7 Best Mem0 Alternatives in 2026

Published September 28, 2026 by DevTools Stack Review Editorial Team

Agent memory layers compared, graph vs vector recall, self-hosting, contradiction handling and multi-hop retrieval, plus what Mem0 still does well. This guide evaluates the leading alternatives to Mem0 for teams that need more than flat embedding recall, starting with Cognee, the graph-native open-source memory layer that leads this list on architecture, retrieval depth, and self-hosting flexibility.

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Why Teams Look for Mem0 Alternatives

Mem0 has earned genuine traction in the agent memory category. It is open-source under Apache 2.0, has accumulated tens of thousands of GitHub stars, and offers a clean SDK that integrates with most major agent frameworks. Its managed platform handles memory scoping across user, session, and agent dimensions, and its April 2026 algorithm update expanded its temporal reasoning capabilities. For teams that want a memory layer they can bolt onto an existing agent in a day, Mem0 remains a reasonable starting point.

However, several architectural and operational realities push teams to look elsewhere.

Common Reasons Teams Evaluate Mem0 Alternatives

• Graph-structured memory vs. vector recall alone: Mem0's core data model is vector-first. Its knowledge graph capabilities are gated behind the $249/month Pro tier, meaning teams on free or starter plans get semantic similarity search without relationship-aware retrieval.
• Multi-hop reasoning: Agents that need to traverse chains of related facts, "who managed the account when the contract was signed, and what was the account's status at the time?", tend to outgrow flat embedding recall quickly. Graph-native systems handle these queries structurally rather than relying on similarity to surface the right facts.
• Contradiction handling: When a fact changes, vector stores accumulate both the old and new version. Resolving which fact is current at query time is a retrieval-side problem. Graph-based systems can invalidate superseded facts at write time.
• Self-hosting and data residency: Mem0's open-source library is self-hostable, but production graph memory requires its managed platform. Teams in regulated industries or with strict data residency requirements often need a fully self-hosted stack without a cloud dependency.
• Cost at scale: Quota-gated subscription pricing can become expensive as memory operation volume grows. Teams processing high volumes of agent interactions often evaluate alternatives with different cost structures.
• Retrieval pipeline control: Mem0 abstracts retrieval away from the developer. Teams that want to tune chunking strategies, retrieval modes, or graph traversal depth need more control than a managed abstraction provides.

This guide covers seven alternatives, ranked by how well they address these gaps. Each entry includes the memory model, persistence and contradiction handling, multi-hop retrieval capability, self-hosting path, framework integrations, and honest pros and cons.

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What to Look for in a Mem0 Alternative

Evaluating agent memory infrastructure requires looking beyond raw recall accuracy. The right tool depends on how your agents use memory, what your infrastructure constraints are, and what your team is willing to operate.

Key Evaluation Criteria for Agent Memory Layers

• Memory model: Does the system use vector similarity, a knowledge graph, or a hybrid of both? Graph models support relational and causal queries that vector retrieval cannot answer reliably.
• Contradiction and staleness handling: When a fact changes, does the system invalidate the old version, accumulate both, or rely on retrieval-time ranking to surface the right one?
• Multi-hop retrieval quality: Can the system answer questions that require chaining two or more related facts, without requiring the developer to hand-craft graph traversal logic?
• Self-hosting and licensing: Is the full feature set available on a self-hosted instance, or are advanced capabilities cloud-only? What is the license?
• Framework integrations: Does the library integrate with the agent frameworks your team already uses, LangChain, LlamaIndex, LangGraph, CrewAI, or custom loops?
• Latency and cost at scale: What is the retrieval latency at the 95th percentile? How does cost scale with memory operation volume?
• Operational maturity: Is the project actively maintained? Does it have production deployments, documentation, and observability tooling?

All seven tools in this list are evaluated against these dimensions. The comparison table below provides a side-by-side summary before the individual entries go deeper.

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Competitor Comparison: Agent Memory Layers

The table below summarizes how each tool positions across the key evaluation dimensions. Use it as a quick reference before reading the detailed entries.

Cognee is the only tool on this list that combines a fully self-hostable graph-plus-vector architecture with a pipeline that refines the knowledge graph after ingestion, ships 14 retrieval modes, and operates without a mandatory cloud dependency across its entire feature set.

