fix(docs): update the cookbooks and remove and update teh depcreataed param (#4814)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Kartik
@@ -56,7 +56,7 @@ class Mem0Teachability(AgentCapability):
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def process_last_received_message(self, text: Union[Dict, str]):
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expanded_text = text
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if self.memory.get_all(agent_id=self.agent_id):
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if self.memory.get_all(filters={"agent_id": self.agent_id}):
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expanded_text = self._consider_memo_retrieval(text)
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self._consider_memo_storage(text)
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return expanded_text
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@@ -139,7 +139,7 @@ class Mem0Teachability(AgentCapability):
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return comment + self._concatenate_memo_texts(memo_list)
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def _retrieve_relevant_memos(self, input_text: str) -> list:
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search_results = self.memory.search(input_text, agent_id=self.agent_id, limit=self.max_num_retrievals)
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search_results = self.memory.search(input_text, filters={"agent_id": self.agent_id}, top_k=self.max_num_retrievals)
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memo_list = [result["memory"] for result in search_results if result["score"] <= self.recall_threshold]
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if self.verbosity >= 1 and not memo_list:
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@@ -158,7 +158,7 @@ class PersonalTravelAssistant:
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return [m['memory'] for m in memories.get('results', [])]
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def search_memories(self, query, user_id):
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memories = self.memory.search(query, user_id=user_id)
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memories = self.memory.search(query, filters={"user_id": user_id})
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return [m['memory'] for m in memories.get('results', [])]
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# Usage example
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@@ -354,10 +354,10 @@ Exclude:
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```
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</Tab>
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<Tab title="Open Source">
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Tell Mem0 what matters by including `custom_fact_extraction_prompt` in the config dict:
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Tell Mem0 what matters by including `custom_instructions` in the config dict:
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```python
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MEMORY_CONFIG["custom_fact_extraction_prompt"] = """
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MEMORY_CONFIG["custom_instructions"] = """
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Extract from running coach conversations:
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- Training goals and race targets
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- Physical constraints or injuries
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@@ -375,7 +375,7 @@ Return JSON with key "facts" as a list of strings (use [] if nothing to store).
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memory = Memory.from_config(MEMORY_CONFIG)
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```
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<Note>`custom_fact_extraction_prompt` is a top-level key in the config dictionary passed to `Memory.from_config()`. Make sure it's set before creating the Memory instance — not after.</Note>
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<Note>`custom_instructions` is a top-level key in the config dictionary passed to `Memory.from_config()`. Make sure it's set before creating the Memory instance — not after.</Note>
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</Tab>
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</Tabs>
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@@ -520,7 +520,7 @@ memory.add(
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# "hey" → don't store
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# "cool thanks" → don't store
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# Or rely on custom_fact_extraction_prompt to filter automatically
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# Or rely on custom_instructions to filter automatically
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```
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</Tab>
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</Tabs>
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@@ -545,11 +545,11 @@ expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
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mem0_client.add(
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[{"role": "user", "content": "Rolled my left ankle, needs rest"}],
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user_id="max",
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expiration_date=expiration
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metadata={"memory_bucket": "constraints", "expires_on": expiration}
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)
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```
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In 14 days, this memory disappears automatically. Ray stops asking about the ankle.
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Store `expires_on` in metadata and periodically clean up expired memories. Ray stops asking about the ankle once it's removed.
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</Tab>
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<Tab title="Open Source">
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```python
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@@ -627,7 +627,7 @@ MEMORY_CONFIG = {
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"ollama_base_url": "http://localhost:11434",
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},
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},
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"custom_fact_extraction_prompt": """
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"custom_instructions": """
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Extract: goals, constraints, preferences, progress
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Exclude: greetings, filler, casual chat
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Return JSON with key "facts" as a list of strings.
