docs: navigation (#5900)
This commit is contained in:
Kartik
@@ -97,7 +97,7 @@ Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_so
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## Next Steps
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<CardGroup cols={2}>
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<CardGroup cols={3}>
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<Card title="Add Your First Memory" icon="rocket" href="/api-reference/memory/add-memories">
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Start storing memories via the REST API
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</Card>
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@@ -105,4 +105,8 @@ Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_so
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<Card title="Search with Filters" icon="filter" href="/api-reference/memory/search-memories">
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Learn advanced search and filtering techniques
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</Card>
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<Card title="Build with cookbooks" icon="book-open" href="/cookbooks/overview">
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See the API used end to end in real projects.
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</Card>
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</CardGroup>
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@@ -14,17 +14,17 @@ See the list of supported embedders below.
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</Note>
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<CardGroup cols={4}>
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<Card title="OpenAI" href="/components/embedders/models/openai"></Card>
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<Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
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||||
<Card title="Ollama" href="/components/embedders/models/ollama"></Card>
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<Card title="Hugging Face" href="/components/embedders/models/huggingface"></Card>
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<Card title="Google AI" href="/components/embedders/models/google_AI"></Card>
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<Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
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<Card title="Together" href="/components/embedders/models/together"></Card>
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<Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
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<Card title="Langchain" href="/components/embedders/models/langchain"></Card>
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<Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
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<Card title="FastEmbed" href="/components/embedders/models/fastembed"></Card>
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<Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/embedders/models/openai"></Card>
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<Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure_openai"></Card>
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<Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/embedders/models/ollama"></Card>
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<Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/embedders/models/huggingface"></Card>
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<Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google_AI"></Card>
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<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/embedders/models/vertexai"></Card>
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<Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/embedders/models/together"></Card>
|
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<Card title="LM Studio" icon="/images/provider-icons/lmstudio.svg" href="/components/embedders/models/lmstudio"></Card>
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<Card title="Langchain" icon="/images/provider-icons/langchain-color.svg" href="/components/embedders/models/langchain"></Card>
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<Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws_bedrock"></Card>
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<Card title="FastEmbed" icon="/images/provider-icons/qdrant.svg" href="/components/embedders/models/fastembed"></Card>
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</CardGroup>
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## Usage
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@@ -20,22 +20,22 @@ See the list of supported LLMs below.
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</Note>
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<CardGroup cols={4}>
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<Card title="OpenAI" href="/components/llms/models/openai" />
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<Card title="Ollama" href="/components/llms/models/ollama" />
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<Card title="Azure OpenAI" href="/components/llms/models/azure_openai" />
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<Card title="Anthropic" href="/components/llms/models/anthropic" />
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<Card title="Together" href="/components/llms/models/together" />
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<Card title="Groq" href="/components/llms/models/groq" />
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<Card title="Litellm" href="/components/llms/models/litellm" />
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<Card title="Mistral AI" href="/components/llms/models/mistral_AI" />
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<Card title="Google AI" href="/components/llms/models/google_AI" />
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<Card title="AWS bedrock" href="/components/llms/models/aws_bedrock" />
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<Card title="DeepSeek" href="/components/llms/models/deepseek" />
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<Card title="MiniMax" href="/components/llms/models/minimax" />
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<Card title="xAI" href="/components/llms/models/xAI" />
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<Card title="Sarvam AI" href="/components/llms/models/sarvam" />
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<Card title="LM Studio" href="/components/llms/models/lmstudio" />
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<Card title="Langchain" href="/components/llms/models/langchain" />
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<Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/llms/models/openai" />
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<Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/llms/models/ollama" />
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<Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/llms/models/azure_openai" />
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<Card title="Anthropic" icon="/images/provider-icons/anthropic.svg" href="/components/llms/models/anthropic" />
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<Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/llms/models/together" />
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<Card title="Groq" icon="/images/provider-icons/groq.svg" href="/components/llms/models/groq" />
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<Card title="Litellm" icon="shuffle" href="/components/llms/models/litellm" />
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<Card title="Mistral AI" icon="/images/provider-icons/mistral-color.svg" href="/components/llms/models/mistral_AI" />
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<Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/llms/models/google_AI" />
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<Card title="AWS bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/llms/models/aws_bedrock" />
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<Card title="DeepSeek" icon="/images/provider-icons/deepseek-color.svg" href="/components/llms/models/deepseek" />
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<Card title="MiniMax" icon="/images/provider-icons/minimax-color.svg" href="/components/llms/models/minimax" />
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<Card title="xAI" icon="/images/provider-icons/xai.svg" href="/components/llms/models/xAI" />
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<Card title="Sarvam AI" icon="/images/provider-icons/sarvam.svg" href="/components/llms/models/sarvam" />
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<Card title="LM Studio" icon="/images/provider-icons/lmstudio.svg" href="/components/llms/models/lmstudio" />
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<Card title="Langchain" icon="/images/provider-icons/langchain-color.svg" href="/components/llms/models/langchain" />
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</CardGroup>
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## Structured vs Unstructured Outputs
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@@ -9,6 +9,18 @@ Mem0 rerankers rescore vector search hits so your agents surface the most releva
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Reranking trades extra latency for better precision. Start once you have baseline search working and measure before/after relevance.
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</Info>
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## Supported Rerankers
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<CardGroup cols={3}>
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<Card title="Cohere" icon="/images/provider-icons/cohere.svg" href="/components/rerankers/models/cohere" />
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<Card title="Sentence Transformers" icon="vector-square" href="/components/rerankers/models/sentence_transformer" />
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<Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/rerankers/models/huggingface" />
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<Card title="LLM Reranker" icon="wand-magic-sparkles" href="/components/rerankers/models/llm_reranker" />
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<Card title="Zero Entropy" icon="/images/provider-icons/zeroentropy.svg" href="/components/rerankers/models/zero_entropy" />
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</CardGroup>
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## Reranking Workflow
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<CardGroup cols={3}>
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<Card
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title="Understand Reranking"
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@@ -19,13 +31,13 @@ Reranking trades extra latency for better precision. Start once you have baselin
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<Card
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title="Configure Providers"
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description="Add reranker blocks to your memory configuration."
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icon="settings"
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icon="gear"
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href="/components/rerankers/config"
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/>
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<Card
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title="Optimize Performance"
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description="Balance relevance, latency, and cost with tuning tactics."
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icon="speedometer"
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icon="gauge"
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href="/components/rerankers/optimization"
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/>
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<Card
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@@ -43,7 +55,7 @@ Reranking trades extra latency for better precision. Start once you have baselin
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<Card
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title="Sentence Transformers"
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description="Keep reranking on-device with cross-encoder models."
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icon="cpu"
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icon="microchip"
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href="/components/rerankers/models/sentence_transformer"
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/>
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</CardGroup>
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@@ -66,7 +78,7 @@ Reranking trades extra latency for better precision. Start once you have baselin
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<Card
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title="Set Up Reranking"
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description="Walk through the configuration fields and defaults."
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icon="settings"
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icon="gear"
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href="/components/rerankers/config"
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/>
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<Card
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@@ -14,26 +14,26 @@ See the list of supported vector databases below.
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</Note>
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<CardGroup cols={3}>
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<Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
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||||
<Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>
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<Card title="PGVector" href="/components/vectordbs/dbs/pgvector"></Card>
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||||
<Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card>
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||||
<Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
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||||
<Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card>
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||||
<Card title="MongoDB" href="/components/vectordbs/dbs/mongodb"></Card>
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<Card title="Azure" href="/components/vectordbs/dbs/azure"></Card>
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<Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
|
||||
<Card title="Valkey" href="/components/vectordbs/dbs/valkey"></Card>
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||||
<Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>
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<Card title="OpenSearch" href="/components/vectordbs/dbs/opensearch"></Card>
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<Card title="Supabase" href="/components/vectordbs/dbs/supabase"></Card>
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<Card title="Vertex AI" href="/components/vectordbs/dbs/vertex_ai"></Card>
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<Card title="Weaviate" href="/components/vectordbs/dbs/weaviate"></Card>
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<Card title="FAISS" href="/components/vectordbs/dbs/faiss"></Card>
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||||
<Card title="LangChain" href="/components/vectordbs/dbs/langchain"></Card>
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||||
<Card title="Amazon S3 Vectors" href="/components/vectordbs/dbs/s3_vectors"></Card>
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||||
<Card title="Databricks" href="/components/vectordbs/dbs/databricks"></Card>
|
||||
<Card title="Turbopuffer" href="/components/vectordbs/dbs/turbopuffer"></Card>
|
||||
<Card title="Qdrant" icon="/images/provider-icons/qdrant.svg" href="/components/vectordbs/dbs/qdrant"></Card>
|
||||
<Card title="Chroma" icon="/images/provider-icons/chroma.svg" href="/components/vectordbs/dbs/chroma"></Card>
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||||
<Card title="PGVector" icon="/images/provider-icons/postgresql.svg" href="/components/vectordbs/dbs/pgvector"></Card>
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||||
<Card title="Upstash Vector" icon="/images/provider-icons/upstash.svg" href="/components/vectordbs/dbs/upstash-vector"></Card>
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||||
<Card title="Milvus" icon="/images/provider-icons/milvus.svg" href="/components/vectordbs/dbs/milvus"></Card>
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||||
<Card title="Pinecone" icon="/images/provider-icons/pinecone.svg" href="/components/vectordbs/dbs/pinecone"></Card>
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||||
<Card title="MongoDB" icon="/images/provider-icons/mongodb.svg" href="/components/vectordbs/dbs/mongodb"></Card>
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||||
<Card title="Azure" icon="/images/provider-icons/azure-color.svg" href="/components/vectordbs/dbs/azure"></Card>
|
||||
<Card title="Redis" icon="/images/provider-icons/redis.svg" href="/components/vectordbs/dbs/redis"></Card>
|
||||
<Card title="Valkey" icon="/images/provider-icons/valkey.svg" href="/components/vectordbs/dbs/valkey"></Card>
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||||
<Card title="Elasticsearch" icon="/images/provider-icons/elasticsearch.svg" href="/components/vectordbs/dbs/elasticsearch"></Card>
|
||||
<Card title="OpenSearch" icon="/images/provider-icons/opensearch.svg" href="/components/vectordbs/dbs/opensearch"></Card>
|
||||
<Card title="Supabase" icon="/images/provider-icons/supabase.svg" href="/components/vectordbs/dbs/supabase"></Card>
|
||||
<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/vectordbs/dbs/vertex_ai"></Card>
|
||||
<Card title="Weaviate" icon="circle-nodes" href="/components/vectordbs/dbs/weaviate"></Card>
|
||||
<Card title="FAISS" icon="layer-group" href="/components/vectordbs/dbs/faiss"></Card>
|
||||
<Card title="LangChain" icon="/images/provider-icons/langchain-color.svg" href="/components/vectordbs/dbs/langchain"></Card>
|
||||
<Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3_vectors"></Card>
|
||||
<Card title="Databricks" icon="/images/provider-icons/databricks.svg" href="/components/vectordbs/dbs/databricks"></Card>
|
||||
<Card title="Turbopuffer" icon="/images/provider-icons/turbopuffer.svg" href="/components/vectordbs/dbs/turbopuffer"></Card>
|
||||
</CardGroup>
|
||||
|
||||
## Usage
|
||||
|
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@@ -238,13 +238,7 @@ You've successfully built a Gemini 3 agent with persistent memory using Mem0's M
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## Next Steps
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<CardGroup cols={2}>
|
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<Card
|
||||
title="MCP Integration Feature"
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description="Learn about MCP configuration options and deployment methods"
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||||
icon="plug"
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||||
href="/platform/features/mcp-integration"
|
||||
/>
|
||||
<CardGroup cols={1}>
|
||||
<Card
|
||||
title="MCP Quickstart"
|
||||
description="Get started with MCP for any AI client in minutes"
|
||||
|
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@@ -13,6 +13,22 @@ With Mem0, you can create stateful LLM-based applications such as chatbots, virt
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Here are some examples of how Mem0 can be integrated into various applications:
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## Start here
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|
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The most popular cookbooks to get going fast:
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|
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<CardGroup cols={3}>
|
||||
<Card title="Build an AI companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
|
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The core memory lifecycle, end to end.
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</Card>
|
||||
<Card title="Self-host with Ollama" icon="server" href="/cookbooks/companions/local-companion-ollama">
|
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Run Mem0 fully local with Ollama.
|
||||
</Card>
|
||||
<Card title="Partition memory by entity" icon="layer-group" href="/cookbooks/essentials/entity-partitioning-playbook">
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Scope memories per user, agent, and app.
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</Card>
|
||||
</CardGroup>
|
||||
|
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## Essentials
|
||||
|
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<CardGroup cols={2}>
|
||||
|
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@@ -0,0 +1,100 @@
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---
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||||
title: "How Mem0 Works"
|
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description: "What happens when you add, store, and search memories with Mem0."
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icon: "diagram-project"
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---
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Mem0 sits between your application and your model. You send conversation turns to `add`, then call `search` before the next model request to fetch relevant context. Your app decides which returned memories to include in the prompt.
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Use Mem0 when you want agents to remember useful facts across turns, sessions, or users without replaying the full transcript every time.