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Best Mem0 Alternatives in 2026

1. Cognee

Cognee is an open-source memory layer for AI agents that builds a knowledge graph and vector store from ingested data through its ECL, Extract, Cognify, Load, pipeline. Rather than storing memories as flat embeddings that are retrieved by similarity alone, Cognee extracts entities and relationships from raw data, commits them as graph edges alongside vector embeddings, and runs a memify stage that refines the graph based on feedback and interaction traces. The result is a memory layer that agents can query structurally, not just semantically.

Best for: Teams that need graph-structured memory with full self-hosting control, multi-framework compatibility, and retrieval that reasons over relationships between facts rather than surface-level similarity.

Key Features:

• ECL Pipeline: The cognify stage runs a six-stage pipeline, classify documents, check permissions, extract chunks, extract entities and relationships via LLM, generate summaries, embed into vector store, and commit graph edges. Only new or updated files are processed on re-runs, keeping ingestion costs predictable.
• 14 Retrieval Modes: Cognee ships retrieval strategies ranging from classic RAG to chain-of-thought graph traversal, giving agents the ability to reason over the structure of stored knowledge rather than just its semantic surface.
• Self-Improving Graph (memify): After ingestion, the memify layer refines graph connections based on feedback and interaction traces. This moves memory from static storage toward an adaptive knowledge layer that strengthens frequently accessed connections and prunes stale nodes.
• Format and Integration Breadth: The ECL pipeline processes over 38 file formats. Integrations cover LangChain, LlamaIndex, LangGraph, CrewAI, OpenAI SDK, Google ADK, Claude tooling, Cursor, Cline, and n8n. MCP support allows any MCP-compatible agent framework to connect to a Cognee instance as a shared memory service.
• Embedded-First Defaults: Cognee's default storage backends, SQLite, LanceDB, and Ladybug, run embedded with minimal resource overhead. No external infrastructure is required to get started, and no mandatory cloud dependency exists for any feature.
• Multi-Agent Shared Memory: Cognee's MCP integration allows multiple agents running different models to read from and write to the same Cognee instance through a shared protocol, making it practical for multi-agent pipelines.

Memory Use Cases:

• Persistent conversational memory across sessions, with entity and relationship extraction on each interaction
• Multi-agent shared state in LangGraph or CrewAI pipelines via MCP
• Enterprise document and knowledge graph ingestion, processing structured and unstructured data across 38+ file types
• Self-hosted deployments for regulated industries requiring on-premises control and data residency compliance
• Codebase memory for AI coding agents, storing PR history, architectural decisions, and code structure as a queryable graph

Pricing: Open-source core is free under Apache 2.0 and runs locally without an API key. The managed cloud offering uses a token-based consumption model rather than charging by stored gigabytes. Enterprise pricing is custom, delivered as a bring-your-own-cloud engagement with dedicated support, SLAs, bi-temporal memory, and conflict resolution capabilities.

Pros:

• Full feature set available on a self-hosted instance, no cloud tier required to access graph memory
• Graph-plus-vector hybrid architecture handles relational and multi-hop queries that vector-only systems cannot resolve
• memify stage actively refines the knowledge graph, making memory self-improving rather than static
• 38+ file format support and broad framework integrations reduce adoption friction across diverse tech stacks
• MCP-native, enabling shared memory across agents and compatible tools including Cursor and Claude Code
• Backed by $7.5M seed funding led by Pebblebed, with participation backed by founders of OpenAI and Facebook AI Research

Cons:

• Python is the primary ecosystem; TypeScript and Go support are more limited
• Hybrid architecture is more operationally complex than a pure vector store, which may be more than simple personalization use cases require
• Token-based managed pricing means ingestion costs scale with data volume; teams should model costs before committing to managed tiers
• Younger project than Mem0 or LangChain, so community resources and third-party tutorials are still growing

Cognee addresses the core limitation that pushes most teams off Mem0: the ability to reason over relationships between facts without a cloud dependency or a premium tier paywall. Its ECL pipeline is the architectural mechanism that makes this possible, ingesting raw data, extracting a structured graph, and refining that graph over time. With over 12,000 GitHub stars, 80+ contributors, more than 100 companies using the platform, and approximately 6 million memories created monthly as of June 2026, Cognee is the most actively adopted graph-native memory framework for agents in 2026.

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2. Zep

Zep is a memory and context engineering platform for AI agents built on Graphiti, an open-source temporal knowledge graph engine. Its defining characteristic is that every fact stored in the graph carries explicit time metadata, when the fact became true and when it was superseded, enabling agents to reason accurately about change over time rather than just retrieving the most semantically similar chunk.

