[pull] main from mem0ai:main

integrations/mem0-plugin/scripts/_search.py

@@ -7,12 +7,31 @@
 from __future__ import annotations
 
 import json
+import os
 import urllib.request
 
 SEARCH_URL = "https://api.mem0.ai/v3/memories/search/"
 SEARCH_TIMEOUT = 5
 
 
+def should_rerank() -> bool:
+    """Whether auto-injection searches should request Platform reranking.
+
+    The REST search endpoint does not rerank when ``rerank`` is omitted, so
+    auto-injected context is ordered by raw vector similarity and the single
+    most relevant memory can fall outside the injected top_k window. We default
+    reranking ON for the hook-driven injection path (the extra ~150-200ms is
+    well within the hook's curl budget) and let users opt out via MEM0_RERANK.
+
+    MEM0_RERANK is read case-insensitively; ``0``, ``false``, ``no``, and
+    ``off`` disable reranking. Anything else (including unset) enables it.
+    """
+    raw = os.environ.get("MEM0_RERANK")
+    if raw is None:
+        return True
+    return raw.strip().lower() not in ("0", "false", "no", "off", "")
+
+
 def _do_search(api_key: str, payload: dict) -> list[dict]:
     body = json.dumps(payload).encode()
     req = urllib.request.Request(

integrations/mem0-plugin/scripts/file_context.py

@@ -23,7 +23,7 @@
 from _formatting import TYPE_ICONS, format_age
 from _identity import resolve_api_key, resolve_user_id
 from _project import resolve_project_id
-from _search import search_memories
+from _search import search_memories, should_rerank
 
 FILE_READ_GATE_MIN_BYTES = 1500
 MAX_RESULTS = 5
@@ -93,6 +93,7 @@ def search_file_context(
         api_key, user_id, project_id, query,
         top_k=MAX_RESULTS, threshold=0.3,
         global_search=global_search,
+        rerank=should_rerank(),
     )
 
     results = results[:MAX_RESULTS]

integrations/mem0-plugin/scripts/on_bash_output.sh

@@ -77,15 +77,16 @@ RESULTS=$(PYTHONPATH="$SCRIPT_DIR" MEM0_SEARCH_QUERY="$ERROR_QUERY" MEM0_SEARCH_
   python3 -c "
 import os, sys
 sys.path.insert(0, os.environ.get('PYTHONPATH', '.'))
-from _search import search_memories, format_results_for_context
+from _search import search_memories, format_results_for_context, should_rerank
 
 api_key = os.environ.get('MEM0_API_KEY', '')
 user_id = os.environ.get('MEM0_SEARCH_USER', 'default')
 project_id = os.environ.get('MEM0_PROJECT_ID', 'unknown')
 query = os.environ.get('MEM0_SEARCH_QUERY', '')
+rerank = should_rerank()
 
-r1 = search_memories(api_key, user_id, project_id, query, metadata_type='anti_pattern', top_k=3)
-r2 = search_memories(api_key, user_id, project_id, query, metadata_type='bug_fix', top_k=3)
+r1 = search_memories(api_key, user_id, project_id, query, metadata_type='anti_pattern', top_k=3, rerank=rerank)
+r2 = search_memories(api_key, user_id, project_id, query, metadata_type='bug_fix', top_k=3, rerank=rerank)
 
 seen = set()
 combined = []

integrations/mem0-plugin/scripts/on_user_prompt.sh

@@ -120,14 +120,15 @@ if [ -n "$HAS_RESUME" ]; then
   RESUME_RESULTS=$(PYTHONPATH="$SCRIPT_DIR" MEM0_SEARCH_USER="$USER_ID" python3 -c "
 import os, sys
 sys.path.insert(0, os.environ.get('PYTHONPATH', '.'))
-from _search import search_memories, format_results_for_context
+from _search import search_memories, format_results_for_context, should_rerank
 
 api_key = os.environ.get('MEM0_API_KEY', '')
 user_id = os.environ.get('MEM0_SEARCH_USER', 'default')
 project_id = os.environ.get('MEM0_PROJECT_ID', 'unknown')
+rerank = should_rerank()
 