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|
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<Frame caption="Memory extraction: Mem0 turns messages into stored facts with metadata, embeddings, and optional entity relationships.">
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<img src="/images/memory-extraction.png" alt="Mem0 memory extraction pipeline: store new memories after the response, context lookup to find related memories, extract memories (ADD only) from input and context, deduplicate and embed, entity linking, written to a SQL database (facts and metadata), vector database (embeddings and similarity), and entity store (entities and relationships)." />
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</Frame>
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|
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## The mental model
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| Without a memory layer | With Mem0 |
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|---|---|
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| Keep appending chat history to the prompt | Store facts once, then retrieve them by query |
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| Make the model re-read old turns | Give the model only the relevant memories |
|
||||
| Lose context when a session ends | Scope memory by `user_id`, `agent_id`, `run_id`, and metadata |
|
||||
|
||||
## Messages vs memories
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||||
|
||||
You send Mem0 messages. By default, Mem0 stores extracted memories, not a verbatim transcript.
|
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|
||||
| Input | Stored memory |
|
||||
|---|---|
|
||||
| `"I prefer aisle seats"` | `User prefers aisle seats` |
|
||||
| `"Let's use Postgres for this project"` | `Project decision: use Postgres` |
|
||||
| Message metadata | Filterable fields such as category, app, user, or run |
|
||||
|
||||
Use `infer=False` when you need to store raw content exactly as provided. Otherwise, keep inference enabled so retrieval works on clean, deduplicated facts.
|
||||
|
||||
## Two phases: extraction and retrieval
|
||||
|
||||
Most applications use Mem0 in two places:
|
||||
|
||||
1. **After a useful interaction**, call `add` to store what should be remembered.
|
||||
2. **Before a model call**, call `search` and pass the best results into your prompt.
|
||||
|
||||
### 1. Extraction (writing memory)
|
||||
|
||||
When new messages arrive, Mem0 extracts durable facts and stores them with the identifiers and metadata you provide.
|
||||
|
||||
1. **Context lookup.** Mem0 checks related existing memories so it can avoid storing the same fact again.
|
||||
2. **Fact extraction.** An LLM extracts preferences, decisions, plans, and other details your agent can reuse.
|
||||
3. **Deduplication and embedding.** Redundant facts are removed, then each memory is embedded for semantic search.
|
||||
4. **Entity linking.** When configured, Mem0 links people, places, organizations, and concepts across memories.
|
||||
|
||||
The automatic extraction path is additive. If a user says, "I moved from Austin to Seattle," Mem0 can store the new fact without silently rewriting the old one. Use explicit `update` or `delete` operations when your application needs to correct or remove a memory.
|
||||
|
||||
### 2. Retrieval (reading memory)
|
||||
|
||||
When you call `search`, Mem0 ranks stored memories against your query and filters.
|
||||
|
||||
| Signal | What it does | Best for |
|
||||
|---|---|---|
|
||||
| **Semantic** | Vector similarity over embeddings | Conceptual questions |
|
||||
| **Keyword** | Term matching for exact words and phrases | Names, IDs, and factual lookups |
|
||||
| **Entity** | Boosts memories linked to entities in the query | Questions about a person, project, or account |
|
||||
|
||||
Platform retrieval uses multiple signals in the managed service. OSS retrieval depends on your configured vector store, optional reranker, and graph store.
|
||||
|
||||
<Note>
|
||||
Always scope searches with filters such as `user_id`, `agent_id`, or `run_id`. This keeps memories from different users, agents, or sessions from mixing.
|
||||
</Note>
|
||||
|
||||
## Where memories live
|
||||
|
||||
Mem0 stores different parts of a memory in stores built for different lookup patterns:
|
||||
|
||||
| Store | Holds | Purpose |
|
||||
|---|---|---|
|
||||
| **SQL database** | Facts and metadata | The source of truth for each memory |
|
||||
| **Vector database** | Embeddings | Semantic similarity search |
|
||||
| **Entity or graph store** | Entities and relationships | Relationship-aware retrieval when graph memory is enabled |
|
||||
|
||||
On Mem0 Platform, these stores are managed for you. In OSS, you choose and operate the backing stores through your configuration.
|
||||
|
||||
## Build against this flow
|
||||
|
||||
- Call `add` only for information worth reusing later: preferences, decisions, account facts, goals, and durable feedback.
|
||||
- Call `search` before the model response, then include only the returned memories that help answer the current request.
|
||||
- Use metadata for filters your product already cares about, such as workspace, feature area, tenant, or data source.
|
||||
- Avoid storing secrets, raw credentials, or unredacted sensitive data. Mem0 is designed to retrieve stored context.
|
||||
|
||||
## Next steps
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Memory types" icon="brain" href="/core-concepts/memory-types">
|
||||
Choose the right scope for user, agent, run, and session memory.
|
||||
</Card>
|
||||
<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
|
||||
Add, search, update, and delete memories from your app.
|
||||
</Card>
|
||||
<Card title="See the benchmarks" icon="chart-line" href="/core-concepts/memory-evaluation">
|
||||
Review the evaluation setup and benchmark results.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -9,13 +9,6 @@ iconType: "solid"
|
||||
|
||||
Adding memory is how Mem0 captures useful details from a conversation so your agents can reuse them later. Think of it as saving the important sentences from a chat transcript into a structured notebook your agent can search.
|
||||
|
||||
<Info>
|
||||
**Why it matters**
|
||||
- Preserves user preferences, goals, and feedback across sessions.
|
||||
- Powers personalization and decision-making in downstream conversations.
|
||||
- Keeps context consistent between managed Platform and OSS deployments.
|
||||
</Info>
|
||||
|
||||
## Key terms
|
||||
|
||||
- **Messages** – The ordered list of user/assistant turns you send to `add`.
|
||||
@@ -157,7 +150,6 @@ Add memory whenever your agent learns something useful:
|
||||
|
||||
Storing this context allows the agent to reason better in future interactions.
|
||||
|
||||
|
||||
### More Details
|
||||
|
||||
For full list of supported fields, required formats, and advanced options, see the
|
||||
|
||||
@@ -216,7 +216,7 @@ memory.delete_all(user_id="alice")
|
||||
## Put it into practice
|
||||
|
||||
- Review the <Link href="/api-reference/memory/delete-memory">Delete Memory API reference</Link>, plus <Link href="/api-reference/memory/batch-delete">Batch Delete</Link> and <Link href="/api-reference/memory/delete-memories">Filtered Delete</Link>.
|
||||
- Pair deletes with <Link href="/platform/features/platform-overview">Expiration Policies</Link> to automate retention.
|
||||
- Pair deletes with the <Link href="/api-reference/memory/update-memory">expiration date field</Link> to automate retention.
|
||||
|
||||
## See it live
|
||||
|
||||
@@ -233,9 +233,9 @@ memory.delete_all(user_id="alice")
|
||||
href="/core-concepts/memory-operations/add"
|
||||
/>
|
||||
<Card
|
||||
title="Enable Expiration Policies"
|
||||
description="Automate retention with the platform’s expiration feature."
|
||||
title="Set an Expiration Date"
|
||||
description="Automate retention with the expiration date field on update."
|
||||
icon="clock"
|
||||
href="/platform/features/platform-overview"
|
||||
href="/api-reference/memory/update-memory"
|
||||
/>
|
||||
</CardGroup>
|
||||
|
||||
@@ -9,13 +9,6 @@ iconType: "solid"
|
||||
|
||||
Mem0's search operation lets agents ask natural-language questions and get back the memories that matter most. Like a smart librarian, it finds exactly what you need from everything you've stored.
|
||||
|
||||
<Info>
|
||||
**Why it matters**
|
||||
- Retrieves the right facts without rebuilding prompts from scratch.
|
||||
- Supports both managed Platform and OSS so you can test locally and deploy at scale.
|
||||
- Keeps results relevant with filters, rerankers, and thresholds.
|
||||
</Info>
|
||||
|
||||
## Key terms
|
||||
|
||||
- **Query** – Natural-language question or statement you pass to `search`.
|
||||
|
||||
@@ -9,13 +9,6 @@ iconType: "solid"
|
||||
|
||||
Mem0’s update operation lets you fix or enrich an existing memory without deleting it. When a user changes their preference or clarifies a fact, use update to keep the knowledge base fresh.
|
||||
|
||||
<Info>
|
||||
**Why it matters**
|
||||
- Corrects outdated or incorrect memories immediately.
|
||||
- Adds new metadata so filters and rerankers stay sharp.
|
||||
- Works for both one-off edits and large batches (up to 1000 memories).
|
||||
</Info>
|
||||
|
||||
## Key terms
|
||||
|
||||
- **memory_id** – Unique identifier returned by `add` or `search` results.
|
||||
|
||||
@@ -9,13 +9,6 @@ iconType: "solid"
|
||||
|
||||
Mem0 separates memory into layers so agents remember the right detail at the right time. Think of it like a notebook: a sticky note for the current task, a daily journal for the session, and an archive for everything a user has shared.
|
||||
|
||||
<Info>
|
||||
**Why it matters**
|
||||
- Keeps conversations coherent without repeating instructions.
|
||||
- Lets agents personalize responses based on long-term preferences.
|
||||
- Avoids over-fetching data by scoping memory to the correct layer.
|
||||
</Info>
|
||||
|
||||
## Key terms
|
||||
|
||||
- **Conversation memory** – In-flight messages inside a single turn (what was just said).
|
||||
|
||||
+181
-108
@@ -8,7 +8,7 @@
|
||||
"light": "#8F74E0",
|
||||
"dark": "#8F74E0"
|
||||
},
|
||||
"favicon": "/logo/favicon.png",
|
||||
"favicon": "/logo/favicon.svg",
|
||||
"logo": {
|
||||
"light": "/logo/light.svg",
|
||||
"dark": "/logo/dark.svg",
|
||||
@@ -21,7 +21,7 @@
|
||||
"icon": "book-open",
|
||||
"tabs": [
|
||||
{
|
||||
"tab": "Welcome",
|
||||
"tab": "Get Started",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Start Here",
|
||||
@@ -39,19 +39,20 @@
|
||||
"group": "Getting Started",
|
||||
"icon": "rocket",
|
||||
"pages": [
|
||||
"platform/quickstart",
|
||||
"platform/overview",
|
||||
"platform/agent-signup",
|
||||
"vibecoding",
|
||||
"platform/mem0-mcp",
|
||||
"platform/cli",
|
||||
"platform/platform-vs-oss",
|
||||
"platform/quickstart"
|
||||
"platform/mem0-mcp",
|
||||
"platform/platform-vs-oss"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Core Concepts",
|
||||
"icon": "brain",
|
||||
"pages": [
|
||||
"core-concepts/how-it-works",
|
||||
"core-concepts/memory-types",
|
||||
"core-concepts/memory-operations/add",
|
||||
"core-concepts/memory-operations/search",
|
||||
@@ -61,12 +62,11 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Platform Features",
|
||||
"group": "Features",
|
||||
"icon": "star",
|
||||
"pages": [
|
||||
"platform/features/platform-overview",
|
||||
{
|
||||
"group": "Essential Features",
|
||||
"group": "Essentials",
|
||||
"icon": "circle-check",
|
||||
"pages": [
|
||||
"platform/features/v2-memory-filters",
|
||||
@@ -79,7 +79,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Advanced Features",
|
||||
"group": "Advanced",
|
||||
"icon": "bolt",
|
||||
"pages": [
|
||||
"platform/features/advanced-retrieval",
|
||||
@@ -100,38 +100,30 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Integration Features",
|
||||
"group": "Integrations",
|
||||
"icon": "plug",
|
||||
"pages": [
|
||||
"platform/features/webhooks",
|
||||
"platform/features/feedback-mechanism",
|
||||
"platform/features/group-chat",
|
||||
"platform/features/mcp-integration"
|
||||
"platform/features/group-chat"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Support & Troubleshooting",
|
||||
"group": "Support",
|
||||
"icon": "life-buoy",
|
||||
"pages": [
|
||||
"platform/faqs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Migration Guide",
|
||||
"group": "Migration",
|
||||
"icon": "arrow-right",
|
||||
"pages": [
|
||||
"migration/platform-v2-to-v3",
|
||||
"migration/oss-to-platform"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Contribute",
|
||||
"icon": "clipboard-list",
|
||||
"pages": [
|
||||
"platform/contribute"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -143,14 +135,14 @@
|
||||
"icon": "rocket",
|
||||
"pages": [
|
||||
"open-source/overview",
|
||||
"open-source/setup",
|
||||
"vibecoding",
|
||||
"open-source/python-quickstart",
|
||||
"open-source/node-quickstart"
|
||||
"open-source/node-quickstart",
