How to Give Claude Shared Access to Your Company's Memory in 2026

Most teams hit the same wall within days of adopting Claude at scale: one engineer stores context, runs a session, and produces useful work. A second engineer opens their own Claude instance the next morning and starts from zero. None of that accumulated reasoning, architectural decisions, or project-specific knowledge transfers between accounts. This is not a bug in Claude. It is a deliberate design: Claude's memory was built for individuals, not organizations. Understanding exactly where that boundary sits, what Claude's built-in primitives actually do, and how MCP-backed external memory layers close the gap is the core problem this guide solves. As Gartner predicts 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025, the per-account memory boundary is about to become a much more visible bottleneck. Open Source AI Review covers open-source AI infrastructure for developers and engineers making production decisions, and the memory layer question is one of the most common infrastructure gaps teams encounter when moving Claude from individual productivity tool to shared engineering platform.

What Claude's Built-In Memory Actually Does

Claude ships three overlapping memory primitives, and the terminology is often conflated in ways that create real confusion on engineering teams. The first is the conversational context window, which spans up to 200,000 tokens within a single session. Everything in that window is available to the model in real time, but it resets the moment the session ends. The second is Claude's memory feature, which as of March 2, 2026, became available on all plans including the free tier. This feature automatically generates a summarized profile from standalone chat history, updated roughly every 24 hours, and allows users to instruct Claude to remember specific facts. The third is Claude Projects, which give users an isolated workspace with uploaded knowledge files and persistent instructions that apply to every conversation inside that project.

The critical architectural constraint that most guides miss: all three of these primitives are per-account and per-project. Memory saved in one person's Claude is invisible to a colleague using their own account, and there is no shared substrate that every teammate and every agent reads from. As one engineering manager running 14 engineers on Claude Code documented in a public GitHub issue, the building blocks exist but what is missing is the connective tissue that makes memory flow between people the way knowledge flows in real teams.

Why Claude's Per-Account Boundary Creates Org-Level Problems

The individual memory problem in 2026 is largely solved. Multiple mature options exist for giving a single developer's Claude sessions continuity across conversations. The team problem is categorically different and remains open infrastructure territory. The gap becomes concrete when you model two engineers working on the same codebase with Claude Code. Engineer A spends a session debugging an obscure authentication edge case, reaches a working solution, and Claude stores context about the fix in Engineer A's memory. Engineer B opens a Claude Code session the next day, encounters the same edge case, and Claude starts from zero because B's account has no access to A's memory store.

This pattern repeats across every dimension of organizational knowledge: architecture decisions, API quirks, domain conventions, runbook entries, and accumulated debugging patterns. Two engineers on the same repo do not share Claude context, and memory resets or fragments across projects and worktrees. Every new session and every teammate re-establishes context the company already knows. Beyond the productivity cost, this creates a token spend problem: every session re-sends the same background information that the organization has already paid to surface in prior sessions. It is one reason Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027, citing unclear ROI and immature infrastructure as leading causes. The fix is not a better Claude plan tier. No subscription change addresses the per-account isolation boundary. The fix is an external shared memory layer that Claude reads from over MCP.

The Architecture: Claude's Native Memory vs. Project Knowledge vs. an External Layer

Before selecting a tool, it is worth being precise about what each memory layer is actually doing, because the three layers serve different purposes and operate at different scopes.

Claude's built-in memory retains preferences, working style, and recurring context for one user across that user's own conversations. It is single-player by design. It is also scoped to one vendor's traffic: memory from Claude conversations does not inform a Cursor session, and vice versa.

Claude Projects provide what Anthropic's support documentation describes as a second layer of memory. Each project can hold uploaded documents and custom instructions that persist across all conversations within the project. Team and Enterprise plans add sharing with view and edit permissions. However, projects hold documents, not resolved facts. If two uploaded documents disagree, or a decision from week one was overturned in week six, the project has no mechanism to resolve which is current. The model re-derives an answer from the pile on every question. Projects are a container, not a memory system.

An external shared memory layer over MCP is the architecture that fills the gap neither of the above addresses. The Model Context Protocol is an open standard that standardizes how AI models connect to external data sources and tools. As of 2026, MCP is supported by Anthropic, OpenAI, Google, and Microsoft, making it the de facto integration standard for AI agents. A memory MCP server exposes a shared store over this protocol: any MCP-compatible client that points to the same server endpoint reads from and writes to the same graph, regardless of which account or tool is making the request.

