fix(openclaw): improve credential detection in extraction instructions

The previous exclude rule for credentials was too generic ("even if shared in conversation") and the extraction LLM failed to recognize credentials embedded in config blocks, setup logs, and tool output. New rule gives the LLM concrete patterns to watch for (sk-, m0-, ak_, ghp_, bot tokens, bearer tokens, webhook URLs, pairing codes) and WRONG/RIGHT examples showing what to store instead of the raw secret. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
fix(openclaw): prevent extraction of standalone timestamps as memories
2026-03-26 15:39:59 +05:30 · 2026-03-26 15:12:29 +05:30
34 changed files with 314 additions and 1557 deletions
@@ -8,56 +8,20 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">

-<Update label="2026-03-26" description="v1.0.8">
-
-**New Features & Updates:**
- **Vector Stores:** Integrated Turbopuffer as a vector database provider ([#4428](https://github.com/mem0ai/mem0/pull/4428))
- **LLMs:** Added MiniMax LLM provider ([#4431](https://github.com/mem0ai/mem0/pull/4431))
-
-**Bug Fixes:**
- **Core:** Fixed merging of multiple filter operators for the same key ([#4559](https://github.com/mem0ai/mem0/pull/4559))
- **Core:** Prevented in-place mutation of metadata in `_create_memory` ([#4529](https://github.com/mem0ai/mem0/pull/4529))
- **Core:** Preserved custom metadata when updating memory ([#4495](https://github.com/mem0ai/mem0/pull/4495))
- **Core:** Handled chatty LLM responses in JSON parsing ([#4525](https://github.com/mem0ai/mem0/pull/4525))
- **Core:** Prevented double embedding in `mem0.add` ([#3996](https://github.com/mem0ai/mem0/pull/3996))
- **Core:** Raised `ValueError` when deleting nonexistent memory ([#4455](https://github.com/mem0ai/mem0/pull/4455))
- **Core:** Cleaned up graph store data on `Memory.delete()` ([#4505](https://github.com/mem0ai/mem0/pull/4505))
- **Vector Stores:** Prevented SQL injection in Databricks vector store ([#4558](https://github.com/mem0ai/mem0/pull/4558))
- **Vector Stores:** Upgraded MongoDB vector store from deprecated `knnVector` to GA `vectorSearch` ([#3995](https://github.com/mem0ai/mem0/pull/3995))
- **Vector Stores:** Prevented embedding corruption in Valkey and Redis when vector is `None` ([#4362](https://github.com/mem0ai/mem0/pull/4362))
- **Vector Stores:** Accepted default `/tmp/chroma` path in `ChromaDbConfig` validator ([#4179](https://github.com/mem0ai/mem0/pull/4179))
- **Vector Stores:** Wrapped vector and payload in lists for `Langchain.update` ([#4446](https://github.com/mem0ai/mem0/pull/4446))
- **Graph:** Soft-delete graph relationships instead of hard `DELETE` ([#4188](https://github.com/mem0ai/mem0/pull/4188))
- **Graph:** Sanitized hyphens in Neo4j Cypher relationship names ([#4154](https://github.com/mem0ai/mem0/pull/4154))
- **Graph:** Used root LLM config as fallback for graph store instead of hardcoded OpenAI default ([#4466](https://github.com/mem0ai/mem0/pull/4466))
- **Qdrant:** Fixed `do not remove local path on init` ([#4475](https://github.com/mem0ai/mem0/pull/4475))
- **Qdrant:** Implemented enhanced metadata filtering operators ([#4127](https://github.com/mem0ai/mem0/pull/4127))
- **Embeddings:** Fixed OpenAI embedding dimensions ([#4481](https://github.com/mem0ai/mem0/pull/4481))
- **LLMs:** Omitted `topP` for Anthropic Converse in Bedrock; used `AWSBedrockConfig` in `LlmFactory` ([#4469](https://github.com/mem0ai/mem0/pull/4469))
- **LLMs:** Avoided sending both `temperature` and `top_p` to Anthropic API ([#4471](https://github.com/mem0ai/mem0/pull/4471))
- **LLMs:** Handled `None` content and empty candidates in `GeminiLLM` parsing ([#4462](https://github.com/mem0ai/mem0/pull/4462))
- **LLMs:** Added missing `_parse_response` to `AzureOpenAIStructuredLLM` ([#4434](https://github.com/mem0ai/mem0/pull/4434))
- **History:** Added timestamps for `DELETE` operations in history ([#4492](https://github.com/mem0ai/mem0/pull/4492))
-
-**Improvements:**
- **Vector Stores:** Added vector validation to OpenSearchDB to ensure non-null, non-empty, and correct-dimension vectors ([#4472](https://github.com/mem0ai/mem0/pull/4472))
-
-</Update>
-
 <Update label="2026-03-19" description="v1.0.7">

 **Bug Fixes:**
- **Core:** Fixed control characters in LLM JSON responses causing parse failures ([#4420](https://github.com/mem0ai/mem0/pull/4420))
- **Core:** Replaced hardcoded US/Pacific timezone references with `timezone.utc` ([#4404](https://github.com/mem0ai/mem0/pull/4404))
- **Core:** Preserved `http_auth` in `_safe_deepcopy_config` for OpenSearch ([#4418](https://github.com/mem0ai/mem0/pull/4418))
- **Core:** Normalized malformed LLM fact output before embedding ([#4224](https://github.com/mem0ai/mem0/pull/4224))
- **Embeddings:** Pass `encoding_format='float'` in OpenAI embeddings for proxy compatibility ([#4058](https://github.com/mem0ai/mem0/pull/4058))
- **LLMs:** Fixed Ollama to pass tools to `client.chat` and parse `tool_calls` from response ([#4176](https://github.com/mem0ai/mem0/pull/4176))
- **Reranker:** Support nested LLM config in `LLMReranker` for non-OpenAI providers ([#4405](https://github.com/mem0ai/mem0/pull/4405))
- **Vector Stores:** Cast `vector_distance` to float in Redis search ([#4377](https://github.com/mem0ai/mem0/pull/4377))
+- **Core:** Fixed control characters in LLM JSON responses causing parse failures (#4420)
+- **Core:** Replaced hardcoded US/Pacific timezone references with `timezone.utc` (#4404)
+- **Core:** Preserved `http_auth` in `_safe_deepcopy_config` for OpenSearch (#4418)
+- **Core:** Normalized malformed LLM fact output before embedding (#4224)
+- **Embeddings:** Pass `encoding_format='float'` in OpenAI embeddings for proxy compatibility (#4058)
+- **LLMs:** Fixed Ollama to pass tools to `client.chat` and parse `tool_calls` from response (#4176)
+- **Reranker:** Support nested LLM config in `LLMReranker` for non-OpenAI providers (#4405)
+- **Vector Stores:** Cast `vector_distance` to float in Redis search (#4377)

 **Improvements:**
- **Embeddings:** Improved Ollama embedder with model name normalization and error handling ([#4403](https://github.com/mem0ai/mem0/pull/4403))
+- **Embeddings:** Improved Ollama embedder with model name normalization and error handling (#4403)

 </Update>

@@ -799,20 +763,6 @@ mode: "wide"

 <Tab title="TypeScript">

-<Update label="2026-03-26" description="v2.4.3">
-
-**New Features & Updates:**
- **OSS:** Added pgvector support to NodeJS OSS `VectorStoreFactory` ([#3997](https://github.com/mem0ai/mem0/pull/3997))
-
-**Bug Fixes:**
- **OSS:** Made pgvector `pg` import compatible with ESM ([#4544](https://github.com/mem0ai/mem0/pull/4544))
- **OSS:** Registered pgvector in `VectorStoreFactory` ([#4502](https://github.com/mem0ai/mem0/pull/4502))
- **OSS:** Used root LLM config as fallback for graph store instead of hardcoded OpenAI default ([#4466](https://github.com/mem0ai/mem0/pull/4466))
- **OSS:** Fixed `toCamelCase` in Redis `get` method for the payload ([#3172](https://github.com/mem0ai/mem0/pull/3172))
- **Client:** Fixed Zod Schema incompatibility with OpenAI Structured Outputs API ([#3462](https://github.com/mem0ai/mem0/pull/3462))
-
-</Update>
-
 <Update label="2026-03-19" description="v2.4.2">

 **Bug Fixes:**
@@ -7,51 +7,58 @@ When using LLM rerankers, you can customize the prompts used for ranking to bett

 ## Default Prompt

-The default LLM reranker prompt scores each memory individually on a 0.0-1.0 scale:
+The default LLM reranker prompt is designed to be general-purpose:

 ```
-You are a relevance scoring assistant. Given a query and a document, you need to score how relevant the document is to the query.
+Given a query and a list of memory entries, rank the memory entries based on their relevance to the query.
+Rate each memory on a scale of 1-10 where 10 is most relevant.

-Score the relevance on a scale from 0.0 to 1.0, where:
- 1.0 = Perfectly relevant and directly answers the query
- 0.8-0.9 = Highly relevant with good information
- 0.6-0.7 = Moderately relevant with some useful information
- 0.4-0.5 = Slightly relevant with limited useful information
- 0.0-0.3 = Not relevant or no useful information
+Query: {query}

-Query: "{query}"
-Document: "{document}"
+Memory entries:
+{memories}

-Provide only a single numerical score between 0.0 and 1.0. Do not include any explanation or additional text.
+Provide your ranking as a JSON array with scores for each memory.
 ```

 ## Custom Prompt Configuration

-You can provide a custom prompt template using the `scoring_prompt` parameter:
+You can provide a custom prompt template when configuring the LLM reranker:

 ```python
 from mem0 import Memory

 custom_prompt = """
-You are an expert at evaluating memories for a personal AI assistant.
-Given a user query and a memory entry, score how relevant the memory is.
-Consider direct relevance, temporal relevance, and actionability.
+You are an expert at ranking memories for a personal AI assistant.
+Given a user query and a list of memory entries, rank each memory based on:
+1. Direct relevance to the query
+2. Temporal relevance (recent memories may be more important)
+3. Emotional significance
+4. Actionability

-Query: "{query}"
-Memory: "{document}"
+Query: {query}
+User Context: {user_context}

-Provide only a single numerical score between 0.0 and 1.0.
+Memory entries:
+{memories}
+
+Rate each memory from 1-10 and provide reasoning.
+Return as JSON: {{"rankings": [{{"index": 0, "score": 8, "reason": "..."}}]}}
 """

 config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "openai",
-            "model": "gpt-4o-mini",
-            "api_key": "your-openai-key",
-            "scoring_prompt": custom_prompt,
-            "top_k": 5
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-4.1-nano-2025-04-14",
+                    "api_key": "your-openai-key"
+                }
+            },
+            "custom_prompt": custom_prompt,
+            "top_n": 5
        }
    }
 }
@@ -63,14 +70,12 @@ memory = Memory.from_config(config)

 Your custom prompt can use the following variables:

-| Variable     | Description                   |
-| ------------ | ----------------------------- |
-| `{query}`    | The search query              |
-| `{document}` | The memory entry being scored |
-
-<Note>
-  Both `{query}` and `{document}` are required in your custom prompt. The LLM reranker scores each memory individually against the query, so the prompt is called once per candidate memory.
-</Note>
+| Variable         | Description                           |
+| ---------------- | ------------------------------------- |
+| `{query}`        | The search query                      |
+| `{memories}`     | The list of memory entries to rank    |
+| `{user_id}`      | The user ID (if available)            |
+| `{user_context}` | Additional user context (if provided) |

 ## Domain-Specific Examples

@@ -84,10 +89,13 @@ Prioritize memories that:
 - Show previous resolution patterns
 - Indicate customer preferences or constraints

-Query: "{query}"
-Memory: "{document}"
+Query: {query}
+Customer Context: Previous interactions with this customer

-Score relevance from 0.0 to 1.0.
+Memories:
+{memories}
+
+Rank each memory 1-10 based on support relevance.
 """
 ```

@@ -95,16 +103,19 @@ Score relevance from 0.0 to 1.0.

 ```python
 educational_prompt = """
-Score this learning memory for relevance to a student query.
+Rank these learning memories for a student query.
 Consider:
 - Prerequisite knowledge requirements
 - Learning progression and difficulty
 - Relevance to current learning objectives

-Student Query: "{query}"
-Memory: "{document}"
+Student Query: {query}
+Learning Context: {user_context}

-Score educational relevance from 0.0 to 1.0.
+Available memories:
+{memories}
+
+Score each memory for educational value (1-10).
 """
 ```

@@ -112,64 +123,73 @@ Score educational relevance from 0.0 to 1.0.

 ```python
 personal_assistant_prompt = """
-Score this personal memory for relevance to the user's query.
+Rank personal memories for relevance to the user's query.
 Consider:
 - Recent vs. historical importance
 - Personal preferences and habits
- Contextual relationships
+- Contextual relationships between memories

-Query: "{query}"
-Memory: "{document}"
+Query: {query}
+Personal context: {user_context}

-Provide relevance score from 0.0 to 1.0.
+Memories to rank:
+{memories}
+
+Provide relevance scores (1-10) with brief explanations.
 """
 ```

 ## Advanced Prompt Techniques

-### Multi-Criteria Scoring
+### Multi-Criteria Ranking

 ```python
 multi_criteria_prompt = """
-Evaluate this memory using multiple criteria:
+Evaluate memories using multiple criteria:

 1. RELEVANCE (40%): How directly related to the query
-2. RECENCY (20%): How recent the memory appears to be
+2. RECENCY (20%): How recent the memory is
 3. IMPORTANCE (25%): Personal or business significance
 4. ACTIONABILITY (15%): How useful for next steps

-Query: "{query}"
-Memory: "{document}"
+Query: {query}
+Context: {user_context}

-Compute a weighted score from 0.0 to 1.0 based on these criteria.
-Provide only the final numerical score.
+Memories:
+{memories}
+
+For each memory, provide:
+- Overall score (1-10)
+- Breakdown by criteria
+- Final ranking recommendation
+
+Format: JSON with detailed scoring
 """
 ```