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@@ -684,8 +684,7 @@ expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
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mem0_client.add(
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[{"role": "user", "content": "Rolled ankle, need light workouts"}],
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user_id="max",
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categories=["constraints"],
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expiration_date=expiration
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metadata={"memory_bucket": "constraints", "expires_on": expiration}
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)
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```
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</Tab>
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@@ -112,19 +112,17 @@ Let's add the same information with graph memory enabled:
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```python
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client.add(
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"Emma works with David on the mobile app redesign",
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user_id="company_kb",
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enable_graph=True
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user_id="company_kb"
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)
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client.add(
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"David reports to Rachel, who manages the design team",
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user_id="company_kb",
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enable_graph=True
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user_id="company_kb"
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)
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```
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When you set `enable_graph=True`, Mem0 extracts entities and relationships:
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When graph memory is enabled, Mem0 extracts entities and relationships:
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- `emma --[works_with]--> david`
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- `david --[reports_to]--> rachel`
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@@ -135,8 +133,7 @@ Now the same query works differently:
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```python
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results = client.search(
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"Who is Emma's teammate's manager?",
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filters={"user_id": "company_kb"},
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enable_graph=True
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filters={"user_id": "company_kb"}
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)
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print(results['results'][0]['memory'])
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@@ -209,30 +206,27 @@ For our company knowledge base, we'll use both:
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## Putting It Together
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Let's build a small company knowledge base with both approaches:
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Let's build a small company knowledge base:
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```python
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# Facts about individuals - vector store is fine
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# Facts about individuals
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client.add("Emma specializes in React and TypeScript", user_id="company_kb")
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client.add("David has 5 years of product management experience", user_id="company_kb")
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# Relationships - use graph memory
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# Relationships
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client.add(
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"Emma and David work together on the mobile app",
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user_id="company_kb",
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enable_graph=True
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user_id="company_kb"
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)
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client.add(
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"David reports to Rachel",
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user_id="company_kb",
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enable_graph=True
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user_id="company_kb"
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)
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client.add(
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"Rachel runs weekly team syncs every Tuesday",
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user_id="company_kb",
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enable_graph=True
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user_id="company_kb"
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)
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```
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@@ -257,8 +251,7 @@ Emma specializes in React and TypeScript
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# Multi-hop relationship - graph search
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results = client.search(
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"What meetings does Emma's project manager's boss run?",
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filters={"user_id": "company_kb"},
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enable_graph=True
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filters={"user_id": "company_kb"}
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)
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print(results['results'][0]['memory'])
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@@ -281,23 +274,22 @@ Enable graph memory when your queries need multi-hop traversal: org charts (who
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## The Tradeoff
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Graph memory adds processing time and cost. When you call `client.add()` with `enable_graph=True`, Mem0 makes extra LLM calls to extract entities and relationships.
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Graph memory adds processing time and cost. Mem0 makes extra LLM calls to extract entities and relationships from each memory.
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<Note>
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**Cost consideration:** Graph memory extraction adds ~2-3 extra LLM calls per `add()` operation to identify entities and relationships. Use it selectively—enable graph for organizational structure and long-term relationships, skip it for temporary notes and simple facts.
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**Cost consideration:** Graph memory extraction adds ~2-3 extra LLM calls per `add()` operation to identify entities and relationships. Use it when your use case benefits from relationship traversal—organizational structures, team hierarchies, and long-term connections.
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</Note>
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Use graph memory when the relationship traversal adds real value. For most use cases, vector search is sufficient and faster.
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```python
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# Long-term organizational structure - worth using graph
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# Long-term organizational structure - benefits from graph
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client.add(
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"Emma mentors two junior engineers on the frontend team",
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user_id="company_kb",
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enable_graph=True
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user_id="company_kb"
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)
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# Temporary notes - skip graph, not worth the cost
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# Temporary notes stored with a run_id for session isolation
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client.add(
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"Emma is out sick today",
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user_id="company_kb",
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@@ -308,38 +300,13 @@ client.add(
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---
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## Enabling Graph Memory
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You can enable graph memory in two ways:
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**Per-call** (recommended to start):
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```python
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client.add("Emma works with David", user_id="company_kb", enable_graph=True)
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client.search("team structure", filters={"user_id": "company_kb"}, enable_graph=True)
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```
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**Project-wide** (if most of your data has relationships):
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```python
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client.project.update(enable_graph=True)
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# Now every add uses graph automatically
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client.add("Emma mentors Jordan", user_id="company_kb")
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```
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---
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## What You Built
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A hybrid company knowledge base that combines both architectures:
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- **Vector search** - Fast semantic lookups for individual facts (Emma's skills, David's experience)
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- **Graph memory** - Multi-hop relationship traversal (Emma's teammate's manager, project hierarchies)
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- **Selective enablement** - Graph only for long-term organizational structure, vector for everything else
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- **Cost optimization** - Skip graph extraction for temporary notes and simple facts
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- **Cost optimization** - Use graph for long-term organizational structure, vector for temporary notes and simple facts
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This pattern scales from 10-person startups to enterprise org charts with thousands of employees.