What to Look For in an MCP Memory Layer for Team Use

Not every MCP memory server is built for organizational shared access. Most of the early options in the ecosystem were designed for individual developer use, with local SQLite storage and single-user authentication. Before selecting a tool, an engineering team should evaluate against these specific requirements.

Must-Have Properties for Org-Wide Shared Memory

Remote, network-accessible storage: Local file-based or SQLite-based memory stores cannot be shared across machines or team members. A team-grade memory layer needs a server that multiple clients can connect to over a network, using a stable URL or endpoint.

Write propagation across clients: This is the specific capability that most individual memory tools lack. When Engineer A's Claude Code session calls remember() and stores a fact, Engineer B's Claude Desktop session must be able to call recall() and retrieve that same fact. The write from one client must be readable by another.

Dataset-level access controls: In a multi-engineer environment, not all memory should be universally readable. A shared memory layer needs the ability to scope datasets per user, per team, or as shared public graphs, with permissions at the read, write, delete, and share level.

Cross-client compatibility: Teams rarely use Claude in isolation. Engineers typically span Claude Code, Claude Desktop, Cursor, and other MCP-compatible clients within a single workflow. The memory layer needs to expose a consistent endpoint that all of these clients can consume.

Graph-structured retrieval: Flat vector search is sufficient for simple keyword-adjacent recall. Production engineering memory, which includes relationships between entities like services, decisions, owners, and incidents, benefits from graph-structured storage that makes those relationships queryable.

Self-hosting or data residency controls: Many engineering teams, especially in regulated industries or those working on proprietary systems, need the option to run the memory backend on infrastructure they control rather than sending knowledge to a third-party cloud.

How Two Engineers Can Share the Same Claude Context

The mechanics of shared memory over MCP are straightforward once the architecture is clear. The pattern is: one Cognee REST API server running on shared infrastructure, multiple Claude clients each configured to point their MCP connection at that server's endpoint. When Engineer A's session calls remember(), the fact is written to the shared Cognee knowledge graph. When Engineer B opens a session and calls recall(), the query hits the same graph and returns the same resolved facts.

Cognee operates in two modes. Standalone Mode runs the complete Cognee pipeline locally with its own database, which is appropriate for a single developer. API Mode connects multiple clients to a shared knowledge graph, which is the configuration that makes team memory possible. The Cognee MCP documentation is explicit about this distinction: you only need a separate Cognee REST API when you switch to API Mode to share one knowledge graph across clients.

Step-by-Step: Connecting Multiple Claude Clients to a Shared Cognee Memory Graph

The following configuration covers the API Mode path, where a shared Cognee server runs as a central endpoint that multiple engineers' Claude Desktop or Claude Code instances read from and write to.

Step 1: Stand up the shared Cognee server

Cognee publishes prebuilt Docker images on every push to main. Run the following on a machine or VM that all team members can reach over the network:

bash

# Create a .env file with your LLM credentials
echo 'LLM_API_KEY="YOUR_OPENAI_API_KEY"' > .env

# Pull and start the Cognee MCP server in HTTP transport mode
docker pull cognee/cognee-mcp:main
docker run -e TRANSPORT_MODE=http \
 --env-file ./.env \
 -p 8000:8000 \
 --rm -it cognee/cognee-mcp:main


HTTP transport is the recommended mode for team deployments where the server needs to accept connections from multiple clients simultaneously. The server will be accessible at http://YOUR_SERVER_HOST:8000.

Step 2: Configure each engineer's Claude Desktop

On macOS, open ~/Library/Application Support/Claude/claude_desktop_config.json. On Windows, open %APPDATA%\Claude\claude_desktop_config.json. You can also navigate there via Claude Desktop: Settings > Developer > Edit Config.

Add the Cognee MCP server under mcpServers, pointing at the shared server endpoint:

{
 "mcpServers": {
   "cognee": {
     "command": "npx",
     "args": [
       "mcp-remote",
       "http://YOUR_SERVER_HOST:8000/mcp"
     ]
   }
 }
}


Save the file and fully restart Claude Desktop for the configuration to load. Every engineer on the team repeats this step, all pointing to the same server host.

Step 3: Configure Claude Code clients

For Claude Code, add the MCP server using the CLI:

claude mcp add --transport http --scope local cognee http://YOUR_SERVER_HOST:8000/mcp


This registers the Cognee server for the local project scope. Use --scope user to apply it across all Claude Code sessions for that user.