-### Chain-of-Thought Scoring
+### Contextual Ranking

 ```python
-reasoning_prompt = """
-Evaluate this memory's relevance step by step:
+contextual_prompt = """
+Consider the following context when ranking memories:
+- Current user situation: {user_context}
+- Time of day: {current_time}
+- Recent activities: {recent_activities}

-1. What is the main intent of the query?
-2. What key information does the memory contain?
-3. How directly does the memory address the query?
+Query: {query}

-Based on this analysis, provide a single relevance score from 0.0 to 1.0.
+Rank these memories considering both direct relevance and contextual appropriateness:
+{memories}

-Query: "{query}"
-Memory: "{document}"
-
-Score:
+Provide contextually-aware relevance scores (1-10).
 """
 ```

 ## Best Practices

 1. **Be Specific**: Clearly define what makes a memory relevant for your use case
-2. **Use 0.0-1.0 Scale**: The score extractor expects values between 0.0 and 1.0
-3. **Request Only the Score**: Ask for just the numerical score to improve extraction reliability
+2. **Use Examples**: Include examples in your prompt for better model understanding
+3. **Structure Output**: Specify the exact JSON format you want returned
 4. **Test Iteratively**: Refine your prompt based on actual ranking performance
 5. **Consider Token Limits**: Keep prompts concise while being comprehensive

@@ -186,7 +206,7 @@ prompts = [
 ]

 for i, prompt in enumerate(prompts):
-    config["reranker"]["config"]["scoring_prompt"] = prompt
+    config["reranker"]["config"]["custom_prompt"] = prompt
    memory = Memory.from_config(config)

    results = memory.search("test query", user_id="test_user")
@@ -196,6 +216,6 @@ for i, prompt in enumerate(prompts):
 ## Common Issues

 - **Too Long**: Keep prompts under token limits for your chosen LLM
- **Too Vague**: Be specific about scoring criteria
- **Wrong Scale**: Use 0.0-1.0 scale to match the default score extractor
- **Extra Output**: Ask for only the numeric score — extra text can confuse score extraction
+- **Too Vague**: Be specific about ranking criteria
+- **Inconsistent Format**: Ensure JSON output format is clearly specified
+- **Missing Context**: Include relevant variables for your use case
@@ -18,9 +18,13 @@ config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "openai",
-            "model": "gpt-4o-mini",
-            "api_key": "your-openai-api-key"
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-4",
+                    "api_key": "your-openai-api-key"
+                }
+            }
        }
    }
 }
@@ -32,14 +36,11 @@ m = Memory.from_config(config)

 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
-| `provider` | str | `"openai"` | LLM provider (openai, anthropic, etc.) |
-| `model` | str | `"gpt-4o-mini"` | LLM model to use for reranking |
-| `api_key` | str | None | API key for the LLM provider |
-| `top_k` | int | None | Number of top documents to return after reranking |
+| `llm` | dict | Required | LLM configuration object |
+| `top_k` | int | 10 | Number of results to rerank |
 | `temperature` | float | 0.0 | LLM temperature for consistency |
-| `max_tokens` | int | 100 | Maximum tokens for LLM response |
-| `scoring_prompt` | str | None | Custom prompt template for scoring documents |
-| `llm` | dict | None | Optional nested LLM config for provider-specific fields (e.g., `ollama_base_url`, `azure_endpoint`). Overrides top-level `provider`/`model`/`api_key` when provided. |
+| `custom_prompt` | str | None | Custom reranking prompt |
+| `score_range` | tuple | (0, 10) | Score range for relevance |

 ### Advanced Configuration

@@ -48,19 +49,20 @@ config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "anthropic",
-            "model": "claude-sonnet-4-20250514",
-            "api_key": "your-anthropic-api-key",
+            "llm": {
+                "provider": "anthropic",
+                "config": {
+                    "model": "claude-3-sonnet-20240229",
+                    "api_key": "your-anthropic-api-key"
+                }
+            },
            "top_k": 15,
            "temperature": 0.0,
-            "scoring_prompt": """
-            Rate the relevance of each memory to the query on a scale of 0.0-1.0.
+            "score_range": (1, 5),
+            "custom_prompt": """
+            Rate the relevance of each memory to the query on a scale of 1-5.
            Consider semantic similarity, context, and practical utility.
            Only provide the numeric score.
-
-            Query: "{query}"
-            Document: "{document}"
-            Score:
            """
        }
    }
@@ -76,10 +78,14 @@ config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "openai",
-            "model": "gpt-4o-mini",
-            "api_key": "your-openai-api-key",
-            "temperature": 0.0
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-4",
+                    "api_key": "your-openai-api-key",
+                    "temperature": 0.0
+                }
+            }
        }
    }
 }
@@ -92,9 +98,13 @@ config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "anthropic",
-            "model": "claude-sonnet-4-20250514",
-            "api_key": "your-anthropic-api-key"
+            "llm": {
+                "provider": "anthropic",
+                "config": {
+                    "model": "claude-3-sonnet-20240229",
+                    "api_key": "your-anthropic-api-key"
+                }
+            }
        }
    }
 }
@@ -107,12 +117,10 @@ config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "ollama",
-            "model": "llama3.2",
            "llm": {
                "provider": "ollama",
                "config": {
-                    "model": "llama3.2",
+                    "model": "llama2",
                    "ollama_base_url": "http://localhost:11434"
                }
            }
@@ -121,10 +129,6 @@ config = {
 }
 ```

-<Note>
-  For providers like Ollama that need extra fields (e.g., `ollama_base_url`), use the optional nested `llm` key to pass provider-specific configuration. The nested `llm` config overrides top-level `provider`/`model`/`api_key` when provided.
-</Note>
-
 ### Azure OpenAI

 ```python
@@ -132,16 +136,13 @@ config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "azure_openai",
-            "model": "gpt-4o-mini",
-            "api_key": "your-azure-api-key",
            "llm": {
                "provider": "azure_openai",
                "config": {
-                    "model": "gpt-4o-mini",
+                    "model": "gpt-4",
                    "api_key": "your-azure-api-key",
                    "azure_endpoint": "https://your-resource.openai.azure.com/",
-                    "azure_deployment": "gpt-4o-mini-deployment"
+                    "azure_deployment": "gpt-4-deployment"
                }
            }
        }
@@ -153,22 +154,16 @@ config = {

 ### Default Prompt Behavior

-The default prompt asks the LLM to score relevance on a 0.0-1.0 scale:
+The default prompt asks the LLM to score relevance on a 0-10 scale:

 ```
-You are a relevance scoring assistant. Given a query and a document, you need to score how relevant the document is to the query.
+Given a query and a memory, rate how relevant the memory is to answering the query.
+Score from 0 (completely irrelevant) to 10 (perfectly relevant).
+Only provide the numeric score.

-Score the relevance on a scale from 0.0 to 1.0, where:
- 1.0 = Perfectly relevant and directly answers the query
- 0.8-0.9 = Highly relevant with good information
- 0.6-0.7 = Moderately relevant with some useful information
- 0.4-0.5 = Slightly relevant with limited useful information
- 0.0-0.3 = Not relevant or no useful information
-
-Query: "{query}"
-Document: "{document}"
-
-Provide only a single numerical score between 0.0 and 1.0. Do not include any explanation or additional text.
+Query: {query}
+Memory: {memory}
+Score:
 ```

 ### Custom Prompt Examples
@@ -179,14 +174,14 @@ Provide only a single numerical score between 0.0 and 1.0. Do not include any ex
 custom_prompt = """
 You are a medical information specialist. Rate how relevant each memory is for answering the medical query.
 Consider clinical accuracy, specificity, and practical applicability.
-Rate from 0.0 to 1.0 where:
- 0.0-0.3: Irrelevant or potentially harmful
- 0.4-0.6: Somewhat relevant but incomplete
- 0.7-0.8: Relevant and helpful
- 0.9-1.0: Highly relevant and clinically useful
+Rate from 1-10 where:
+- 1-3: Irrelevant or potentially harmful
+- 4-6: Somewhat relevant but incomplete
+- 7-8: Relevant and helpful
+- 9-10: Highly relevant and clinically useful

-Query: "{query}"
-Document: "{document}"
+Query: {query}
+Memory: {memory}
 Score:
 """

@@ -194,10 +189,14 @@ config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "openai",
-            "model": "gpt-4o-mini",
-            "api_key": "your-api-key",
-            "scoring_prompt": custom_prompt
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-4",
+                    "api_key": "your-api-key"
+                }
+            },
+            "custom_prompt": custom_prompt
        }
    }
 }
@@ -214,15 +213,15 @@ Consider:
 - Recency and accuracy
 - Practical usefulness

-Rate 0.0-1.0:
-0.0 = Not relevant
-0.25 = Slightly relevant
-0.5 = Moderately relevant
-0.75 = Very relevant
-1.0 = Perfectly answers the question
+Rate 1-5:
+1 = Not relevant
+2 = Slightly relevant
+3 = Moderately relevant
+4 = Very relevant
+5 = Perfectly answers the question

-Query: "{query}"
-Document: "{document}"
+Query: {query}
+Memory: {memory}
 Score:
 """
 ```
@@ -240,17 +239,13 @@ Consider:
 - Factual accuracy
 - Conversation flow

-Rate 0.0-1.0:
-Query: "{query}"
-Document: "{document}"
+Rate 0-10:
+Query: {query}
+Memory: {memory}
 Score:
 """
 ```

-<Note>
-  Custom prompts must include `{query}` and `{document}` placeholders. The LLM response should contain a numerical score which is automatically extracted.
-</Note>
-
 ## Usage Examples

 ### Basic Usage
@@ -300,9 +295,9 @@ results = safe_llm_rerank_search("What are my preferences?", "alice")

 | Model Type | Speed | Quality | Cost | Best For |
 |------------|-------|---------|------|----------|
-| GPT-4o mini | Fast | Good | Low | High-volume applications |
-| GPT-4o | Medium | Excellent | Medium | Quality-critical applications |
-| Claude Sonnet | Medium | Excellent | Medium | Balanced performance |
+| GPT-3.5 Turbo | Fast | Good | Low | High-volume applications |
+| GPT-4 | Medium | Excellent | Medium | Quality-critical applications |
+| Claude 3 Sonnet | Medium | Excellent | Medium | Balanced performance |
 | Ollama Local | Variable | Good | Free | Privacy-sensitive applications |

 ### Optimization Strategies
@@ -313,10 +308,14 @@ fast_config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "openai",
-            "model": "gpt-4o-mini",
-            "api_key": "your-api-key",
-            "top_k": 5,
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-3.5-turbo",
+                    "api_key": "your-api-key"
+                }
+            },
+            "top_k": 5,  # Limit candidates
            "temperature": 0.0
        }
    }
@@ -327,9 +326,13 @@ quality_config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "openai",
-            "model": "gpt-4o",
-            "api_key": "your-api-key",
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-4",
+                    "api_key": "your-api-key"
+                }
+            },
            "top_k": 15,
            "temperature": 0.0
        }
@@ -350,10 +353,10 @@ Evaluate this memory's relevance using multi-step reasoning:
 3. How directly does the memory address the query?
 4. What additional context might be needed?

-Based on this analysis, rate relevance 0.0-1.0:
+Based on this analysis, rate relevance 1-10:

-Query: "{query}"
-Document: "{document}"
+Query: {query}
+Memory: {memory}

 Analysis:
 Step 1 (Intent):
@@ -364,6 +367,42 @@ Final Score:
 """
 ```

+### Comparative Ranking
+
+```python
+comparative_prompt = """
+You will see a query and multiple memories. Rank them in order of relevance.
+Consider which memories best answer the question and would be most helpful.
+
+Query: {query}
+
+Memories to rank:
+{memories}
+
+Provide scores 1-10 for each memory, considering their relative usefulness.
+"""
+```
+
+### Emotional Intelligence
+
+```python
+emotional_prompt = """
+Consider both factual relevance and emotional appropriateness.
+Rate how suitable this memory is for responding to the user's query.
+
+Factors to consider:
+- Factual accuracy and relevance
+- Emotional tone and sensitivity
+- User's likely emotional state
+- Appropriateness of response
+
+Query: {query}
+Memory: {memory}
+Emotional Context: {context}
+Score (1-10):
+"""
+```
+
 ## Error Handling and Fallbacks

 ```python
@@ -394,22 +433,14 @@ class RobustLLMReranker:
 primary_config = {
    "reranker": {
        "provider": "llm_reranker",
-        "config": {
-            "provider": "openai",
-            "model": "gpt-4o",
-            "api_key": "your-api-key"
-        }
+        "config": {"llm": {"provider": "openai", "config": {"model": "gpt-4"}}}
    }
 }

 fallback_config = {
    "reranker": {
        "provider": "llm_reranker",
-        "config": {
-            "provider": "openai",
-            "model": "gpt-4o-mini",
-            "api_key": "your-api-key"
-        }
+        "config": {"llm": {"provider": "openai", "config": {"model": "gpt-3.5-turbo"}}}
    }
 }

@@ -455,4 +486,4 @@ results = reranker.search("What are my preferences?", "alice")
  <Card title="Performance Optimization" icon="bolt" href="/components/rerankers/optimization">
    Optimize LLM reranker performance
  </Card>
-</CardGroup>
+</CardGroup>
@@ -123,9 +123,13 @@ config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
-            "provider": "openai",
-            "model": "gpt-4o-mini",
-            "api_key": "your-openai-api-key",
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-4",
+                    "api_key": "your-openai-api-key"
+                }
+            },
            "top_k": 5
        }
    }
@@ -13,10 +13,6 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
  - You plan to explore or debug endpoints through the built-in OpenAPI page at `/docs`.
 </Info>

-<Warning>
-  **OSS vs Platform API paths:** The self-hosted OSS server does **not** use the `/v1/` prefix. For example, the endpoint is `POST /memories`, not `POST /v1/memories/`. The [API Reference](/api-reference) documents the hosted platform at `api.mem0.ai` which uses `/v1/` paths — those do not apply to the OSS server.
-</Warning>
-
 <Warning>
  Enable API key authentication (see below) and HTTPS before exposing the server to anything beyond your internal network.
 </Warning>
@@ -151,18 +147,13 @@ curl -X POST http://localhost:8000/memories \
 </Info>

 ```bash
-curl -X POST http://localhost:8000/search \
-  -H "Content-Type: application/json" \
-  -d '{
-    "query": "vegetable",
-    "user_id": "alice"
-  }'
+curl "http://localhost:8000/memories/search?user_id=alice&query=vegetable"
 ```

 ### Explore with OpenAPI docs

-1. Navigate to `http://localhost:8000/docs`.
-2. Pick an endpoint (e.g., `POST /search`).  
+1. Navigate to `http://localhost:8000/docs`.  
+2. Pick an endpoint (e.g., `POST /memories/search`).  
 3. Fill in parameters and click **Execute** to try requests in-browser.

