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const model = OpenAIChat.route .with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") }) .model({ id: "gpt-4o-mini" })const Json = Schema.fromJsonString(Schema.Unknown)const decodeJson = Schema.decodeUnknownSync(Json)
const baseRequest = LLM.request({ id: "req_1", model, prompt: "Use the tool.",})const weatherFailureCause = new Error("weather lookup denied")
test("dispatches namespaced calls by qualified identity", async () => { let context: ToolExecuteContext | undefined const lookup = Tool.make({ description: "Look up a customer.", parameters: Schema.Struct({}), success: Schema.String, execute: (_, value) => { context = value return Effect.succeed("customer") }, }) const call = ToolCallPart.make({ id: "call_1", namespace: "crm", name: "lookup", input: {} }) const result = await Effect.runPromise( ToolRuntime.dispatch({ "crm.lookup": lookup, lookup: schema_only_weather }, call), )
expect(result.result).toEqual({ type: "text", value: "customer" }) expect(context).toEqual({ id: "call_1", namespace: "crm", name: "lookup" }) expect(result.events).toEqual([expect.objectContaining({ type: "tool-result", namespace: "crm", name: "lookup" })])})
const get_weather = Tool.make({ 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}`, error: weatherFailureCause }) return { temperature: 22, condition: "sunny" } }),})
const schema_only_weather = Tool.make({ 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 selected 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] if (!second || typeof second !== "object") throw new Error("Expected second request body") const messages = "messages" in second ? second.messages : undefined const tools = "tools" in second ? second.tools : undefined
expect("max_completion_tokens" in second ? second.max_completion_tokens : undefined).toBe(50) expect("tool_choice" in second ? second.tool_choice : undefined).toBe("auto") expect(tools).toHaveLength(1) expect( Array.isArray(messages) ? messages.map((message) => message && typeof message === "object" && "role" in message ? message.role : undefined, ) : undefined, ).toEqual(["user", "assistant", "tool"]) expect(Array.isArray(messages) ? messages[1] : undefined).toMatchObject({ role: "assistant", content: null, tool_calls: [{ id: "call_1", type: "function", function: { name: "get_weather" } }], }) expect(Array.isArray(messages) ? messages[2] : undefined).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("projects encoded typed tool success into canonical model content", () => Effect.gen(function* () { const calls: unknown[] = [] const projected = Tool.make({ description: "Project an encoded success.", parameters: Schema.Struct({ prefix: Schema.String }), success: Schema.Struct({ count: Schema.NumberFromString }), execute: () => Effect.succeed({ count: 2 }), toModelOutput: (input) => { calls.push(input) return [{ type: "text", text: `${input.parameters.prefix}:${input.output.count}` }] }, })
const dispatched = yield* ToolRuntime.dispatch( { projected }, LLMEvent.toolCall({ id: "call_projected", name: "projected", input: { prefix: "count" } }), )
expect(calls).toEqual([{ id: "call_projected", parameters: { prefix: "count" }, output: { count: "2" } }]) expect(dispatched.result).toEqual({ type: "text", value: "count:2" }) expect(dispatched.output).toEqual({ structured: { count: "2" }, content: [{ type: "text", text: "count:2" }] }) expect(dispatched.events).toEqual([ LLMEvent.toolResult({ id: "call_projected", name: "projected", result: { type: "text", value: "count:2" }, output: { structured: { count: "2" }, content: [{ type: "text", text: "count:2" }] }, }), ]) }), )
it.effect("preserves provider metadata on dispatched tool results", () => Effect.gen(function* () { const tool = Tool.make({ description: "Return text.", parameters: Schema.Struct({}), success: Schema.String, execute: () => Effect.succeed("hello"), }) const providerMetadata = { google: { thoughtSignature: "provider_sig" } } const dispatched = yield* ToolRuntime.dispatch( { tool }, LLMEvent.toolCall({ id: "call_1", name: "tool", input: {}, providerMetadata }), )