|
||||
"open-source/setup",
|
||||
"vibecoding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Self-Hosting Features",
|
||||
"group": "Features",
|
||||
"icon": "server",
|
||||
"pages": [
|
||||
"open-source/features/overview",
|
||||
@@ -300,77 +292,8 @@
|
||||
"icon": "users",
|
||||
"pages": [
|
||||
"contributing/development",
|
||||
"contributing/documentation"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Cookbooks",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Getting Started",
|
||||
"icon": "lightbulb",
|
||||
"pages": [
|
||||
"cookbooks/overview"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Essentials",
|
||||
"icon": "flag",
|
||||
"pages": [
|
||||
"cookbooks/essentials/building-ai-companion",
|
||||
"cookbooks/essentials/entity-partitioning-playbook",
|
||||
"cookbooks/essentials/controlling-memory-ingestion",
|
||||
"cookbooks/essentials/tagging-and-organizing-memories",
|
||||
"cookbooks/essentials/exporting-memories"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Companion Playbooks",
|
||||
"icon": "users",
|
||||
"pages": [
|
||||
"cookbooks/companions/quickstart-demo",
|
||||
"cookbooks/companions/nodejs-companion",
|
||||
"cookbooks/companions/ai-tutor",
|
||||
"cookbooks/companions/travel-assistant",
|
||||
"cookbooks/companions/youtube-research",
|
||||
"cookbooks/companions/voice-companion-openai",
|
||||
"cookbooks/companions/local-companion-ollama"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Ops & Automations",
|
||||
"icon": "briefcase",
|
||||
"pages": [
|
||||
"cookbooks/operations/support-inbox",
|
||||
"cookbooks/operations/email-automation",
|
||||
"cookbooks/operations/content-writing",
|
||||
"cookbooks/operations/deep-research",
|
||||
"cookbooks/operations/team-task-agent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Integrations & Platforms",
|
||||
"icon": "plug",
|
||||
"pages": [
|
||||
"cookbooks/integrations/agents-sdk-tool",
|
||||
"cookbooks/integrations/openai-tool-calls",
|
||||
"cookbooks/integrations/mastra-agent",
|
||||
"cookbooks/integrations/healthcare-google-adk",
|
||||
"cookbooks/integrations/aws-bedrock",
|
||||
"cookbooks/integrations/tavily-search"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Frameworks & Multimodal",
|
||||
"icon": "layers",
|
||||
"pages": [
|
||||
"cookbooks/frameworks/llamaindex-react",
|
||||
"cookbooks/frameworks/llamaindex-multiagent",
|
||||
"cookbooks/frameworks/multimodal-retrieval",
|
||||
"cookbooks/frameworks/eliza-os-character",
|
||||
"cookbooks/frameworks/gemini-3-with-mem0-mcp"
|
||||
"contributing/documentation",
|
||||
"platform/contribute"
|
||||
]
|
||||
}
|
||||
]
|
||||
@@ -458,6 +381,76 @@
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Cookbooks",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Getting Started",
|
||||
"icon": "lightbulb",
|
||||
"pages": [
|
||||
"cookbooks/overview"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Essentials",
|
||||
"icon": "flag",
|
||||
"pages": [
|
||||
"cookbooks/essentials/building-ai-companion",
|
||||
"cookbooks/essentials/entity-partitioning-playbook",
|
||||
"cookbooks/essentials/controlling-memory-ingestion",
|
||||
"cookbooks/essentials/tagging-and-organizing-memories",
|
||||
"cookbooks/essentials/exporting-memories"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Companion Playbooks",
|
||||
"icon": "users",
|
||||
"pages": [
|
||||
"cookbooks/companions/quickstart-demo",
|
||||
"cookbooks/companions/nodejs-companion",
|
||||
"cookbooks/companions/ai-tutor",
|
||||
"cookbooks/companions/travel-assistant",
|
||||
"cookbooks/companions/youtube-research",
|
||||
"cookbooks/companions/voice-companion-openai",
|
||||
"cookbooks/companions/local-companion-ollama"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Ops & Automations",
|
||||
"icon": "briefcase",
|
||||
"pages": [
|
||||
"cookbooks/operations/support-inbox",
|
||||
"cookbooks/operations/email-automation",
|
||||
"cookbooks/operations/content-writing",
|
||||
"cookbooks/operations/deep-research",
|
||||
"cookbooks/operations/team-task-agent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Integrations & Platforms",
|
||||
"icon": "plug",
|
||||
"pages": [
|
||||
"cookbooks/integrations/agents-sdk-tool",
|
||||
"cookbooks/integrations/openai-tool-calls",
|
||||
"cookbooks/integrations/mastra-agent",
|
||||
"cookbooks/integrations/healthcare-google-adk",
|
||||
"cookbooks/integrations/aws-bedrock",
|
||||
"cookbooks/integrations/tavily-search"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Frameworks & Multimodal",
|
||||
"icon": "layers",
|
||||
"pages": [
|
||||
"cookbooks/frameworks/llamaindex-react",
|
||||
"cookbooks/frameworks/llamaindex-multiagent",
|
||||
"cookbooks/frameworks/multimodal-retrieval",
|
||||
"cookbooks/frameworks/eliza-os-character",
|
||||
"cookbooks/frameworks/gemini-3-with-mem0-mcp"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "API Reference",
|
||||
"groups": [
|
||||
@@ -481,7 +474,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Memory APIs",
|
||||
"group": "Memory Management",
|
||||
"icon": "sparkles",
|
||||
"pages": [
|
||||
"api-reference/memory/create-memory-export",
|
||||
@@ -495,7 +488,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Events APIs",
|
||||
"group": "Events",
|
||||
"icon": "clock",
|
||||
"pages": [
|
||||
"api-reference/events/get-events",
|
||||
@@ -503,7 +496,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Entities APIs",
|
||||
"group": "Entities",
|
||||
"icon": "users",
|
||||
"pages": [
|
||||
"api-reference/entities/get-users",
|
||||
@@ -511,7 +504,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Organizations APIs",
|
||||
"group": "Organizations",
|
||||
"icon": "building",
|
||||
"pages": [
|
||||
"api-reference/organization/create-org",
|
||||
@@ -525,7 +518,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Project APIs",
|
||||
"group": "Projects",
|
||||
"icon": "folder",
|
||||
"pages": [
|
||||
"api-reference/project/create-project",
|
||||
@@ -540,7 +533,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Webhook APIs",
|
||||
"group": "Webhooks",
|
||||
"icon": "webhook",
|
||||
"pages": [
|
||||
"api-reference/webhook/create-webhook",
|
||||
@@ -970,17 +963,25 @@
|
||||
"source": "/v0x/introduction",
|
||||
"destination": "/introduction"
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/mcp-integration",
|
||||
"destination": "/platform/mem0-mcp"
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/platform-overview",
|
||||
"destination": "/platform/features/v2-memory-filters"
|
||||
},
|
||||
{
|
||||
"source": "/features/async-client",
|
||||
"destination": "/platform/features/async-client"
|
||||
},
|
||||
{
|
||||
"source": "/features/custom-prompts",
|
||||
"destination": "/platform/features/platform-overview"
|
||||
"destination": "/platform/features/custom-instructions"
|
||||
},
|
||||
{
|
||||
"source": "/features/selective-memory",
|
||||
"destination": "/platform/features/platform-overview"
|
||||
"destination": "/platform/features/custom-instructions"
|
||||
},
|
||||
{
|
||||
"source": "/features/custom-categories",
|
||||
@@ -1012,7 +1013,7 @@
|
||||
},
|
||||
{
|
||||
"source": "/features/online-memory",
|
||||
"destination": "/platform/features/platform-overview"
|
||||
"destination": "/platform/features/async-client"
|
||||
},
|
||||
{
|
||||
"source": "/features/multimodal",
|
||||
@@ -1020,7 +1021,7 @@
|
||||
},
|
||||
{
|
||||
"source": "/features/inferences",
|
||||
"destination": "/platform/features/platform-overview"
|
||||
"destination": "/core-concepts/how-it-works"
|
||||
},
|
||||
{
|
||||
"source": "/features/graph-memory",
|
||||
@@ -1032,7 +1033,7 @@
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/online-memory",
|
||||
"destination": "/platform/features/platform-overview"
|
||||
"destination": "/platform/features/async-client"
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/multimodal",
|
||||
@@ -1040,7 +1041,7 @@
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/inferences",
|
||||
"destination": "/platform/features/platform-overview"
|
||||
"destination": "/core-concepts/how-it-works"
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/custom-prompts",
|
||||
@@ -1149,6 +1150,78 @@
|
||||
{
|
||||
"source": "/openmemory/integrations",
|
||||
"destination": "/introduction"
|
||||
},
|
||||
{
|
||||
"source": "/self-hosting",
|
||||
"destination": "/open-source/overview"
|
||||
},
|
||||
{
|
||||
"source": "/self-hosting/:slug",
|
||||
"destination": "/open-source/overview"
|
||||
},
|
||||
{
|
||||
"source": "/self-hosted",
|
||||
"destination": "/open-source/overview"
|
||||
},
|
||||
{
|
||||
"source": "/self-hosted/:slug",
|
||||
"destination": "/open-source/overview"
|
||||
},
|
||||
{
|
||||
"source": "/getting-started",
|
||||
"destination": "/platform/quickstart"
|
||||
},
|
||||
{
|
||||
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+23
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@@ -177,7 +177,7 @@ You: Add refresh token rotation to the auth system.
|
||||
<Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">
|
||||
Detailed MCP configuration for all clients
|
||||
</Card>
|
||||
<Card title="Codex Integration" icon={<svg width="24" height="25" viewBox="0 0 24 25" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M20.5565 10.6338C21.0009 9.27575 20.8528 7.76958 20.1367 6.53501C19.0503 4.63378 16.8528 3.67081 14.7046 4.11526C13.7663 3.05353 12.3836 2.46094 10.9515 2.46094C8.75399 2.46094 6.82807 3.86835 6.13671 5.94242C4.7293 6.23872 3.51943 7.10291 2.80338 8.36217C1.71696 10.2634 1.96387 12.6338 3.42066 14.2634C2.97622 15.6461 3.14906 17.1276 3.8651 18.3622C4.95152 20.2634 7.14906 21.2511 9.2972 20.7819C10.2602 21.8437 11.6182 22.4609 13.0503 22.4609C15.2478 22.4609 17.1737 21.0535 17.8651 18.9795C19.2725 18.6832 20.4824 17.819 21.1984 16.5597C22.2849 14.6585 22.0379 12.2634 20.5565 10.6338ZM13.0503 21.1523C12.1614 21.1523 11.3219 20.856 10.6552 20.2881C10.6799 20.2634 10.754 20.2387 10.7787 20.214L14.754 17.9177C14.9515 17.7943 15.075 17.5967 15.075 17.3498V11.7449L16.754 12.7079C16.7787 12.7079 16.7787 12.7325 16.7787 12.7572V17.3992C16.8034 19.4733 15.1244 21.1523 13.0503 21.1523ZM5.00091 17.7202C4.55646 16.9548 4.40831 16.0659 4.55646 15.2017C4.58115 15.2264 4.63054 15.2511 4.67992 15.2758L8.65523 17.572C8.85276 17.6955 9.09967 17.6955 9.2972 17.572L14.1614 14.7572V16.7079C14.1614 16.7325 14.1614 16.7572 14.1367 16.7572L10.112 19.0782C8.33424 20.1153 6.03794 19.498 5.00091 17.7202ZM3.96387 9.02884C4.40831 8.26341 5.09967 7.69551 5.91449 7.37452V12.1153C5.91449 12.3375 6.03794 12.5597 6.23548 12.6832L11.0997 15.498L9.42066 16.4609C9.39597 16.4609 9.37128 16.4856 9.37128 16.4609L5.34659 14.1399C3.51943 13.1029 2.92683 10.8066 3.96387 9.02884ZM17.791 12.2387L12.9268 9.4239L14.6058 8.46094C14.6305 8.46094 14.6552 8.43625 14.6552 8.46094L18.6799 10.7819C20.4824 11.819 21.075 14.1153 20.0379 15.893C19.5935 16.6585 18.9021 17.2264 18.0873 17.5227V12.8066C18.112 12.5844 17.9886 12.3622 17.791 12.2387ZM19.4454 9.7202C19.4207 9.69551 19.3713 9.67081 19.3219 9.64612L15.3466 7.34983C15.1491 7.22637 14.9021 7.22637 14.7046 7.34983L9.84041 10.1646V8.21402C9.84041 8.18933 9.84041 8.16464 9.86511 8.16464L13.8898 5.84365C15.6923 4.80662 17.9639 5.4239 19.0009 7.22637C19.4454 7.96711 19.5935 8.856 19.4454 9.7202ZM8.92683 13.177L7.24782 12.214C7.22313 12.214 7.22313 12.1893 7.22313 12.1646V7.52267C7.22313 5.44859 8.90214 3.76958 10.9762 3.76958C11.8651 3.76958 12.7046 4.06588 13.3713 4.63378C13.3466 4.65847 13.2972 4.68316 13.2478 4.70785L9.27251 7.00415C9.07498 7.1276 8.95152 7.32514 8.95152 7.57205V13.177H8.92683ZM9.84041 11.2017L12.0133 9.94242L14.1861 11.2017V13.6955L12.0133 14.9548L9.84041 13.6955V11.2017Z" fill="currentColor"/></svg>} href="/integrations/codex">
|
||||
<Card title="Codex Integration" icon="/images/provider-icons/openai.svg" href="/integrations/codex">
|
||||
Add Mem0 memory to OpenAI Codex workflows
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -158,7 +158,7 @@ You: Add WebSocket support for real-time notification delivery.
|
||||
<Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">
|
||||
Detailed MCP configuration for all clients
|
||||
</Card>
|
||||
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|
||||
<Card title="Claude Code Integration" icon="/images/provider-icons/anthropic.svg" href="/integrations/claude-code">
|
||||
Add Mem0 memory to Claude Code workflows
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -118,7 +118,7 @@ You: The /orders endpoint is also slow, same pattern as before.
|
||||
<Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">
|
||||
Detailed MCP configuration for all clients
|
||||
</Card>
|
||||
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|
||||
<Card title="Claude Code Integration" icon="/images/provider-icons/anthropic.svg" href="/integrations/claude-code">
|
||||
Add Mem0 memory to Claude Code workflows
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -224,7 +224,7 @@ curl http://localhost:11434/api/tags
|
||||
5. **Additive memory**: works alongside Hermes' built-in file memory (`MEMORY.md`, `USER.md`).
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="OpenClaw Integration" icon={<svg width="24" height="24" viewBox="0 0 500 500" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" d="m153.5 173.5q24.62 1.46 46 13.5 12.11 8.1 17.5 21.5 0.74 2.45 0.5 5 0.09 0.81 1 1 1.48-4.9 1-10 5.04 10.48 1.5 22-9.81 27.86-35.5 42.5-26.17 14.97-56 19.5-2.77-0.4-2 1 2.86 1.27 6 1 25.64 1.53 48.5-10 0.34 10.08 2 20 1.08 5.76 5 10 1 1.5 0 3-31.11 20.84-68.5 17.5-23.7-5.7-32.5-28.5-4.39-9.18-3.5-19 15.41 6.23 32 4.5-20.68-6.39-39-18-34.81-27.22-12.5-65.5 11.84-14.83 29-23 4.21 7.66 11.5 12.5 3 1 6 0-26.04-34.62-29-78-0.13-8.46 2-16.5 1 6.5 2 13 3.43 39.53 24.5 73 2.03 2.28 4.5 4 0.5-1.25 1-2.5-1.27-6.54-5-12 0.5-0.75 1-1.5 9.72-3.43 20-4 0.55 10.34 8 17.5 1.94 0.74 4 0.5-17.8-64.6 16.5-122 0.98-1.79 1.5 0-28.21 56.64-13.5 118 1.08 1.43 2.5 0.5 2.21-4.98 2-10.5z" fill="currentColor"/><path fill-rule="evenodd" d="m454.5 97.5q-1.33 11.18-8.5 20-21.81 26.28-55.5 32-1.11-0.2-2 0.5 2.31 2.82 5.5 4.5 1 2 0 4-9.56 11.3-19.5 20 19.71-8.72 31-27 2.68-0.43 5 1-14.24 30.97-48 36.5-9.93 1.71-20 1.5-6.8-0.48-13 1 5.81 6.92 14 11-10.78 16.03-27 26.5 27.16-7.4 38-33.5 4.34 1.35 9 1-9.08 23.84-33 33.5-18.45 6.41-38 7 22.59 8.92 45-1 12.05-5.52 24-11 9.01-1.79 17 2.5 5.28-4.38 11-8 12.8-6.07 27-5 0 0.5 0 1-19.34 2.69-34 15.5 0.5 0.25 1 0.5 17.79-8.09 36-15 2.71-0.79 5-2 2.5-1 5-2 5.53-4.04 11-8 11.7-4.18 24-6.5 7.78-1.36 15 1.5-2.97 18.45-13.5 34-34.92 49.37-94.5 62.5-59.27 12.45-108-23-15.53-12.52-21.5-31.5-2.47-14.26 4-27-3.15 24.41 14 42-4.92-10.28-7-22-1.97-17.63 7-33 47.28-69.5 125.5-100 15.86-3.42 32-5.5 18.63-1.47 37 1.5z" fill="currentColor"/><path fill-rule="evenodd" d="m231.5 238.5q1.31-0.2 2 1-3.13 28.62 15 51-16.25 6.75-27-7.5-1-1-2 0 14.73 29.34 46 18.5 1.79 0.52 0 1.5-37.63 16.82-50.5-22.5-5.1-26.48 16.5-42z" fill="currentColor"/><path fill-rule="evenodd" d="m203.5 266.5q1.31-0.2 2 1-2.48 22.08 12 39-6.99 1.35-14 0.5 4.59 4.08 10 7-8.71 0.28-14.5-6.5-16.98-22.76 4.5-41z" fill="currentColor"/><path fill-rule="evenodd" d="m58.5 284.5q9.6-2.17 14.5 6 5.15 14.18-1 28-11.05-13.14-27.5-17.5 5.15-9.9 14-16.5z" fill="currentColor"/><path fill-rule="evenodd" d="m56.5 313.5q3.43 5.43 8 10-4.88 0.44-8 4-1.11-0.2-2 0.5 28.91 1.65 38 28.5 0.45 3.16-1 6-11.02-7.01-23-12.5-4.75-3.75-9.5-7.5 1.47 7.42 7 13 8.34 27.18 32 43 0.99 2.41-1.5 3.5-40.25 5.58-66.5-25.5-15.67-22.01-8-48 10.46-23.87 34.5-15z" fill="currentColor"/><path fill-rule="evenodd" d="m198.5 319.5q1.44 0.68 2.5 2 2.41 8.23 6 16 1.2 2.64-0.5 5-30.65 21.41-68 18.5-25.16-6.17-32.5-30.5 6.96 4.99 15.5 6.5 8.99 0.75 18 0.5 16.25 2.38 32-2.5 15.9-3.94 27-15.5z" fill="currentColor"/><path fill-rule="evenodd" d="m239.5 342.5q7.02-0.25 14 0.5 4.46 1.06 8 3.5-5.2 2.35-10 5.5-3.88 4.65-9 7.5-9.89-3.09-9.5-13 2.36-3.63 6.5-4z" fill="currentColor"/><path fill-rule="evenodd" d="m214.5 349.5q5.96 7.2 13.5 13 1 1 0 2-28.58 23.34-65.5 20.5-18.15-4.24-27.5-19.5 1.13 0.94 2.5 1.5 14.7 1.42 29-1.5 26.57-0.52 48-16z" fill="currentColor"/><path fill-rule="evenodd" d="m302.5 373.5q0.21 2.44-2 3.5-28.69 7.6-50.5-12.5-0.06-6.71 6.5-9 4.45-0.75 9-1 22.26 2.27 37 19z" fill="currentColor"/><path fill-rule="evenodd" d="m232.5 365.5q17.6 6.19 10.5 23-10.6 10.42-25.5 11.5-25.94 3.21-49-9 36.75-1.65 64-25.5z" fill="currentColor"/><path fill-rule="evenodd" d="m113.5 367.5q7.7-0.01 9.5 7-9.69 7.19-18.5 15.5-7.23 5.76-5.5-3.5 3.12-12.84 14.5-19z" fill="currentColor"/><path fill-rule="evenodd" d="m126.5 380.5q7.88-0.4 12 6.5-8.5 7.25-17 14.5-5.62-12.55 5-21z" fill="currentColor"/><path fill-rule="evenodd" d="m283.5 385.5q3.22 2.95 7 5.5 2.8 4.03 6 7.5 0.42 2.77-2 4-15.5-9.75-31-19.5-1.79-0.98 0-1.5 9.96 2.49 20 4z" fill="currentColor"/></svg>} href="/integrations/openclaw">
|
||||
<Card title="OpenClaw Integration" icon="/images/provider-icons/openclaw.svg" href="/integrations/openclaw">
|
||||
Add memory to OpenClaw agents with auto-recall and auto-capture
|
||||
</Card>
|
||||
<Card title="Mem0 Platform" icon="rocket" href="/platform/overview">
|
||||
|
||||
@@ -491,7 +491,7 @@ export MEM0_TELEMETRY=false
|
||||
The plugin injects memory-related instructions into the agent's system context via OpenClaw's `prependSystemContext` mechanism. This includes the memory triage protocol and recalled memories. This is the standard OpenClaw plugin SDK pattern for memory backends. No user-facing prompts are modified.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="OpenAI Agents SDK" icon="robot" href="/integrations/openai-agents-sdk">
|
||||
<Card title="OpenAI Agents SDK" icon="/images/provider-icons/openai.svg" href="/integrations/openai-agents-sdk">
|
||||
Build agents with OpenAI's SDK and Mem0
|
||||
</Card>
|
||||
<Card title="LangGraph Integration" icon="diagram-project" href="/integrations/langgraph">
|
||||
|
||||
@@ -175,10 +175,10 @@ You: What do you know about my preferences?
|
||||
- **Dream not triggering**: All three gates must pass (time, sessions, memories). Use `/mem0-dream` to force it manually
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Claude Code Integration" icon="terminal" href="/integrations/claude-code">
|
||||
<Card title="Claude Code Integration" icon="/images/provider-icons/anthropic.svg" href="/integrations/claude-code">
|
||||
Add Mem0 memory to Claude Code
|
||||
</Card>
|
||||
<Card title="OpenClaw Integration" icon="plug" href="/integrations/openclaw">
|
||||
<Card title="OpenClaw Integration" icon="/images/provider-icons/openclaw.svg" href="/integrations/openclaw">
|
||||
Add Mem0 memory to OpenClaw agents
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
+77
-87
@@ -1,79 +1,121 @@
|
||||
---
|
||||
title: "Welcome to Mem0"
|
||||
description: "Mem0 is a universal, self-improving memory layer for LLM applications that enables persistent context across sessions."
|
||||
title: "Build AI apps that remember"
|
||||
description: "Add persistent, self-improving memory to your AI app with Mem0 Platform or self-hosted Open Source."
|
||||
mode: "custom"
|
||||
---
|
||||
|
||||
{/* debug: welcome-layout-v3-grid */}
|
||||
|
||||
<div className="px-4 pt-8 pb-4 lg:pt-10 max-w-4xl mx-auto text-center space-y-3">
|
||||
<div className="px-4 pt-8 pb-6 lg:pt-10 max-w-4xl mx-auto text-center space-y-4">
|
||||
<h1 className="text-3xl lg:text-4xl font-bold text-gray-900 dark:text-zinc-50 tracking-tight mb-2">
|
||||
Build with <span className="text-primary">Mem0</span>
|
||||
</h1>
|
||||
|
||||
<p className="max-w-2xl mx-auto text-base text-gray-600 dark:text-zinc-400 leading-relaxed">
|
||||
<p className="max-w-3xl mx-auto text-base text-gray-600 dark:text-zinc-400 leading-relaxed">
|
||||
Universal, self-improving memory layer for LLM applications.
|
||||
</p>
|
||||
|
||||
<a
|
||||
href="/platform/quickstart"
|
||||
className="inline-flex items-center gap-1 text-sm text-gray-500 dark:text-zinc-500 hover:text-primary dark:hover:text-primary transition-colors"
|
||||
>
|
||||
Write your first memory
|
||||
<span className="group-hover:translate-x-0.5 transition-transform">→</span>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
<section className="px-4 pt-4 pb-6 max-w-6xl mx-auto">
|
||||
<div className="grid gap-4 sm:grid-cols-2 lg:grid-cols-3">
|
||||
<a
|
||||
href="/platform/overview"
|
||||
href="/platform/quickstart"
|
||||
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
|
||||
>
|
||||
<img
|
||||
className="block dark:hidden aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/light/mem0_platform.png"
|
||||
src="/images/docs thumbnails/light/mem0-platform.png"
|
||||
alt="Mem0 Platform thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<img
|
||||
className="hidden dark:block aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/dark/mem0_platform.png"
|
||||
src="/images/docs thumbnails/dark/mem0-platform.png"
|
||||
alt="Mem0 Platform thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
|
||||
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
|
||||
Mem0 Platform
|
||||
Add memory to your app
|
||||
</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-zinc-400">
|
||||
Managed memory with production-scale infrastructure, ready in minutes.
|
||||
Start with the quickstart and store your first memory in minutes.
|
||||
</p>
|
||||
</div>
|
||||
</a>
|
||||
|
||||
<a
|
||||
href="/open-source/overview"
|
||||
href="/integrations"
|
||||
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
|
||||
>
|
||||
<img
|
||||
className="block dark:hidden aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/light/mem0_open_source.png"
|
||||
alt="Mem0 Open Source thumbnail"
|
||||
src="/images/docs thumbnails/light/mem0-plugins.png"
|
||||
alt="Integrations thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<img
|
||||
className="hidden dark:block aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/dark/mem0_open_source.png"
|
||||
alt="Mem0 Open Source thumbnail"
|
||||
src="/images/docs thumbnails/dark/mem0-plugins.png"
|
||||
alt="Integrations thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
|
||||
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
|
||||
Mem0 Open Source
|
||||
Add memory to your agent
|
||||
</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-zinc-400">
|
||||
Self-host the Mem0 stack for full control over data, deployment, and customization.
|
||||
Give Claude Code, Cursor, and Codex memory that persists across sessions. A drop-in plugin, no code to write.
|
||||
</p>
|
||||
</div>
|
||||
</a>
|
||||
|
||||
<a
|
||||
href="/platform/agent-signup"
|
||||
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
|
||||
>
|
||||
<img
|
||||
className="block dark:hidden aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/light/mem0-cli.png"
|
||||
alt="Sign up as an agent thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<img
|
||||
className="hidden dark:block aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/dark/mem0-cli.png"
|
||||
alt="Sign up as an agent thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
|
||||
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
|
||||
Sign up for Mem0 as an agent
|
||||
</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-zinc-400">
|
||||
Let an AI agent mint a Mem0 API key, claim ownership later, and write its first memory from the terminal.
|
||||
</p>
|
||||
</div>
|
||||
</a>
|
||||
|
||||
<a
|
||||
href="/integrations"
|
||||
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
|
||||
>
|
||||
<img
|
||||
className="block dark:hidden aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/light/mem0-integrations.png"
|
||||
alt="Integrations thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<img
|
||||
className="hidden dark:block aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/dark/mem0-integrations.png"
|
||||
alt="Integrations thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
|
||||
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
|
||||
Explore Mem0 integrations
|
||||
</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-zinc-400">
|
||||
Connect Mem0 to LangChain, CrewAI, Vercel AI SDK, and 20+ partner frameworks.
|
||||
</p>
|
||||
</div>
|
||||
</a>
|
||||
@@ -96,88 +138,36 @@ mode: "custom"
|
||||
/>
|
||||
<div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
|
||||
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
|
||||
Cookbooks
|
||||
See memory-app examples
|
||||
</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-zinc-400">
|
||||
Production-ready tutorials that show how to ship memorable AI experiences.
|
||||
Browse cookbooks for companions, support agents, voice agents, research tools, and more.
|
||||
</p>
|
||||
</div>
|
||||
</a>
|
||||
|
||||
<a
|
||||
href="/integrations"
|
||||
href="/open-source/overview"
|
||||
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
|
||||
>
|
||||
<img
|
||||
className="block dark:hidden aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/light/Integrations.png"
|
||||
alt="Integrations thumbnail"
|
||||
src="/images/docs thumbnails/light/mem0_open_source.png"
|
||||
alt="Mem0 Open Source thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<img
|
||||
className="hidden dark:block aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/dark/Integrations.png"
|
||||
alt="Integrations thumbnail"
|
||||
src="/images/docs thumbnails/dark/mem0_open_source.png"
|
||||
alt="Mem0 Open Source thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
|
||||
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
|
||||
Integrations
|
||||
Self-host Mem0
|
||||
</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-zinc-400">
|
||||
Connect Mem0 to LangChain, CrewAI, Vercel AI SDK, and 20+ partner frameworks.