 <Tip>
@@ -171,25 +162,6 @@ curl -X POST http://localhost:8000/search \

 ---

-## Endpoint reference
-
-The OSS REST server exposes the following endpoints. None use the `/v1/` prefix.
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `POST` | `/configure` | Set memory configuration |
-| `POST` | `/memories` | Create memories |
-| `GET` | `/memories` | Get all memories (filter by `user_id`, `agent_id`, or `run_id`) |
-| `GET` | `/memories/{memory_id}` | Get a specific memory |
-| `PUT` | `/memories/{memory_id}` | Update a memory |
-| `DELETE` | `/memories/{memory_id}` | Delete a specific memory |
-| `DELETE` | `/memories` | Delete all memories for an identifier |
-| `GET` | `/memories/{memory_id}/history` | Get memory history |
-| `POST` | `/search` | Search memories |
-| `POST` | `/reset` | Reset all memories |
-
---
-
 ## Verify the feature is working

 - Hit the root route and `/docs` to confirm the server is reachable.
@@ -1,6 +1,6 @@
 ---
 title: Integration Guide Template
-description: "A reusable template for writing integration guides that pair Mem0 with third-party tools and services."
+description: "Pattern for pairing Mem0 with third-party tools."
 icon: "plug"
 ---

@@ -4,9 +4,8 @@
  "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows using the Mem0 Platform MCP server.",
  "author": {
    "name": "Mem0",
-    "email": "support@mem0.ai"
+    "url": "https://github.com/mem0ai"
  },
-  "homepage": "https://mem0.ai",
  "repository": "https://github.com/mem0ai/mem0",
  "logo": "logo.svg",
  "license": "Apache-2.0"
@@ -1,10 +0,0 @@
-{
-  "mcpServers": {
-    "mem0": {
-      "url": "https://mcp.mem0.ai/mcp/",
-      "headers": {
-        "Authorization": "Token ${env:MEM0_API_KEY}"
-      }
-    }
-  }
-}
@@ -4,14 +4,9 @@
  "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
  "author": {
    "name": "Mem0",
-    "email": "support@mem0.ai"
+    "url": "https://github.com/mem0ai"
  },
-  "homepage": "https://mem0.ai",
  "repository": "https://github.com/mem0ai/mem0",
  "logo": "logo.svg",
-  "license": "Apache-2.0",
-  "keywords": ["mem0", "memory", "mcp", "personalization", "semantic-search"],
-  "skills": "./skills/",
-  "hooks": "./hooks/cursor-hooks.json",
-  "mcpServers": ".cursor-mcp.json"
+  "license": "Apache-2.0"
 }
@@ -1,10 +1,10 @@
-# Mem0 Plugin for Claude Code, Claude Cowork & Cursor
+# Mem0 Plugin for Claude & Cursor

-Add persistent memory to your AI workflows. Store, retrieve, and manage memories across sessions using the Mem0 Platform. Works with **Claude Code** (CLI), **Claude Cowork** (desktop app), and **Cursor**.
+Add persistent memory to your AI coding workflows. Store, retrieve, and manage memories across sessions using the Mem0 Platform. Works with both **Claude Code** and **Cursor**.

 ## Step 1: Set your API key

-> **You must complete this step before installing the plugin.**
+> **You must complete this step before installing the plugin for either Claude Code or Cursor.**

 1. Sign up at [app.mem0.ai](https://app.mem0.ai) if you haven't already
 2. Go to [app.mem0.ai/dashboard/api-keys](https://app.mem0.ai/dashboard/api-keys)
@@ -30,51 +30,22 @@ Add persistent memory to your AI workflows. Store, retrieve, and manage memories

 ## Step 2: Install the plugin

-Choose one of the options below. All require `MEM0_API_KEY` to be set first (see above).
+Choose one of the options below. Both require `MEM0_API_KEY` to be set first (see above).

-### Claude Code (CLI) / Claude Cowork (Desktop)
-
-Claude Code and Claude Cowork share the same plugin system.
-
-**CLI:**
+### Claude Code

 ```
 /plugin marketplace add mem0ai/mem0
 /plugin install mem0@mem0-plugins
 ```

-**Cowork desktop app:** Open the Cowork tab, click **Customize** in the sidebar, click **Browse plugins**, and install Mem0.
-
-This installs the full plugin including the MCP server, lifecycle hooks (automatic memory capture), and the Mem0 SDK skill.
-
 ### Cursor

-> **Already have `mem0` configured as an MCP server?** Remove the existing entry from your Cursor MCP settings before installing to avoid duplicate tools.
+Click the deeplink below to install the Mem0 MCP server in Cursor:

-**Option A — One-click deeplink** (installs MCP server only):
+[Install Mem0 MCP in Cursor](cursor://anysphere.cursor-deeplink/mcp/install?name=mem0&config=eyJtY3BTZXJ2ZXJzIjp7Im1lbTAiOnsidHlwZSI6Imh0dHAiLCJ1cmwiOiJodHRwczovL21jcC5tZW0wLmFpL21jcC8iLCJoZWFkZXJzIjp7IkF1dGhvcml6YXRpb24iOiJUb2tlbiAke01FTTBfQVBJX0tFWX0ifX19fQ==)

-[Install Mem0 MCP in Cursor](cursor://anysphere.cursor-deeplink/mcp/install?name=mem0&config=eyJtY3BTZXJ2ZXJzIjp7Im1lbTAiOnsidXJsIjoiaHR0cHM6Ly9tY3AubWVtMC5haS9tY3AvIiwiaGVhZGVycyI6eyJBdXRob3JpemF0aW9uIjoiVG9rZW4gJHtlbnY6TUVNMF9BUElfS0VZfSJ9fX19)
-
-**Option B — Manual configuration** (MCP server only):
-
-Add the following to your `.cursor/mcp.json`:
-
-```json
-{
-  "mcpServers": {
-    "mem0": {
-      "url": "https://mcp.mem0.ai/mcp/",
-      "headers": {
-        "Authorization": "Token ${env:MEM0_API_KEY}"
-      }
-    }
-  }
-}
-```
-
-**Option C — Cursor Marketplace** (full plugin with hooks and skills):
-
-Install from the [Cursor Marketplace](https://cursor.com/marketplace) for the complete experience including lifecycle hooks and the Mem0 SDK skill.
+> **Already have `mem0` configured as an MCP server?** Remove the existing entry from your `.mcp.json` or settings before installing this plugin to avoid duplicate tools.

 ## Verify it works

@@ -86,15 +57,8 @@ After installing, confirm the MCP server is connected:

 ## What's included

-| Component | Claude Code / Cowork | Cursor (Marketplace) | Cursor (Deeplink/Manual) |
-|-----------|:--------------------:|:--------------------:|:------------------------:|
-| MCP Server | Yes | Yes | Yes |
-| Lifecycle Hooks | Yes | Yes | No |
-| Mem0 SDK Skill | Yes | Yes | No |
-
 - **MCP Server** — Connects to the Mem0 remote MCP server (`mcp.mem0.ai`), providing tools to add, search, update, and delete memories. No local dependencies required.
- **Lifecycle Hooks** — Automatic memory capture at key points: session start, context compaction, task completion, and session end.
- **Mem0 SDK Skill** — Guides the AI on how to integrate the Mem0 SDK (Python & TypeScript) into your applications.
+- **Mem0 Skill** — Guides Claude on how to integrate the Mem0 SDK (Python & TypeScript) into your applications

 ## MCP Tools

@@ -1,37 +0,0 @@
-{
-  "hooks": {
-    "sessionStart": [
-      {
-        "command": "${CURSOR_PLUGIN_ROOT}/scripts/on_session_start.sh",
-        "matcher": "startup|resume|compact"
-      }
-    ],
-    "preToolUse": [
-      {
-        "command": "${CURSOR_PLUGIN_ROOT}/scripts/block_memory_write.sh",
-        "matcher": "Write|Edit"
-      }
-    ],
-    "preCompact": [
-      {
-        "command": "${CURSOR_PLUGIN_ROOT}/scripts/on_pre_compact.sh"
-      },
-      {
-        "command": "python3 ${CURSOR_PLUGIN_ROOT}/scripts/on_pre_compact.py",
-        "timeout": 30
-      }
-    ],
-    "stop": [
-      {
-        "command": "${CURSOR_PLUGIN_ROOT}/scripts/on_stop.sh",
-        "timeout": 10
-      }
-    ],
-    "beforeSubmitPrompt": [
-      {
-        "command": "${CURSOR_PLUGIN_ROOT}/scripts/on_user_prompt.sh",
-        "timeout": 5
-      }
-    ]
-  }
-}
@@ -2,7 +2,6 @@
 module.exports = {
  ...require("./jest.config"),
  testMatch: ["**/integration/**/*.test.ts"],
-  globalSetup: "<rootDir>/src/client/tests/integration/global-setup.ts",
  globalTeardown: "<rootDir>/src/client/tests/integration/global-teardown.ts",
  // Run integration tests serially to avoid rate limiting and race conditions
  maxWorkers: 1,
@@ -1,6 +1,6 @@
 {
  "name": "mem0ai",
-  "version": "2.4.3",
+  "version": "2.4.2",
  "description": "The Memory Layer For Your AI Apps",
  "main": "./dist/index.js",
  "module": "./dist/index.mjs",
@@ -1,6 +1,4 @@
-import type { Client as ClientType } from "pg";
-import pkg from "pg";
-const { Client } = pkg;
+import { Client } from "pg";
 import { VectorStore } from "./base";
 import { SearchFilters, VectorStoreConfig, VectorStoreResult } from "../types";

@@ -16,7 +14,7 @@ interface PGVectorConfig extends VectorStoreConfig {
 }

 export class PGVector implements VectorStore {
-  private client: ClientType;
+  private client: Client;
  private collectionName: string;
  private useDiskann: boolean;
  private useHnsw: boolean;
@@ -21,7 +21,6 @@ class AzureOpenAIConfig(BaseLlmConfig):
        top_k: int = 1,
        enable_vision: bool = False,
        vision_details: Optional[str] = "auto",
-        reasoning_effort: Optional[str] = None,
        http_client_proxies: Optional[dict] = None,
        # Azure OpenAI-specific parameters
        azure_kwargs: Optional[Dict[str, Any]] = None,
@@ -38,7 +37,6 @@ class AzureOpenAIConfig(BaseLlmConfig):
            top_k: Top-k sampling parameter, defaults to 1
            enable_vision: Enable vision capabilities, defaults to False
            vision_details: Vision detail level, defaults to "auto"
-            reasoning_effort: Effort level for reasoning models ("low", "medium", "high"), defaults to None
            http_client_proxies: HTTP client proxy settings, defaults to None
            azure_kwargs: Azure-specific configuration, defaults to None
        """
@@ -52,7 +50,6 @@ class AzureOpenAIConfig(BaseLlmConfig):
            top_k=top_k,
            enable_vision=enable_vision,
            vision_details=vision_details,
-            reasoning_effort=reasoning_effort,
            http_client_proxies=http_client_proxies,
        )

@@ -23,7 +23,6 @@ class BaseLlmConfig(ABC):
        top_k: int = 1,
        enable_vision: bool = False,
        vision_details: Optional[str] = "auto",
-        reasoning_effort: Optional[str] = None,
        http_client_proxies: Optional[Union[Dict, str]] = None,
    ):
        """
@@ -49,9 +48,6 @@ class BaseLlmConfig(ABC):
                Only applicable to vision-enabled models. Defaults to False
            vision_details: Level of detail for vision processing.
                Options: "low", "high", "auto". Defaults to "auto"
-            reasoning_effort: Effort level for reasoning models (e.g., o1, o3, gpt-5).
-                Options: "low", "medium", "high". Only applicable to reasoning models.
-                Defaults to None (uses the model's default reasoning effort)
            http_client_proxies: Proxy settings for HTTP client.
                Can be a dict or string. Defaults to None
        """
@@ -63,5 +59,4 @@ class BaseLlmConfig(ABC):
        self.top_k = top_k
        self.enable_vision = enable_vision
        self.vision_details = vision_details
-        self.reasoning_effort = reasoning_effort
        self.http_client = httpx.Client(proxies=http_client_proxies) if http_client_proxies else None
@@ -20,7 +20,6 @@ class OpenAIConfig(BaseLlmConfig):
        top_k: int = 1,
        enable_vision: bool = False,
        vision_details: Optional[str] = "auto",
-        reasoning_effort: Optional[str] = None,
        http_client_proxies: Optional[dict] = None,
        # OpenAI-specific parameters
        openai_base_url: Optional[str] = None,
@@ -45,7 +44,6 @@ class OpenAIConfig(BaseLlmConfig):
            top_k: Top-k sampling parameter, defaults to 1
            enable_vision: Enable vision capabilities, defaults to False
            vision_details: Vision detail level, defaults to "auto"
-            reasoning_effort: Effort level for reasoning models ("low", "medium", "high"), defaults to None
            http_client_proxies: HTTP client proxy settings, defaults to None
            openai_base_url: OpenAI API base URL, defaults to None
            models: List of models for OpenRouter, defaults to None
@@ -65,7 +63,6 @@ class OpenAIConfig(BaseLlmConfig):
            top_k=top_k,
            enable_vision=enable_vision,
            vision_details=vision_details,
-            reasoning_effort=reasoning_effort,
            http_client_proxies=http_client_proxies,
        )

