fix(anthropic): tool_choice format and tool response parsing (#5537)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

mem0-ts/src/oss/src/config/manager.ts

@@ -112,6 +112,13 @@ export class ConfigManager {
               | undefined) ??
             userConf?.url ??
             defaultConf.baseURL;
+          const llmRaw = userConf as Record<string, unknown> | undefined;
+          const temperature =
+            userConf?.temperature ??
+            (llmRaw?.temperature as number | undefined);
+          const topP = userConf?.topP ?? (llmRaw?.top_p as number | undefined);
+          const maxTokens =
+            userConf?.maxTokens ?? (llmRaw?.max_tokens as number | undefined);
 
           return {
             baseURL: llmBaseURL,
@@ -125,6 +132,9 @@ export class ConfigManager {
               userConf?.modelProperties !== undefined
                 ? userConf.modelProperties
                 : defaultConf.modelProperties,
+            temperature,
+            topP,
+            maxTokens,
           };
         })(),
       },

mem0-ts/src/oss/src/llms/anthropic.ts

@@ -5,6 +5,9 @@ import { LLMConfig, Message } from "../types";
 export class AnthropicLLM implements LLM {
   private client: Anthropic;
   private model: string;
+  private maxTokens: number;
+  private temperature?: number;
+  private topP?: number;
 
   constructor(config: LLMConfig) {
     const apiKey = config.apiKey || process.env.ANTHROPIC_API_KEY;
@@ -12,18 +15,24 @@ export class AnthropicLLM implements LLM {
       throw new Error("Anthropic API key is required");
     }
     this.client = new Anthropic({ apiKey });
-    this.model = config.model || "claude-3-sonnet-20240229";
+    this.model = config.model || "claude-sonnet-4-6";
+    // Defaults mirror the Python provider's AnthropicConfig
+    // (max_tokens=2000, temperature=0.1, top_p omitted).
+    this.maxTokens = config.maxTokens ?? 2000;
+    this.temperature = config.temperature ?? 0.1;
+    this.topP = config.topP;
   }
 
   async generateResponse(
     messages: Message[],
     responseFormat?: { type: string },
-  ): Promise<string> {
+    tools?: any[],
+  ): Promise<string | LLMResponse> {
     // Extract system message if present
     const systemMessage = messages.find((msg) => msg.role === "system");
     const otherMessages = messages.filter((msg) => msg.role !== "system");
 
-    const response = await this.client.messages.create({
+    const params: Anthropic.MessageCreateParamsNonStreaming = {
       model: this.model,
       messages: otherMessages.map((msg) => ({
         role: msg.role as "user" | "assistant",
@@ -36,8 +45,41 @@ export class AnthropicLLM implements LLM {
         typeof systemMessage?.content === "string"
           ? systemMessage.content
           : undefined,
-      max_tokens: 4096,
+      max_tokens: this.maxTokens,
-    });
+    };
+
+    // Anthropic rejects requests that include both temperature and top_p;
+    // prefer temperature, matching the Python provider's _get_common_params.
+    if (this.temperature !== undefined) {
+      params.temperature = this.temperature;
+    } else if (this.topP !== undefined) {
+      params.top_p = this.topP;
+    }
+
+    if (tools) {
+      params.tools = tools;
+      params.tool_choice = { type: "auto" };
+    }
+
+    const response = await this.client.messages.create(params);
+
+    if (tools) {
+      let content = "";
+      const toolCalls: Array<{ name: string; arguments: string }> = [];
+
+      for (const block of response.content) {
+        if (block.type === "text") {
+          content = block.text;
+        } else if (block.type === "tool_use") {
+          toolCalls.push({
+            name: block.name,
+            arguments: JSON.stringify(block.input),
+          });
+        }
+      }
+
+      return { content, role: "assistant", toolCalls };
+    }
 
     const firstBlock = response.content[0];
     if (firstBlock.type === "text") {
@@ -49,9 +91,9 @@ export class AnthropicLLM implements LLM {
 
   async generateChat(messages: Message[]): Promise<LLMResponse> {
     const response = await this.generateResponse(messages);
-    return {
+    if (typeof response === "string") {
-      content: response,
+      return { content: response, role: "assistant" };
-      role: "assistant",
+    }
-    };
+    return response;
   }
 }

mem0-ts/src/oss/src/types/index.ts

@@ -49,6 +49,9 @@ export interface LLMConfig {
   model?: string | any;
   modelProperties?: Record<string, any>;
   timeout?: number;
+  temperature?: number;
+  topP?: number;
+  maxTokens?: number;
 }
 
 export interface MemoryConfig {
@@ -131,6 +134,9 @@ export const MemoryConfigSchema = z.object({
       baseURL: z.string().optional(),
       url: z.string().optional(),
       timeout: z.number().optional(),
+      temperature: z.number().optional(),
+      topP: z.number().optional(),
+      maxTokens: z.number().optional(),
     }),
   }),
   historyDbPath: z.string().optional(),

mem0-ts/src/oss/tests/anthropic-llm.test.ts

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

mem0/llms/anthropic.py

@@ -35,7 +35,7 @@ class AnthropicLLM(LLMBase):
         super().__init__(config)
 
         if not self.config.model:
-            self.config.model = "claude-3-5-sonnet-20240620"
+            self.config.model = "claude-sonnet-4-6"
 
         api_key = self.config.api_key or os.getenv("ANTHROPIC_API_KEY")
         self.client = anthropic.Anthropic(api_key=api_key)
@@ -106,7 +106,20 @@ class AnthropicLLM(LLMBase):
 
         if tools:  # TODO: Remove tools if no issues found with new memory addition logic
             params["tools"] = tools
-            params["tool_choice"] = tool_choice
+            params["tool_choice"] = {"type": tool_choice}
 
         response = self.client.messages.create(**params)
+        return self._parse_response(response, tools)
+
+    def _parse_response(self, response, tools):
+        if tools:
+            result = {"content": None, "tool_calls": []}
+            for block in response.content:
+                if block.type == "text":
+                    result["content"] = block.text
+                elif block.type == "tool_use":
+                    result["tool_calls"].append(
+                        {"name": block.name, "arguments": block.input}
+                    )
+            return result
         return response.content[0].text