-state = search_memories(api_key, user_id, project_id, 'session state current task', metadata_type='session_state', top_k=3)
-decisions = search_memories(api_key, user_id, project_id, 'recent decisions and learnings', metadata_type='decision', top_k=3)
+state = search_memories(api_key, user_id, project_id, 'session state current task', metadata_type='session_state', top_k=3, rerank=rerank)
+decisions = search_memories(api_key, user_id, project_id, 'recent decisions and learnings', metadata_type='decision', top_k=3, rerank=rerank)
 
 all_r = state + decisions
 seen = set()

integrations/mem0-plugin/tests/test_search.py

@@ -101,6 +101,67 @@ def test_search_memories_no_api_key_returns_empty():
     assert results == []
 
 
+def test_search_memories_omits_rerank_by_default():
+    """Regression for #5684: rerank must not be sent unless requested."""
+    from _search import search_memories
+
+    captured_body = {}
+
+    def mock_urlopen(req, timeout=None):
+        captured_body.update(json.loads(req.data.decode()))
+        resp = MagicMock()
+        resp.read.return_value = json.dumps({"results": []}).encode()
+        resp.__enter__ = lambda s: s
+        resp.__exit__ = MagicMock(return_value=False)
+        return resp
+
+    with patch("urllib.request.urlopen", side_effect=mock_urlopen):
+        search_memories("key", "user", "proj", "query")
+
+    assert "rerank" not in captured_body
+
+
+def test_search_memories_forwards_rerank_true():
+    """Regression for #5684: rerank=True must reach the request body so the
+    REST endpoint actually reranks (it does not rerank when omitted)."""
+    from _search import search_memories
+
+    captured_body = {}
+
+    def mock_urlopen(req, timeout=None):
+        captured_body.update(json.loads(req.data.decode()))
+        resp = MagicMock()
+        resp.read.return_value = json.dumps({"results": []}).encode()
+        resp.__enter__ = lambda s: s
+        resp.__exit__ = MagicMock(return_value=False)
+        return resp
+
+    with patch("urllib.request.urlopen", side_effect=mock_urlopen):
+        search_memories("key", "user", "proj", "query", rerank=True)
+
+    assert captured_body.get("rerank") is True
+
+
+def test_should_rerank_defaults_true(monkeypatch):
+    """Regression for #5684: auto-injection reranks by default."""
+    from _search import should_rerank
+
+    monkeypatch.delenv("MEM0_RERANK", raising=False)
+    assert should_rerank() is True
+
+
+def test_should_rerank_opt_out_values(monkeypatch):
+    from _search import should_rerank
+
+    for falsey in ("0", "false", "False", "NO", "off", ""):
+        monkeypatch.setenv("MEM0_RERANK", falsey)
+        assert should_rerank() is False, falsey
+
+    for truthy in ("1", "true", "yes", "on"):
+        monkeypatch.setenv("MEM0_RERANK", truthy)
+        assert should_rerank() is True, truthy
+
+
 def test_format_results_for_context():
     from _search import format_results_for_context
 

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

@@ -14,7 +14,13 @@ export class AnthropicLLM implements LLM {
     if (!apiKey) {
       throw new Error("Anthropic API key is required");
     }
-    this.client = new Anthropic({ apiKey });
+    // Forward baseURL to the client when set so proxy/gateway users are
+    // honored (parity with the OpenAI provider and the Python fix in #5626).
+    const clientArgs: { apiKey: string; baseURL?: string } = { apiKey };
+    if (config.baseURL) {
+      clientArgs.baseURL = config.baseURL;
+    }
+    this.client = new Anthropic(clientArgs);
     this.model = config.model || "claude-sonnet-4-6";
     // Defaults mirror the Python provider's AnthropicConfig
     // (max_tokens=2000, temperature=0.1, top_p omitted).