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@@ -514,8 +514,8 @@ These controls prevent retrieval failures and ensure your AI assistant works wit
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Start with conservative filters (only store confirmed facts) and iterate based on your application's needs. Combine custom instructions with confidence thresholds for the most reliable memory ingestion pipeline.
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<CardGroup cols={2}>
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<Card title="Expire Short-Term Data" icon="timer" href="/cookbooks/essentials/memory-expiration-short-and-long-term">
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Automatically clean up session context before it clutters retrieval.
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<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
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Learn core memory patterns including temporary vs permanent data handling.
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</Card>
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<Card title="Choose Your Memory Architecture" icon="sitemap" href="/cookbooks/essentials/choosing-memory-architecture-vector-vs-graph">
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Learn when to layer graph memory alongside vectors for multi-hop queries.
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@@ -280,8 +280,8 @@ This covers data portability, GDPR compliance, system migrations, and manual rev
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Use **`get_all()`** for bulk retrieval, **`search()`** for specific questions, and **`create_memory_export()`** for structured data exports with custom schemas. Remember exports expire after 7 days—download them locally for long-term archives.
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<CardGroup cols={2}>
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<Card title="Expire Short-Term Data" icon="timer" href="/cookbooks/essentials/memory-expiration-short-and-long-term">
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Keep exports lean by clearing session context before you archive it.
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<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
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Learn core memory patterns including temporary vs permanent data handling.
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</Card>
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<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
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Ensure only verified insights make it into your export pipeline.
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@@ -1,277 +0,0 @@
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---
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title: Set Memory Expiration
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description: "Define short-term versus long-term retention so the store stays fresh."
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---
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While building memory systems, we realized their size grows fast. Session notes, temporary context, chat history - everything starts accumulating and bogging down the system. This pollutes search results and increase storage costs. Not every memory needs to persist forever.
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In this cookbook, we'll go through how to use short-term vs long-term memories and see where it's best to use them.
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---
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## Overview
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By default, Mem0 memories persist forever. This works for user preferences and core facts, but temporary data should expire automatically.
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In this tutorial, we will:
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- Understand default (permanent) memory behavior
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- Add expiration dates for temporary memories
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- Decide what should be temporary vs permanent
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---
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## Setup
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```python
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from mem0 import MemoryClient
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from datetime import datetime, timedelta
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client = MemoryClient(api_key="your-api-key")
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```
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<Note>
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Import `datetime` and `timedelta` to calculate expiration dates. Without these imports, you'll need to manually format ISO timestamps—error-prone and harder to read.
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</Note>
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---
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## Default Behavior: Everything Persists
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By default, all memories persist forever:
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```python
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# Store user preference
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client.add("User prefers dark mode", user_id="sarah")
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# Store session context
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client.add("Currently browsing electronics category", user_id="sarah")
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# 6 months later - both still exist
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results = client.get_all(filters={"user_id": "sarah"})
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print(f"Total memories: {len(results['results'])}")
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```
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**Output:**
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```
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Total memories: 2
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```
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Both the preference and session context persist. The preference is useful, but the 6-month-old session context is not.
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---
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## The Problem: Memory Bloat
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Without expiration, memories accumulate forever. Session notes from weeks ago mix with current preferences. Storage grows, search results get polluted with irrelevant old context, and retrieval quality degrades.
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<Warning>
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Memory bloat degrades search quality. When "User prefers dark mode" competes with "Currently browsing electronics" from 6 months ago, semantic search returns stale session data instead of actual preferences. Old memories pollute retrieval.