@@ -31,7 +31,6 @@ class AzureOpenAILLM(LLMBase):
                top_k=config.top_k,
                enable_vision=config.enable_vision,
                vision_details=config.vision_details,
-                reasoning_effort=getattr(config, 'reasoning_effort', None),
                http_client_proxies=config.http_client,
            )

@@ -88,12 +88,7 @@ class LLMBase(ABC):
                supported_params["tools"] = kwargs["tools"]
            if "tool_choice" in kwargs:
                supported_params["tool_choice"] = kwargs["tool_choice"]
-
-            # Add reasoning_effort if configured
-            reasoning_effort = getattr(self.config, 'reasoning_effort', None)
-            if reasoning_effort:
-                supported_params["reasoning_effort"] = reasoning_effort
-
+                
            return supported_params
        else:
            # For regular models, include all common parameters
@@ -29,7 +29,6 @@ class OpenAILLM(LLMBase):
                top_k=config.top_k,
                enable_vision=config.enable_vision,
                vision_details=config.vision_details,
-                reasoning_effort=getattr(config, 'reasoning_effort', None),
                http_client_proxies=config.http_client,
            )

@@ -516,15 +516,15 @@ class Memory(MemoryBase):
        )

        try:
-            cleaned_response = remove_code_blocks(response)
-            if not cleaned_response.strip():
+            response = remove_code_blocks(response)
+            if not response.strip():
                new_retrieved_facts = []
            else:
                try:
                    # First try direct JSON parsing
-                    new_retrieved_facts = json.loads(cleaned_response, strict=False)["facts"]
+                    new_retrieved_facts = json.loads(response, strict=False)["facts"]
                except json.JSONDecodeError:
-                    # Try extracting JSON from response (handles chatty LLM output)
+                    # Try extracting JSON from response using built-in function
                    extracted_json = extract_json(response)
                    new_retrieved_facts = json.loads(extracted_json, strict=False)["facts"]
                new_retrieved_facts = normalize_facts(new_retrieved_facts)
@@ -589,11 +589,8 @@ class Memory(MemoryBase):
                    logger.warning("Empty response from LLM, no memories to extract")
                    new_memories_with_actions = {}
                else:
-                    try:
-                        new_memories_with_actions = json.loads(remove_code_blocks(response), strict=False)
-                    except json.JSONDecodeError:
-                        extracted_json = extract_json(response)
-                        new_memories_with_actions = json.loads(extracted_json, strict=False)
+                    response = remove_code_blocks(response)
+                    new_memories_with_actions = json.loads(response, strict=False)
            except Exception as e:
                logger.error(f"Invalid JSON response: {e}")
                new_memories_with_actions = {}
@@ -977,11 +974,11 @@ class Memory(MemoryBase):
                }
                
                if operator in operator_map:
-                    result.setdefault(key, {})[operator_map[operator]] = value
+                    result[key] = {operator_map[operator]: value}
                else:
                    raise ValueError(f"Unsupported metadata filter operator: {operator}")
            return result
-
+        
        for key, value in metadata_filters.items():
            if key == "AND":
                # Logical AND: combine multiple conditions
@@ -1081,15 +1078,13 @@ class Memory(MemoryBase):

        return original_memories

-    def update(self, memory_id, data, metadata: Optional[Dict[str, Any]] = None):
+    def update(self, memory_id, data):
        """
        Update a memory by ID.

        Args:
            memory_id (str): ID of the memory to update.
            data (str): New content to update the memory with.
-            metadata (dict, optional): Additional metadata to update. Existing metadata fields
-                not specified here will be preserved. Defaults to None.

        Returns:
            dict: Success message indicating the memory was updated.
@@ -1097,14 +1092,12 @@ class Memory(MemoryBase):
        Example:
            >>> m.update(memory_id="mem_123", data="Likes to play tennis on weekends")
            {'message': 'Memory updated successfully!'}
-            >>> m.update(memory_id="mem_123", data="Likes tennis", metadata={"category": "sports"})
-            {'message': 'Memory updated successfully!'}
        """
        capture_event("mem0.update", self, {"memory_id": memory_id, "sync_type": "sync"})

        existing_embeddings = {data: self.embedding_model.embed(data, "update")}

-        self._update_memory(memory_id, data, existing_embeddings, metadata)
+        self._update_memory(memory_id, data, existing_embeddings)
        return {"message": "Memory updated successfully!"}

    def delete(self, memory_id):
@@ -1200,9 +1193,7 @@ class Memory(MemoryBase):
        new_metadata = deepcopy(metadata) if metadata is not None else {}
        new_metadata["data"] = data
        new_metadata["hash"] = hashlib.md5(data.encode()).hexdigest()
-        if "created_at" not in new_metadata:
-            new_metadata["created_at"] = datetime.now(timezone.utc).isoformat()
-        new_metadata["updated_at"] = new_metadata["created_at"]
+        new_metadata["created_at"] = datetime.now(timezone.utc).isoformat()

        self.vector_store.insert(
            vectors=[embeddings],
@@ -1215,7 +1206,6 @@ class Memory(MemoryBase):
            data,
            "ADD",
            created_at=new_metadata.get("created_at"),
-            updated_at=new_metadata.get("updated_at"),
            actor_id=new_metadata.get("actor_id"),
            role=new_metadata.get("role"),
        )
@@ -1289,7 +1279,7 @@ class Memory(MemoryBase):
            new_metadata["agent_id"] = existing_memory.payload["agent_id"]
        if "run_id" not in new_metadata and "run_id" in existing_memory.payload:
            new_metadata["run_id"] = existing_memory.payload["run_id"]
-        if "actor_id" in existing_memory.payload:
+        if "actor_id" not in new_metadata and "actor_id" in existing_memory.payload:
            new_metadata["actor_id"] = existing_memory.payload["actor_id"]
        if "role" not in new_metadata and "role" in existing_memory.payload:
            new_metadata["role"] = existing_memory.payload["role"]
@@ -1327,19 +1317,12 @@ class Memory(MemoryBase):
            if existing_memory is None:
                raise ValueError(f"Memory with id {memory_id} not found")
        prev_value = existing_memory.payload.get("data", "")
-
-        # Preserve original created_at and record deletion time
-        created_at = _normalize_iso_timestamp_to_utc(existing_memory.payload.get("created_at"))
-        updated_at = datetime.now(timezone.utc).isoformat()
-
        self.vector_store.delete(vector_id=memory_id)
        self.db.add_history(
            memory_id,
            prev_value,
            None,
            "DELETE",
-            created_at=created_at,
-            updated_at=updated_at,
            actor_id=existing_memory.payload.get("actor_id"),
            role=existing_memory.payload.get("role"),
            is_deleted=1,
@@ -1607,15 +1590,15 @@ class AsyncMemory(MemoryBase):
            response_format={"type": "json_object"},
        )
        try:
-            cleaned_response = remove_code_blocks(response)
-            if not cleaned_response.strip():
+            response = remove_code_blocks(response)
+            if not response.strip():
                new_retrieved_facts = []
            else:
                try:
                    # First try direct JSON parsing
-                    new_retrieved_facts = json.loads(cleaned_response, strict=False)["facts"]
+                    new_retrieved_facts = json.loads(response, strict=False)["facts"]
                except json.JSONDecodeError:
-                    # Try extracting JSON from response (handles chatty LLM output)
+                    # Try extracting JSON from response using built-in function
                    extracted_json = extract_json(response)
                    new_retrieved_facts = json.loads(extracted_json, strict=False)["facts"]
                new_retrieved_facts = normalize_facts(new_retrieved_facts)
@@ -1683,11 +1666,8 @@ class AsyncMemory(MemoryBase):
                    logger.warning("Empty response from LLM, no memories to extract")
                    new_memories_with_actions = {}
                else:
-                    try:
-                        new_memories_with_actions = json.loads(remove_code_blocks(response), strict=False)
-                    except json.JSONDecodeError:
-                        extracted_json = extract_json(response)
-                        new_memories_with_actions = json.loads(extracted_json, strict=False)
+                    response = remove_code_blocks(response)
+                    new_memories_with_actions = json.loads(response, strict=False)
            except Exception as e:
                logger.error(f"Invalid JSON response: {e}")
                new_memories_with_actions = {}
@@ -2102,7 +2082,7 @@ class AsyncMemory(MemoryBase):
                }

                if operator in operator_map:
-                    result.setdefault(key, {})[operator_map[operator]] = value
+                    result[key] = {operator_map[operator]: value}
                else:
                    raise ValueError(f"Unsupported metadata filter operator: {operator}")
            return result
@@ -2208,15 +2188,13 @@ class AsyncMemory(MemoryBase):

        return original_memories

-    async def update(self, memory_id, data, metadata: Optional[Dict[str, Any]] = None):
+    async def update(self, memory_id, data):
        """
        Update a memory by ID asynchronously.

        Args:
            memory_id (str): ID of the memory to update.
            data (str): New content to update the memory with.
-            metadata (dict, optional): Additional metadata to update. Existing metadata fields
-                not specified here will be preserved. Defaults to None.

        Returns:
            dict: Success message indicating the memory was updated.
@@ -2224,15 +2202,13 @@ class AsyncMemory(MemoryBase):
        Example:
            >>> await m.update(memory_id="mem_123", data="Likes to play tennis on weekends")
            {'message': 'Memory updated successfully!'}
-            >>> await m.update(memory_id="mem_123", data="Likes tennis", metadata={"category": "sports"})
-            {'message': 'Memory updated successfully!'}
        """
        capture_event("mem0.update", self, {"memory_id": memory_id, "sync_type": "async"})

        embeddings = await asyncio.to_thread(self.embedding_model.embed, data, "update")
        existing_embeddings = {data: embeddings}

-        await self._update_memory(memory_id, data, existing_embeddings, metadata)
+        await self._update_memory(memory_id, data, existing_embeddings)
        return {"message": "Memory updated successfully!"}

    async def delete(self, memory_id):
@@ -2332,9 +2308,7 @@ class AsyncMemory(MemoryBase):
        new_metadata = deepcopy(metadata) if metadata is not None else {}
        new_metadata["data"] = data
        new_metadata["hash"] = hashlib.md5(data.encode()).hexdigest()
-        if "created_at" not in new_metadata:
-            new_metadata["created_at"] = datetime.now(timezone.utc).isoformat()
-        new_metadata["updated_at"] = new_metadata["created_at"]
+        new_metadata["created_at"] = datetime.now(timezone.utc).isoformat()

        await asyncio.to_thread(
            self.vector_store.insert,
@@ -2350,7 +2324,6 @@ class AsyncMemory(MemoryBase):
            data,
            "ADD",
            created_at=new_metadata.get("created_at"),
-            updated_at=new_metadata.get("updated_at"),
            actor_id=new_metadata.get("actor_id"),
            role=new_metadata.get("role"),
        )
@@ -2440,7 +2413,7 @@ class AsyncMemory(MemoryBase):
        if "run_id" not in new_metadata and "run_id" in existing_memory.payload:
            new_metadata["run_id"] = existing_memory.payload["run_id"]

-        if "actor_id" in existing_memory.payload:
+        if "actor_id" not in new_metadata and "actor_id" in existing_memory.payload:
            new_metadata["actor_id"] = existing_memory.payload["actor_id"]
        if "role" not in new_metadata and "role" in existing_memory.payload:
            new_metadata["role"] = existing_memory.payload["role"]
@@ -2481,10 +2454,6 @@ class AsyncMemory(MemoryBase):
                raise ValueError(f"Memory with id {memory_id} not found")
        prev_value = existing_memory.payload.get("data", "")

-        # Preserve original created_at and record deletion time
-        created_at = _normalize_iso_timestamp_to_utc(existing_memory.payload.get("created_at"))
-        updated_at = datetime.now(timezone.utc).isoformat()
-
        await asyncio.to_thread(self.vector_store.delete, vector_id=memory_id)
        await asyncio.to_thread(
            self.db.add_history,
@@ -2492,8 +2461,6 @@ class AsyncMemory(MemoryBase):
            prev_value,
            None,
            "DELETE",
-            created_at=created_at,
-            updated_at=updated_at,
            actor_id=existing_memory.payload.get("actor_id"),
            role=existing_memory.payload.get("role"),
            is_deleted=1,
@@ -124,20 +124,14 @@ def remove_code_blocks(content: str) -> str:
 def extract_json(text):
    """
    Extracts JSON content from a string, removing enclosing triple backticks and optional 'json' tag if present.
-    If no code block is found, attempts to locate JSON by finding the first '{' and last '}'.
-    If that also fails, returns the text as-is.
+    If no code block is found, returns the text as-is.
    """
    text = text.strip()
    match = re.search(r"```(?:json)?\s*(.*?)\s*```", text, re.DOTALL)
    if match:
        json_str = match.group(1)
    else:
-        start_idx = text.find("{")
-        end_idx = text.rfind("}")
-        if start_idx != -1 and end_idx != -1 and end_idx > start_idx:
-            json_str = text[start_idx : end_idx + 1]
-        else:
-            json_str = text
+        json_str = text  # assume it's raw JSON
    return json_str


@@ -1,13 +1,11 @@
 import json
 import logging
-import re
 import uuid
 from typing import Optional, List
 from datetime import datetime, date
 from databricks.sdk.service.catalog import ColumnInfo, ColumnTypeName, TableType, DataSourceFormat
 from databricks.sdk.service.catalog import TableConstraint, PrimaryKeyConstraint
 from databricks.sdk import WorkspaceClient
-from databricks.sdk.service.sql import StatementParameterListItem
 from databricks.sdk.service.vectorsearch import (
    VectorIndexType,
    DeltaSyncVectorIndexSpecRequest,
@@ -30,9 +28,6 @@ class MemoryResult(BaseModel):

 excluded_keys = {"user_id", "agent_id", "run_id", "hash", "data", "created_at", "updated_at"}