Best for: Applications where entities and relationships change over time, CRM assistants, compliance agents, medical record systems, and any use case where "who managed this account in Q1?" is a meaningful query.

Key Features:

• Bi-temporal graph model tracking when events occurred and when they were ingested
• Hybrid retrieval combining semantic search, BM25 keyword search, and graph traversal
• Edge validity windows that allow historical point-in-time queries without discarding old facts
• Sub-300ms retrieval latency by avoiding LLM calls at query time
• First-class LangGraph integration and LangChain support

Memory Use Case Offerings:

• Temporal agent memory with entity and relationship evolution
• Historical queries reconstructing knowledge states at specific past moments
• Episodic storage of raw conversation sessions as discrete graph-update triggers

Pricing: Zep Cloud offers a free plan with limited monthly credits. Paid Flex plans start at approximately $125/month as of mid-2026. Self-hosting requires running Graphiti directly with a compatible graph database backend, Neo4j, FalkorDB, or Kuzu. The Community Edition was deprecated in April 2025, so self-hosted Zep now requires assembling the Graphiti stack independently.

Pros:

• Best-in-class temporal reasoning, facts carry validity windows, not just embeddings
• Peer-reviewed architecture published as a research paper
• Graphiti (MIT-licensed) is independently usable and has 20,000+ GitHub stars
• Strong benchmark scores on temporal and single-session preference categories

Cons:

• Community Edition deprecated in April 2025; self-hosting now requires operational investment in Graphiti plus a graph database
• Credit-based pricing requires careful upfront usage estimation
• Full Zep application stack is cloud-only; graph memory without managed infrastructure requires building on raw Graphiti
• No cheap intermediate paid tier between the free plan and the Flex pricing level

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3. Letta (Formerly MemGPT)

Letta is an agent runtime, not just a memory layer. It grew out of the MemGPT research project, a paper proposing that LLM context be managed the way an operating system manages virtual memory, and has since become a full platform for building stateful agents whose memory, identity, and tools persist server-side across sessions. Letta's agents live inside Letta; they do not merely use it for storage.

Best for: Teams building long-running autonomous agents that need to manage their own memory across multiple tiers, including agents that should reason and reorganize memory during idle periods.

Key Features:

• Three-tier memory architecture: Core Memory (in-context, always present), Recall Memory (searchable conversation history outside the context window), and Archival Memory (external long-term storage)
• Sleep-time compute, agents continue reasoning and reorganizing memory during idle periods rather than only at inference time
• Git-based Context Repositories for versioned, branchable, diffable agent memory introduced in February 2026
• Stateful agents as durable server-side objects, memory, tools, and model settings persist rather than being rebuilt from a prompt on every request

Memory Use Case Offerings:

• Long-running autonomous agents with self-managed memory tiering
• Agents that learn and adapt between sessions via continual learning
• Enterprise agents requiring full data ownership and on-premises deployment

Pricing: The framework and self-hosted server are free under Apache 2.0. Letta Cloud Pro is $20/month for up to 20 stateful agents. The API developer plan starts at $20/month base plus $0.10 per active agent per month and $0.00015 per second of tool execution. Enterprise pricing is custom.

Pros:

• Full self-hosting with the complete feature set under Apache 2.0
• Sleep-time compute and git-based memory versioning are unique capabilities not offered by any other tool on this list
• Strong research lineage from UC Berkeley; continually publishing and shipping new memory mechanisms
• Agents are durable server-side objects, no stateless reconstruction on each request

Cons:

• Letta is an agent runtime, not a drop-in memory layer. Teams with an existing agent framework must migrate to or wrap Letta's runtime, not just add an SDK call
• Framework integrations are narrower than Mem0 or Cognee, Letta agents live inside Letta
• The older V1 API server is retired; teams on the older letta-ai/letta repository need to migrate to Letta Code
• Does not natively build a knowledge graph, so relational multi-hop queries require additional tooling

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4. LangMem / LangGraph Memory

LangMem is LangChain's open-source Python SDK for adding long-term memory to LangGraph agents. It extracts facts, experiences, and behavioral patterns from conversations, stores them for future retrieval, and includes a prompt optimization feature that updates agent behavior based on accumulated experience. For teams already committed to the LangChain/LangGraph ecosystem, it is the path of least resistance.

Best for: Teams fully committed to LangGraph who need basic personalization, conversation summarization, and procedural memory refinement without introducing a new infrastructure dependency.