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</Warning>
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---
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## Short-Term Memories: Adding Expiration
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Set `expiration_date` to make memories temporary:
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```python
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from datetime import datetime, timedelta
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# Session context - expires in 7 days
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expires_at = (datetime.now() + timedelta(days=7)).isoformat()
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client.add(
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"Currently browsing electronics category",
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user_id="sarah",
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expiration_date=expires_at
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)
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# User preference - no expiration, persists forever
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client.add(
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"User prefers dark mode",
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user_id="sarah"
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)
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```
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<Info icon="check">
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**Expected behavior:** After 7 days, the session context automatically disappears—no cron jobs, no manual cleanup. The preference persists forever. Mem0 handles expiration transparently.
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</Info>
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Memories with `expiration_date` are automatically removed after expiring. No cleanup job needed - Mem0 handles it.
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<Tip>
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Start conservative with short expiration windows (7 days), then extend them based on usage patterns. It's easier to increase retention than to clean up over-retained stale data. Monitor search quality to find the right balance.
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</Tip>
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---
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## When to Use Each
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### Permanent Memories (no expiration_date):
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**Use for:**
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- User preferences and settings
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- Account information
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- Important facts and milestones
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- Historical data that matters long-term
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```python
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client.add("User prefers email notifications", user_id="sarah")
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client.add("User's birthday is March 15th", user_id="sarah")
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client.add("User completed onboarding on Jan 5th", user_id="sarah")
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```
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### Temporary Memories (with expiration_date):
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**Use for:**
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- Session context (current page, browsing history)
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- Temporary reminders
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- Recent chat history
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- Cached data
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||||
|
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```python
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expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
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client.add(
|
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"Currently viewing product ABC123",
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user_id="sarah",
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expiration_date=expires_7d
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)
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|
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client.add(
|
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"Asked about return policy",
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user_id="sarah",
|
||||
expiration_date=expires_7d
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)
|
||||
|
||||
```
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||||
---
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||||
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## Setting Different Expiration Periods
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Different data needs different lifetimes:
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```python
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# Session context - 7 days
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expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
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client.add("Browsing electronics", user_id="sarah", expiration_date=expires_7d)
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# Recent chat - 30 days
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expires_30d = (datetime.now() + timedelta(days=30)).isoformat()
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client.add("User asked about warranty", user_id="sarah", expiration_date=expires_30d)
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|
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# Important preference - no expiration
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client.add("User prefers dark mode", user_id="sarah")
|
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|
||||
```
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|
||||
---
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||||
|
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## Using Metadata to Track Memory Types
|
||||
|
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Tag memories to make filtering easier:
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|
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```python
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expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
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|
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# Tag session context
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client.add(
|
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"Browsing electronics",
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user_id="sarah",
|
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expiration_date=expires_7d,
|
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metadata={"type": "session"}
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)
|
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|
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# Tag preference
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||||
client.add(
|
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"User prefers dark mode",
|
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user_id="sarah",
|
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metadata={"type": "preference"}
|
||||
)
|
||||
|
||||
# Query only preferences
|
||||
preferences = client.get_all(
|
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filters={
|
||||
"AND": [
|
||||
{"user_id": "sarah"},
|
||||
{"metadata": {"type": "preference"}}
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Checking Expiration Status
|
||||
|
||||
See which memories will expire and when:
|
||||
|
||||
```python
|
||||
results = client.get_all(filters={"user_id": "sarah"})
|
||||
|
||||
for memory in results['results']:
|
||||
exp_date = memory.get('expiration_date')
|
||||
|
||||
if exp_date:
|
||||
print(f"Temporary: {memory['memory']}")
|
||||
print(f" Expires: {exp_date}\\n")
|
||||
else:
|
||||
print(f"Permanent: {memory['memory']}\\n")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Temporary: Browsing electronics
|
||||
Expires: 2025-11-01T10:30:00Z
|
||||
|
||||
Temporary: Viewed MacBook Pro and Dell XPS
|
||||
Expires: 2025-11-01T10:30:00Z
|
||||
|
||||
Permanent: User prefers dark mode
|
||||
|
||||
Permanent: User prefers email notifications
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## What You Built
|
||||
|
||||
A self-cleaning memory system with automatic retention policies:
|
||||
|
||||
- **Automatic expiration** - Memories self-destruct after defined periods, no cron jobs needed
|
||||
- **Tiered retention** - 7-day session context, 30-day chat history, permanent preferences
|
||||
- **Metadata tagging** - Classify memories by type (session, preference, chat) for filtered retrieval
|
||||
- **Expiration tracking** - Check which memories will expire and when using `get_all()`
|
||||
|
||||
This pattern keeps storage costs low and search quality high as your memory store scales.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
Memory expiration keeps storage clean and search results relevant. Use **`expiration_date`** for temporary data (session context, recent chats), skip it for permanent facts (preferences, account info). Mem0 handles cleanup automatically—no background jobs required.