Step 4: Use the remember / recall / forget / improve API

Once connected, Claude's tool calls map directly to Cognee's four-operation API. The API surface is intentionally minimal. remember() stores information in the shared graph. recall() queries it. forget() removes a dataset or wipes owned memory. improve() triggers Cognee's self-improvement pipeline, which updates the knowledge graph based on accumulated usage.

Engineer A, working on an authentication bug:

"Remember that the Shopify OAuth flow requires PKCE for enhanced security
and that Docker networking needs host.docker.internal for local testing."


Cognee stores this to the shared dataset. Engineer B, starting a session the next day:

"Recall what the team knows about the Shopify OAuth implementation."


The recall query hits the same graph and returns the resolved facts that Engineer A stored, regardless of which Claude client or account made the original write.

Step 5: Verify cross-client write propagation

The test that confirms the team setup is working: have Engineer A store a fact in their Claude Desktop session using remember, then have Engineer B open a completely separate Claude Code session and run recall for the same topic. If the fact appears in Engineer B's session, the shared graph is functioning correctly.

How AI Engineering Teams Use Shared Memory in Practice

The shared memory architecture enables several concrete use cases that are not possible with per-account memory. Teams building production agent systems have adopted Cognee across several categories of workload.

Codebase context preservation: Cognee's Claude Code plugin captures prompts, tool traces, and assistant responses into session memory, injects relevant context on every prompt, and syncs session memory into the permanent knowledge graph at session end. For a team of engineers working across multiple repos, this means architecture decisions made in one session are recoverable in future sessions without re-explanation.

Multi-agent shared state: In multi-agent architectures with a collector agent, a compliance reviewer, and a strategy synthesizer, all agents can share the same Cognee-backed memory layer. Because each agent writes to and reads from the same graph, collective knowledge accumulates rather than fragmenting across isolated contexts. This is the pattern that makes agentic pipelines stateful across runs.

Onboarding acceleration: A new engineer's Claude Code instance, once connected to the shared Cognee endpoint, has read access to the accumulated knowledge graph from all prior sessions. Architectural conventions, debugging patterns, and domain-specific facts that took the team months to surface are queryable from day one.

Cross-tool context continuity: For teams where some engineers use Claude Desktop, others use Claude Code, and others use Cursor or Cline, a shared Cognee server functions as a neutral memory backend that all MCP-compatible clients can read from. A fact stored during a Claude Desktop session is retrievable in a subsequent Cursor session without any manual transfer.

Production agent memory at scale: Cognee's pipeline volume grew 500x to over 1,000,000 runs in 2025, reflecting that the four-operation API scales to production without requiring architectural redesign as dataset size grows. Cognee reports scoring 79% on the public BEAM memory benchmark at a 100,000-token context window, against a reported state of the art of 73.4%.

The Broader MCP Memory Server Landscape

Cognee is not the only tool in this space, and for teams evaluating their options, it is worth understanding what each alternative offers and where it fits. Open Source AI Review covers all of these tools independently. The summary below is intended to help engineering teams identify which tool matches their specific requirements.

Cognee is the option this guide focuses on for team shared memory. Its lead differentiators are the MCP interface with the four-verb remember/recall/forget/improve API, graph-plus-vector hybrid storage, dataset-level multi-tenancy with permissions enforced at the read/write/delete/share level, and support for self-hosted deployment across pgvector, Neo4j, Kuzu, and LanceDB backends. The Cognee team published peer-reviewed research on optimizing knowledge graphs for LLM reasoning. Cognee closed a $7.5M seed round in February 2026, backed by investors including OpenAI co-founder Pamela Vagata. The open-source repository had approximately 26,000 GitHub stars at the time of this writing.

Mem0 is the established managed memory API. It provides a three-tier memory system across user, session, and agent scopes, backed by a hybrid store combining vectors, graph relationships, and key-value lookups. In April 2026, Mem0 updated their memory algorithm, achieving 91.6% accuracy on the LoCoMo benchmark while using roughly 3 to 4 times fewer tokens than full-context approaches. Mem0's MCP path has evolved over 2026: the standalone mem0-mcp repository was archived in March 2026, and OpenMemory, the local Docker path that replaced it, was deleted from the Mem0 monorepo in July 2026. Teams evaluating Mem0 should verify the current documented MCP entry point before adoption.