expect(dispatched.events).toEqual([ LLMEvent.toolResult({ id: "call_1", name: "tool", result: { type: "text", value: "hello" }, output: { structured: "hello", content: [{ type: "text", text: "hello" }] }, providerMetadata, }), ])
const failed = yield* ToolRuntime.dispatch( {}, LLMEvent.toolCall({ id: "call_2", name: "missing", input: {}, providerMetadata }), ) expect(failed.events).toEqual([ LLMEvent.toolError({ id: "call_2", name: "missing", message: 'No tool named "missing" is currently available. Please use a tool from the available tool list.', providerMetadata, }), LLMEvent.toolResult({ id: "call_2", name: "missing", result: { type: "error", value: 'No tool named "missing" is currently available. Please use a tool from the available tool list.', }, providerMetadata, }), ]) }), )
it.effect("uses the narrow default projection for encoded typed success", () => Effect.gen(function* () { const text = Tool.make({ description: "Return text.", parameters: Schema.Struct({}), success: Schema.String, execute: () => Effect.succeed("hello"), }) const json = Tool.make({ description: "Return JSON.", parameters: Schema.Struct({}), success: Schema.Struct({ ok: Schema.Boolean }), execute: () => Effect.succeed({ ok: true }), })
expect( (yield* ToolRuntime.dispatch({ text }, LLMEvent.toolCall({ id: "call_text", name: "text", input: {} }))).output, ).toEqual({ structured: "hello", content: [{ type: "text", text: "hello" }] }) expect( (yield* ToolRuntime.dispatch({ json }, LLMEvent.toolCall({ id: "call_json", name: "json", input: {} }))).output, ).toEqual({ structured: { ok: true }, content: [] }) }), )
it.effect("can retain model media while redacting duplicated structured payloads", () => Effect.gen(function* () { const image = Tool.make({ description: "Return an image.", parameters: Schema.Struct({}), success: Schema.Struct({ mime: Schema.String, data: Schema.String }), execute: () => Effect.succeed({ mime: "image/png", data: "AAECAw==" }), toStructuredOutput: (output) => ({ mime: output.mime }), toModelOutput: ({ output }) => [ { type: "file", uri: `data:${output.mime};base64,${output.data}`, mime: output.mime }, ], })
const dispatched = yield* ToolRuntime.dispatch( { image }, LLMEvent.toolCall({ id: "call_image", name: "image", input: {} }), )
expect(dispatched.output).toEqual({ structured: { mime: "image/png" }, content: [{ type: "file", uri: "data:image/png;base64,AAECAw==", mime: "image/png" }], }) }), )
test("models canonical tool files with URIs", () => { const decode = Schema.decodeUnknownSync(Content)
expect(decode({ type: "file", uri: "data:image/png;base64,AAAA", mime: "image/png" })).toEqual({ type: "file", uri: "data:image/png;base64,AAAA", mime: "image/png", }) expect(decode({ type: "file", uri: "https://example.test/image.png", mime: "image/png" })).toEqual({ type: "file", uri: "https://example.test/image.png", mime: "image/png", }) expect(decode({ type: "file", uri: "file:///tmp/image.png", mime: "image/png" })).toEqual({ type: "file", uri: "file:///tmp/image.png", mime: "image/png", }) })
test("preserves canonical tool file URIs", () => { expect( ToolOutput.toResultValue( ToolOutput.make({}, [{ type: "file", uri: "data:image/png;base64,AAAA", mime: "image/png" }]), ), ).toEqual({ type: "content", value: [{ type: "file", uri: "data:image/png;base64,AAAA", mime: "image/png" }], }) expect( ToolOutput.toResultValue( ToolOutput.make({}, [{ type: "file", uri: "https://example.test/image.png", mime: "image/png" }]), ), ).toEqual({ type: "content", value: [{ type: "file", uri: "https://example.test/image.png", mime: "image/png" }], }) expect( ToolOutput.toResultValue( ToolOutput.make({}, [{ type: "file", uri: "file:///tmp/image.png", mime: "image/png" }]), ), ).toEqual({ type: "content", value: [{ type: "file", uri: "file:///tmp/image.png", mime: "image/png" }], }) expect( ToolOutput.fromResultValue({ type: "content", value: [{ type: "file", uri: "https://example.test/image.png", mime: "image/png" }], }), ).toEqual({ structured: {}, content: [{ type: "file", uri: "https://example.test/image.png", mime: "image/png" }], }) })
it.effect("settles projected URL files as canonical tool results", () => Effect.gen(function* () { const remote = Tool.make({ description: "Return a remote file.", parameters: Schema.Struct({}), success: Schema.Struct({ ok: Schema.Boolean }), execute: () => Effect.succeed({ ok: true }), toModelOutput: () => [{ type: "file", uri: "https://example.test/image.png", mime: "image/png" }], })
const dispatched = yield* ToolRuntime.dispatch( { remote }, LLMEvent.toolCall({ id: "call_remote", name: "remote", input: {} }), )
expect(dispatched.output).toEqual({ structured: { ok: true }, content: [{ type: "file", uri: "https://example.test/image.png", mime: "image/png" }], }) expect(dispatched.result).toEqual({ type: "content", value: [{ type: "file", uri: "https://example.test/image.png", mime: "image/png" }], }) expect(dispatched.events.map((event) => event.type)).toEqual(["tool-result"]) }), )
test("derives typed output schemas and preserves dynamic output schemas", () => { const [typed] = toDefinitions({ get_weather }) const schema = { type: "object", properties: { result: { type: "string" } } } as const const [dynamic] = toDefinitions({ dynamic: Tool.make({ description: "Dynamic tool.", jsonSchema: { type: "object" }, outputSchema: schema }), })
expect(typed?.outputSchema).toMatchObject({ type: "object", properties: { condition: { type: "string" } }, required: ["temperature", "condition"], additionalProperties: false, }) const properties = typed?.outputSchema && "properties" in typed.outputSchema ? typed.outputSchema.properties : undefined expect(properties && "temperature" in properties ? properties.temperature : undefined).toBeDefined() expect(dynamic?.outputSchema).toEqual(schema) })
it.effect("preserves content tool results from dynamic tools", () => Effect.gen(function* () { const screenshot = Tool.make({ description: "Capture a screenshot.", jsonSchema: { type: "object", properties: {} }, execute: () => Effect.succeed({ type: "content" as const, value: [ { type: "text" as const, text: "Screenshot captured." }, { type: "file" as const, uri: "data:image/png;base64,AAAA", mime: "image/png" }, ], }), })
const events = Array.from( yield* TestToolRuntime.runTools({ request: baseRequest, tools: { screenshot }, maxSteps: 1 }).pipe( Stream.runCollect, Effect.provide( scriptedResponses([sseEvents(toolCallChunk("call_1", "screenshot", "{}"), finishChunk("tool_calls"))]), ), ), )
expect(events.find(LLMEvent.is.toolResult)).toMatchObject({ type: "tool-result", id: "call_1", name: "screenshot", result: { type: "content", value: [ { type: "text", text: "Screenshot captured." }, { type: "file", uri: "data:image/png;base64,AAAA", mime: "image/png" }, ], }, }) }), )
it.effect("does not mistake dynamic tool output fields for dispatcher state", () => Effect.gen(function* () { const callerOwned = { type: "json" as const, value: { ok: true }, events: ["caller-owned"] } const eventful = Tool.make({ description: "Return an events field.", jsonSchema: { type: "object", properties: {} }, execute: () => Effect.succeed(callerOwned), })
const dispatched = yield* ToolRuntime.dispatch( { eventful }, LLMEvent.toolCall({ id: "call_1", name: "eventful", input: {} }), )
expect(dispatched.result).toEqual(callerOwned) expect(dispatched.events).toEqual([ LLMEvent.toolResult({ id: "call_1", name: "eventful", result: callerOwned, output: { structured: { ok: true }, content: [] }, }), ]) }), )
it.effect("projects malformed tagged dynamic output as opaque JSON", () => Effect.gen(function* () { const malformed = { type: "content", value: [{ type: "text" }] } const dynamic = Tool.make({ description: "Return caller-defined JSON.", jsonSchema: { type: "object", properties: {} }, execute: () => Effect.succeed(malformed), })
const dispatched = yield* ToolRuntime.dispatch( { dynamic }, LLMEvent.toolCall({ id: "call_1", name: "dynamic", input: {} }), )