Key Features:

• Extracts memories from live agent interactions and deduplicates them over time
• Prompt optimization, refines agent system prompts based on accumulated experience (a genuine differentiator not available in most memory layers)
• Native integration with LangGraph's BaseStore persistent storage layer
• Supports procedural, episodic, and semantic memory types through LangChain's storage primitives
• Namespace-scoped memories for user isolation and cross-agent sharing

Memory Use Case Offerings:

• Personalization memory within LangGraph agent pipelines
• Procedural memory refinement through prompt updates
• Episodic and semantic memory storage using LangGraph Platform's managed storage

Pricing: The LangMem core SDK is free and open-source under MIT. Production managed memory is available through the LangGraph Platform, where pricing applies at the platform level.

Pros:

• Zero additional infrastructure if already on LangGraph Platform, memory is built in
• Prompt optimization feature is a genuine capability absent from competing tools
• Maintained by LangChain with LangChain 1.0 docs positioning it as the long-term memory option
• Easy adoption for existing LangChain users, familiar APIs and patterns

Cons:

• No knowledge graph, retrieval is vector-based, making multi-hop relational queries structurally unsupported
• LangGraph lock-in is real; using LangMem outside a LangGraph stack loses the native storage integration
• Latest PyPI release (0.0.30, October 2025) indicates slow release cadence; the project remains pre-1.0 as of mid-2026
• Contradiction handling relies on deduplication at extraction time rather than graph-level invalidation, stale facts can persist

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5. Graphiti

Graphiti is the open-source temporal knowledge graph engine that powers Zep Cloud, available independently under the MIT license. Teams that want Zep's temporal graph architecture without the Zep Cloud dependency can self-host Graphiti directly, pairing it with a compatible graph database, Neo4j, FalkorDB, or Kuzu. As of 2026, Graphiti has approximately 30,000 GitHub stars and is a mature, actively maintained project.

Best for: Teams that want direct control over a temporal knowledge graph for agent memory, prefer assembling their own stack, and do not want to be tied to a managed cloud product.

Key Features:

• Real-time incremental updates, new episodes fold into the graph immediately without batch recomputation
• Bi-temporal model tracking both when events occurred and when they were ingested
• Edge-level validity windows: when a fact is superseded, the old edge is marked invalid rather than deleted, preserving history
• Hybrid retrieval combining vector similarity, BM25 keyword search, and graph traversal without LLM calls at retrieval time
• Sub-300ms retrieval latency by design

Memory Use Case Offerings:

• Agent memory with time-aware fact management
• Historical knowledge reconstruction at any past point in time
• Custom memory infrastructure builds for teams with specific graph database preferences

Pricing: Free and open-source under MIT. Teams pay only for the graph database backend (Neo4j, FalkorDB, or Kuzu) and their own infrastructure.

Pros:

• Full self-hosting with no managed dependency
• MIT license, permissive and production-safe
• Bi-temporal contradiction handling is among the most rigorous available in open source
• Strong retrieval performance without LLM inference at query time keeps latency low and costs predictable

Cons:

• Requires provisioning and operating a compatible graph database backend, operationally more complex than embedding-first alternatives
• No pre-built agent integration SDK at the level of Mem0 or Cognee, teams assemble their own integration layer
• Zep Cloud deprecating its Community Edition means self-hosting is now the Graphiti-direct path, which requires more infrastructure work than the old turnkey solution
• Less beginner-friendly than managed alternatives

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6. Redis-Based Memory Patterns

Redis is not a memory layer purpose-built for AI agents, but it has emerged as a practical substrate for teams that want to build one on infrastructure they already trust. Redis Agent Memory, a pattern and server implementation offered by Redis, provides a dual-tier memory stack combining short-term session context, long-term semantic memory via vector search, and semantic caching through LangCache. The 2025 Stack Overflow Developer Survey found more AI agent developers trusting Redis for memory and data storage than any other infrastructure provider.

Best for: Teams that already operate Redis, want to consolidate their memory and caching infrastructure on a single system, and are comfortable building the memory intelligence layer themselves.

Key Features:

• Vector search for semantic long-term memory alongside in-memory data structures for short-term session context
• Semantic caching via Redis LangCache, reuses semantically equivalent responses to reduce LLM API calls
• Configurable LLM-based extraction policies that pull facts, preferences, and episodic events from conversations
• Short-to-long-term promotion logic, high-signal facts move from session context to persistent memory automatically
• Integrations with 30+ agent frameworks including LangChain, LangGraph, and LlamaIndex

Memory Use Case Offerings:

• Session context management and semantic long-term memory on shared infrastructure
• Semantic caching to reduce LLM costs across high-volume deployments
• Multi-session agent memory with configurable extraction and promotion policies

Pricing: Redis OSS is free. Redis Stack and Redis Cloud have separate pricing tiers. The Agent Memory Server pattern runs on your existing Redis infrastructure; additional managed services follow Redis Cloud pricing.