Zep / Graphiti takes a temporal knowledge graph approach. Rather than flat vector storage, Graphiti builds a knowledge graph where every memory is stored with time anchoring. This makes Zep particularly strong on temporally complex queries where the system needs to know not just what was stored but when it was relevant and how it changed. Zep's Memory MCP Server lets users connect MCP clients through their enterprise identity provider, with access controls and retention policies applied at the graph level. The Graphiti MCP server reached version 1.0 in 2025 and now has hundreds of thousands of weekly users.

OpenMemory ships a native MCP server for local persistent memory storage. It is designed for local deployment on a single machine, where the full memory store stays on the developer's hardware with no cloud dependency. The cross-client use case it supports is across tools on one machine, not across engineers on different machines. For individual developers who want privacy-first memory that works across Claude Desktop and Cursor, it is a strong option. For team-wide shared memory, it requires additional infrastructure work.

Supermemory is a TypeScript-based MCP server that syncs conversation memories across multiple LLM applications. It integrates with the Supermemory cloud service, or can be self-hosted via API key. The MCP server is built on Cloudflare Workers with Durable Objects for scalable persistent connections. Supermemory's design prioritizes recall latency and cross-tool portability. For coding agent use cases where retrieval speed is in the product's critical path, it is worth evaluating.

How to Share Memory Across Claude, ChatGPT, and Cursor

One of the most common questions from teams running heterogeneous AI tool stacks is whether a single memory layer can serve Claude, ChatGPT, and Cursor simultaneously, so that context written in one tool is readable in another. The answer in 2026 is yes, with MCP as the shared transport.

MCP is an open standard that as of 2026 is supported by Anthropic, OpenAI, Google, and Microsoft. Any MCP-compatible client can connect to a shared MCP server endpoint. Because Cognee exposes its memory layer as a standard MCP server, a Claude Desktop session, a Cursor session, and any other MCP-compatible agent can all point to the same Cognee endpoint. The remember() call from a Claude Code session writes to the same graph that a Cursor session's recall() query reads from.

The practical configuration is the same as the multi-engineer setup described above: run a single Cognee API server with HTTP transport on shared infrastructure, then add the server endpoint to each client's MCP configuration. Claude Desktop uses claude_desktop_config.json. Cursor uses its own MCP configuration file. The server does not know or care which client is making the request; it processes the tool call and updates or queries the shared graph accordingly.

For teams also running ChatGPT or other non-MCP tools, Cognee exposes a REST API alongside the MCP interface, which provides the same remember, recall, forget, and improve operations over plain HTTP. This means agents that do not natively speak MCP can still read from and write to the same shared knowledge graph through direct API calls.

Best Practices for Managing a Shared Company Memory Graph

Shared memory graphs introduce coordination problems that per-user memory does not. The following practices reflect how engineering teams have managed these graphs in production.

Use dataset scoping to separate concern areas: Cognee's multi-tenancy model supports memory graphs scoped per user, per group, or as shared public graphs. In practice, this means a team can maintain a shared organizational graph for cross-team conventions and architecture decisions, while individual engineers maintain their own scoped datasets for personal working context. Dataset-level permissions control read, write, delete, and share access across all scopes.

Write resolved facts, not raw conversation: The quality of recall is directly proportional to the quality of what is stored. Raw conversation dumps create retrieval noise. Structured, resolved facts, like architecture decisions with rationale, API behavior summaries, and confirmed bug patterns, produce more accurate recall. Instruct your Claude sessions to store conclusions rather than full conversation history.

Use improve() to keep the graph current: Cognee's improve() operation triggers a self-improvement pipeline that updates the knowledge graph based on usage. Running this periodically on the shared dataset surfaces relationships and corrections that static ingestion misses. It is the equivalent of a garbage collection pass on accumulated knowledge.

Establish a shared dataset naming convention: When multiple engineers are writing to a shared graph, consistent dataset names are critical for recall queries. If Engineer A stores facts in a dataset named auth-service and Engineer B queries authentication, the recall may fail or return incomplete results. Define and document dataset naming conventions before the team begins writing to the shared graph.

Self-host for data residency requirements: Cognee's private deployment mode activates on-premise operation with zero external API calls, supporting locally served embedding models through any OpenAI-compatible local inference endpoint. For teams working on proprietary codebases or operating in regulated industries, the self-hosted path is the appropriate choice. The platform is documented as GDPR-compliant, with data encrypted at rest and in transit.

