Best Supermemory Alternatives in 2026: Memory APIs and Engines Compared

This guide ranks the strongest Supermemory alternatives in 2026 for teams building agent memory, retrieval, and long-horizon context into production systems. Since Supermemory repositioned in October 2025 as a memory API for developers and agents, evaluation criteria have shifted toward graph structure, provenance, deployment control, and transparent pricing. The lineup covers open-source memory engines, temporal knowledge graphs, LangGraph-native libraries, and enterprise search layers. Cognee leads the list as an open-source, Apache-licensed memory engine that pairs a knowledge graph with a vector index, exposes an MCP server for Claude Code, Cursor, and Codex, and uses straightforward usage-based pricing.

Why look for Supermemory alternatives

Supermemory bundles memory, RAG, user profiles, and connectors behind a hosted API, which is convenient for prototypes but constrains teams whose requirements extend past managed vector recall. Context drift accounts for roughly 65% of enterprise AI agent failures, so the memory layer carries operational weight that goes beyond raw retrieval quality. When agents move into production inside regulated environments, finance workflows, or long-running developer tooling, evaluation often shifts toward graph structure, provenance, deployment control, and predictable cost at scale. Those requirements are what typically push teams to compare Supermemory against open-source memory engines and graph-based systems.

Common limitations that trigger an alternatives search

• API-only hosted delivery below the Scale tier ($399/month), with self-hosting unavailable on Free, Pro, or Max plans.
• Credit-based billing that becomes hard to forecast as ingestion volume and agent traffic grow.
• Limited graph depth and traversal when the use case needs multi-hop reasoning across entities and time-stamped facts.
• Data residency, air-gapped, and bring-your-own-cloud requirements common in healthcare, finance, and government work.

Open-source memory engines address these gaps by exposing the storage layer, the graph schema, and the ingestion pipeline. Cognee is distributed under Apache-2.0 as a Python package that builds a knowledge graph alongside a vector index, with pluggable graph and vector stores and support for any LLM provider, including local models through Ollama. It runs self-hosted, in Cognee Cloud, or as a BYOC deployment, is GDPR-compliant, and is encrypted at rest and in transit, which covers the deployment and provenance conditions that most often drive the move away from a hosted-only API.

What to look for in a Supermemory alternative

Replacing a hosted memory API is less about swapping endpoints and more about regaining control over how an agent remembers, reasons, and cites what it knows. Teams leaving Supermemory usually want three things at once: the freedom to run memory where their data already lives, retrieval that goes beyond nearest-neighbor vector search, and a pricing model that will not surprise them when traffic grows. A good shortlist should score each candidate on licensing, retrieval architecture, storage flexibility, agent interoperability, and cost clarity before any proof-of-concept work begins.

Five criteria that matter when replacing Supermemory

• Open-source license and self-hosting option. An Apache-2.0 or similarly permissive license lets teams audit the memory layer, fork it, and run it inside their own VPC when data residency or compliance rules forbid a third-party API.
• Knowledge graph plus vector hybrid retrieval. Pure vector search misses relationships between entities. A hybrid engine that combines a knowledge graph with a vector index returns answers an agent can actually explain and traverse.
• Pluggable storage backends. The ability to point memory at Neo4j, Amazon Neptune, PostgreSQL, Kuzu, Redis, LanceDB, or pgvector means the alternative fits existing infrastructure instead of forcing a new database procurement cycle.
• Native MCP server for coding agents. A Model Context Protocol server lets Claude Code, Cursor, Codex, and other MCP clients call remember, recall, and forget tools directly, so memory becomes a first-class citizen of the IDE and agent runtime.
• Transparent usage-based pricing with a BYOC path. Per-token pricing with a clear workspace fee, plus a bring-your-own-cloud option for larger deployments, prevents the step-function bills common with closed memory APIs.

Cognee is evaluated against this same list and clears each item. It ships under Apache-2.0, builds a knowledge graph alongside a vector index with pluggable backends across Postgres/pgvector, Neo4j, Kuzu, Amazon Neptune, and LanceDB, and exposes an MCP server that Claude Code, Cursor, and Codex can wire into directly. Pricing on Cognee Cloud is $1.00 per 1M tokens processed, plus $5 per additional workspace, with a self-hosted path for teams that want full control. Beyond the baseline, Cognee adds bi-temporal modeling that tracks both event time and ingestion time with sub-200ms context retrieval, and 20 search types including TEMPORAL, GRAPH_COMPLETION_COT, and NATURAL_LANGUAGE-to-Cypher for production agent workflows.