-# Pattern for valid SQL identifiers to prevent column name injection
-_VALID_SQL_IDENTIFIER = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
-

 class Databricks(VectorStoreBase):
    def __init__(
@@ -393,46 +388,28 @@ class Databricks(VectorStoreBase):
        # Determine the number of items to process
        num_items = len(payloads) if payloads else len(vectors) if vectors else 0

-        params = []
        value_tuples = []
        for i in range(num_items):
-            placeholders = []
+            values = []
            for col in self.columns:
-                param_name = f"{col.name}_{i}"
                if col.name == "memory_id":
                    val = ids[i] if ids and i < len(ids) else str(uuid.uuid4())
                elif col.name == "embedding":
                    val = vectors[i] if vectors and i < len(vectors) else []
-                    # Vectors are numeric arrays — ARRAY type not supported by StatementParameterListItem,
-                    # so we inline using _format_sql_value (values are floats from the embedding model).
-                    placeholders.append(self._format_sql_value(val))
-                    continue
                elif col.name == "memory":
                    val = payloads[i].get("data") if payloads and i < len(payloads) else None
                else:
                    val = payloads[i].get(col.name) if payloads and i < len(payloads) else None
-
-                if val is None:
-                    placeholders.append("NULL")
-                else:
-                    placeholders.append(f":{param_name}")
-                    if isinstance(val, dict):
-                        val = json.dumps(val)
-                    # Use explicit type for TIMESTAMP columns so Databricks doesn't
-                    # rely on implicit STRING→TIMESTAMP casting.
-                    param_type = "TIMESTAMP" if col.type_name == ColumnTypeName.TIMESTAMP else None
-                    params.append(StatementParameterListItem(name=param_name, value=str(val), type=param_type))
-            value_tuples.append(f"({', '.join(placeholders)})")
+                values.append(val)
+            formatted = [self._format_sql_value(v) for v in values]
+            value_tuples.append(f"({', '.join(formatted)})")

        insert_sql = f"INSERT INTO {self.fully_qualified_table_name} ({', '.join(self.column_names)}) VALUES {', '.join(value_tuples)}"

        # Execute the insert
        try:
            response = self.client.statement_execution.execute_statement(
-                statement=insert_sql,
-                warehouse_id=self.warehouse_id,
-                wait_timeout="30s",
-                parameters=params,
+                statement=insert_sql, warehouse_id=self.warehouse_id, wait_timeout="30s"
            )
            if response.status.state.value == "SUCCEEDED":
                logger.info(
@@ -517,13 +494,10 @@ class Databricks(VectorStoreBase):
        try:
            logger.info(f"Deleting vector with ID {vector_id} from Delta table {self.fully_qualified_table_name}")

-            delete_sql = f"DELETE FROM {self.fully_qualified_table_name} WHERE memory_id = :vector_id"
+            delete_sql = f"DELETE FROM {self.fully_qualified_table_name} WHERE memory_id = '{vector_id}'"

            response = self.client.statement_execution.execute_statement(
-                statement=delete_sql,
-                warehouse_id=self.warehouse_id,
-                wait_timeout="30s",
-                parameters=[StatementParameterListItem(name="vector_id", value=str(vector_id))],
+                statement=delete_sql, warehouse_id=self.warehouse_id, wait_timeout="30s"
            )

            if response.status.state.value == "SUCCEEDED":
@@ -545,8 +519,8 @@ class Databricks(VectorStoreBase):
            payload (dict, optional): New payload data.
        """

+        update_sql = f"UPDATE {self.fully_qualified_table_name} SET "
        set_clauses = []
-        params = []
        if not vector_id:
            logger.error("vector_id is required for update operation")
            return
@@ -554,38 +528,25 @@ class Databricks(VectorStoreBase):
            if not isinstance(vector, list):
                logger.error("vector must be a list of float values")
                return
-            # Vectors are numeric arrays — safe to inline since StatementParameterListItem
-            # doesn't support ARRAY types, and values are validated as list of floats above.
-            # Use array() SQL syntax, not Python list repr which is invalid Databricks SQL.
-            set_clauses.append(f"embedding = {self._format_sql_value(vector)}")
+            set_clauses.append(f"embedding = {vector}")
        if payload:
            if not isinstance(payload, dict):
                logger.error("payload must be a dictionary")
                return
            for key, value in payload.items():
                if key not in excluded_keys:
-                    if not _VALID_SQL_IDENTIFIER.match(key):
-                        logger.warning(f"Skipping invalid column name in payload: {key!r}")
-                        continue
-                    param_name = f"payload_{key}"
-                    set_clauses.append(f"{key} = :{param_name}")
-                    params.append(StatementParameterListItem(name=param_name, value=str(value)))
+                    set_clauses.append(f"{key} = '{value}'")

        if not set_clauses:
            logger.error("No fields to update")
            return
-        update_sql = f"UPDATE {self.fully_qualified_table_name} SET "
        update_sql += ", ".join(set_clauses)
-        update_sql += " WHERE memory_id = :vector_id"
-        params.append(StatementParameterListItem(name="vector_id", value=str(vector_id)))
+        update_sql += f" WHERE memory_id = '{vector_id}'"
        try:
            logger.info(f"Updating vector with ID {vector_id} in Delta table {self.fully_qualified_table_name}")

            response = self.client.statement_execution.execute_statement(
-                statement=update_sql,
-                warehouse_id=self.warehouse_id,
-                wait_timeout="30s",
-                parameters=params,
+                statement=update_sql, warehouse_id=self.warehouse_id, wait_timeout="30s"
            )

            if response.status.state.value == "SUCCEEDED":
@@ -2,15 +2,6 @@

 All notable changes to the `@mem0/openclaw-mem0` plugin will be documented in this file.

-## [0.4.1] - 2026-03-26
-
-### Added
- **Improved extraction quality**: Enhanced noise filtering, deduplication, and better extraction instructions for higher-quality memory capture (#4302)
-
-### Fixed
- **Credential detection in extraction**: Improved detection of credentials, API keys, and secrets in extraction instructions to prevent them from being stored as memories (#4552)
- **Standalone timestamp extraction**: Prevented extraction of standalone timestamps as memories when no meaningful content accompanies them (#4550)
-
 ## [0.4.0] - 2026-03-16

 ### Added
@@ -1,6 +1,6 @@
 {
  "name": "@mem0/openclaw-mem0",
-  "version": "0.4.1",
+  "version": "0.4.0",
  "type": "module",
  "description": "Mem0 memory backend for OpenClaw — platform or self-hosted open-source",
  "license": "Apache-2.0",
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "mem0ai"
-version = "1.0.8"
+version = "1.0.7"
 description = "Long-term memory for AI Agents"
 authors = [
    { name = "Mem0", email = "support@mem0.ai" }
@@ -91,57 +91,6 @@ def test_generate_response_with_tools(mock_openai_client):
    assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}


-def test_reasoning_model_with_reasoning_effort(mock_openai_client):
-    """Test that reasoning_effort is passed to the API for Azure reasoning models."""
-    config = AzureOpenAIConfig(model="o3-mini", reasoning_effort="low")
-    llm = AzureOpenAILLM(config)
-    messages = [
-        {"role": "system", "content": "You are a helpful ai."},
-        {"role": "user", "content": "Hello"},
-    ]
-
-    mock_response = Mock()
-    mock_response.choices = [Mock(message=Mock(content="Response from o3-mini"))]
-    mock_openai_client.chat.completions.create.return_value = mock_response
-
-    response = llm.generate_response(messages)
-
-    call_kwargs = mock_openai_client.chat.completions.create.call_args
-    assert call_kwargs[1]["reasoning_effort"] == "low"
-    assert "temperature" not in call_kwargs[1]
-    assert response == "Response from o3-mini"
-
-
-def test_azure_reasoning_effort_not_passed_when_none(mock_openai_client):
-    """Test that reasoning_effort is not passed when not configured on Azure."""
-    config = AzureOpenAIConfig(model="o3-mini")
-    llm = AzureOpenAILLM(config)
-    messages = [
-        {"role": "system", "content": "You are a helpful ai."},
-        {"role": "user", "content": "Hello"},
-    ]
-
-    mock_response = Mock()
-    mock_response.choices = [Mock(message=Mock(content="Response"))]
-    mock_openai_client.chat.completions.create.return_value = mock_response
-
-    llm.generate_response(messages)
-
-    call_kwargs = mock_openai_client.chat.completions.create.call_args
-    assert "reasoning_effort" not in call_kwargs[1]
-
-
-def test_azure_config_accepts_reasoning_effort():
-    """Test that AzureOpenAIConfig accepts reasoning_effort without TypeError (issue #3651)."""
-    config = AzureOpenAIConfig(
-        model="o3-mini",
-        reasoning_effort="low",
-        azure_kwargs={"api_key": "test"},
-    )
-    assert config.reasoning_effort == "low"
-    assert config.model == "o3-mini"
-
-
@pytest.mark.parametrize(
    "default_headers",
    [None, {"Firstkey": "FirstVal", "SecondKey": "SecondVal"}],
@@ -170,67 +170,6 @@ def test_callback_exception_handling(mock_openai_client):
    assert llm.config.response_callback is faulty_callback


-def test_reasoning_model_with_reasoning_effort(mock_openai_client):
-    """Test that reasoning_effort is passed to the API for reasoning models."""
-    config = OpenAIConfig(model="o3-mini", reasoning_effort="low")
-    llm = OpenAILLM(config)
-    messages = [{"role": "user", "content": "Hello"}]
-
-    mock_response = Mock()
-    mock_response.choices = [Mock(message=Mock(content="Response from o3-mini"))]
-    mock_openai_client.chat.completions.create.return_value = mock_response
-
-    response = llm.generate_response(messages)
-
-    call_kwargs = mock_openai_client.chat.completions.create.call_args
-    assert call_kwargs[1]["reasoning_effort"] == "low"
-    assert "temperature" not in call_kwargs[1]  # reasoning models don't get temperature
-    assert response == "Response from o3-mini"
-
-
-def test_reasoning_model_without_reasoning_effort(mock_openai_client):
-    """Test that reasoning_effort is not passed when not configured."""
-    config = OpenAIConfig(model="o3-mini")
-    llm = OpenAILLM(config)
-    messages = [{"role": "user", "content": "Hello"}]
-
-    mock_response = Mock()
-    mock_response.choices = [Mock(message=Mock(content="Response"))]
-    mock_openai_client.chat.completions.create.return_value = mock_response
-
-    llm.generate_response(messages)
-
-    call_kwargs = mock_openai_client.chat.completions.create.call_args
-    assert "reasoning_effort" not in call_kwargs[1]
-
-
-def test_non_reasoning_model_ignores_reasoning_effort(mock_openai_client):
-    """Test that reasoning_effort is not passed for non-reasoning models."""
-    config = OpenAIConfig(model="gpt-4.1-nano-2025-04-14", reasoning_effort="high")
-    llm = OpenAILLM(config)
-    messages = [{"role": "user", "content": "Hello"}]
-
-    mock_response = Mock()
-    mock_response.choices = [Mock(message=Mock(content="Response"))]
-    mock_openai_client.chat.completions.create.return_value = mock_response
-
-    llm.generate_response(messages)
-
-    call_kwargs = mock_openai_client.chat.completions.create.call_args
-    # Non-reasoning models use common params path, reasoning_effort not added there
-    assert "reasoning_effort" not in call_kwargs[1]
-
-
-def test_reasoning_effort_config_values():
-    """Test that reasoning_effort can be set to all valid values."""
-    for effort in ["low", "medium", "high"]:
-        config = OpenAIConfig(model="o3", reasoning_effort=effort)
-        assert config.reasoning_effort == effort
-
-    config = OpenAIConfig(model="o3")
-    assert config.reasoning_effort is None
-
-
 def test_callback_with_tools(mock_openai_client):
    mock_callback = Mock()
    config = OpenAIConfig(model="gpt-4.1-nano-2025-04-14", response_callback=mock_callback)
@@ -1,6 +1,6 @@
 import logging
 from datetime import datetime, timezone
-from unittest.mock import MagicMock, Mock
+from unittest.mock import MagicMock

 import pytest

@@ -79,57 +79,6 @@ class TestAddToVectorStoreErrors:
        assert "Empty response from LLM, no memories to extract" in caplog.text


-class TestAsyncUpdate:
-    @pytest.fixture
-    def mock_async_memory(self, mocker):
-        """Fixture for AsyncMemory with mocker-based mocks"""
-        _setup_mocks(mocker)
-        memory = AsyncMemory()
-        return memory
-
-    @pytest.mark.asyncio
-    async def test_async_update_without_metadata(self, mock_async_memory, mocker):
-        """Test async update passes None metadata by default"""
-        mock_async_memory.embedding_model = Mock()
-        mock_async_memory.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
-        mock_async_memory._update_memory = mocker.AsyncMock()
-
-        result = await mock_async_memory.update("test_id", "Updated memory")
-
-        mock_async_memory._update_memory.assert_called_once_with(
-            "test_id", "Updated memory", {"Updated memory": [0.1, 0.2, 0.3]}, None
-        )
-        assert result["message"] == "Memory updated successfully!"
-
-    @pytest.mark.asyncio
-    async def test_async_update_with_metadata(self, mock_async_memory, mocker):
-        """Test async update correctly forwards metadata"""
-        mock_async_memory.embedding_model = Mock()
-        mock_async_memory.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
-        mock_async_memory._update_memory = mocker.AsyncMock()
-        metadata = {"category": "sports", "priority": "high"}
-
-        result = await mock_async_memory.update("test_id", "Updated memory", metadata=metadata)
-
-        mock_async_memory._update_memory.assert_called_once_with(
-            "test_id", "Updated memory", {"Updated memory": [0.1, 0.2, 0.3]}, metadata
-        )
-        assert result["message"] == "Memory updated successfully!"
-
-    @pytest.mark.asyncio
-    async def test_async_update_with_empty_metadata(self, mock_async_memory, mocker):
-        """Test async update with empty metadata dict"""
-        mock_async_memory.embedding_model = Mock()
-        mock_async_memory.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
-        mock_async_memory._update_memory = mocker.AsyncMock()
-
-        await mock_async_memory.update("test_id", "Updated memory", metadata={})
-
-        mock_async_memory._update_memory.assert_called_once_with(
-            "test_id", "Updated memory", {"Updated memory": [0.1, 0.2, 0.3]}, {}
-        )
-
-
@pytest.mark.asyncio
 class TestAsyncAddToVectorStoreErrors:
    @pytest.fixture
@@ -208,28 +157,6 @@ def test_create_memory_uses_utc_timestamps(mocker):
    _assert_utc_timestamp(payload["created_at"])