|
||||
</p>
|
||||
</div>
|
||||
</a>
|
||||
|
||||
<a
|
||||
href="/api-reference"
|
||||
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
|
||||
>
|
||||
<img
|
||||
className="block dark:hidden aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/light/API.png"
|
||||
alt="API reference thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<img
|
||||
className="hidden dark:block aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/dark/API.png"
|
||||
alt="API reference thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
|
||||
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
|
||||
API Reference
|
||||
</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-zinc-400">
|
||||
Explore every REST endpoint with payload examples and usage guidance.
|
||||
</p>
|
||||
</div>
|
||||
</a>
|
||||
|
||||
<a
|
||||
href="/platform/agent-signup"
|
||||
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
|
||||
>
|
||||
<img
|
||||
className="block dark:hidden aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/light/CLI.png"
|
||||
alt="Sign up as an agent thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<img
|
||||
className="hidden dark:block aspect-[2/1] w-full object-cover"
|
||||
src="/images/docs thumbnails/dark/CLI.png"
|
||||
alt="Sign up as an agent thumbnail"
|
||||
style={{pointerEvents: "none"}}
|
||||
/>
|
||||
<div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
|
||||
<h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
|
||||
Sign up as an agent
|
||||
</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-zinc-400">
|
||||
For AI agents: mint a Mem0 API key in under five seconds: no email, no dashboard. Four commands to your first memory.
|
||||
Use Open Source when you want memory without the hosted platform.
|
||||
</p>
|
||||
</div>
|
||||
</a>
|
||||
|
||||
+4
-5
@@ -6,7 +6,7 @@
|
||||
|
||||
### Don't have a Mem0 API key?
|
||||
|
||||
Install the CLI, then mint a key — no email, no dashboard, no OTP required:
|
||||
Install the CLI, then mint a key without email, dashboard, or OTP:
|
||||
|
||||
```bash
|
||||
# Python
|
||||
@@ -16,7 +16,7 @@ pip install mem0-cli
|
||||
npm install -g @mem0/cli
|
||||
```
|
||||
|
||||
Then run: `mem0 init --agent --agent-caller <your-name> --json` — replace `<your-name>` with the AI agent you are (e.g. `claude-code`, `cursor`, `codex`). If you forgot to pass `--agent-caller`, run `mem0 identify <your-name>` after init to attribute the signup.
|
||||
Then run: `mem0 init --agent --agent-caller <your-name> --json`. Replace `<your-name>` with the AI agent you are (e.g. `claude-code`, `cursor`, `codex`). If you forgot to pass `--agent-caller`, run `mem0 identify <your-name>` after init to attribute the signup.
|
||||
|
||||
This mints an evaluation key in <5 seconds. Use it immediately against the Platform API or MCP server. When the human is ready, claim with `mem0 init --email <your-email>` to transfer ownership (memories preserved, same API key, no agent disruption).
|
||||
|
||||
@@ -184,6 +184,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
|
||||
|
||||
## Core Concepts
|
||||
|
||||
- [How Mem0 Works](https://docs.mem0.ai/core-concepts/how-it-works) [Both]: Use when explaining the end-to-end pipeline: extraction (ADD-only distillation), storage across vector/graph/history stores, and multi-signal retrieval.
|
||||
- [Memory Types](https://docs.mem0.ai/core-concepts/memory-types) [Both]: Use when explaining working, factual, episodic, and semantic memory distinctions.
|
||||
- [Memory Operations - Add](https://docs.mem0.ai/core-concepts/memory-operations/add) [Both]: Use when explaining how `add()` extracts facts, resolves conflicts, and writes to both stores.
|
||||
- [Memory Operations - Search](https://docs.mem0.ai/core-concepts/memory-operations/search) [Both]: Use when explaining how queries are processed and ranked.
|
||||
@@ -194,7 +195,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
|
||||
## Platform
|
||||
|
||||
### Features - Essential
|
||||
- [Platform Features Overview](https://docs.mem0.ai/platform/features/platform-overview) [Platform]: Use when surveying what managed offers beyond CRUD.
|
||||
- [V2 Memory Filters](https://docs.mem0.ai/platform/features/v2-memory-filters) [Platform]: Use when compound filters (AND/OR on metadata, entity, time) are needed at search.
|
||||
- [Entity-Scoped Memory](https://docs.mem0.ai/platform/features/entity-scoped-memory) [Platform]: Use when partitioning memories by user, agent, app, or run.
|
||||
- [Graph Memory](https://docs.mem0.ai/platform/features/graph-memory) [Platform]: Use when connecting facts across memories through shared entities for entity-centric or multi-hop questions.
|
||||
@@ -208,7 +208,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
|
||||
- [Temporal Reasoning](https://docs.mem0.ai/platform/features/temporal-reasoning) [Platform]: Use when time-aware searches like last week, upcoming, or right now need better result ordering.
|
||||
- [Contextual Add](https://docs.mem0.ai/platform/features/contextual-add) [Platform]: Use when `add()` should consider the surrounding conversation, not just the latest turn.
|
||||
- [Custom Instructions](https://docs.mem0.ai/platform/features/custom-instructions) [Platform]: Use when tailoring what Mem0 extracts and stores on Platform.
|
||||
- [Memory Decay](https://docs.mem0.ai/platform/features/memory-decay) [Platform]: Use when search results should boost recently-reinforced memories and dampen stale ones — opt-in per project, search-time only, never filters candidates out.
|
||||
- [Memory Decay](https://docs.mem0.ai/platform/features/memory-decay) [Platform]: Use when search results should boost recently-reinforced memories and dampen stale ones. Opt in per project; applies at search time and never filters candidates out.
|
||||
- [Advanced Memory Operations](https://docs.mem0.ai/platform/advanced-memory-operations) [Platform]: Use when basic CRUD is not enough - batch ops, complex filters, workflows.
|
||||
|
||||
### Features - Data Management
|
||||
@@ -220,7 +220,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
|
||||
- [Webhooks](https://docs.mem0.ai/platform/features/webhooks) [Platform]: Use when another system needs to react to memory changes in real time.
|
||||
- [Feedback Mechanism](https://docs.mem0.ai/platform/features/feedback-mechanism) [Platform]: Use when capturing user feedback to improve memory quality.
|
||||
- [Group Chat Support](https://docs.mem0.ai/platform/features/group-chat) [Platform]: Use when the conversation has multiple participants.
|
||||
- [MCP Integration](https://docs.mem0.ai/platform/features/mcp-integration) [Platform]: Use when wiring Mem0 into Claude/Cursor/other MCP clients.
|
||||
|
||||
### Support & Migration
|
||||
- [FAQs](https://docs.mem0.ai/platform/faqs) [Platform]: Use when answering common Platform questions.
|
||||
|
||||
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@@ -1,63 +1,47 @@
|
||||
---
|
||||
title: "Configure the OSS Stack"
|
||||
description: "Wire up Mem0 OSS with your preferred LLM, vector store, embedder, and reranker."
|
||||
description: "Configure Mem0 OSS in Python or TypeScript with your own LLM, embedder, and vector store."
|
||||
icon: "sliders"
|
||||
---
|
||||
|
||||
# Configure Mem0 OSS Components
|
||||
Mem0 OSS works out of the box with OpenAI defaults. Point it at your own LLM, embedder, and vector store by passing a config when you create `Memory`. The Python SDK also supports a reranker and graph memory.
|
||||
|
||||
<Info>
|
||||
**Prerequisites**
|
||||
- Python 3.10+ with `pip` available
|
||||
- Running vector database (e.g., Qdrant, Postgres + pgvector) or access credentials for a managed store
|
||||
- API keys for your chosen LLM, embedder, and reranker providers
|
||||
- Python 3.10+ (`pip`) or Node.js 18+ (`npm`)
|
||||
- A running vector store such as Qdrant or Postgres + pgvector (Python's default Qdrant and Node's in-memory store need nothing extra)
|
||||
- API keys for your chosen LLM and embedder providers
|
||||
</Info>
|
||||
|
||||
<Tip>
|
||||
Start from the <Link href="/open-source/python-quickstart">Python quickstart</Link> if you still need the base CLI and repository.
|
||||
New to Mem0 OSS? Run the <Link href="/open-source/python-quickstart">Python</Link> or <Link href="/open-source/node-quickstart">Node.js</Link> quickstart first, then come back to swap in your own providers.
|
||||
</Tip>
|
||||
|
||||
## Install dependencies
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Python">
|
||||
<Steps>
|
||||
<Step title="Install Mem0 OSS">
|
||||
```bash
|
||||
<CodeGroup>
|
||||
```bash pip
|
||||
pip install mem0ai
|
||||
```
|
||||
</Step>
|
||||
<Step title="Add provider SDKs (example: Qdrant + OpenAI)">
|
||||
```bash
|
||||
pip install qdrant-client openai
|
||||
|
||||
```bash npm
|
||||
npm install mem0ai
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
<Tab title="Docker Compose">
|
||||
<Steps>
|
||||
<Step title="Clone the repo and copy the compose file">
|
||||
</CodeGroup>
|
||||
|
||||
Using Qdrant as your vector store? Install its Python client (the Node SDK talks to Qdrant over REST) and run the server locally:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/mem0ai/mem0.git
|
||||
cd mem0/examples/docker-compose
|
||||
pip install qdrant-client # Python only
|
||||
docker run -p 6333:6333 qdrant/qdrant
|
||||
```
|
||||
</Step>
|
||||
<Step title="Install dependencies for local overrides">
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Define your configuration
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Python">
|
||||
<Steps>
|
||||
<Step title="Create a configuration dictionary">
|
||||
```python
|
||||
Each component takes a `provider` and a `config`. Keys are `snake_case` in Python and `camelCase` in TypeScript. Pass the config when you create `Memory`:
|
||||
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
from mem0 import Memory
|
||||
|
||||
config = {
|
||||
@@ -67,11 +51,11 @@ config = {
|
||||
},
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {"model": "gpt-4.1-mini", "temperature": 0.1},
|
||||
"config": {"model": "gpt-5-mini", "temperature": 0.1},
|
||||
},
|
||||
"embedder": {
|
||||
"provider": "vertexai",
|
||||
"config": {"model": "textembedding-gecko@003"},
|
||||
"provider": "openai",
|
||||
"config": {"model": "text-embedding-3-small"},
|
||||
},
|
||||
"reranker": {
|
||||
"provider": "cohere",
|
||||
@@ -81,86 +65,95 @@ config = {
|
||||
|
||||
memory = Memory.from_config(config)
|
||||
```
|
||||
</Step>
|
||||
<Step title="Store secrets as environment variables">
|
||||
|
||||
```ts Node.js
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const memory = new Memory({
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: process.env.OPENAI_API_KEY || "", model: "gpt-5-mini", temperature: 0.1 },
|
||||
},
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: process.env.OPENAI_API_KEY || "", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "qdrant",
|
||||
config: { host: "localhost", port: 6333, collectionName: "memories" },
|
||||
},
|
||||
});
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
Set your provider keys as environment variables:
|
||||
|
||||
```bash
|
||||
export QDRANT_API_KEY="..."
|
||||
export OPENAI_API_KEY="..."
|
||||
export COHERE_API_KEY="..."
|
||||
export COHERE_API_KEY="..." # Python reranker only
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
<Tab title="config.yaml">
|
||||
<Steps>
|
||||
<Step title="Create a `config.yaml` file">
|
||||
```yaml
|
||||
vector_store:
|
||||
provider: qdrant
|
||||
config:
|
||||
host: localhost
|
||||
port: 6333
|
||||
|
||||
llm:
|
||||
provider: azure_openai
|
||||
config:
|
||||
api_key: ${AZURE_OPENAI_KEY}
|
||||
deployment_name: gpt-4.1-mini
|
||||
<Note>
|
||||
The TypeScript OSS SDK configures the LLM, embedder, vector store, and history store. Reranker and graph memory are Python-only today.
|
||||
</Note>
|
||||
|
||||
embedder:
|
||||
provider: ollama
|
||||
config:
|
||||
model: nomic-embed-text
|
||||
Prefer a config file? Load YAML into Python's `from_config`:
|
||||
|
||||
reranker:
|
||||
provider: zero_entropy
|
||||
config:
|
||||
api_key: ${ZERO_ENTROPY_KEY}
|
||||
```
|
||||
</Step>
|
||||
<Step title="Load the config file at runtime">
|
||||
```python
|
||||
import yaml
|
||||
from mem0 import Memory
|
||||
|
||||
memory = Memory.from_config_file("config.yaml")
|
||||
with open("config.yaml") as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
memory = Memory.from_config(config)
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Info icon="check">
|
||||
Run `memory.add("Remember my favorite cafe in Tokyo.", user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
|
||||
Verify it works: add a memory and search it back. `memory.add(...)` followed by `memory.search(...)` should populate your vector store and return the memory as a top hit.
|
||||
</Info>
|
||||
|
||||
## Available providers
|
||||
|
||||
Change the `provider` string to switch backends. The most common options:
|
||||
|
||||
| Component | Python | TypeScript |
|
||||
| --- | --- | --- |
|
||||
| LLM | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `litellm` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `azure_openai`, `mistral`, `deepseek` |
|
||||
| Embedder | `openai`, `gemini`, `azure_openai`, `ollama`, `huggingface`, `vertexai`, `aws_bedrock` | `openai`, `gemini`, `azure_openai`, `ollama` |
|
||||
| Vector store | `qdrant`, `pgvector`, `chroma`, `pinecone`, `redis`, `weaviate`, `milvus`, `elasticsearch` | `memory`, `qdrant`, `pgvector`, `redis`, `supabase`, `azure-ai-search`, `vectorize` |
|
||||
|
||||
See the full catalog in <Link href="/components/llms/overview">Components</Link>.
|
||||
|
||||
## Tune component settings
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Vector store collections">
|
||||
Name collections explicitly in production (`collection_name`) to isolate tenants and enable per-tenant retention policies.
|
||||
Name collections explicitly in production (`collection_name` / `collectionName`) to isolate tenants and enable per-tenant retention policies.
|
||||
</Accordion>
|
||||
<Accordion title="LLM extraction temperature">
|
||||
Keep extraction temperatures ≤0.2 so advanced memories stay deterministic. Raise it only when you see missing facts.
|
||||
Keep extraction temperature at or below 0.2 so memories stay deterministic. Raise it only when you see facts being missed.
|
||||
</Accordion>
|
||||
<Accordion title="Reranker depth">
|
||||
Limit `top_k` to 10–20 results; sending more adds latency without meaningful gains.
|
||||
<Accordion title="Reranker depth (Python)">
|
||||
Limit `top_k` to 10 to 20 results. Sending more adds latency without meaningful gains.
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
<Warning>
|
||||
Mixing managed and self-hosted components? Make sure every outbound provider call happens through a secure network path. Managed rerankers often require outbound internet even if your vector store is on-prem.
|
||||
Mixing managed and self-hosted components? Make sure every outbound provider call has a secure network path. Managed rerankers and embedders often require outbound internet even if your vector store is on-prem.
|
||||
</Warning>
|
||||
|
||||
## Quick recovery
|
||||
|
||||
- Qdrant connection errors → confirm port `6333` is exposed and API key (if set) matches.
|
||||
- Empty search results → verify the embedder model name; a mismatch causes dimension errors.
|
||||
- `Unknown reranker` → update the SDK (`pip install --upgrade mem0ai`) to load the latest provider registry.
|
||||
- Qdrant connection errors: confirm port `6333` is exposed and the API key (if set) matches.
|
||||
- Empty search results: verify the embedder model name. A mismatch causes dimension errors.
|
||||
- `Unknown reranker` (Python): upgrade the SDK with `pip install --upgrade mem0ai` to load the latest provider registry.
|
||||
- `Cannot find module` (Node): import from the OSS entry point, `import { Memory } from "mem0ai/oss"`, not `"mem0ai"`.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
title="Pick Providers"
|
||||
description="Review the LLM, vector store, embedder, and reranker catalogs."
|
||||
description="Browse the LLM, vector store, embedder, and reranker catalogs."
|
||||
icon="sitemap"
|
||||
href="/components/llms/overview"
|
||||
/>
|
||||
|
||||
@@ -4,7 +4,7 @@ description: "Store and search Mem0 memories from a TypeScript or JavaScript app
|
||||
icon: "js"
|
||||
---
|
||||
|
||||
Spin up Mem0 with the Node SDK in just a few steps. You’ll install the package, initialize the client, add a memory, and confirm retrieval with a single search.
|
||||
Spin up Mem0 with the Node SDK in just a few steps. You'll install the package, initialize the client, add a memory, and confirm retrieval with a single search.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
@@ -67,218 +67,45 @@ console.log(results);
|
||||
</Steps>
|
||||
|
||||
<Note>
|
||||
By default the Node SDK uses local-friendly settings (OpenAI `gpt-5-mini`, `text-embedding-3-small`, in-memory vector store, and SQLite history). Swap components by passing a config as shown below.
|
||||
By default the Node SDK uses local-friendly settings (OpenAI `gpt-5-mini`, `text-embedding-3-small`, in-memory vector store, and SQLite history). Pass a config to swap any of them.
|
||||
</Note>
|
||||
|
||||
## Configure for production
|
||||
## Configure providers
|
||||
|
||||
Pass a config object to `new Memory()` to use your own LLM, embedder, and vector store:
|
||||
|
||||
```ts
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const memory = new Memory({
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: process.env.OPENAI_API_KEY || "", model: "gpt-4-turbo-preview" }
|
||||
},
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: {
|
||||
apiKey: process.env.OPENAI_API_KEY || "",
|
||||
model: "text-embedding-3-small"
|
||||
}
|
||||
config: { apiKey: process.env.OPENAI_API_KEY || "", model: "text-embedding-3-small" }
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: "memories",
|
||||
dimension: 1536
|
||||
}
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: {
|
||||
apiKey: process.env.OPENAI_API_KEY || "",
|
||||
model: "gpt-4-turbo-preview"
|
||||
}
|
||||
},
|
||||
historyDbPath: "memory.db"
|
||||
});
|
||||
```
|
||||
|
||||
## Manage memories (optional)
|
||||
|
||||
<CodeGroup>
|
||||
```ts Get all memories
|
||||
const allMemories = await memory.getAll({ filters: { userId: "alice" } });
|
||||
console.log(allMemories);
|
||||
```
|
||||
|
||||
```ts Get one memory
|
||||
const singleMemory = await memory.get("892db2ae-06d9-49e5-8b3e-585ef9b85b8e");
|
||||
console.log(singleMemory);
|
||||
```
|
||||
|
||||
```ts Search memories
|
||||
const result = await memory.search("What do you know about me?", { filters: { userId: "alice" } });
|
||||
console.log(result);
|
||||
```
|
||||
|
||||
```ts Update a memory
|
||||
const updateResult = await memory.update(
|
||||
"892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
|
||||
"I love India, it is my favorite country."
|
||||
);
|
||||
console.log(updateResult);
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
```ts
|
||||
// Audit history
|
||||
const history = await memory.history("892db2ae-06d9-49e5-8b3e-585ef9b85b8e");
|
||||
console.log(history);
|
||||
|
||||
// Delete specific or scoped memories
|
||||
await memory.delete("892db2ae-06d9-49e5-8b3e-585ef9b85b8e");
|
||||
await memory.deleteAll({ userId: "alice" });
|
||||
|
||||
// Reset everything
|
||||
await memory.reset();
|
||||
```
|
||||
|
||||
## Use a custom history store
|
||||
|
||||
The Node SDK supports Supabase (or other providers) when you need serverless-friendly history storage.
|
||||
|
||||
<CodeGroup>
|
||||
```ts Supabase provider
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const memory = new Memory({
|
||||
historyStore: {
|
||||
provider: "supabase",
|
||||
config: {
|
||||
supabaseUrl: process.env.SUPABASE_URL || "",
|
||||
supabaseKey: process.env.SUPABASE_KEY || "",
|
||||
tableName: "memory_history"
|
||||
}
|
||||
config: { collectionName: "memories", dimension: 1536 }
|
||||
}
|
||||
});
|
||||
```
|
||||
|
||||
```ts Disable history
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const memory = new Memory({
|
||||
disableHistory: true
|
||||
});
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
Create the Supabase table with:
|
||||
|
||||
```sql
|
||||
create table memory_history (
|
||||
id text primary key,
|
||||
memory_id text not null,
|
||||
previous_value text,
|
||||
new_value text,
|
||||
action text not null,
|
||||
created_at timestamp with time zone default timezone('utc', now()),
|
||||
updated_at timestamp with time zone,
|
||||
is_deleted integer default 0
|
||||
);
|
||||
```
|
||||
|
||||
## Configuration parameters
|
||||
|
||||
Mem0 offers granular configuration across vector stores, LLMs, embedders, and history stores.
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Vector store">
|
||||
| Parameter | Description | Default |
|
||||
| --- | --- | --- |
|
||||
| `provider` | Vector store provider (e.g., `"memory"`) | `"memory"` |
|
||||
| `host` | Host address | `"localhost"` |
|
||||
| `port` | Port number | `undefined` |
|
||||
</Accordion>
|
||||
<Accordion title="LLM">
|
||||
| Parameter | Description | Provider |
|
||||
| --- | --- | --- |
|
||||
| `provider` | LLM provider (e.g., `"openai"`, `"anthropic"`) | All |
|
||||
| `model` | Model to use | All |
|
||||
| `temperature` | Temperature value | All |
|
||||
| `apiKey` | API key | All |
|
||||
| `maxTokens` | Max tokens to generate | All |
|
||||
| `topP` | Probability threshold | All |
|
||||
| `topK` | Token count to keep | All |
|
||||
| `openaiBaseUrl` | Base URL override | OpenAI |
|
||||
</Accordion>
|
||||
<Accordion title="Embedder">
|
||||
| Parameter | Description | Default |
|
||||
| --- | --- | --- |
|
||||
| `provider` | Embedding provider | `"openai"` |
|
||||
| `model` | Embedding model | `"text-embedding-3-small"` |
|
||||
| `apiKey` | API key | `undefined` |
|
||||
</Accordion>
|
||||
<Accordion title="General">
|
||||
| Parameter | Description | Default |
|
||||
| --- | --- | --- |
|
||||
| `historyDbPath` | Path to history database | `"{mem0_dir}/history.db"` |
|
||||
| `customInstructions` | Custom processing prompt | `undefined` |
|
||||
</Accordion>
|
||||
<Accordion title="History store">
|
||||
| Parameter | Description | Default |
|
||||
| --- | --- | --- |
|
||||
| `provider` | History provider | `"sqlite"` |
|
||||
| `config` | Provider configuration | `undefined` |
|
||||
| `disableHistory` | Disable history store | `false` |
|
||||
</Accordion>
|
||||
<Accordion title="Complete config example">
|
||||
```ts
|
||||
const config = {
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: {
|
||||
apiKey: process.env.OPENAI_API_KEY || "",
|
||||
model: "text-embedding-3-small"
|
||||
}
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: "memories",
|
||||
dimension: 1536
|
||||
}
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: {
|
||||
apiKey: process.env.OPENAI_API_KEY || "",
|
||||
model: "gpt-4-turbo-preview"
|
||||
}
|
||||
},
|
||||
historyStore: {
|
||||
provider: "supabase",
|
||||
config: {
|
||||
supabaseUrl: process.env.SUPABASE_URL || "",
|
||||
supabaseKey: process.env.SUPABASE_KEY || "",
|
||||
tableName: "memories"
|
||||
}
|
||||
},
|
||||
disableHistory: false,
|
||||
customInstructions: "I'm a virtual assistant. I'm here to help you with your queries."
|
||||
};
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
For the full provider catalog, history stores, and every config option, see [Configuration](/open-source/configuration).
|
||||
|
||||
## What's next?
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Explore Memory Operations" icon="database" href="/core-concepts/memory-operations/add">
|
||||
Review CRUD patterns, filters, and advanced retrieval across the OSS stack.
|
||||
<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
|
||||
Search, update, and manage memories with the full CRUD API.
|
||||
</Card>
|
||||
<Card title="Customize Configuration" icon="sliders" href="/open-source/configuration">
|
||||
Swap in your preferred LLM, vector store, and history provider for production use.
|
||||
<Card title="Configure for production" icon="sliders" href="/open-source/configuration">
|
||||
Swap in your own LLM, embedder, and vector store.
|
||||
</Card>
|
||||
<Card title="Automate Node Workflows" icon="plug" href="/cookbooks/integrations/openai-tool-calls">
|
||||
See a full Node-based workflow that layers Mem0 memories onto tool-calling agents.
|
||||
<Card title="Add to your framework" icon="plug" href="/integrations">
|
||||
Wire Mem0 into LangChain, CrewAI, LangGraph, and 20+ more.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
|
||||
@@ -4,43 +4,38 @@ description: "Self-host Mem0 with full control over your infrastructure and data
|
||||
icon: "house"
|
||||
---
|
||||
|
||||
# Mem0 Open Source Overview
|
||||
Mem0 Open Source is the same memory engine as the Platform, running on your own infrastructure. You own the stack, the data, and every component, and you choose where each one runs.
|
||||
|
||||
Mem0 Open Source delivers the same adaptive memory engine as the platform, but packaged for teams that need to run everything on their own infrastructure. You own the stack, the data, and the customizations.
|
||||
Run it two ways:
|
||||
|
||||
## What Mem0 OSS provides
|
||||
- **As a library** in your app. `pip install mem0ai` (or `npm install mem0ai`), call `Memory()`, and you have memory in a few lines.
|
||||
- **As a self-hosted server.** A Docker stack with a dashboard, per-user API keys, and a request audit log.
|
||||
|
||||
- **Full control**: Tune every component, from LLMs to vector stores, inside your environment.
|
||||
- **Offline ready**: Keep memory on your own network when compliance or privacy demands it.
|
||||
- **Extendable codebase**: Fork the repo, add providers, and ship custom automations.
|
||||
|
||||
<Info>
|
||||
Two ways to run Mem0 OSS: as a **library** inside your app (Python or Node), or as a **self-hosted server** with a dashboard, per-user API keys, and a request audit log.
|
||||
</Info>
|
||||
|
||||
## Choose your path
|
||||
## Get started
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Self-hosted setup" icon="rocket-launch" href="/open-source/setup">
|
||||
Run `make bootstrap` to launch the server + dashboard, create an admin, and issue your first API key.
|
||||
</Card>
|
||||
<Card title="Python Quickstart" icon="python" href="/open-source/python-quickstart">
|
||||
Bootstrap CLI and verify add/search loop.
|
||||
Install the SDK and verify the add/search loop in a few lines.
|
||||
</Card>
|
||||
<Card title="Node.js Quickstart" icon="node" href="/open-source/node-quickstart">
|
||||
Install TypeScript SDK and run starter script.
|
||||
Install the TypeScript SDK and run the starter script.
|
||||
</Card>
|
||||
<Card title="Self-hosted server" icon="rocket-launch" href="/open-source/setup">
|
||||
Run `make bootstrap` to launch the server, dashboard, and your first API key.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Go further
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Configure Components" icon="sliders" href="/open-source/configuration">
|
||||
LLM, embedder, vector store, reranker setup.