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

@@ -717,7 +717,7 @@ export class Memory {
     if (!infer) {
       const returnedMemories: MemoryItem[] = [];
       for (const message of messages) {
-        if (message.content === "system") {
+        if (message.role === "system") {
           continue;
         }
         const memoryId = await this.createMemory(
@@ -1189,7 +1189,13 @@ export class Memory {
       }
     }
 
-    const result = { ...memoryItem, ...filters };
+    const result = {
+      ...memoryItem,
+      ...filters,
+      ...(memory.payload.attributedTo && {
+        attributedTo: memory.payload.attributedTo,
+      }),
+    };
     await this._displayFirstRunNotice("get");
     return result;
   }
@@ -1453,6 +1459,7 @@ export class Memory {
           ...(payload.user_id && { user_id: payload.user_id }),
           ...(payload.agent_id && { agent_id: payload.agent_id }),
           ...(payload.run_id && { run_id: payload.run_id }),
+          ...(payload.attributedTo && { attributedTo: payload.attributedTo }),
           ...(scored.scoreDetails && { score_details: scored.scoreDetails }),
         };
       });
@@ -1687,6 +1694,9 @@ export class Memory {
       ...(mem.payload.user_id && { user_id: mem.payload.user_id }),
       ...(mem.payload.agent_id && { agent_id: mem.payload.agent_id }),
       ...(mem.payload.run_id && { run_id: mem.payload.run_id }),
+      ...(mem.payload.attributedTo && {
+        attributedTo: mem.payload.attributedTo,
+      }),
     }));
 
     const result = { results };

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

@@ -82,6 +82,7 @@ export interface MemoryItem {
   updatedAt?: string;
   score?: number;
   metadata?: Record<string, any>;
+  attributedTo?: string;
 }
 
 export interface SearchFilters {

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

@@ -4,17 +4,46 @@
  */
 
 const mockCreate = jest.fn();
+const mockConstructor = jest.fn();
 
 jest.mock("@anthropic-ai/sdk", () => {
-  return jest.fn().mockImplementation(() => ({
-    messages: { create: mockCreate },
-  }));
+  return jest.fn().mockImplementation((args) => {
+    mockConstructor(args);
+    return { messages: { create: mockCreate } };
+  });
 });
 
 import { AnthropicLLM } from "../src/llms/anthropic";
 
 describe("AnthropicLLM (unit)", () => {
-  beforeEach(() => mockCreate.mockClear());
+  beforeEach(() => {
+    mockCreate.mockClear();
+    mockConstructor.mockClear();
+  });
+
+  // Regression #5665: a configured baseURL must reach the Anthropic client so
+  // proxy/gateway users are not silently bypassed (TS parity with #5626).
+  it("forwards baseURL to the Anthropic client when set", () => {
+    new AnthropicLLM({
+      apiKey: "test-key",
+      baseURL: "https://proxy.example/v1",
+    });
+
+    expect(mockConstructor).toHaveBeenCalledTimes(1);
+    const ctorArgs = mockConstructor.mock.calls[0][0];
+    expect(ctorArgs.apiKey).toBe("test-key");
+    expect(ctorArgs.baseURL).toBe("https://proxy.example/v1");
+  });
+
+  // When no baseURL is configured the client must not receive a baseURL key
+  // (so the SDK default endpoint is used).
+  it("does NOT set baseURL when none is configured", () => {
+    new AnthropicLLM({ apiKey: "test-key" });
+
+    expect(mockConstructor).toHaveBeenCalledTimes(1);
+    const ctorArgs = mockConstructor.mock.calls[0][0];
+    expect(ctorArgs.baseURL).toBeUndefined();
+  });
 
   it("returns text when no tools are provided and model returns a text block", async () => {
     mockCreate.mockResolvedValueOnce({

mem0-ts/src/oss/tests/memory.crud.test.ts

@@ -361,6 +361,45 @@ describe("Memory - search()", () => {
   });
 });
 