-def test_create_memory_sets_updated_at(mocker):
-    memory = _build_memory_instance(mocker, Memory)
-    memory._create_memory("new memory", {"new memory": [0.1, 0.2, 0.3]}, metadata={})
-    payload = memory.vector_store.insert.call_args.kwargs["payloads"][0]
-    assert "updated_at" in payload
-    assert payload["updated_at"] == payload["created_at"]
-    _assert_utc_timestamp(payload["updated_at"])
-
-    # History should also receive updated_at
-    history_kwargs = memory.db.add_history.call_args
-    assert history_kwargs.kwargs["updated_at"] == payload["updated_at"]
-
-
-def test_create_memory_preserves_existing_created_at(mocker):
-    memory = _build_memory_instance(mocker, Memory)
-    custom_ts = "2023-05-06T09:19:20+00:00"
-    memory._create_memory("new memory", {"new memory": [0.1, 0.2, 0.3]}, metadata={"created_at": custom_ts})
-    payload = memory.vector_store.insert.call_args.kwargs["payloads"][0]
-    assert payload["created_at"] == custom_ts
-    assert payload["updated_at"] == custom_ts
-
-
 def test_update_memory_uses_utc_timestamps(mocker):
    memory = _build_memory_instance(mocker, Memory)
    memory.vector_store.get.return_value = MagicMock(
@@ -249,30 +176,6 @@ async def test_async_create_memory_uses_utc_timestamps(mocker):
    _assert_utc_timestamp(payload["created_at"])


-@pytest.mark.asyncio
-async def test_async_create_memory_sets_updated_at(mocker):
-    memory = _build_memory_instance(mocker, AsyncMemory)
-    await memory._create_memory("new memory", {"new memory": [0.1, 0.2, 0.3]}, metadata={})
-    payload = memory.vector_store.insert.call_args.kwargs["payloads"][0]
-    assert "updated_at" in payload
-    assert payload["updated_at"] == payload["created_at"]
-    _assert_utc_timestamp(payload["updated_at"])
-
-    # History should also receive updated_at
-    history_kwargs = memory.db.add_history.call_args
-    assert history_kwargs.kwargs["updated_at"] == payload["updated_at"]
-
-
-@pytest.mark.asyncio
-async def test_async_create_memory_preserves_existing_created_at(mocker):
-    memory = _build_memory_instance(mocker, AsyncMemory)
-    custom_ts = "2023-05-06T09:19:20+00:00"
-    await memory._create_memory("new memory", {"new memory": [0.1, 0.2, 0.3]}, metadata={"created_at": custom_ts})
-    payload = memory.vector_store.insert.call_args.kwargs["payloads"][0]
-    assert payload["created_at"] == custom_ts
-    assert payload["updated_at"] == custom_ts
-
-
@pytest.mark.asyncio
 async def test_async_update_memory_uses_utc_timestamps(mocker):
    memory = _build_memory_instance(mocker, AsyncMemory)
@@ -285,107 +188,6 @@ async def test_async_update_memory_uses_utc_timestamps(mocker):
    _assert_utc_timestamp(payload["updated_at"])


-def test_create_then_search_and_get_all_return_same_timestamps(mocker):
-    """Reproduces issue #3720: created_at must be identical in search() and get_all()."""
-    memory = _build_memory_instance(mocker, Memory)
-
-    # Step 1: Create a memory — capture the payload stored in the vector store
-    memory._create_memory("Likes pizza", {"Likes pizza": [0.1, 0.2, 0.3]}, metadata={"user_id": "alice"})
-    stored_payload = memory.vector_store.insert.call_args.kwargs["payloads"][0]
-    stored_id = memory.vector_store.insert.call_args.kwargs["ids"][0]
-
-    # Verify both timestamps were stored
-    assert stored_payload["created_at"] is not None
-    assert stored_payload["updated_at"] is not None
-    assert stored_payload["updated_at"] == stored_payload["created_at"]
-
-    # Step 2: Simulate the vector store returning this memory for both search and get_all
-    mem_result = MagicMock()
-    mem_result.id = stored_id
-    mem_result.payload = stored_payload
-    mem_result.score = 0.95
-
-    memory.vector_store.search.return_value = [mem_result]
-    memory.vector_store.list.return_value = [[mem_result]]
-
-    # Step 3: Call search and get_all, compare timestamps
-    search_results = memory._search_vector_store("pizza", filters={"user_id": "alice"}, limit=10, threshold=None)
-    get_all_results = memory._get_all_from_vector_store(filters={"user_id": "alice"}, limit=100)
-
-    search_item = search_results[0]
-    get_all_item = get_all_results[0]
-
-    # The core assertion from issue #3720: created_at must be the same
-    assert search_item["created_at"] == get_all_item["created_at"], (
-        f"created_at mismatch: search={search_item['created_at']}, get_all={get_all_item['created_at']}"
-    )
-    assert search_item["updated_at"] == get_all_item["updated_at"], (
-        f"updated_at mismatch: search={search_item['updated_at']}, get_all={get_all_item['updated_at']}"
-    )
-
-    # Neither should be None
-    assert search_item["created_at"] is not None
-    assert search_item["updated_at"] is not None
-    assert get_all_item["created_at"] is not None
-    assert get_all_item["updated_at"] is not None
-
-
-def test_update_preserves_created_at_and_updates_updated_at(mocker):
-    """After an update, created_at must stay the same and updated_at must change."""
-    memory = _build_memory_instance(mocker, Memory)
-
-    # Create a memory
-    memory._create_memory("Likes pizza", {"Likes pizza": [0.1, 0.2, 0.3]}, metadata={"user_id": "alice"})
-    created_payload = memory.vector_store.insert.call_args.kwargs["payloads"][0]
-    created_id = memory.vector_store.insert.call_args.kwargs["ids"][0]
-    original_created_at = created_payload["created_at"]
-
-    # Update the memory — simulate existing memory in vector store
-    memory.vector_store.get.return_value = MagicMock(
-        id=created_id,
-        payload=created_payload,
-    )
-    memory._update_memory(created_id, "Loves pizza", {"Loves pizza": [0.2, 0.3, 0.4]}, metadata={})
-    updated_payload = memory.vector_store.update.call_args.kwargs["payload"]
-
-    # created_at must be preserved
-    assert updated_payload["created_at"] == original_created_at
-    # updated_at must be set and different from creation time (or at least present)
-    assert updated_payload["updated_at"] is not None
-    _assert_utc_timestamp(updated_payload["updated_at"])
-
-
-def test_search_and_get_all_consistent_after_update(mocker):
-    """After update, search and get_all must still return the same timestamps."""
-    memory = _build_memory_instance(mocker, Memory)
-
-    # Simulate a memory that was created then updated
-    updated_payload = {
-        "data": "Loves pizza",
-        "hash": "abc123",
-        "user_id": "alice",
-        "created_at": "2023-05-06T09:19:20+00:00",
-        "updated_at": "2026-03-23T10:00:00+00:00",
-    }
-
-    mem_result = MagicMock()
-    mem_result.id = "mem-1"
-    mem_result.payload = updated_payload
-    mem_result.score = 0.9
-
-    memory.vector_store.search.return_value = [mem_result]
-    memory.vector_store.list.return_value = [[mem_result]]
-
-    search_results = memory._search_vector_store("pizza", filters={"user_id": "alice"}, limit=10, threshold=None)
-    get_all_results = memory._get_all_from_vector_store(filters={"user_id": "alice"}, limit=100)
-
-    assert search_results[0]["created_at"] == get_all_results[0]["created_at"]
-    assert search_results[0]["updated_at"] == get_all_results[0]["updated_at"]
-    # created_at should be the original, not the updated time
-    assert search_results[0]["created_at"] == "2023-05-06T09:19:20+00:00"
-    assert search_results[0]["updated_at"] == "2026-03-23T10:00:00+00:00"
-
-
 class TestMetadataNotMutated:
    """Tests that metadata dicts passed to memory methods are not mutated in-place (issue #2648)."""

@@ -581,52 +383,6 @@ class TestMetadataNotMutated:
        )


-def test_update_preserves_actor_id_when_different_actor_updates(mocker):
-    """actor_id must be preserved from the original memory even when the
-    updating caller passes a different actor_id in metadata (issue #4490)."""
-    memory = _build_memory_instance(mocker, Memory)
-    memory.vector_store.get.return_value = MagicMock(
-        payload={
-            "data": "I am player #1",
-            "user_id": "team",
-            "actor_id": "Alice",
-            "created_at": "2026-01-01T00:00:00+00:00",
-        }
-    )
-
-    memory._update_memory(
-        "mem-id", "Player #1 is a good person",
-        {"Player #1 is a good person": [0.1, 0.2, 0.3]},
-        metadata={"user_id": "team", "actor_id": "Bob"},
-    )
-
-    stored = memory.vector_store.update.call_args.kwargs["payload"]
-    assert stored["actor_id"] == "Alice"
-
-
-@pytest.mark.asyncio
-async def test_async_update_preserves_actor_id_when_different_actor_updates(mocker):
-    """Async variant: actor_id must be preserved from the original memory (issue #4490)."""
-    memory = _build_memory_instance(mocker, AsyncMemory)
-    memory.vector_store.get.return_value = MagicMock(
-        payload={
-            "data": "I am player #1",
-            "user_id": "team",
-            "actor_id": "Alice",
-            "created_at": "2026-01-01T00:00:00+00:00",
-        }
-    )
-
-    await memory._update_memory(
-        "mem-id", "Player #1 is a good person",
-        {"Player #1 is a good person": [0.1, 0.2, 0.3]},
-        metadata={"user_id": "team", "actor_id": "Bob"},
-    )
-
-    stored = memory.vector_store.update.call_args.kwargs["payload"]
-    assert stored["actor_id"] == "Alice"
-
-
 def test_normalize_iso_timestamp_to_utc_preserves_naive_values():
    assert _normalize_iso_timestamp_to_utc("2026-03-18T00:00:00") == "2026-03-18T00:00:00"