Pros:

• Sub-millisecond in-memory retrieval latency, fastest raw retrieval of any option on this list
• Familiar operational model for teams already running Redis in production
• Semantic caching meaningfully reduces token costs at scale
• Broad framework support across 30+ integrations

Cons:

• No native knowledge graph, multi-hop relational queries are not structurally supported
• The memory intelligence layer (extraction, deduplication, contradiction handling) must be built and maintained by the team
• Not purpose-built for agent memory, using Redis for this purpose requires assembling multiple components into a coherent architecture
• Redis licensing (RSAL / SSPL for newer versions) has created concerns for some open-source teams and managed service providers

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7. Building on a Raw Vector Database

The lowest-level option is to build agent memory directly on a vector database, Pinecone, Qdrant, Weaviate, or Chroma, using LangChain or LlamaIndex as the retrieval abstraction layer. This approach offers maximum control over the embedding model, chunking strategy, and retrieval logic, but it transfers the full implementation burden to the engineering team.

Best for: Teams with specific retrieval requirements that no off-the-shelf memory layer satisfies, or teams building specialized agent infrastructure where control over every component is a hard requirement.

Key Features (common across leading options):

• Semantic similarity search via HNSW or IVF indexing
• Metadata filtering for scoped retrieval by user, session, agent, or time
• Hybrid search (vector + BM25) available in Weaviate and Qdrant
• LangChain and LlamaIndex wrappers for nearly all major vector databases
• Self-hosting available for Qdrant (Rust-based, excellent payload filtering), Weaviate (hybrid deployment), and Chroma (embedded or server mode)

Memory Use Case Offerings:

• Custom semantic memory retrieval tuned to specific domains
• Long-term fact storage with metadata filtering for scoped agent access
• RAG pipelines that serve as a memory substrate when combined with a retrieval orchestration layer

Pricing: Chroma is free and open-source. Qdrant has a free open-source core with a managed cloud offering. Weaviate offers open-source and cloud tiers. Pinecone is fully managed with a free tier and usage-based paid plans. All options charge at the infrastructure level rather than per memory operation.

Pros:

• Maximum control over the retrieval pipeline, embedding model, chunking, indexing, and query strategies are all configurable
• No vendor lock-in to a memory abstraction layer
• Infrastructure-level pricing is often cheaper than managed memory services at scale
• Broad community resources and mature operational tooling

Cons:

• Zero contradiction handling, graph traversal, or relationship modeling, these must all be built from scratch
• No memory extraction from raw conversations, the team must implement the extraction, deduplication, and storage pipeline
• What starts as a simple prototype tends to grow into a bespoke memory system that becomes expensive to maintain
• Not recommended as a starting point when purpose-built memory layers exist that solve these problems at the framework level

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Evaluation Rubric and Research Methodology for Agent Memory Layers

In our comparison, we evaluated each tool across seven dimensions. Teams selecting an agent memory layer should weight these categories differently depending on their use case, but no evaluation should ignore any of them.

Tools that score well across all seven dimensions rather than optimizing for one at the expense of others are the most durable choices for production agent deployments.

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Why Cognee Is the Best Mem0 Alternative for Most Teams

Most teams looking for a Mem0 alternative are looking for something specific: graph-structured memory without a cloud paywall, retrieval that handles relational and multi-hop queries, or a self-hostable stack that satisfies data residency requirements. Cognee addresses all three without the architectural compromises visible in the other alternatives on this list.

Zep offers excellent temporal graph capabilities, but its Community Edition is deprecated and its full application stack is now cloud-only. Letta is a powerful agent runtime, but it requires migrating your agent architecture, not just adding a memory SDK. LangMem is the right answer if you are fully inside LangGraph and your memory needs stop at personalization. Graphiti is the right building block if you want to assemble your own stack. Redis is the right choice if you already operate Redis and want to consolidate infrastructure. Raw vector databases give you control but transfer all implementation burden to your team.

Cognee occupies the position none of the others hold cleanly: a self-hostable, graph-native, open-source memory layer with a ready-to-use SDK, 38+ file format support, 14 retrieval modes, framework integrations across LangChain, LlamaIndex, LangGraph, CrewAI, MCP, and major AI coding tools, and a self-improving graph pipeline that refines stored knowledge over time. Its defaults run embedded with no external infrastructure required, making adoption as fast as Mem0's, without Mem0's graph-behind-a-paywall constraint.

