokens: 2, totalTokens: 3 },
|
|
}),
|
|
LLMEvent.finish({
|
|
reason: "tool-calls",
|
|
usage: { inputTokens: 1, outputTokens: 2, totalTokens: 3 },
|
|
}),
|
|
]
|
|
: [
|
|
LLMEvent.stepStart({ index: 0 }),
|
|
LLMEvent.textDelta({ id: "text_1", text: "Done." }),
|
|
LLMEvent.stepFinish({
|
|
index: 0,
|
|
reason: "stop",
|
|
usage: { inputTokens: 4, outputTokens: 5, totalTokens: 9 },
|
|
}),
|
|
LLMEvent.finish({ reason: "stop", usage: { inputTokens: 4, outputTokens: 5, totalTokens: 9 } }),
|
|
],
|
|
),
|
|
}).pipe(Stream.runCollect),
|
|
)
|
|
|
|
expect(events.filter(LLMEvent.is.stepFinish).map((event) => event.index)).toEqual([0, 1])
|
|
expect(events.filter(LLMEvent.is.finish)).toHaveLength(1)
|
|
expect(events.find(LLMEvent.is.finish)?.usage).toMatchObject({
|
|
inputTokens: 5,
|
|
outputTokens: 7,
|
|
totalTokens: 12,
|
|
})
|
|
}),
|
|
)
|
|
|
|
it.effect("stops follow-up when stopWhen returns true after the first step", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(toolCallChunk("call_1", "get_weather", '{"city":"Paris"}'), finishChunk("tool_calls")),
|
|
sseEvents(deltaChunk({ role: "assistant", content: "Should not run." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({
|
|
request: baseRequest,
|
|
tools: { get_weather },
|
|
stopWhen: (state) => state.step >= 0,
|
|
}).pipe(Stream.runCollect, Effect.provide(layer)),
|
|
)
|
|
|
|
expect(events.filter(LLMEvent.is.finish)).toHaveLength(1)
|
|
expect(events.find(LLMEvent.is.toolResult)).toMatchObject({ type: "tool-result", id: "call_1" })
|
|
}),
|
|
)
|
|
|
|
it.effect("does not dispatch provider-executed tool calls", () =>
|
|
Effect.gen(function* () {
|
|
let streams = 0
|
|
const layer = dynamicResponse((input) =>
|
|
Effect.sync(() => {
|
|
streams++
|
|
return input.respond(
|
|
sseEvents(
|
|
{ type: "message_start", message: { usage: { input_tokens: 5 } } },
|
|
{
|
|
type: "content_block_start",
|
|
index: 0,
|
|
content_block: { type: "server_tool_use", id: "srvtoolu_abc", name: "web_search" },
|
|
},
|
|
{
|
|
type: "content_block_delta",
|
|
index: 0,
|
|
delta: { type: "input_json_delta", partial_json: '{"query":"x"}' },
|
|
},
|
|
{ type: "content_block_stop", index: 0 },
|
|
{
|
|
type: "content_block_start",
|
|
index: 1,
|
|
content_block: {
|
|
type: "web_search_tool_result",
|
|
tool_use_id: "srvtoolu_abc",
|
|
content: [{ type: "web_search_result", url: "https://example.com", title: "Example" }],
|
|
},
|
|
},
|
|
{ type: "content_block_stop", index: 1 },
|
|
{ type: "content_block_start", index: 2, content_block: { type: "text", text: "" } },
|
|
{ type: "content_block_delta", index: 2, delta: { type: "text_delta", text: "Done." } },
|
|
{ type: "content_block_stop", index: 2 },
|
|
{ type: "message_delta", delta: { stop_reason: "end_turn" }, usage: { output_tokens: 8 } },
|
|
),
|
|
{ headers: { "content-type": "text/event-stream" } },
|
|
)
|
|
}),
|
|
)
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({
|
|
request: LLM.updateRequest(baseRequest, {
|
|
model: AnthropicMessages.model({ id: "claude-sonnet-4-5", apiKey: "test" }),
|
|
}),
|
|
tools: {},
|
|
}).pipe(Stream.runCollect, Effect.provide(layer)),
|
|
)
|
|
|
|
expect(streams).toBe(1)
|
|
expect(events.find(LLMEvent.is.toolError)).toBeUndefined()
|
|
expect(events.filter(LLMEvent.is.toolCall)).toEqual([
|
|
{
|
|
type: "tool-call",
|
|
id: "srvtoolu_abc",
|
|
name: "web_search",
|
|
input: { query: "x" },
|
|
providerExecuted: true,
|
|
},
|
|
])
|
|
expect(LLMResponse.text({ events })).toBe("Done.")
|
|
}),
|
|
)
|
|
|
|
it.effect("dispatches multiple tool calls in one step concurrently", () =>
|
|
Effect.gen(function* () {
|
|
const layer = scriptedResponses([
|
|
sseEvents(
|
|
deltaChunk({
|
|
role: "assistant",
|
|
tool_calls: [
|
|
{ index: 0, id: "c1", function: { name: "get_weather", arguments: '{"city":"Paris"}' } },
|
|
{ index: 1, id: "c2", function: { name: "get_weather", arguments: '{"city":"Tokyo"}' } },
|
|
],
|
|
}),
|
|
finishChunk("tool_calls"),
|
|
),
|
|
sseEvents(deltaChunk({ role: "assistant", content: "Both done." }), finishChunk("stop")),
|
|
])
|
|
|
|
const events = Array.from(
|
|
yield* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather } }).pipe(
|
|
Stream.runCollect,
|
|
Effect.provide(layer),
|
|
),
|
|
)
|
|
|
|
const results = events.filter(LLMEvent.is.toolResult)
|
|
expect(results).toHaveLength(2)
|
|
expect(results.map((event) => event.id).toSorted()).toEqual(["c1", "c2"])
|
|
}),
|
|
)
|
|
})
|