expect(dispatched.result).toEqual({ type: "json", value: malformed }) expect(dispatched.output).toEqual({ structured: malformed, content: [] }) expect(dispatched.events).toEqual([ LLMEvent.toolResult({ id: "call_1", name: "dynamic", result: { type: "json", value: malformed }, output: { structured: malformed, content: [] }, }), ]) }), )
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* TestToolRuntime.runTools({ request: baseRequest, tools: { get_weather }, maxSteps: 1 }).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.make({ 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( LLMRequest.update(baseRequest, { tools: toDefinitions({ get_weather: schema_only_weather }) }), ).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 } }, { type: "message_stop" }, ) : 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 } }, { type: "message_stop" }, ), { headers: { "content-type": "text/event-stream" } }, ) }), )
yield* TestToolRuntime.runTools({ request: LLMRequest.update(baseRequest, { model: AnthropicMessages.route .with({ auth: Auth.header("x-api-key", "test") }) .model({ id: "claude-sonnet-4-5" }), }), 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("replays encrypted OpenAI reasoning items with tool outputs", () => 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: "response.output_item.added", item: { type: "reasoning", id: "rs_1", encrypted_content: null }, }, { type: "response.reasoning_summary_part.added", item_id: "rs_1", summary_index: 0 }, { type: "response.reasoning_summary_part.done", item_id: "rs_1", summary_index: 0 }, { type: "response.output_item.done", item: { type: "reasoning", id: "rs_1", encrypted_content: "encrypted-state" }, }, { type: "response.output_item.added", item: { type: "function_call", id: "item_1", call_id: "call_1", name: "get_weather", arguments: "", }, }, { type: "response.function_call_arguments.delta", item_id: "item_1", delta: '{"city":"Paris"}' }, { type: "response.output_item.done", item: { type: "function_call", id: "item_1", call_id: "call_1", name: "get_weather", arguments: '{"city":"Paris"}', }, }, { type: "response.completed", response: {} }, ) : sseEvents( { type: "response.output_text.delta", item_id: "msg_1", delta: "Done." }, { type: "response.completed", response: {} }, ), { headers: { "content-type": "text/event-stream" } }, ) }), )
yield* TestToolRuntime.runTools({ request: LLM.request({ model: OpenAIResponses.route .with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") }) .model({ id: "gpt-5.5" }), prompt: "Use the tool.", providerOptions: { store: false, include: ["reasoning.encrypted_content"] }, }), tools: { get_weather }, }).pipe(Stream.runCollect, Effect.provide(layer))
expect(bodies[1]).toMatchObject({ include: ["reasoning.encrypted_content"], input: [ { role: "user" }, { type: "reasoning", summary: [], encrypted_content: "encrypted-state" }, { type: "function_call", call_id: "call_1", name: "get_weather" }, { type: "function_call_output", call_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).toBe( 'No tool named "missing_tool" is currently available. Please use a tool from the available tool list.', ) expect(events.find(LLMEvent.is.toolResult)).toMatchObject({ type: "tool-result", id: "call_1", name: "missing_tool", result: { type: "error", value: 'No tool named "missing_tool" is currently available. Please use a tool from the available tool list.', }, }) }), )
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") expect(toolError?.error).toBe(weatherFailureCause) }), )
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("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 } }, { type: "message_stop" }, ), { headers: { "content-type": "text/event-stream" } }, ) }), ) const events = Array.from( yield* TestToolRuntime.runTools({ request: LLMRequest.update(baseRequest, { model: AnthropicMessages.route .with({ auth: Auth.header("x-api-key", "test") }) .model({ id: "claude-sonnet-4-5" }), }), 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"]) }), )})