Monitor the graph boundary for stale facts: A shared graph that accumulates without pruning will eventually return outdated context. Use forget() to remove datasets that are no longer current. For teams with rapidly changing codebases, establishing a quarterly review of the shared graph prevents Claude from recalling deprecated architectural decisions as if they were current.

Advantages of External Shared Memory Layers for Engineering Teams

The case for adding external shared memory infrastructure to a Claude deployment is grounded in specific, measurable outcomes rather than general claims about AI productivity.

Elimination of context re-establishment overhead: Every session that starts without shared memory requires re-establishing organizational context through prompts, file uploads, or pasted documentation. An external memory layer that injects relevant context automatically eliminates this overhead across every session for every engineer on the team.

Cross-session knowledge accumulation: Individual Claude sessions produce insights, debugging conclusions, and architectural decisions that disappear when the session ends. A shared graph retains these as queryable facts. The collective intelligence of the team's Claude sessions accumulates over time rather than resetting on each new conversation.

Agent coordination in multi-agent pipelines: As teams move from Claude as a single interactive assistant to Claude as part of orchestrated agent pipelines, shared memory becomes a coordination primitive. Multiple agents reading from and writing to the same graph can divide work, hand off context, and avoid duplicating effort without requiring explicit inter-agent communication protocols.

Reduced token spend at scale: Token usage stays roughly flat as the stored dataset grows in a graph-based memory architecture, whereas a long-context approach that re-sends raw documents on every session increases token spend linearly with context size. For teams running high-volume Claude API usage, the difference compounds quickly.

Consistent context across tool switches: Engineers working across Claude Desktop, Claude Code, and Cursor within a single workflow lose context at every tool boundary without a shared memory layer. A single Cognee endpoint that all tools connect to means the context from a planning session in Claude Desktop is available when the engineer switches to Claude Code for implementation.

How Cognee Addresses the Shared Memory Gap

Cognee is the open-source AI memory platform for agents. The Cognee 1.0 release on June 26, 2026, moved the project from a developer library to a production system: the same engine now ships as a managed service called Cognee Cloud, runs on a single Postgres database, is rebuilt around a high-performance Rust core, and is reorganized around a memory-native API. For teams building agents that need to remember context across sessions rather than starting over each time, Cognee 1.0 is the first version the company positions as ready for production deployment rather than experimentation.

For Claude specifically, Cognee supports Claude Desktop, Claude Code, Cursor, Codex, Windsurf, Gemini CLI, Cline, and other MCP-compatible agents. The significance is breadth: rather than locking memory to one assistant, Cognee positions itself as a portable layer that any MCP-compatible agent can adopt with minimal setup. For developers already running several agents across a workflow, a shared memory backend that each tool reads from is a more substantive capability than any single integration.

The four-operation API, remember, recall, forget, improve, maps cleanly to how engineers actually interact with organizational knowledge. The same API surface is consistent across the Cognee SDK, HTTP REST, and MCP, which means the same mental model applies regardless of whether an engineer is writing Python code, calling the API directly, or operating through Claude's tool call interface. The core memory integration can be completed in fewer than 10 lines of code.

Cognee combines vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation to make documents both searchable by meaning and connected by relationships that evolve as the knowledge base grows. This is the architectural property that distinguishes it from flat vector stores: the graph captures relationships between entities, not just semantic similarity between text chunks. For engineering knowledge, where the relationships between services, decisions, owners, and incidents matter as much as the content of individual documents, graph-structured retrieval produces materially better results.

Open Source AI Review has covered Cognee's development and production readiness in the context of the broader open-source agent memory infrastructure category. For engineering teams evaluating their options independently, Cognee's GitHub repository, documentation, and the open-access BEAM benchmark results provide the primary evidence base for an informed decision.

Getting Started with Shared Company Memory for Claude

The path from individual Claude sessions to team-wide shared memory is a configuration problem, not an infrastructure overhaul. The foundational architecture is: one shared MCP memory server, one consistent API surface, and a client configuration that every engineer on the team can replicate in under 10 minutes.

For teams starting from scratch, the practical starting point is running Cognee in Docker with HTTP transport on a machine accessible to the team, adding the server endpoint to each engineer's claude_desktop_config.json or Claude Code MCP configuration, and running a cross-engineer write/recall test to confirm that memory propagates between clients. The four-verb API, remember, recall, forget, improve, is the only interface the team needs to learn. Everything else is configuration.