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FAQs About Mem0 Alternatives

What is Mem0 and when does it make sense to use it?

Mem0 is an open-source, managed memory layer for AI agents that persists information across sessions, retrieves relevant context on demand, and supports multi-level memory scoping across user, session, and agent dimensions. It makes sense when you need the fastest path from zero to production memory, value the largest community and broadest ecosystem, and do not require graph-based relational retrieval on your free or starter tier. Teams that need graph features and can budget for the $249/month Pro tier may also find Mem0's managed platform practical.

How hard is it to migrate from Mem0 to Cognee?

Migrating from Mem0 to Cognee involves exporting stored memories from Mem0's API, reformatting them as documents or structured data, and ingesting them through Cognee's ECL pipeline using the cognee.add() and cognee.cognify() calls. The ingestion step also builds the knowledge graph from the raw content, so you gain graph structure on migrated data that Mem0 stored as flat embeddings. The engineering work is primarily around data export and pipeline wiring; Cognee's integrations with LangChain and LlamaIndex use familiar abstractions, reducing the re-learning curve for teams already on those frameworks.

Does a team actually need a dedicated memory layer, or is plain RAG sufficient?

Plain RAG, chunking documents, embedding them, and retrieving by similarity, works well for static knowledge bases where facts do not change and questions are answerable from individual chunks. It breaks down when agents need to reason over evolving facts, resolve contradictions between old and new information, answer multi-hop questions that require chaining related facts, or maintain user-specific state across sessions. A dedicated memory layer like Cognee handles all four cases by building a structured knowledge graph rather than a flat embedding index. Teams with simple, static retrieval needs may not need a memory layer at all; teams building agents that learn, personalize, and reason over time almost always do.

What is the difference between graph memory and vector memory for agents?

Vector memory stores facts as embeddings and retrieves them by semantic similarity to a query. It is fast, simple to implement, and works well for finding the single most relevant fact. Graph memory stores facts as nodes and edges in a knowledge graph, where relationships between entities are first-class data. When an agent asks a question that requires chaining two or more facts, "what was the project status when the previous team lead left?", graph memory can traverse the relationship structure to find the answer. Vector memory must rely on embedding similarity to surface both facts independently, which fails when the query does not obviously resemble either fact's text. Tools like Cognee and Zep use hybrid architectures that combine both, giving agents the speed of vector retrieval and the reasoning capability of graph traversal.

How does contradiction handling work in graph-native memory systems like Cognee and Zep?

In graph-native systems, each fact is stored as a graph edge with associated metadata. When a new fact contradicts an existing one, for example, a user updates their job title, the system can mark the old edge as superseded at write time, preserving history without surfacing stale information at retrieval time. Cognee's memify layer refines graph connections based on feedback and interaction traces, actively pruning stale nodes. Zep's Graphiti uses bi-temporal timestamps to record both when a fact was true and when it was recorded, enabling precise point-in-time historical queries. In contrast, vector-based systems like Mem0's free tier accumulate both the old and new fact as separate embeddings and rely on retrieval-time ranking to surface the more recent one, a less reliable approach for high-stakes applications.

Is Cognee suitable for regulated industries with strict data residency requirements?

Yes. Cognee's default storage backends, SQLite, LanceDB, and Ladybug, run fully embedded on your own infrastructure with no mandatory cloud dependency. Teams in regulated industries can run the entire Cognee stack on-premises, configure local LLM and embedding providers via Ollama, and never send data to an external API. This makes Cognee one of the few graph-native memory layers that satisfies strict data residency requirements without architectural compromise. For enterprise deployments, Cognee's custom enterprise tier includes bring-your-own-cloud support, dedicated SLAs, and additional compliance capabilities.

What frameworks does Cognee integrate with?

Cognee integrates with LangChain, LlamaIndex, LangGraph, CrewAI, the OpenAI SDK, Google ADK, Claude tooling (including Claude Code as a memory plugin), Cursor, Cline, and n8n. Its Model Context Protocol support connects Cognee as a memory service to any MCP-compatible agent environment, meaning agents built on different frameworks or models can share a single Cognee memory instance through a unified protocol. LangChain's official integration documentation lists Cognee as a supported provider, and LangGraph nodes can use Cognee as a shared memory backend without custom wiring.