+// ─── attributedTo (#5666) ────────────────────────────────
+
+describe("Memory - attributedTo round-trip (#5666)", () => {
+  let memory: Memory;
+  const userId = `attributed_test_${Date.now()}`;
+  let id: string;
+
+  beforeAll(async () => {
+    memory = createMemory();
+    // The mocked LLM tags every extracted fact with attributed_to: "user".
+    const addResult: SearchResult = await memory.add("I love AI", { userId });
+    id = addResult.results[0].id;
+  });
+
+  afterAll(async () => {
+    await memory.reset();
+  });
+
+  test("get() surfaces attributedTo", async () => {
+    const item: MemoryItem | null = await memory.get(id);
+    expect(item!.attributedTo).toBe("user");
+  });
+
+  test("getAll() surfaces attributedTo", async () => {
+    const result: SearchResult = await memory.getAll({
+      filters: { user_id: userId },
+    });
+    expect(result.results[0].attributedTo).toBe("user");
+  });
+
+  test("search() surfaces attributedTo", async () => {
+    const result: SearchResult = await memory.search("AI", {
+      filters: { user_id: userId },
+    });
+    expect(result.results.length).toBeGreaterThan(0);
+    expect(result.results[0].attributedTo).toBe("user");
+  });
+});
+
 // ─── history() ───────────────────────────────────────────
 
 describe("Memory - history()", () => {

mem0/llms/anthropic.py

@@ -38,7 +38,11 @@ def __init__(self, config: Optional[Union[BaseLlmConfig, AnthropicConfig, Dict]]
             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)
+        base_url = self.config.anthropic_base_url or os.getenv("ANTHROPIC_BASE_URL")
+        client_kwargs = {"api_key": api_key}
+        if base_url:
+            client_kwargs["base_url"] = base_url
+        self.client = anthropic.Anthropic(**client_kwargs)
 
     def _get_common_params(self, **kwargs) -> Dict:
         """Get common parameters, avoiding sending both temperature and top_p together.

mem0/memory/main.py

@@ -1102,6 +1102,7 @@ def get(self, memory_id):
             "run_id",
             "actor_id",
             "role",
+            "attributed_to",
         ]
 
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
@@ -1215,6 +1216,7 @@ def _get_all_from_vector_store(self, filters, limit):
             "run_id",
             "actor_id",
             "role",
+            "attributed_to",
         ]
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
 
@@ -1550,6 +1552,7 @@ def _search_vector_store(self, query, filters, limit, threshold=0.1, explain=Fal
             "run_id",
             "actor_id",
             "role",
+            "attributed_to",
         ]
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
 
@@ -2638,6 +2641,7 @@ async def get(self, memory_id):
             "run_id",
             "actor_id",
             "role",
+            "attributed_to",
         ]
 
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
@@ -2751,6 +2755,7 @@ async def _get_all_from_vector_store(self, filters, limit):
             "run_id",
             "actor_id",
             "role",
+            "attributed_to",
         ]
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
 
@@ -3092,6 +3097,7 @@ async def _search_vector_store(self, query, filters, limit, threshold=0.1, expla
             "run_id",
             "actor_id",
             "role",
+            "attributed_to",
         ]
         core_and_promoted_keys = {"data", "hash", "created_at", "updated_at", "id", "text_lemmatized", "attributed_to", *promoted_payload_keys}
 

mem0/utils/factory.py

@@ -1,4 +1,5 @@
 import importlib
+import inspect
 from typing import Dict, Optional, Union
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
@@ -102,6 +103,14 @@ def create(cls, provider_name: str, config: Optional[Union[BaseLlmConfig, Dict]]
                     "vision_details": config.vision_details,
                     "http_client_proxies": config.http_client_proxies,
                 }
+                # Only forward reasoning fields to provider configs that accept them
+                # (explicitly or via **kwargs); others would raise on unexpected kwargs.
+                params = inspect.signature(config_class).parameters
+                accepts_kwargs = any(p.kind == p.VAR_KEYWORD for p in params.values())
+                if accepts_kwargs or "reasoning_effort" in params:
+                    config_dict["reasoning_effort"] = config.reasoning_effort
+                if accepts_kwargs or "is_reasoning_model" in params:
+                    config_dict["is_reasoning_model"] = config.is_reasoning_model
                 config_dict.update(kwargs)
                 config = config_class(**config_dict)
             else: