> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Llms core

# Cognee Core Documentation

> Foundational docs for getting started, core concepts, setup, guides, examples, and contributor-facing documentation.

See the full overview at [https://docs.cognee.ai/llms.txt](https://docs.cognee.ai/llms.txt).

## Getting Started

* [Introduction](https://docs.cognee.ai/getting-started/introduction.md): Cognee organizes your data into AI memory.
* [Installation](https://docs.cognee.ai/getting-started/installation.md): Set up your environment and install Cognee
* [LLM Quickstart Skill](https://docs.cognee.ai/getting-started/llm-quickstart-skill.md): Copy a Claude-compatible skill that helps an LLM install Cognee, configure dependencies, and run a first smoke test.
* [Quickstart](https://docs.cognee.ai/getting-started/quickstart.md): Get started with Cognee quickly and efficiently

## Core Concepts

* [Overview](https://docs.cognee.ai/core-concepts/overview.md): Learn about Cognee's core concepts, architecture, and how to get started
* [Architecture](https://docs.cognee.ai/core-concepts/architecture.md): Understanding Cognee's storage architecture and system components
* [DataPoints](https://docs.cognee.ai/core-concepts/building-blocks/datapoints.md): Atomic units of knowledge in Cognee
* [Tasks](https://docs.cognee.ai/core-concepts/building-blocks/tasks.md): Building blocks of processing that transform data in Cognee pipelines
* [Pipelines](https://docs.cognee.ai/core-concepts/building-blocks/pipelines.md): Orchestrating tasks into coordinated workflows for data processing
* [PipelineContext](https://docs.cognee.ai/core-concepts/building-blocks/pipeline-context.md): Typed runtime context automatically injected into pipeline task functions
* [Remember](https://docs.cognee.ai/core-concepts/main-operations/remember.md): Store data in Cognee's permanent graph or session memory
* [Recall](https://docs.cognee.ai/core-concepts/main-operations/recall.md): Query Cognee memory with auto-routing and session-aware retrieval
* [Improve](https://docs.cognee.ai/core-concepts/main-operations/improve.md): Enrich the graph, apply feedback, and bridge session memory into permanent memory
* [Forget](https://docs.cognee.ai/core-concepts/main-operations/forget.md): Remove data from Cognee with one unified deletion operation
* [Add](https://docs.cognee.ai/core-concepts/main-operations/legacy-operations/add.md): Ingesting and preparing data for processing in Cognee
* [Cognify](https://docs.cognee.ai/core-concepts/main-operations/legacy-operations/cognify.md): Transforming ingested data into a knowledge graph with embeddings, chunks, and summaries
* [Search](https://docs.cognee.ai/core-concepts/main-operations/legacy-operations/search.md): Query your AI memory with vectors, graphs, and LLMs
* [Memify](https://docs.cognee.ai/core-concepts/main-operations/legacy-operations/memify.md): Enrich an existing knowledge graph with derived knowledge via configurable pipelines
* [Delete](https://docs.cognee.ai/core-concepts/main-operations/legacy-operations/delete.md): Remove data from your knowledge graph
* [Datasets](https://docs.cognee.ai/core-concepts/further-concepts/datasets.md): Project-level containers for organization, permissions, and processing
* [NodeSets](https://docs.cognee.ai/core-concepts/further-concepts/node-sets.md): Tagging and grouping data in Cognee
* [Ontologies](https://docs.cognee.ai/core-concepts/further-concepts/ontologies.md): Enrich your knowledge graph with external vocabularies
* [Global Context Index](https://docs.cognee.ai/core-concepts/further-concepts/global-context-index.md): Build dataset-level orientation summaries for graph completion retrieval
* [Agent Memory Decorator](https://docs.cognee.ai/core-concepts/further-concepts/agent-memory-decorator.md): Attach Cognee memory retrieval to an async agent function at the function boundary
* [Sessions and Caching](https://docs.cognee.ai/core-concepts/sessions-and-caching.md): Understanding how Cognee maintains conversational memory through sessions and cache adapters
* [Loaders](https://docs.cognee.ai/core-concepts/further-concepts/loaders.md): How Cognee handles different file formats
* [Chunkers](https://docs.cognee.ai/core-concepts/further-concepts/chunkers.md): How Cognee splits documents into smaller pieces
* [Multi-User Mode Overview](https://docs.cognee.ai/core-concepts/multi-user-mode/multi-user-mode-overview.md): How Cognee handles multiple users and data isolation between users.
* [Dataset Database Handlers: What are they?](https://docs.cognee.ai/core-concepts/multi-user-mode/dataset-database-handlers/dataset-database-handlers-what-are-they.md): How Cognee maps datasets to graph and vector storage backends
* [Dataset Database Handlers: How to use them?](https://docs.cognee.ai/core-concepts/multi-user-mode/dataset-database-handlers/dataset-database-handlers-how-to-use-them.md): How to use Dataset Database Handlers in Cognee for multi-user mode
* [Kuzu Dataset Database Handler](https://docs.cognee.ai/core-concepts/multi-user-mode/dataset-database-handlers/existing-dataset-database-handlers/kuzu.md): Handler for connecting to a Kuzu database, enabling multi-user mode.
* [LanceDB Dataset Database Handler](https://docs.cognee.ai/core-concepts/multi-user-mode/dataset-database-handlers/existing-dataset-database-handlers/lancedb.md): Handler for connecting to a LanceDB database, enabling multi-user mode.
* [PGVector Dataset Database Handler](https://docs.cognee.ai/core-concepts/multi-user-mode/dataset-database-handlers/existing-dataset-database-handlers/pgvector.md): Handler for connecting to a Postgres database with the PGVector extension, enabling multi-user mode on the Postgres database instance.
* [FalkorDB Dataset Database Handler](https://docs.cognee.ai/core-concepts/multi-user-mode/dataset-database-handlers/existing-dataset-database-handlers/falkor.md): Handler for connecting to a FalkorDB database, enabling multi-user mode on the FalkorDB database instance.
* [Neo4j Aura Dataset Database Handler](https://docs.cognee.ai/core-concepts/multi-user-mode/dataset-database-handlers/existing-dataset-database-handlers/neo4j-aura-dev.md): Handler for connecting to a Neo4j database, enabling multi-user mode on a Neo4j database instance hosted on their cloud, Neo4j Aura.
* [Qdrant Dataset Database Handler](https://docs.cognee.ai/core-concepts/multi-user-mode/dataset-database-handlers/existing-dataset-database-handlers/qdrant.md): Handler for connecting to a Qdrant database, enabling multi-user mode on the Qdrant database instance.
* [Overview](https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/overview.md): Introduction to Cognee's permission system and access control architecture
* [Datasets](https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/datasets.md): The core unit of data in Cognee's permission system
* [Principals](https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/principals.md): The unified abstraction for entities that can hold permissions in Cognee
* [Users](https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/users.md): Individual users and authentication in Cognee's permission system
* [Tenants](https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/tenants.md): Organization-level access control and permission inheritance in Cognee
* [Roles](https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/roles.md): Role-based permissions within tenants for granular access control
* [ACL](https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/acl.md): Access Control List system for permission storage and inheritance in Cognee

## Setup & Configuration

* [Setup Configuration](https://docs.cognee.ai/setup-configuration/overview.md): Configure Cognee to use your preferred LLM, embedding engine, and storage backends
* [LLM Providers](https://docs.cognee.ai/setup-configuration/llm-providers.md): Configure LLM providers for text generation and reasoning in Cognee
* [Structured Output Backends](https://docs.cognee.ai/setup-configuration/structured-output-backends.md): Configure structured output frameworks for reliable data extraction in Cognee
* [Embedding Providers](https://docs.cognee.ai/setup-configuration/embedding-providers.md): Configure embedding providers for semantic search in Cognee
* [Relational Databases](https://docs.cognee.ai/setup-configuration/relational-databases.md): Configure relational databases for metadata and state storage in Cognee
* [Vector Stores](https://docs.cognee.ai/setup-configuration/vector-stores.md): Configure vector databases for embedding storage and semantic search in Cognee
* [Graph Stores](https://docs.cognee.ai/setup-configuration/graph-stores.md): Configure graph databases for knowledge graph storage and relationship reasoning in Cognee
* [Permissions Setup](https://docs.cognee.ai/setup-configuration/permissions.md): Configure Cognee's permission system and access control
* [Security & Privacy](https://docs.cognee.ai/setup-configuration/security.md): Configure security controls for sensitive data, API key protection, and access control in self-hosted Cognee deployments
* [Logging](https://docs.cognee.ai/setup-configuration/logging.md): Understand how Cognee logs work, where log files are stored, and how to control log output
* [Adapters Overview](https://docs.cognee.ai/setup-configuration/community-maintained/overview.md): Adapters and extensions built by the Cognee community
* [Qdrant](https://docs.cognee.ai/setup-configuration/community-maintained/qdrant.md): Use Qdrant as a vector store through a community-maintained adapter
* [Redis](https://docs.cognee.ai/setup-configuration/community-maintained/redis.md): Use Redis as a vector store through a community-maintained adapter
* [FalkorDB](https://docs.cognee.ai/setup-configuration/community-maintained/falkordb.md): Use FalkorDB as both a graph and vector store (hybrid store) through a community-maintained adapter
* [Memgraph](https://docs.cognee.ai/setup-configuration/community-maintained/memgraph.md): Use Memgraph as a graph store through a community-maintained adapter
* [Pinecone](https://docs.cognee.ai/setup-configuration/community-maintained/pinecone.md): Use Pinecone as a vector store through a community-maintained adapter
* [Turbopuffer](https://docs.cognee.ai/setup-configuration/community-maintained/turbopuffer.md): Use Turbopuffer as a vector store through a community-maintained adapter

## Guides

* [Permission Snippets](https://docs.cognee.ai/guides/permission-snippets.md): Practical code snippets and scenarios for Cognee's permission system
* [Search Basics](https://docs.cognee.ai/guides/search-basics.md): Step-by-step guide to running your first Cognee search and understanding core parameters
* [Local Setup (No API Key)](https://docs.cognee.ai/guides/local-setup.md): Run Cognee entirely on your own machine using Ollama and Fastembed — no cloud API key required
* [Sessions](https://docs.cognee.ai/guides/sessions.md): Step-by-step guide to using sessions for conversational memory in Cognee
* [Agent Memory Quickstart](https://docs.cognee.ai/guides/agent-memory-quickstart.md): Minimal end-to-end example showing session memory and graph memory with cognee.agent\_memory
* [Deploy REST API Server](https://docs.cognee.ai/guides/deploy-rest-api-server.md): Deploy Cognee as a REST API server using Docker or Python
* [Time Awareness](https://docs.cognee.ai/guides/time-awareness.md): Step-by-step guide to using temporal mode for time-aware queries
* [Ontology Quickstart](https://docs.cognee.ai/guides/ontology-support.md): Step-by-step guide to using OWL ontologies to ground Cognee knowledge graphs
* [S3 Storage](https://docs.cognee.ai/guides/s3-storage.md): Step-by-step guide to using S3 for data ingestion and storage
* [Graph Visualization](https://docs.cognee.ai/guides/graph-visualization.md): Step-by-step guide to rendering interactive knowledge graphs
* [Low-Level LLM](https://docs.cognee.ai/guides/low-level-llm.md): Step-by-step guide to using acreate\_structured\_output for direct LLM interaction
* [Distributed Execution](https://docs.cognee.ai/guides/distributed-execution.md): Step-by-step guide to running Cognee pipelines across Modal containers
* [Feedback System](https://docs.cognee.ai/guides/feedback-system.md): Step-by-step guide to using feedback with Cognee sessions
* [Multilingual Ingestion](https://docs.cognee.ai/guides/multilingual-ingestion.md): Translate non-English content before building the knowledge graph
* [Self-Improvement Quickstart](https://docs.cognee.ai/guides/self-improvement-quickstart.md): Step-by-step guide to enriching memory and bridging session content with improve
* [Triplet Embeddings](https://docs.cognee.ai/guides/memify-triplet-embeddings.md): Index graph triplets as embeddings to enable TRIPLET\_COMPLETION search
* [Session Persistence](https://docs.cognee.ai/guides/memify-session-persistence.md): Persist cached conversation sessions into the knowledge graph
* [Entity Consolidation](https://docs.cognee.ai/guides/memify-entity-consolidation.md): Rewrite fragmented entity descriptions using LLM analysis of graph neighborhoods
* [Custom Data Models](https://docs.cognee.ai/guides/custom-data-models.md): Step-by-step guide to creating custom data models and using add\_data\_points
* [Custom Graph Model](https://docs.cognee.ai/guides/custom-graph-model.md): Step-by-step guide to creating custom graph models and using remember with them
* [Custom Tasks and Pipelines](https://docs.cognee.ai/guides/custom-tasks-pipelines.md): Step-by-step guide to creating custom tasks and pipelines
* [Custom Prompts](https://docs.cognee.ai/guides/custom-prompts.md): Step-by-step guide to using custom prompts to control graph extraction

## How-To Guides

* [Deployment Overview](https://docs.cognee.ai/how-to-guides/cognee-sdk/deployment.md): Deploy Cognee with flexible data storage options for any scale
* [Deployment Options](https://docs.cognee.ai/how-to-guides/cognee-sdk/deployment/deployment-options.md): Choose a Cognee deployment pattern based on writer ownership, storage, and read scaling
* [Docker Deployment](https://docs.cognee.ai/how-to-guides/cognee-sdk/deployment/docker.md): Deploy Cognee and its supporting services using Docker Compose profiles
* [EC2 Deployment](https://docs.cognee.ai/how-to-guides/cognee-sdk/deployment/ec2.md): Deploy Cognee on Amazon EC2 for traditional cloud server deployments with custom configurations
* [Kubernetes (Helm)](https://docs.cognee.ai/how-to-guides/cognee-sdk/deployment/helm.md): Deploy Cognee on Kubernetes with Helm charts for enterprise-grade, production-ready deployments
* [Modal Deployment](https://docs.cognee.ai/how-to-guides/cognee-sdk/deployment/modal.md): Deploy Cognee on Modal for serverless, auto-scaling knowledge graph processing

## Examples

* [Overview](https://docs.cognee.ai/examples/overview.md): AI systems still struggle with the messy realities of data.
* [Vertical AI Agents](https://docs.cognee.ai/examples/vertical-ai-agents.md): The future of AI is autonomous agents that execute complex, multi-step tasks in specialized domains. But agents without memory are agents without context. They can't learn from past interactions, can't understand orga...
* [Data Silos](https://docs.cognee.ai/examples/data-silos.md): Every enterprise has the same problem: valuable data locked in silos. Your CRM doesn't talk to your ERP. Your knowledge base doesn't connect to your support tickets. Your strategic documents live in SharePoint while o...
* [Edge AI](https://docs.cognee.ai/examples/edge-ai.md): Cognee is bringing AI memory to the edge with **cognee-RS**, our Rust-based SDK designed for resource-constrained devices. Run the full memory pipeline (ingestion, semantic organization, retrieval) directly on-device,...
* [Cognee Walkthrough](https://docs.cognee.ai/examples/getting-started-with-cognee.md): From Data to Interactive Memory: End-to-end tutorial with nodesets, ontologies, memify, graph visualization, and feedback system using a coding assistant example
* [Migrate from Mem0](https://docs.cognee.ai/examples/migrate-from-mem0.md): Move from Mem0 to Cognee
* [Migrate a Relational Database into a Knowledge Graph](https://docs.cognee.ai/examples/relational-db-migration.md): Convert a relational database schema and data into a searchable knowledge graph

## Cognee CLI

* [Cognee CLI Overview](https://docs.cognee.ai/cognee-cli/overview.md): Command line interface for Cognee AI memory operations

## Contributing

* [Contributing](https://docs.cognee.ai/contributing/contributing-overview.md): Contribute to the cognee project

## Additional Docs

* [Changelog](https://docs.cognee.ai/changelog.md): Recent Cognee releases