@@ -1,232 +0,0 @@
-"""Tests for chatty LLM JSON parsing robustness.
-
-Validates that extract_json and the full fallback chain correctly handle
-responses from local LLMs (LM Studio, Ollama) that wrap JSON in
-conversational text, markdown code blocks, or both.
-"""
-
-import json
-
-import pytest
-
-from mem0.memory.utils import extract_json, remove_code_blocks
-
-
-# --- Test extract_json ---
-
-class TestExtractJson:
-    """Tests for extract_json utility."""
-
-    def test_pure_json(self):
-        text = '{"memory": [{"id": "0", "text": "likes basketball", "event": "ADD"}]}'
-        result = extract_json(text)
-        parsed = json.loads(result)
-        assert parsed["memory"][0]["text"] == "likes basketball"
-
-    def test_json_in_markdown_code_block(self):
-        text = '```json\n{"memory": [{"id": "0", "text": "likes basketball", "event": "ADD"}]}\n```'
-        result = extract_json(text)
-        parsed = json.loads(result)
-        assert parsed["memory"][0]["text"] == "likes basketball"
-
-    def test_json_in_plain_code_block(self):
-        text = '```\n{"memory": [{"id": "0", "text": "likes basketball", "event": "ADD"}]}\n```'
-        result = extract_json(text)
-        parsed = json.loads(result)
-        assert parsed["memory"][0]["text"] == "likes basketball"
-
-    def test_chatty_with_markdown(self):
-        """extract_json can handle embedded code blocks via re.search."""
-        text = """Here is the extracted memory:
-```json
-{"memory": [{"id": "0", "text": "likes basketball", "event": "ADD"}]}
-```
-I hope this helps!"""
-        result = extract_json(text)
-        parsed = json.loads(result)
-        assert parsed["memory"][0]["text"] == "likes basketball"
-
-    def test_chatty_without_markdown(self):
-        """extract_json finds JSON by {/} boundaries when no code block is present."""
-        text = """Here is the memory update you requested:
-{"memory": [{"id": "0", "text": "likes gaming", "event": "ADD"}]}
-That's the result."""
-        result = extract_json(text)
-        parsed = json.loads(result)
-        assert parsed["memory"][0]["text"] == "likes gaming"
-
-    def test_chatty_multiline_json_without_markdown(self):
-        """extract_json finds multi-line JSON by {/} boundaries."""
-        text = """Here is the memory update:
-{
-  "memory": [
-    {
-      "id": "0",
-      "text": "User likes gaming",
-      "event": "ADD"
-    }
-  ]
-}
-"""
-        result = extract_json(text)
-        parsed = json.loads(result)
-        assert parsed["memory"][0]["text"] == "User likes gaming"
-
-    def test_no_json_at_all(self):
-        """No JSON in the text — should return as-is."""
-        text = "I don't have any memory updates."
-        result = extract_json(text)
-        assert result == text
-
-    def test_whitespace_padding(self):
-        text = '  \n  {"memory": []}  \n  '
-        result = extract_json(text)
-        parsed = json.loads(result)
-        assert parsed == {"memory": []}
-
-    def test_nested_json_objects(self):
-        """JSON with nested objects should find outermost braces."""
-        text = 'Sure! {"memory": [{"id": "0", "text": "test", "event": "ADD"}]} Done.'
-        result = extract_json(text)
-        parsed = json.loads(result)
-        assert parsed["memory"][0]["id"] == "0"
-
-
-# --- Test remove_code_blocks ---
-
-class TestRemoveCodeBlocks:
-    """Tests for remove_code_blocks — verify it does NOT handle chatty text."""
-
-    def test_clean_code_block(self):
-        text = '```json\n{"memory": []}\n```'
-        result = remove_code_blocks(text)
-        parsed = json.loads(result)
-        assert parsed == {"memory": []}
-
-    def test_no_code_block(self):
-        """Without code blocks, returns content as-is (may not be valid JSON)."""
-        text = 'Here is the result: {"memory": []}'
-        result = remove_code_blocks(text)
-        # This should NOT be parseable as JSON because it has leading text
-        with pytest.raises(json.JSONDecodeError):
-            json.loads(result)
-
-    def test_think_tags_removed_inside_code_block(self):
-        """Think tags inside a code block response are removed."""
-        text = '```json\n<think>reasoning here</think>\n{"memory": []}\n```'
-        result = remove_code_blocks(text)
-        parsed = json.loads(result)
-        assert parsed == {"memory": []}
-
-    def test_think_tags_before_code_block_not_handled(self):
-        """Think tags before code block cause regex to miss — fallback needed.
-
-        This is a known limitation of remove_code_blocks: it matches the code
-        block regex first, and only strips think tags from the result. When
-        think tags come before the code block, the regex fails entirely.
-        The fallback chain (extract_json) handles this case.
-        """
-        text = '<think>reasoning here</think>\n```json\n{"memory": []}\n```'
-        result = remove_code_blocks(text)
-        with pytest.raises(json.JSONDecodeError):
-            json.loads(result)
-
-
-# --- Test the full fallback chain (remove_code_blocks -> extract_json) ---
-
-class TestFallbackChain:
-    """Tests the actual fallback pattern used in _add_to_vector_store:
-    try json.loads(remove_code_blocks(response))
-    except JSONDecodeError: json.loads(extract_json(response))
-    """
-
-    def _parse_with_fallback(self, response):
-        """Mimics the parsing logic in _add_to_vector_store."""
-        try:
-            return json.loads(remove_code_blocks(response), strict=False)
-        except json.JSONDecodeError:
-            extracted = extract_json(response)
-            return json.loads(extracted, strict=False)
-
-    def test_clean_json(self):
-        response = '{"memory": [{"id": "0", "text": "Name is Alex", "event": "ADD"}]}'
-        result = self._parse_with_fallback(response)
-        assert result["memory"][0]["text"] == "Name is Alex"
-
-    def test_markdown_wrapped(self):
-        response = '```json\n{"memory": [{"id": "0", "text": "Name is Alex", "event": "ADD"}]}\n```'
-        result = self._parse_with_fallback(response)
-        assert result["memory"][0]["text"] == "Name is Alex"
-
-    def test_chatty_markdown(self):
-        """Issue #3788: LLM wraps in conversational text + markdown."""
-        response = """Here is the extracted memory:
-```json
-{
-  "memory": [
-    {"id": "0", "text": "Name is Alex", "event": "ADD"},
-    {"id": "1", "text": "Love basketball", "event": "ADD"},
-    {"id": "2", "text": "Love gaming", "event": "ADD"}
-  ]
-}
-```
-I hope this helps!"""
-        result = self._parse_with_fallback(response)
-        assert len(result["memory"]) == 3
-        assert result["memory"][0]["text"] == "Name is Alex"
-        assert result["memory"][1]["text"] == "Love basketball"
-        assert result["memory"][2]["text"] == "Love gaming"
-
-    def test_chatty_no_markdown(self):
-        """Issue #3788: LLM wraps in conversational text without markdown."""
-        response = """Here is the memory update you requested:
-{
-  "memory": [
-    {"id": "0", "text": "Name is Alex", "event": "ADD"},
-    {"id": "1", "text": "Love basketball", "event": "ADD"}
-  ]
-}"""
-        result = self._parse_with_fallback(response)
-        assert len(result["memory"]) == 2
-
-    def test_think_tags_with_json(self):
-        """Reasoning model with <think> tags."""
-        response = '<think>Let me process this...</think>\n```json\n{"memory": [{"id": "0", "text": "test", "event": "ADD"}]}\n```'
-        result = self._parse_with_fallback(response)
-        assert result["memory"][0]["text"] == "test"
-
-    def test_lmstudio_style_response(self):
-        """Mimics the actual LM Studio response format from the issue."""
-        response = """{
-  "memory": [
-    {
-      "id": "0",
-      "text": "Name is Alex",
-      "event": "ADD"
-    },
-    {
-      "id": "1",
-      "text": "Love basketball",
-      "event": "ADD"
-    },
-    {
-      "id": "2",
-      "text": "Love gaming",
-      "event": "ADD"
-    }
-  ]
-}"""
-        result = self._parse_with_fallback(response)
-        assert len(result["memory"]) == 3
-
-    def test_completely_invalid_response(self):
-        """If both attempts fail, the outer except in main.py catches it."""
-        response = "I don't understand the question"
-        with pytest.raises(json.JSONDecodeError):
-            self._parse_with_fallback(response)
-
-    def test_facts_extraction_fallback(self):
-        """Also verify the pattern works for facts extraction responses."""
-        response = 'Sure! Here are the facts:\n{"facts": ["Name is Alex", "Loves basketball"]}\nHope that helps!'
-        result = self._parse_with_fallback(response)
-        assert result["facts"] == ["Name is Alex", "Loves basketball"]
@@ -175,41 +175,12 @@ def test_update(memory_instance):
    result = memory_instance.update("test_id", "Updated memory")

    memory_instance._update_memory.assert_called_once_with(
-        "test_id", "Updated memory", {"Updated memory": [0.1, 0.2, 0.3]}, None
+        "test_id", "Updated memory", {"Updated memory": [0.1, 0.2, 0.3]}
    )

    assert result["message"] == "Memory updated successfully!"


-def test_update_with_metadata(memory_instance):
-    memory_instance.embedding_model = Mock()
-    memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
-
-    memory_instance._update_memory = Mock()
-    metadata = {"category": "sports", "priority": "high"}
-
-    result = memory_instance.update("test_id", "Updated memory", metadata=metadata)
-
-    memory_instance._update_memory.assert_called_once_with(
-        "test_id", "Updated memory", {"Updated memory": [0.1, 0.2, 0.3]}, metadata
-    )
-
-    assert result["message"] == "Memory updated successfully!"
-
-
-def test_update_with_empty_metadata(memory_instance):
-    memory_instance.embedding_model = Mock()
-    memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
-
-    memory_instance._update_memory = Mock()
-
-    memory_instance.update("test_id", "Updated memory", metadata={})
-
-    memory_instance._update_memory.assert_called_once_with(
-        "test_id", "Updated memory", {"Updated memory": [0.1, 0.2, 0.3]}, {}
-    )
-
-
 def test_delete(memory_instance):
    memory_instance._delete_memory = Mock()

@@ -1,5 +1,4 @@
 import json
-from datetime import datetime
 from unittest.mock import MagicMock, patch

 import pytest
@@ -11,7 +10,7 @@ from mem0.memory.utils import normalize_facts

 class MockVectorMemory:
    """Mock memory object for testing incomplete payloads."""
-
+    
    def __init__(self, memory_id: str, payload: dict, score: float = 0.8):
        self.id = memory_id
        self.payload = payload
@@ -105,7 +104,7 @@ def test_collection_name_preserved_after_reset(mock_sqlite, mock_llm_factory, mo

    reset_calls = [call for call in mock_vector_factory.call_args_list if len(mock_vector_factory.call_args_list) > 2]
    if reset_calls:
-        reset_config = reset_calls[-1][0][1]
+        reset_config = reset_calls[-1][0][1]  
        assert reset_config.collection_name == test_collection_name, f"Reset used wrong collection name: {reset_config.collection_name}"


@@ -130,13 +129,13 @@ def test_search_handles_incomplete_payloads(mock_sqlite, mock_llm_factory, mock_
    complete_memory = MockVectorMemory("mem_2", {"data": "content", "hash": "def456"})

    mock_vector_store.search.return_value = [incomplete_memory, complete_memory]
-
+    
    mock_embedder = MagicMock()
    mock_embedder.embed.return_value = [0.1, 0.2, 0.3]
    memory.embedding_model = mock_embedder

    result = memory._search_vector_store("test", {"user_id": "test"}, 10)
-
+    
    assert len(result) == 2
    memories_by_id = {mem["id"]: mem for mem in result}

@@ -573,187 +572,3 @@ def test_update_infer_true_caches_embedding_on_llm_rewrite(mock_sqlite, mock_llm
    # It should NOT be called a 3rd time inside _update_memory
    assert embedder.embed.call_count == 2
    mock_vector_store.update.assert_called_once()
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.main.SQLiteManager')
-def test_delete_memory_history_has_timestamps(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Test that deleting a memory records created_at and updated_at in history.
-
-    Ensures DELETE operations preserve the original creation timestamp
-    and record the deletion time for proper audit trails.
-    """
-    mock_embedder_factory.return_value = MagicMock()
-    mock_vector_store = MagicMock()
-    mock_vector_factory.return_value = mock_vector_store
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    config = MemoryConfig()
-    memory = MemoryClass(config)
-
-    existing_memory = MagicMock()
-    existing_memory.payload = {
-        "data": "I like Python.",
-        "created_at": "2024-01-01T00:00:00+00:00",
-        "actor_id": None,
-        "role": None,
-    }
-    mock_vector_store.get.return_value = existing_memory
-
-    memory.delete("mem-123")
-
-    call_kwargs = memory.db.add_history.call_args.kwargs
-    assert call_kwargs["created_at"] == "2024-01-01T00:00:00+00:00"
-    assert call_kwargs["updated_at"] is not None
-    datetime.fromisoformat(call_kwargs["updated_at"])  # verify valid ISO timestamp
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.main.SQLiteManager')
-def test_delete_memory_normalizes_non_utc_created_at(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """Test that non-UTC created_at timestamps are normalized to UTC on delete."""
-    mock_embedder_factory.return_value = MagicMock()
-    mock_vector_store = MagicMock()
-    mock_vector_factory.return_value = mock_vector_store
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    config = MemoryConfig()
-    memory = MemoryClass(config)
-
-    existing_memory = MagicMock()
-    existing_memory.payload = {
-        "data": "I like Python.",
-        "created_at": "2024-01-01T05:00:00+05:00",  # UTC+5
-        "actor_id": None,
-        "role": None,
-    }
-    mock_vector_store.get.return_value = existing_memory
-
-    memory.delete("mem-123")
-
-    call_kwargs = memory.db.add_history.call_args.kwargs
-    assert call_kwargs["created_at"] == "2024-01-01T00:00:00+00:00"  # normalized to UTC
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.main.SQLiteManager')
-def test_delete_memory_missing_created_at(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """Test that delete works when created_at is absent from the payload (pre-existing memories)."""
-    mock_embedder_factory.return_value = MagicMock()
-    mock_vector_store = MagicMock()
-    mock_vector_factory.return_value = mock_vector_store
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    config = MemoryConfig()
-    memory = MemoryClass(config)
-
-    existing_memory = MagicMock()
-    existing_memory.payload = {
-        "data": "I like Python.",
-        "actor_id": None,
-        "role": None,
-    }
-    mock_vector_store.get.return_value = existing_memory
-
-    memory.delete("mem-123")
-
-    call_kwargs = memory.db.add_history.call_args.kwargs
-    assert call_kwargs["created_at"] is None
-    assert call_kwargs["updated_at"] is not None
-    datetime.fromisoformat(call_kwargs["updated_at"])  # verify valid ISO timestamp
-
-
-@pytest.mark.asyncio
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.main.SQLiteManager')
-async def test_async_delete_memory_history_has_timestamps(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Test that async deleting a memory records created_at and updated_at in history.
-
-    Ensures async DELETE operations preserve the original creation timestamp
-    and record the deletion time for proper audit trails.
-    """
-    mock_embedder_factory.return_value = MagicMock()
-    mock_vector_store = MagicMock()
-    mock_vector_factory.return_value = mock_vector_store
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import AsyncMemory
-    config = MemoryConfig()
-    memory = AsyncMemory(config)
-
-    existing_memory = MagicMock()
-    existing_memory.payload = {
-        "data": "I like Python.",
-        "created_at": "2024-01-01T00:00:00+00:00",
-        "actor_id": None,
-        "role": None,
-    }
-    mock_vector_store.get.return_value = existing_memory
-
-    await memory.delete("mem-123")
-
-    call_kwargs = memory.db.add_history.call_args.kwargs
-    assert call_kwargs["created_at"] == "2024-01-01T00:00:00+00:00"
-    assert call_kwargs["updated_at"] is not None
-    datetime.fromisoformat(call_kwargs["updated_at"])  # verify valid ISO timestamp
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-class TestProcessMetadataFiltersMerge:
-    """Regression tests for issue #3952: multiple operators on the same key must be merged."""
-
-    def _make_memory(self, mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-        mock_embedder_factory.return_value = MagicMock()
-        mock_vector_factory.return_value = MagicMock()
-        mock_llm_factory.return_value = MagicMock()
-        mock_sqlite.return_value = MagicMock()
-        from mem0.memory.main import Memory as MemoryClass
-        return MemoryClass(MemoryConfig())
-
-    def test_multiple_operators_same_key_merged(self, mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-        """Filters like created_at: {gte: X, lte: Y} must preserve both operators."""
-        memory = self._make_memory(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory)
-        result = memory._process_metadata_filters({
-            "created_at": {"gte": 1000, "lte": 2000}
-        })
-        assert result == {"created_at": {"gte": 1000, "lte": 2000}}
-
-    def test_single_operator_still_works(self, mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-        """Single operator filters must continue to work."""
-        memory = self._make_memory(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory)
-        result = memory._process_metadata_filters({
-            "created_at": {"gte": 1000}
-        })
-        assert result == {"created_at": {"gte": 1000}}
-
-    def test_multiple_keys_with_multiple_operators(self, mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-        """Multiple keys each with multiple operators."""
-        memory = self._make_memory(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory)
-        result = memory._process_metadata_filters({
-            "created_at": {"gte": 1000, "lte": 2000},
-            "score": {"gt": 0.5, "lt": 0.9},
-        })
-        assert result == {
-            "created_at": {"gte": 1000, "lte": 2000},
-            "score": {"gt": 0.5, "lt": 0.9},
-        }
@@ -153,16 +153,9 @@ def test_insert_generates_sql(db_instance_direct, mock_workspace_client):
    sql = kwargs["statement"] if "statement" in kwargs else args[0]
    assert "INSERT INTO" in sql
    assert "catalog.schema.table" in sql
-    # Embedding list rendered inline (ARRAY type not supported by parameterized queries)
+    assert "id1" in sql
+    # Embedding list rendered
    assert "array(0.1, 0.2, 0.3, 0.4)" in sql
-    # User-supplied values should use parameterized queries, not inline values
-    assert ":memory_id_0" in sql
-    assert "id1" not in sql  # id should be in params, not in SQL
-    params = kwargs["parameters"]
-    param_names = {p.name for p in params}
-    assert "memory_id_0" in param_names
-    id_param = next(p for p in params if p.name == "memory_id_0")
-    assert id_param.value == "id1"


