fix(anthropic): tool_choice format and tool response parsing (#5537)
Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
This commit is contained in:
Hrushikesh Yadav
co-authored by
kartik-mem0
kartik-mem0
parent
4492e75d04
commit
b55c51e004
@@ -0,0 +1,263 @@
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/// <reference types="jest" />
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/**
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* Anthropic LLM — unit tests (mocked @anthropic-ai/sdk).
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*/
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const mockCreate = jest.fn();
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jest.mock("@anthropic-ai/sdk", () => {
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return jest.fn().mockImplementation(() => ({
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messages: { create: mockCreate },
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}));
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});
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import { AnthropicLLM } from "../src/llms/anthropic";
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describe("AnthropicLLM (unit)", () => {
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beforeEach(() => mockCreate.mockClear());
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it("returns text when no tools are provided and model returns a text block", async () => {
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mockCreate.mockResolvedValueOnce({
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content: [{ type: "text", text: '{"facts": ["fact1"]}' }],
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});
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const llm = new AnthropicLLM({ apiKey: "test-key" });
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const result = await llm.generateResponse([
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{ role: "user", content: "Hello" },
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]);
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expect(mockCreate).toHaveBeenCalledTimes(1);
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expect(result).toBe('{"facts": ["fact1"]}');
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// No tools → tool_choice must NOT be forwarded
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const callArgs = mockCreate.mock.calls[0][0];
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expect(callArgs.tool_choice).toBeUndefined();
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});
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// Bug #1 regression: bare string "auto" must NOT be sent; object form required
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it("forwards tool_choice as { type: 'auto' } (not bare string) when tools are provided", async () => {
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mockCreate.mockResolvedValueOnce({
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content: [
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{
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type: "tool_use",
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id: "toolu_1",
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name: "add_graph_memory",
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input: { source: "Alice", destination: "Bob" },
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},
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],
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});
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const tools = [
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{
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name: "add_graph_memory",
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description: "Add a graph memory",
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input_schema: { type: "object", properties: {} },
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},
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];
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const llm = new AnthropicLLM({ apiKey: "test-key" });
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await llm.generateResponse(
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[{ role: "user", content: "Alice knows Bob" }],
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undefined,
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tools,
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);
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const callArgs = mockCreate.mock.calls[0][0];
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// Must be object form, not a bare string
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expect(callArgs.tool_choice).toEqual({ type: "auto" });
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expect(callArgs.tool_choice).not.toBe("auto");
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});
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// Bug #2 regression: must NOT throw on a tool_use block
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it("does NOT throw when the model returns a tool_use block", async () => {
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mockCreate.mockResolvedValueOnce({
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content: [
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{
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type: "tool_use",
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id: "toolu_1",
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name: "add_graph_memory",
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input: { source: "Alice", destination: "Bob" },
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},
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],
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});
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const tools = [
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{
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name: "add_graph_memory",
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description: "Add a graph memory",
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input_schema: { type: "object", properties: {} },
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},
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];
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const llm = new AnthropicLLM({ apiKey: "test-key" });
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await expect(
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llm.generateResponse(
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[{ role: "user", content: "Alice knows Bob" }],
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undefined,
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tools,
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),
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).resolves.not.toThrow();
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});
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it("parses tool_use blocks into toolCalls with JSON-stringified arguments", async () => {
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const inputObj = { source: "Alice", destination: "Bob" };
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mockCreate.mockResolvedValueOnce({
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content: [
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{
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type: "tool_use",
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id: "toolu_1",
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name: "add_graph_memory",
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input: inputObj,
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},
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],
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});
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const tools = [
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{
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name: "add_graph_memory",
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description: "Add a graph memory",
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input_schema: { type: "object", properties: {} },
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},
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];
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const llm = new AnthropicLLM({ apiKey: "test-key" });
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const result = await llm.generateResponse(
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[{ role: "user", content: "Alice knows Bob" }],
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undefined,
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tools,
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);
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expect(result).toHaveProperty("toolCalls");
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const response = result as {
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content: string;
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role: string;
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toolCalls: Array<{ name: string; arguments: string }>;