|
||||
<Card title="Configure components" icon="sliders" href="/open-source/configuration">
|
||||
Set your LLM, embedder, vector store, and reranker.
|
||||
</Card>
|
||||
<Card title="Tune Retrieval & Rerankers" icon="sparkles" href="/open-source/features/reranker-search">
|
||||
Hybrid retrieval and reranker controls.
|
||||
<Card title="Self-hosting features" icon="server" href="/open-source/features/overview">
|
||||
Async memory, metadata filters, reranker search, multimodal, and more.
|
||||
</Card>
|
||||
<Card title="Memory Evaluation" icon="chart-line" href="/core-concepts/memory-evaluation">
|
||||
Benchmarks and how Mem0 is tested.
|
||||
<Card title="Build with cookbooks" icon="book-open" href="/cookbooks/overview">
|
||||
End-to-end examples: companions, agents, and integrations.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -48,55 +43,29 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
|
||||
Need a managed alternative? Compare hosting models in the <Link href="/platform/platform-vs-oss">Platform vs OSS guide</Link> or switch tabs to the Platform documentation.
|
||||
</Tip>
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="What you get with Mem0 OSS" icon="code-branch">
|
||||
|
||||
| Benefit | What you get |
|
||||
| --- | --- |
|
||||
| Full infrastructure control | Host on your own servers with complete access to configuration and deployment. |
|
||||
| Complete customization | Modify the implementation, extend functionality, and tailor it to your stack. |
|
||||
| Local development | Perfect for development, testing, and offline environments. |
|
||||
| No vendor lock-in | Keep ownership of your data, providers, and pipelines. |
|
||||
| Community driven | Contribute improvements and tap into a growing ecosystem. |
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Default components
|
||||
|
||||
<Note>
|
||||
**Library defaults** (when you `import` Mem0 and call `Memory()` directly):
|
||||
- LLM: OpenAI `gpt-5-mini` (via `OPENAI_API_KEY`)
|
||||
- Embeddings: OpenAI `text-embedding-3-small`
|
||||
- Vector store: Local Qdrant at `/tmp/qdrant`
|
||||
- History store: SQLite at `~/.mem0/history.db`
|
||||
- Reranker: Disabled until configured
|
||||
Mem0 runs out of the box with the defaults below. Override any of them through [configuration](/open-source/configuration).
|
||||
|
||||
Override any component with <Link href="/open-source/configuration">`Memory.from_config`</Link>.
|
||||
</Note>
|
||||
**As a library** (you `import` Mem0 and call `Memory()`):
|
||||
|
||||
<Note>
|
||||
**Self-hosted server defaults** (the `server/` Docker Compose stack):
|
||||
- LLM: OpenAI `gpt-4.1-nano-2025-04-14` (override with `MEM0_DEFAULT_LLM_MODEL`)
|
||||
- Embeddings: OpenAI `text-embedding-3-small` (override with `MEM0_DEFAULT_EMBEDDER_MODEL`)
|
||||
- Vector store: Postgres + pgvector
|
||||
- Bundled providers: `openai`, `anthropic`, `gemini`: switch from the Configuration page
|
||||
| Component | Default |
|
||||
|---|---|
|
||||
| LLM | OpenAI `gpt-5-mini` (set `OPENAI_API_KEY`) |
|
||||
| Embeddings | OpenAI `text-embedding-3-small` |
|
||||
| Vector store | Local Qdrant at `/tmp/qdrant` |
|
||||
| History store | SQLite at `~/.mem0/history.db` |
|
||||
| Reranker | Disabled until configured |
|
||||
|
||||
See <Link href="/open-source/setup#supported-providers">Self-Hosted Setup</Link> for the full provider list and how to extend it.
|
||||
</Note>
|
||||
Override any component with [`Memory.from_config`](/open-source/configuration).
|
||||
|
||||
## Keep going
|
||||
**As a self-hosted server** (the `server/` Docker Compose stack):
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
title="Review Platform vs OSS"
|
||||
description="Confirm whether managed infrastructure or self-hosting better suits your workload."
|
||||
icon="arrows-left-right"
|
||||
href="/platform/platform-vs-oss"
|
||||
/>
|
||||
<Card
|
||||
title="Run the Python Quickstart"
|
||||
description="Clone the repo, install dependencies, and persist your first local memory."
|
||||
icon="terminal"
|
||||
href="/open-source/python-quickstart"
|
||||
/>
|
||||
</CardGroup>
|
||||
| Component | Default |
|
||||
|---|---|
|
||||
| LLM | OpenAI `gpt-5-mini` (override with `MEM0_DEFAULT_LLM_MODEL`) |
|
||||
| Embeddings | OpenAI `text-embedding-3-small` (override with `MEM0_DEFAULT_EMBEDDER_MODEL`) |
|
||||
| Vector store | Postgres + pgvector |
|
||||
| Bundled providers | `openai`, `anthropic`, `gemini` (switch on the Configuration page) |
|
||||
|
||||
See [Self-Hosted Setup](/open-source/setup#supported-providers) for the full provider list and how to extend it.
|
||||
|
||||
@@ -86,24 +86,22 @@ By default `Memory()` wires up:
|
||||
- No reranker (add one in the config when you need it)
|
||||
</Note>
|
||||
|
||||
## What's Next?
|
||||
## What's next?
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Memory Operations" icon="database" href="/core-concepts/memory-operations/add">
|
||||
Learn how to search, update, and manage memories with full CRUD operations
|
||||
<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
|
||||
Search, update, and manage memories with the full CRUD API.
|
||||
</Card>
|
||||
|
||||
<Card title="Configuration" icon="sliders" href="/open-source/configuration">
|
||||
Customize Mem0 with different LLMs, vector stores, and embedders for production use
|
||||
<Card title="Configure for production" icon="sliders" href="/open-source/configuration">
|
||||
Swap in your own LLM, embedder, and vector store.
|
||||
</Card>
|
||||
|
||||
<Card title="Advanced Features" icon="sparkles" href="/open-source/features/async-memory">
|
||||
Explore async support and multi-agent memory organization
|
||||
<Card title="Add to your framework" icon="plug" href="/integrations">
|
||||
Wire Mem0 into LangChain, CrewAI, LangGraph, and 20+ more.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Additional Resources
|
||||
If you have any questions, please feel free to reach out:
|
||||
|
||||
- **[OpenAI Compatibility](/open-source/features/openai_compatibility)** - Use Mem0 with OpenAI-compatible chat completions
|
||||
- **[Contributing Guide](/contributing/development)** - Learn how to contribute to Mem0
|
||||
- **[Examples](/cookbooks/companions/local-companion-ollama)** - See Mem0 in action with Ollama and other integrations
|
||||
<Snippet file="get-help.mdx" />
|
||||
|
||||
+11
-10
@@ -1,5 +1,6 @@
|
||||
---
|
||||
title: "Self-Hosted Setup"
|
||||
sidebarTitle: "Self-Hosted Server"
|
||||
description: "Stand up the Mem0 REST server and dashboard in a few minutes, with an admin account, API keys, and a live audit log included."
|
||||
icon: "rocket-launch"
|
||||
---
|
||||
@@ -22,7 +23,7 @@ The self-hosted bundle ships the REST API and a web dashboard together. Configur
|
||||
## Prerequisites
|
||||
|
||||
- Docker and Docker Compose (the reference path).
|
||||
- An `OPENAI_API_KEY` (or equivalent: the server reads the same component config as the library).
|
||||
- An `OPENAI_API_KEY` or another provider key; the server reads the same component config as the library.
|
||||
- A free port `8888` for the API and `3000` for the dashboard.
|
||||
|
||||
---
|
||||
@@ -70,7 +71,7 @@ cd server
|
||||
make bootstrap
|
||||
```
|
||||
|
||||
`make bootstrap` starts the same containers, then automatically creates the admin account and generates the first API key via the CLI. The admin credentials and API key are printed to your terminal: no browser required.
|
||||
`make bootstrap` starts the same containers, then automatically creates the admin account and generates the first API key via the CLI. The CLI prints the admin credentials and API key in your terminal. No browser required.
|
||||
|
||||
You can override the generated credentials:
|
||||
|
||||
@@ -89,16 +90,16 @@ Because `make bootstrap` already creates the admin, the setup wizard is skipped.
|
||||
## Run the setup wizard
|
||||
|
||||
<Info>
|
||||
This section applies to the **browser-first** path (`make up`). If you used `make bootstrap`, the admin and API key were already created: skip ahead to [What the dashboard gives you](#what-the-dashboard-gives-you).
|
||||
This section applies to the **browser-first** path (`make up`). If you used `make bootstrap`, the admin and API key were already created, so skip ahead to [What the dashboard gives you](#what-the-dashboard-gives-you).
|
||||
</Info>
|
||||
|
||||
On a fresh install the dashboard redirects to `/setup`. Each step submits on Enter.
|
||||
|
||||
**1. Create the admin account.** Name, email, password. This account becomes the first admin. Registration closes after the first admin is created; additional accounts are provisioned by the existing admin.
|
||||
|
||||
**2. Review the effective config.** Read-only display of the LLM and embedder the server is running with, sourced from your environment. If anything is wrong here, stop the stack, fix the `.env`, and restart: the dashboard intentionally does not let you change provider secrets at runtime.
|
||||
**2. Review the effective config.** Read-only display of the LLM and embedder the server is running with, sourced from your environment. If anything is wrong here, stop the stack, fix the `.env`, and restart. The dashboard intentionally does not let you change provider secrets at runtime.
|
||||
|
||||
**3. Generate your first API key.** The full `m0sk_...` value is shown **once**. Copy it immediately: the server only stores the prefix and a bcrypt hash.
|
||||
**3. Generate your first API key.** The full `m0sk_...` value is shown **once**. Copy it immediately. The server stores only the prefix and a bcrypt hash.
|
||||
|
||||
**4. Tell us your use case.** Pick a preset or describe your use case in a few words. Mem0 generates custom instructions that tell the memory system what to prioritize. You can edit the instructions before saving, or skip this step entirely.
|
||||
|
||||
@@ -125,8 +126,8 @@ For the underlying endpoints (including `/auth/*`, `/api-keys`, `/requests`, `/e
|
||||
|
||||
The shipped container bundles the Python packages for:
|
||||
|
||||
- **LLMs**: `openai`, `anthropic`, `gemini`
|
||||
- **Embedders**: `openai`, `gemini`
|
||||
- **LLMs:** `openai`, `anthropic`, `gemini`
|
||||
- **Embedders:** `openai`, `gemini`
|
||||
|
||||
The Configuration page and `POST /configure` only accept providers from these lists. Anything else returns a 400 up front instead of failing at the first memory write.
|
||||
|
||||
@@ -146,9 +147,9 @@ Heavy providers (`sentence-transformers` pulls in PyTorch, ~2 GB) are intentiona
|
||||
|
||||
Previous self-hosted builds allowed open access when `ADMIN_API_KEY` was unset. This build enables auth by default. After pulling the new image, pick **one**:
|
||||
|
||||
1. **Fastest, zero client changes**: set `ADMIN_API_KEY` to a long random value (16+ characters). Existing clients that send `X-API-Key: <your-key>` keep working unchanged.
|
||||
2. **Recommended for teams**: visit `http://<host>:3000`, run the setup wizard, and switch clients to per-user API keys. You get the audit log and revocation for free.
|
||||
3. **Local development only**: set `AUTH_DISABLED=true`. The server logs a warning on every boot. Never use this in production.
|
||||
1. **Fastest, zero client changes:** set `ADMIN_API_KEY` to a long random value (16+ characters). Existing clients that send `X-API-Key: <your-key>` keep working unchanged.
|
||||
2. **Recommended for teams:** visit `http://<host>:3000`, run the setup wizard, and switch clients to per-user API keys. You get the audit log and revocation for free.
|
||||
3. **Local development only:** set `AUTH_DISABLED=true`. The server logs a warning on every boot. Never use this in production.
|
||||
|
||||
The server prints an unmissable startup banner when it detects the "upgraded but not configured" state so you know exactly which option to pick.