|
||||
|
||||
Start by identifying what's temporary versus permanent, then set conservative expiration windows and adjust based on retrieval quality.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
|
||||
Pair expirations with ingestion rules so only trusted context persists.
|
||||
</Card>
|
||||
<Card title="Export Memories Safely" icon="download" href="/cookbooks/essentials/exporting-memories">
|
||||
Build compliant archives once your retention windows are dialed in.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -77,7 +77,7 @@ def retrieve_patient_info(query: str) -> dict:
|
||||
results = mem0_client.search(
|
||||
query,
|
||||
user_id=USER_ID,
|
||||
limit=5,
|
||||
top_k=5,
|
||||
threshold=0.7 # Higher threshold for more relevant results
|
||||
)
|
||||
|
||||
|
||||
@@ -28,13 +28,10 @@ Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys"
|
||||
### Configuration
|
||||
|
||||
```javascript
|
||||
const mem0Config = {
|
||||
apiKey: process.env.MEM0_API_KEY,
|
||||
user_id: "sample-user",
|
||||
};
|
||||
const USER_ID = "sample-user";
|
||||
|
||||
const openAIClient = new OpenAI();
|
||||
const mem0Client = new MemoryClient(mem0Config);
|
||||
const mem0Client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
|
||||
```
|
||||
|
||||
## Adding Memories
|
||||
@@ -43,14 +40,14 @@ Store user preferences, past interactions, or any relevant information:
|
||||
<CodeGroup>
|
||||
```javascript JavaScript
|
||||
async function addUserPreferences() {
|
||||
const mem0Client = new MemoryClient(mem0Config);
|
||||
const mem0Client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
|
||||
|
||||
const userPreferences = "I Love BMW, Audi and Porsche. I Hate Mercedes. I love Red cars and Maroon cars. I have a budget of 120K to 150K USD. I like Audi the most.";
|
||||
|
||||
await mem0Client.add([{
|
||||
role: "user",
|
||||
content: userPreferences,
|
||||
}], mem0Config);
|
||||
}], { userId: "sample-user" });
|
||||
}
|
||||
|
||||
await addUserPreferences();
|
||||
@@ -91,7 +88,7 @@ await addUserPreferences();
|
||||
Search for relevant memories based on the current user input:
|
||||
|
||||
```javascript
|
||||
const relevantMemories = await mem0Client.search(userInput, mem0Config);
|
||||
const relevantMemories = await mem0Client.search(userInput, { userId: USER_ID });
|
||||
```
|
||||
|
||||
## Structured Responses with Zod
|
||||
@@ -152,10 +149,7 @@ import dotenv from 'dotenv';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const mem0Config = {
|
||||
apiKey: process.env.MEM0_API_KEY,
|
||||
user_id: "sample-user",
|
||||
};
|
||||
const USER_ID = "sample-user";
|
||||
|
||||
async function run() {
|
||||
// Responses without memories
|
||||
@@ -185,7 +179,7 @@ const Cars = z.object({
|
||||
|
||||
async function main(memory = false) {
|
||||
const openAIClient = new OpenAI();
|
||||
const mem0Client = new MemoryClient(mem0Config);
|
||||
const mem0Client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
|
||||
|
||||
const input = "Suggest me some cars that I can buy today.";
|
||||
|
||||
@@ -195,12 +189,12 @@ async function main(memory = false) {
|
||||
await mem0Client.add([{
|
||||
role: "user",
|
||||
content: input,
|
||||
}], mem0Config);
|
||||
}], { userId: USER_ID });
|
||||
|
||||
// Search for relevant memories
|
||||
let relevantMemories = []
|
||||
if (memory) {
|
||||
relevantMemories = await mem0Client.search(input, mem0Config);