 # ---------------------- Search Tests ---------------------- #
@@ -253,13 +246,7 @@ def test_delete_vector(db_instance_delta, mock_workspace_client):
    db_instance_delta.delete("id-delete")
    args, kwargs = mock_workspace_client.statement_execution.execute_statement.call_args
    sql = kwargs.get("statement") or args[0]
-    assert "DELETE FROM" in sql
-    assert ":vector_id" in sql
-    assert "id-delete" not in sql  # value should be in params, not in SQL
-    params = kwargs["parameters"]
-    assert len(params) == 1
-    assert params[0].name == "vector_id"
-    assert params[0].value == "id-delete"
+    assert "DELETE FROM" in sql and "id-delete" in sql


 # ---------------------- Update Tests ---------------------- #
@@ -273,17 +260,10 @@ def test_update_vector(db_instance_direct, mock_workspace_client):
    )
    args, kwargs = mock_workspace_client.statement_execution.execute_statement.call_args
    sql = kwargs.get("statement") or args[0]
-    assert "UPDATE" in sql
-    assert "embedding = array(0.4, 0.5, 0.6, 0.7)" in sql
-    assert "custom = :payload_custom" in sql
-    assert ":vector_id" in sql
-    assert "id-upd" not in sql  # value should be in params, not in SQL
-    assert "'val'" not in sql  # value should be in params, not in SQL
+    assert "UPDATE" in sql and "id-upd" in sql
+    assert "embedding = [0.4, 0.5, 0.6, 0.7]" in sql
+    assert "custom = 'val'" in sql
    assert "user_id" not in sql  # excluded
-    params = kwargs["parameters"]
-    param_map = {p.name: p.value for p in params}
-    assert param_map["payload_custom"] == "val"
-    assert param_map["vector_id"] == "id-upd"


 # ---------------------- Get Tests ---------------------- #
@@ -723,12 +703,9 @@ def test_e2e_crud_lifecycle_delta_sync(mock_workspace_client):
        payloads=[{"data": "test memory", "user_id": "u1", "hash": "h1"}],
        ids=["mem-001"],
    )
-    insert_call = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    insert_sql = insert_call["statement"]
+    insert_sql = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs["statement"]
    assert "INSERT INTO cat.sch.tbl" in insert_sql
-    assert ":memory_id_0" in insert_sql  # parameterized, not inline
-    insert_params = {p.name: p.value for p in insert_call["parameters"]}
-    assert insert_params["memory_id_0"] == "mem-001"
+    assert "mem-001" in insert_sql

    # SEARCH
    mock_workspace_client.vector_search_indexes.query_index.return_value = SimpleNamespace(
@@ -776,23 +753,16 @@ def test_e2e_crud_lifecycle_delta_sync(mock_workspace_client):

    # UPDATE
    db.update(vector_id="mem-001", payload={"memory": "updated memory"})
-    update_call = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    update_sql = update_call["statement"]
+    update_sql = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs["statement"]
    assert "UPDATE cat.sch.tbl" in update_sql
-    assert ":vector_id" in update_sql
-    assert ":payload_memory" in update_sql
-    update_params = {p.name: p.value for p in update_call["parameters"]}
-    assert update_params["vector_id"] == "mem-001"
-    assert update_params["payload_memory"] == "updated memory"
+    assert "mem-001" in update_sql
+    assert "updated memory" in update_sql

    # DELETE
    db.delete("mem-001")
-    delete_call = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    delete_sql = delete_call["statement"]
+    delete_sql = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs["statement"]
    assert "DELETE FROM cat.sch.tbl" in delete_sql
-    assert ":vector_id" in delete_sql
-    delete_params = {p.name: p.value for p in delete_call["parameters"]}
-    assert delete_params["vector_id"] == "mem-001"
+    assert "mem-001" in delete_sql


 def test_e2e_crud_lifecycle_direct_access(mock_workspace_client):
@@ -826,11 +796,8 @@ def test_e2e_crud_lifecycle_direct_access(mock_workspace_client):
        payloads=[{"data": "direct memory", "user_id": "u1", "hash": "h1"}],
        ids=["mem-da-001"],
    )
-    insert_call = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    insert_sql = insert_call["statement"]
+    insert_sql = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs["statement"]
    assert "array(0.1, 0.2, 0.3, 0.4)" in insert_sql
-    insert_params = {p.name: p.value for p in insert_call["parameters"]}
-    assert insert_params["memory_id_0"] == "mem-da-001"

    # SEARCH with vector
    mock_workspace_client.vector_search_indexes.query_index.return_value = SimpleNamespace(
@@ -871,191 +838,3 @@ def test_e2e_crud_lifecycle_direct_access(mock_workspace_client):
    list_kwargs = mock_workspace_client.vector_search_indexes.query_index.call_args.kwargs
    assert "query_vector" in list_kwargs
    assert "query_text" not in list_kwargs
-
-
-# ---------------------- SQL Injection Prevention Tests (Issue #4073) ---------------------- #
-
-
-SQLI_PAYLOADS = [
-    "'; DELETE FROM table; --",
-    "' OR '1'='1",
-    "'; DROP TABLE memories; --",
-    "1' UNION SELECT * FROM secrets --",
-]
-
-
-@pytest.mark.parametrize("malicious_id", SQLI_PAYLOADS)
-def test_delete_prevents_sql_injection(db_instance_delta, mock_workspace_client, malicious_id):
-    """Verify delete() uses parameterized queries so SQL injection payloads are never in the SQL string."""
-    db_instance_delta.delete(malicious_id)
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    # The malicious payload must NOT appear in the SQL statement itself
-    assert malicious_id not in sql
-    assert ":vector_id" in sql
-    # It should be safely passed as a parameter
-    assert kwargs["parameters"][0].value == malicious_id
-
-
-@pytest.mark.parametrize("malicious_id", SQLI_PAYLOADS)
-def test_update_prevents_sql_injection_in_vector_id(db_instance_delta, mock_workspace_client, malicious_id):
-    """Verify update() parameterizes vector_id."""
-    db_instance_delta.update(vector_id=malicious_id, payload={"memory": "safe value"})
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    assert malicious_id not in sql
-    assert ":vector_id" in sql
-    param_map = {p.name: p.value for p in kwargs["parameters"]}
-    assert param_map["vector_id"] == malicious_id
-
-
-@pytest.mark.parametrize("malicious_value", SQLI_PAYLOADS)
-def test_update_prevents_sql_injection_in_payload(db_instance_delta, mock_workspace_client, malicious_value):
-    """Verify update() parameterizes payload values."""
-    db_instance_delta.update(vector_id="safe-id", payload={"memory": malicious_value})
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    assert malicious_value not in sql
-    assert ":payload_memory" in sql
-    param_map = {p.name: p.value for p in kwargs["parameters"]}
-    assert param_map["payload_memory"] == malicious_value
-
-
-@pytest.mark.parametrize("malicious_id", SQLI_PAYLOADS)
-def test_insert_prevents_sql_injection_in_ids(db_instance_delta, mock_workspace_client, malicious_id):
-    """Verify insert() parameterizes memory IDs."""
-    db_instance_delta.insert(
-        vectors=[[0.1, 0.2]],
-        payloads=[{"data": "test", "hash": "h1"}],
-        ids=[malicious_id],
-    )
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    assert malicious_id not in sql
-    assert ":memory_id_0" in sql
-    param_map = {p.name: p.value for p in kwargs["parameters"]}
-    assert param_map["memory_id_0"] == malicious_id
-
-
-@pytest.mark.parametrize("malicious_data", SQLI_PAYLOADS)
-def test_insert_prevents_sql_injection_in_payload_data(db_instance_delta, mock_workspace_client, malicious_data):
-    """Verify insert() parameterizes payload data values."""
-    db_instance_delta.insert(
-        vectors=[[0.1, 0.2]],
-        payloads=[{"data": malicious_data, "hash": "h1"}],
-        ids=["safe-id"],
-    )
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    assert malicious_data not in sql
-    param_map = {p.name: p.value for p in kwargs["parameters"]}
-    assert param_map["memory_0"] == malicious_data
-
-
-@pytest.mark.parametrize("malicious_key", [
-    "memory; DROP TABLE x--",
-    "col' OR '1'='1",
-    "valid_col; DELETE FROM t",
-    "col\nname",
-])
-def test_update_rejects_malicious_column_names(db_instance_delta, mock_workspace_client, malicious_key):
-    """Verify update() skips payload keys that are not valid SQL identifiers."""
-    db_instance_delta.update(
-        vector_id="safe-id",
-        payload={malicious_key: "some value", "memory": "legit"},
-    )
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    # The malicious key must NOT appear as a column name in the SQL
-    assert malicious_key not in sql
-    # The legitimate key should still be present
-    assert ":payload_memory" in sql
-
-
-def test_insert_multi_row(db_instance_delta, mock_workspace_client):
-    """Verify multi-row insert generates unique parameter names per row."""
-    db_instance_delta.insert(
-        vectors=[[0.1, 0.2], [0.3, 0.4]],
-        payloads=[
-            {"data": "first memory", "hash": "h1"},
-            {"data": "second memory", "hash": "h2"},
-        ],
-        ids=["id-1", "id-2"],
-    )
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    params = kwargs["parameters"]
-    param_map = {p.name: p.value for p in params}
-    # Each row should have distinct parameter names
-    assert ":memory_id_0" in sql
-    assert ":memory_id_1" in sql
-    assert param_map["memory_id_0"] == "id-1"
-    assert param_map["memory_id_1"] == "id-2"
-    assert param_map["memory_0"] == "first memory"
-    assert param_map["memory_1"] == "second memory"
-
-
-def test_insert_with_none_values(db_instance_delta, mock_workspace_client):
-    """Verify insert() uses NULL literal for None values, not parameters."""
-    db_instance_delta.insert(
-        vectors=[[0.1, 0.2]],
-        payloads=[{"data": "test", "hash": "h1"}],  # agent_id, run_id etc will be None
-        ids=["id-1"],
-    )
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    assert "NULL" in sql  # None values should be literal NULL
-    # Only non-None values should have parameters
-    param_names = {p.name for p in kwargs["parameters"]}
-    assert "memory_id_0" in param_names
-    assert "memory_0" in param_names
-    assert "hash_0" in param_names
-
-
-def test_update_payload_only(db_instance_delta, mock_workspace_client):
-    """Verify update() works with payload only (no vector)."""
-    db_instance_delta.update(vector_id="id-1", payload={"memory": "new text"})
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    assert "UPDATE" in sql
-    assert "embedding" not in sql
-    assert ":payload_memory" in sql
-    assert ":vector_id" in sql
-
-
-def test_update_vector_only(db_instance_direct, mock_workspace_client):
-    """Verify update() works with vector only (no payload)."""
-    db_instance_direct.update(vector_id="id-1", vector=[0.1, 0.2, 0.3, 0.4])
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    sql = kwargs["statement"]
-    assert "UPDATE" in sql
-    assert "embedding = array(0.1, 0.2, 0.3, 0.4)" in sql
-    assert ":vector_id" in sql
-    param_map = {p.name: p.value for p in kwargs["parameters"]}
-    assert param_map["vector_id"] == "id-1"
-    assert len(kwargs["parameters"]) == 1  # only vector_id param
-
-
-def test_insert_timestamp_params_have_explicit_type(db_instance_delta, mock_workspace_client):
-    """Verify insert() sets type='TIMESTAMP' on created_at/updated_at parameters
-    so Databricks doesn't rely on implicit STRING->TIMESTAMP casting."""
-    db_instance_delta.insert(
-        vectors=[[0.1, 0.2]],
-        payloads=[{
-            "data": "test",
-            "hash": "h1",
-            "created_at": "2024-01-01T00:00:00+00:00",
-            "updated_at": "2024-01-02T00:00:00+00:00",
-        }],
-        ids=["id-1"],
-    )
-    kwargs = mock_workspace_client.statement_execution.execute_statement.call_args.kwargs
-    params = kwargs["parameters"]
-    ts_params = [p for p in params if "created_at" in p.name or "updated_at" in p.name]
-    assert len(ts_params) == 2
-    for p in ts_params:
-        assert p.type == "TIMESTAMP", f"Parameter {p.name} should have type=TIMESTAMP, got {p.type}"
-    # Non-timestamp params should not have a type set (defaults to STRING)
-    non_ts_params = [p for p in params if "created_at" not in p.name and "updated_at" not in p.name]
-    for p in non_ts_params:
-        assert p.type is None, f"Parameter {p.name} should not have explicit type, got {p.type}"