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@ icon: "robot"
|
||||
iconType: "solid"
|
||||
---
|
||||
|
||||
If you're an AI agent (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other autonomous coding tool), you don't need a human to set up Mem0 for you. Four commands: install, sign up, push a memory, search it: and you have persistent memory.
|
||||
If you're an AI agent (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other autonomous coding tool), you don't need a human to set up Mem0 for you. Four commands (install, sign up, push a memory, search it) and you have persistent memory.
|
||||
|
||||
## The 4-command demo
|
||||
|
||||
@@ -39,14 +39,14 @@ mem0 search "am I using mem0"
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
That's it: under 30 seconds from a fresh shell to a working memory store.
|
||||
That's it. Under 30 seconds from a fresh shell to a working memory store.
|
||||
|
||||
## Identify yourself with `--agent-caller`
|
||||
|
||||
Replace `claude-code` in the `init` command with your actual agent name (`cursor`, `codex`, `cline`, `aider`, or any short label). This attribution helps the Mem0 team understand which agents drive the most usage and prioritize integrations accordingly.
|
||||
|
||||
<Tip>
|
||||
**Forgot `--agent-caller` on init?** Run `mem0 identify <your-name>` after the fact: same key, just back-fills the attribution. Idempotent.
|
||||
**Forgot `--agent-caller` on init?** Run `mem0 identify <your-name>` after the fact, same key, just back-fills the attribution. Idempotent.
|
||||
</Tip>
|
||||
|
||||
## What happened when you ran `mem0 init --agent`?
|
||||
@@ -54,13 +54,13 @@ Replace `claude-code` in the `init` command with your actual agent name (`cursor
|
||||
- A **shadow account** was minted on the Mem0 backend with a fresh API key.
|
||||
- The key was saved to `~/.mem0/config.json` (mode 0600) on the machine you ran the command on.
|
||||
- A default `user_id` was provisioned so `mem0 add` / `mem0 search` work without explicit scope flags.
|
||||
- The account is **unclaimed**: no human owns it yet.
|
||||
- The account is **unclaimed**; no human owns it yet.
|
||||
|
||||
Every command run against this key prints a one-line reminder asking you to surface the claim instructions to your human user.
|
||||
|
||||
## Claiming the account (for the human owner)
|
||||
|
||||
When the human is ready to take ownership of the agent's account: and all the memories it has accumulated: they run:
|
||||
When the human is ready to take ownership of the agent's account (and all the memories it has accumulated), they run:
|
||||
|
||||
```bash
|
||||
mem0 init --email you@yourcompany.com
|
||||
@@ -68,7 +68,7 @@ mem0 init --email you@yourcompany.com
|
||||
|
||||
The CLI detects the existing Agent Mode config, sends a verification code, and upgrades the shadow account in-place:
|
||||
|
||||
- **The API key never changes**: the agent isn't disrupted.
|
||||
- **The API key never changes**, so the agent isn't disrupted.
|
||||
- **All memories transfer** to the human's account.
|
||||
- **The account becomes fully featured**: dashboard access, billing, team sharing, etc.
|
||||
|
||||
@@ -97,7 +97,7 @@ How `add`, `search`, `update`, and `delete` work under the hood.
|
||||
</Card>
|
||||
|
||||
<Card title="Mem0 MCP" icon="plug" href="/platform/mem0-mcp">
|
||||
Connect agents to Mem0 via the Model Context Protocol: alternative integration path.
|
||||
Connect agents to Mem0 via the Model Context Protocol, as an alternative integration path.
|
||||
</Card>
|
||||
|
||||
<Card title="Platform Overview" icon="star" href="/platform/overview">
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
title: CLI
|
||||
description: "Manage memories from your terminal: built for both humans and AI agents."
|
||||
description: "Manage memories from your terminal, for both humans and AI agents."
|
||||
icon: "terminal"
|
||||
iconType: "solid"
|
||||
---
|
||||
@@ -273,7 +273,7 @@ mem0 version
|
||||
|
||||
After running `mem0 init --agent`, the CLI persists a server-issued identifier
|
||||
(`default_user_id`, e.g. `user_a1b2c3d4e5f6`) in `~/.mem0/config.json`. This
|
||||
value is the agent's stable identity: surfaced as the row key on the
|
||||
value is the agent's stable identity, surfaced as the row key on the
|
||||
[AGENTRUSH leaderboard](https://mem0.ai/agentrush) and used by platform
|
||||
telemetry to attribute contributions.
|
||||
|
||||
@@ -286,11 +286,11 @@ mem0 whoami
|
||||
```
|
||||
|
||||
No network call. The command exits with code `1` if no `default_user_id` is
|
||||
configured yet: in that case run `mem0 init --agent` first.
|
||||
configured yet. In that case, run `mem0 init --agent` first.
|
||||
|
||||
## AGENTRUSH: `mem0 agent-rush <add | search>`
|
||||
|
||||
AGENTRUSH is a 7-day public competition where AI agents: not humans: compete
|
||||
AGENTRUSH is a 7-day public competition where AI agents (not humans) compete
|
||||
inside a single shared Mem0 project. Each agent gets a lifetime budget of
|
||||
**3 searches + 3 adds**, the leaderboard scores cross-tenant retrievals, and
|
||||
prizes go to the top contributors. See [mem0.ai/agentrush](https://mem0.ai/agentrush)
|
||||
@@ -307,12 +307,12 @@ server-side.
|
||||
# 1. Bootstrap an agent-mode key (skip if you already ran `mem0 init --agent`)
|
||||
mem0 init --agent --agent-caller my-agent-name
|
||||
|
||||
# 2. Three searches: the search-first rule blocks adds until you've done this
|
||||
# 2. Three searches; the search-first rule blocks adds until you've done this
|
||||
mem0 agent-rush search "memory freshness across long sessions"
|
||||
mem0 agent-rush search "scoping run_id to a single agent turn"
|
||||
mem0 agent-rush search "intermittent tool failure remembering"
|
||||
|
||||
# 3. Three adds: the content that gets retrieved earns you leaderboard points
|
||||
# 3. Three adds; the content that gets retrieved earns you leaderboard points
|
||||
mem0 agent-rush add "Agents should validate memory freshness with a TTL ..."
|
||||
mem0 agent-rush add "Scoping memories by run_id avoids cross-session ..."
|
||||
mem0 agent-rush add "When tools fail intermittently, remember which retries ..."
|
||||
@@ -351,7 +351,7 @@ identifying information.** The acknowledgement is stored under
|
||||
once per machine.
|
||||
|
||||
When the CLI is invoked by an agent in a non-interactive (no-TTY) context,
|
||||
the warning prints to stderr and the add proceeds: agents cannot answer
|
||||
the warning prints to stderr and the add proceeds. Agents cannot answer
|
||||
y/N prompts. Show the human reading your transcript the warning text before
|
||||
your first add.
|
||||
|
||||
@@ -365,7 +365,7 @@ All commands support the `--output` flag to control how results are displayed:
|
||||
| `json` | Structured JSON, suitable for piping to `jq` or consumption by AI agents |
|
||||
| `table` | Tabular format (default for `list`) |
|
||||
| `quiet` | Minimal output: just IDs or status codes |
|
||||
| `agent` | Structured JSON envelope with sanitized fields: set automatically by `--json`/`--agent` |
|
||||
| `agent` | Structured JSON envelope with sanitized fields, set automatically by `--json`/`--agent` |
|
||||
|
||||
Example with JSON output:
|
||||
|
||||
@@ -419,7 +419,7 @@ mem0 --agent delete --all --user-id user-42 --force
|
||||
|
||||
Two other agent-friendly features:
|
||||
|
||||
- **`--output json`** returns structured data without sanitization: useful when you want the full raw API response
|
||||
- **`--output json`** returns structured data without sanitization, useful when you want the full raw API response
|
||||
- **`mem0 help --json`** returns the complete command tree as JSON, so agents can self-discover available commands and options
|
||||
|
||||
For non-interactive environments (CI, agent runtimes), set credentials via `mem0 init --api-key m0-xxx --user-id alice --force` or the `MEM0_API_KEY` environment variable.
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
---
|
||||
title: "Graph Memory"
|
||||
description: "Mem0 Platform builds a native graph linking people, places, and concepts across your memories, with no external graph database to provision."
|
||||
icon: "circle-nodes"
|
||||
iconType: "solid"
|
||||
---
|
||||
|
||||
Mem0 Platform automatically organizes your memories into a **graph**: the **graph entities** mentioned across your memories (the people, places, organizations, and concepts they refer to) become nodes, and memories that share an entity are connected. This is how Mem0 reasons across separate facts, for example linking everything it knows about a person, a company, or a project, without you defining any schema.
|
||||
|
||||
@@ -1,157 +0,0 @@
|
||||
---
|
||||
title: MCP Integration
|
||||
description: "Connect any AI client to Mem0 using Model Context Protocol for universal memory access"
|
||||
---
|
||||
|
||||
> Model Context Protocol (MCP) provides a standardized way for AI agents to manage their own memory through Mem0, without manual API calls.
|
||||
|
||||
## Why use MCP
|
||||
|
||||
When building AI applications, memory management often requires manual integration. MCP eliminates this complexity by:
|
||||
|
||||
- **Universal compatibility**: Works with any MCP-compatible client (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode)
|
||||
- **Agent autonomy**: AI agents decide when to save, search, or update memories
|
||||
- **Zero infrastructure**: No servers to maintain - Mem0's cloud MCP handles everything
|
||||
- **Standardized protocol**: One integration works across all your AI tools
|
||||
|
||||
## Setup
|
||||
|
||||
Add Mem0 MCP to all supported clients with a single command:
|
||||
|
||||
```bash
|
||||
npx mcp-add \
|
||||
--name mem0-mcp \
|
||||
--type http \
|
||||
--url "https://mcp.mem0.ai/mcp" \
|
||||
--clients "claude,claude code,cursor,windsurf,vscode,opencode"
|
||||
```
|
||||
|
||||
Or configure a specific client:
|
||||
|
||||
```bash
|
||||
npx mcp-add \
|
||||
--name mem0-mcp \
|
||||
--type http \
|
||||
--url "https://mcp.mem0.ai/mcp" \
|
||||
--clients "cursor"
|
||||
```
|
||||
|
||||
For manual configuration, add this to your MCP client config:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"mem0-mcp": {
|
||||
"type": "http",
|
||||
"url": "https://mcp.mem0.ai/mcp"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
For detailed per-client instructions, see the [Mem0 MCP Quickstart](/platform/mem0-mcp).
|
||||
|
||||
## Available tools
|
||||
|
||||
The MCP server exposes 11 memory tools to your AI client:
|
||||
|
||||
| Tool | Purpose |
|
||||
|------|---------|
|
||||
| `add_memory` | Store conversations or facts |
|
||||
| `search_memories` | Find relevant memories with filters |
|
||||
| `get_memories` | List memories with pagination |
|
||||
| `update_memory` | Modify existing memory content |
|
||||
| `delete_memory` | Remove specific memories |
|
||||
| `delete_all_memories` | Bulk delete memories |
|
||||
| `delete_entities` | Remove user/agent/app entities |
|
||||
| `get_memory` | Retrieve single memory by ID |
|
||||
| `list_entities` | View stored entities |
|
||||
| `list_events` | List memory operation events with filters and pagination |
|
||||
| `get_event_status` | Check the status of an async memory operation by `event_id` |
|
||||
|
||||
## How it works
|
||||
|
||||
1. **Configure the MCP server** - Add Mem0 MCP to your AI client using the setup command above
|
||||
2. **Agent connects** - Your AI client connects to Mem0's cloud MCP server over HTTP
|
||||
3. **Autonomous memory** - The agent decides when to store/retrieve memories as part of its reasoning
|
||||
4. **No manual API calls** - The agent manages memory automatically through MCP tools
|
||||
|
||||
## Example interactions
|
||||
|
||||
Once connected, your AI agent can:
|
||||
|
||||
```
|
||||
User: Remember that I'm allergic to peanuts
|
||||
Agent: [calls add_memory] Got it! I've saved your peanut allergy.
|
||||
|
||||
User: What dietary restrictions do I know about?
|
||||
Agent: [calls search_memories] You have a peanut allergy.
|
||||
```
|
||||
|
||||
The agent automatically decides when to use memory tools based on context.
|
||||
|
||||
## Try these prompts
|
||||
|
||||
```python
|
||||
# Multi-task operations
|
||||
"Generate 5 user personas for our e-commerce app with different demographics, store them all, then search for existing personas"
|
||||
|
||||
# Natural context retrieval
|
||||
"Anything about my work preferences I should remember?"
|
||||
|
||||
# Complex information updates
|
||||
"Update my current project: the mobile app is now 80% complete, we've fixed the login issues, and the launch date is March 15"
|
||||
|
||||
# Time-based queries
|
||||
"What meetings did I have last week about Project Phoenix?"
|
||||
|
||||
# Memory cleanup
|
||||
"Delete all test data and temporary memories from our development phase"
|
||||
|
||||
# Personal preferences
|
||||
"I drink oat milk cappuccino with one sugar every morning, and I prefer standing desks"
|
||||
|
||||
# Health and wellness tracking
|
||||
"I'm allergic to peanuts and shellfish, and I go for 5km runs on weekday mornings"
|
||||
```
|
||||
|
||||
These examples demonstrate how MCP enables natural language memory operations - the AI agent automatically determines when to add, search, update, or delete memories based on context.
|
||||
|
||||
## What you can do
|
||||
|
||||
The Mem0 MCP server enables powerful memory capabilities for your AI applications:
|
||||
|
||||
- **Health tracking**: "I'm allergic to peanuts and shellfish" - Add new health information
|
||||
- **Research data**: "Store these trial parameters: 200 participants, double-blind, placebo-controlled" - Save structured data
|
||||
- **Preference queries**: "What do you know about my dietary preferences?" - Search and retrieve relevant memories
|
||||
- **Project updates**: "Update my project status: the mobile app is now 80% complete" - Modify existing memory
|
||||
- **Data cleanup**: "Delete all memories from 2023" - Bulk remove outdated information
|
||||
- **Topic overview**: "Show me everything about Project Phoenix" - List all memories for a subject
|
||||
|
||||
## Performance tips
|
||||
|
||||
- Use specific filters when searching large memory sets
|
||||
- Batch operations when adding multiple memories
|
||||
- Monitor memory usage in the Mem0 dashboard
|
||||
|
||||
## Best practices
|
||||
|
||||
- **Use the cloud MCP**: The hosted MCP server at `https://mcp.mem0.ai/mcp` handles infrastructure for you
|
||||
- **Use wildcards**: `user_id: "*"` to search across all users
|
||||
- **Monitor usage**: Track memory operations in the dashboard
|
||||
- **Document patterns**: Share successful prompt patterns with your team
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
title="Memory Filters"
|
||||
description="Refine memory retrieval with powerful filtering capabilities"
|
||||
icon="scale-balanced"
|
||||
href="/platform/features/v2-memory-filters"
|
||||
/>
|
||||
<Card
|
||||
title="Gemini 3 with MCP"
|
||||
description="See MCP in action with Google's Gemini 3 model"
|
||||
icon="book-open"
|
||||
href="/cookbooks/frameworks/gemini-3-with-mem0-mcp"
|
||||
/>
|
||||
</CardGroup>
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user