|
||||
relevantMemories = await mem0Client.search(input, { userId: USER_ID });
|
||||
}
|
||||
|
||||
const response = await openAIClient.responses.create({
|
||||
@@ -213,14 +207,14 @@ async function main(memory = false) {
|
||||
}
|
||||
|
||||
async function addSampleMemories() {
|
||||
const mem0Client = new MemoryClient(mem0Config);
|
||||
const mem0Client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
|
||||
|
||||
const myInterests = "I Love BMW, Audi and Porsche. I Hate Mercedes. I love Red cars and Maroon cars. I have a budget of 120K to 150K USD. I like Audi the most.";
|
||||
|
||||
await mem0Client.add([{
|
||||
role: "user",
|
||||
content: myInterests,
|
||||
}], mem0Config);
|
||||
}], { userId: USER_ID });
|
||||
}
|
||||
|
||||
const getMemoryString = (memories) => {
|
||||
|
||||
@@ -37,13 +37,6 @@ Here are some examples of how Mem0 can be integrated into various applications:
|
||||
>
|
||||
Filter speculation and low-confidence data.
|
||||
</Card>
|
||||
<Card
|
||||
title="Set Memory Expiration"
|
||||
icon="timer"
|
||||
href="/cookbooks/essentials/memory-expiration-short-and-long-term"
|
||||
>
|
||||
Short-term vs long-term retention strategies.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Companion Playbooks
|
||||
|
||||
+4
-1
@@ -328,7 +328,6 @@
|
||||
"cookbooks/essentials/building-ai-companion",
|
||||
"cookbooks/essentials/entity-partitioning-playbook",
|
||||
"cookbooks/essentials/controlling-memory-ingestion",
|
||||
"cookbooks/essentials/memory-expiration-short-and-long-term",
|
||||
"cookbooks/essentials/tagging-and-organizing-memories",
|
||||
"cookbooks/essentials/exporting-memories",
|
||||
"cookbooks/essentials/choosing-memory-architecture-vector-vs-graph"
|
||||
@@ -627,6 +626,10 @@
|
||||
"source": "/platform/features/expiration-date",
|
||||
"destination": "/"
|
||||
},
|
||||
{
|
||||
"source": "/cookbooks/essentials/memory-expiration-short-and-long-term",
|
||||
"destination": "/cookbooks/essentials/building-ai-companion"
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/async-mode-default-change",
|
||||
"destination": "/"
|
||||
|
||||
@@ -208,7 +208,6 @@ Key differentiators:
|
||||
- [Building AI Companion](https://docs.mem0.ai/cookbooks/essentials/building-ai-companion): Core patterns for building AI agents with memory
|
||||
- [Partition Memories by Entity](https://docs.mem0.ai/cookbooks/essentials/entity-partitioning-playbook): Keep multi-tenant assistants isolated by tagging user, agent, app, and session identifiers
|
||||
- [Controlling Memory Ingestion](https://docs.mem0.ai/cookbooks/essentials/controlling-memory-ingestion): Fine-tune what gets stored in memory and when
|
||||
- [Memory Expiration](https://docs.mem0.ai/cookbooks/essentials/memory-expiration-short-and-long-term): Implement short-term and long-term memory strategies
|
||||
- [Tagging and Organizing Memories](https://docs.mem0.ai/cookbooks/essentials/tagging-and-organizing-memories): Advanced memory organization and categorization
|
||||
- [Exporting Memories](https://docs.mem0.ai/cookbooks/essentials/exporting-memories): Backup and transfer memory data between systems
|
||||
- [Choosing Memory Architecture](https://docs.mem0.ai/cookbooks/essentials/choosing-memory-architecture-vector-vs-graph): Vector vs Graph memory architectures comparison
|
||||
|
||||
Reference in New Issue
Block a user