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};
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expect(response.toolCalls).toHaveLength(1);
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expect(response.toolCalls[0].name).toBe("add_graph_memory");
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expect(JSON.parse(response.toolCalls[0].arguments)).toEqual(inputObj);
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});
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it("handles a mixed text + tool_use response", async () => {
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const inputObj = { source: "Alice", destination: "Bob" };
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mockCreate.mockResolvedValueOnce({
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content: [
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{ type: "text", text: "Calling the tool now." },
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{
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type: "tool_use",
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id: "toolu_2",
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name: "add_graph_memory",
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input: inputObj,
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},
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],
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});
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const tools = [
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{
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name: "add_graph_memory",
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description: "Add a graph memory",
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input_schema: { type: "object", properties: {} },
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},
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];
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const llm = new AnthropicLLM({ apiKey: "test-key" });
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const result = await llm.generateResponse(
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[{ role: "user", content: "Alice knows Bob" }],
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undefined,
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tools,
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);
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expect(result).toHaveProperty("toolCalls");
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const response = result as {
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content: string;
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role: string;
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toolCalls: Array<{ name: string; arguments: string }>;
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};
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expect(response.content).toBe("Calling the tool now.");
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expect(response.role).toBe("assistant");
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expect(response.toolCalls).toHaveLength(1);
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expect(response.toolCalls[0].name).toBe("add_graph_memory");
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expect(JSON.parse(response.toolCalls[0].arguments)).toEqual(inputObj);
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});
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it("returns a structured response when tools are provided but the model returns only a text block", async () => {
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mockCreate.mockResolvedValueOnce({
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content: [{ type: "text", text: "Just a text response" }],
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});
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const tools = [
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{
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name: "noop",
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description: "No operation",
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input_schema: { type: "object", properties: {} },
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},
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];
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const llm = new AnthropicLLM({ apiKey: "test-key" });
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const result = await llm.generateResponse(
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[{ role: "user", content: "Hello" }],
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undefined,
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tools,
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);
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expect(result).toEqual({
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content: "Just a text response",
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role: "assistant",
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toolCalls: [],
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});
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});
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// Parity with the Python provider's AnthropicConfig defaults:
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// model claude-sonnet-4-6, max_tokens 2000, temperature 0.1, top_p omitted.
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it("sends Python-parity defaults (model, max_tokens, temperature)", async () => {
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mockCreate.mockResolvedValueOnce({
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content: [{ type: "text", text: "ok" }],
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});
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const llm = new AnthropicLLM({ apiKey: "test-key" });
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await llm.generateResponse([{ role: "user", content: "Hi" }]);
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const callArgs = mockCreate.mock.calls[0][0];
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expect(callArgs.model).toBe("claude-sonnet-4-6");
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expect(callArgs.max_tokens).toBe(2000);
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expect(callArgs.temperature).toBe(0.1);
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expect(callArgs.top_p).toBeUndefined();
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});
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it("forwards maxTokens, temperature, and model from config", async () => {
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mockCreate.mockResolvedValueOnce({
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content: [{ type: "text", text: "ok" }],
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});
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const llm = new AnthropicLLM({
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apiKey: "test-key",
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model: "claude-opus-4-8",
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maxTokens: 1024,
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temperature: 0.7,
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});
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await llm.generateResponse([{ role: "user", content: "Hi" }]);
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const callArgs = mockCreate.mock.calls[0][0];
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expect(callArgs.model).toBe("claude-opus-4-8");
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expect(callArgs.max_tokens).toBe(1024);
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expect(callArgs.temperature).toBe(0.7);
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});
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// Anthropic rejects requests with both temperature and top_p set.
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it("never sends both temperature and top_p (prefers temperature)", async () => {
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mockCreate.mockResolvedValueOnce({
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content: [{ type: "text", text: "ok" }],
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});
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const llm = new AnthropicLLM({
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apiKey: "test-key",
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temperature: 0.5,
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topP: 0.9,
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});
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await llm.generateResponse([{ role: "user", content: "Hi" }]);
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const callArgs = mockCreate.mock.calls[0][0];
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expect(callArgs.temperature).toBe(0.5);
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expect(callArgs.top_p).toBeUndefined();
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});
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});
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