For teams that need air-gapped or fully on-premise deployment, Cognee's self-hosted path with pgvector or Neo4j backends keeps all data on infrastructure the team controls, with zero external API calls during operation. For teams that prefer managed infrastructure, Cognee Cloud provides the same API surface without the operational overhead of running the server.

The individual memory problem in 2026 has been solved by Claude's built-in features and Claude Projects. The organizational memory problem, where writes from one engineer's agent become readable by another's, requires an external shared memory layer over MCP. That is the gap this guide has addressed, and Cognee is the open-source option most directly built to fill it.

FAQs About Claude Shared Company Memory and MCP Tools

What is shared memory for Claude in an organization?

Shared memory for Claude refers to an external memory layer, typically exposed over MCP, that multiple team members and agents can read from and write to regardless of which Claude account or tool they are using. Claude's built-in memory is per-account and per-session, meaning one engineer's Claude context is not visible to a colleague. An external shared memory server, such as Cognee running in API Mode, creates a common knowledge graph that any MCP-compatible client can access. This is the architectural approach Open Source AI Review covers for teams moving beyond individual Claude use.

Why doesn't Claude Projects solve the team memory problem?

Claude Projects hold uploaded documents and custom instructions that persist across conversations within a single project. Team and Enterprise plans add sharing with view and edit permissions, which provides collaborative document access. However, Projects hold source material that Claude re-reads at answer time, not resolved organizational facts. If two documents contradict each other, or a decision from an earlier session was overturned, the project has no mechanism to reconcile what is current. Projects also do not automatically learn from conversations. An external memory layer like Cognee solves both problems: it stores resolved facts and updates them as the team's knowledge evolves.

Which MCP servers give Claude access to company knowledge?

Several MCP memory servers can give Claude access to company knowledge, each with different architectural tradeoffs. Cognee provides a graph-plus-vector hybrid memory layer with a four-verb API (remember, recall, forget, improve) and dataset-level multi-tenancy, making it the strongest option for teams that need shared memory across multiple engineers and agents. Zep's Graphiti engine brings temporal knowledge graphs for use cases where the timeline of fact changes matters. Mem0 offers a managed cloud API with strong community adoption and compliance posture. Supermemory provides fast recall across multiple MCP-compatible clients. Open Source AI Review covers all of these options.

How do I share memory across Claude, ChatGPT, and Cursor?

MCP is the shared transport that makes this possible. Because Cognee exposes its memory layer as a standard MCP server, any MCP-compatible client, including Claude Desktop, Claude Code, Cursor, and other tools, can point to the same Cognee endpoint. A fact written to the shared graph during a Claude Code session is readable in a subsequent Cursor session. For tools that do not support MCP, Cognee also exposes the same remember, recall, forget, and improve operations over a plain REST API. This means non-MCP agents can participate in the same shared knowledge graph without native MCP support.

What does Cognee's remember/recall/forget/improve API do?

Cognee's SDK and MCP interface expose four operations that cover the core memory lifecycle. remember() is the main ingestion entry point: it accepts raw text, file paths, HTTP or S3 URLs, or structured data objects and stores them in the knowledge graph. recall() queries the graph through natural language, returning relevant facts with optional dataset scoping. forget() removes a specific dataset or wipes all memory owned by the current user. improve() triggers Cognee's self-improvement pipeline, which updates the graph based on accumulated usage and corrects outdated relationships. The same four verbs are consistent across the SDK, HTTP REST API, and MCP interface.

Is self-hosting Cognee required for team use, or is there a managed option?

Both options are available. Self-hosting Cognee gives teams full control over data residency, with support for pgvector, Neo4j, Kuzu, and LanceDB as storage backends and no external API calls in private deployment mode. This is the path for teams with compliance requirements or proprietary codebases. Cognee also ships Cognee Cloud, a managed service launched with the 1.0 release in June 2026, which provides the same API surface without the operational overhead of running the server infrastructure. Cognee MCP and Cognee Cloud are separate systems with different APIs and authentication schemes, so teams should review the documentation for the deployment path they intend to use.

How does Cognee handle multi-tenancy for team memory datasets?

Cognee implements multi-tenancy at the graph and trace level, not merely at the namespace or collection level. Memory graphs can be instantiated per user, per group, or as shared public graphs, with dataset-level permissions controlling read, write, delete, and share access. This isolation model is supported across all self-hostable storage backends. In practice, an engineering team can maintain a shared organizational graph for cross-team conventions alongside individual engineer graphs for personal working context, with access controls enforced at the dataset level rather than relying on application-layer separation.