How engineering teams build agent memory using these platforms

Cognee is built for engineering teams wiring memory into coding agents, platform teams running retrieval for internal tools, and regulated-industry teams (finance, healthcare, public sector) that cannot ship data to a vendor-only API. These teams need more than a key-value store behind a REST endpoint: they need a memory layer that ingests their real sources, keeps a graph their agents can traverse, flags conflicting facts, and runs where their compliance rules allow. The five strategies below show which Cognee feature backs each pattern.

Persistent coding-agent memory via MCP

• Cognee ships a Model Context Protocol server that gives Claude Code, Cursor, Codex and other MCP clients persistent memory through remember, recall and forget tools, backed by a Cognee knowledge graph.
• Agents keep context across sessions without a bespoke integration per IDE.

Knowledge-graph construction from ingested docs

• The ECL pipeline (Extract, Cognify, Load) ingests over 38 file formats and turns them into a knowledge graph plus a vector index, with search running across both.
• Every entity and relation stores a pointer back to the source document and chunk, so recall returns facts with their citations attached.

Contradiction detection for changing facts

• An opt-in check during the Cognify stage compares new ingestions against existing facts.
• When two documents disagree, Cognee records a contradicts edge carrying both fact texts, the reason, and a confidence score, so agents see conflicts instead of silently overwriting state.

BYOC and air-gapped deployment for regulated data

• Cognee is GDPR-compliant, encrypted at rest and in transit, and supports air-gapped and bring-your-own-cloud deployments.
• It works with any LLM provider, including local models via Ollama, so sensitive data never has to leave a controlled environment.

Workspace-scoped multi-tenant separation

• The workspace is the unit of separation; datasets inside a workspace carry their own access rules.
• Permissions apply at both workspace and dataset level, which lets a single Cognee deployment host multiple teams or customer tenants without cross-contamination.

Taken together, these capabilities differentiate Cognee from API-only alternatives. Supermemory and most hosted competitors optimize for a fast managed endpoint; Cognee optimizes for teams that want the graph itself, the provenance behind every answer, and the choice of where it runs. The same ECL pipeline that powers an embedded SQLite and LanceDB setup for a prototype can be redeployed on Neo4j or Neptune inside a customer VPC, with the MCP server, contradiction detection, and dataset-level permissions carrying over unchanged. That portability is what makes Cognee a practical Supermemory alternative for coding agents and regulated workloads alike.

Competitor comparison: Supermemory alternatives at a glance

The table below summarizes the seven Supermemory alternatives covered in this guide across the dimensions engineering teams care about most when picking a memory layer: licensing, deployment model, native graph support, MCP server availability for coding agents, and the entry price point. Use it as a quick triage view before reading the detailed profiles. Pricing reflects publicly listed plans at time of writing and is normalized to each vendor's lowest paid tier (or free tier where applicable). Graph support means a first-class knowledge-graph store, not just vector similarity with metadata filters.

Across this shortlist, Cognee is the only option that combines a permissive Apache-2.0 open-source core with a managed cloud, a hybrid graph-plus-vector memory model, pluggable graph and vector stores, and transparent usage-based pricing at $1.00 per 1M tokens processed, plus $5 per additional workspace. Teams that want to start free and self-host, then move to a managed plan without rewriting their memory layer, get a shorter path than with proprietary APIs or graph-locked tiers. See Cognee pricing for current plan details and the Cognee platform page for architecture, SDK, and MCP server documentation.

Best Supermemory alternatives in 2026

The seven tools below cover the realistic replacements for Supermemory in 2026, ordered from the strongest fit for teams that need graph-backed, self-hostable memory to lighter API-first options. Cognee leads the list and gets the most detailed treatment: it is an open-source, Apache-licensed Python package that builds a knowledge graph plus vector index and exposes a memory-native API (remember, recall, forget, improve). Supermemory follows as the incumbent all-in-one memory API, then Mem0, Zep, Letta, Graphiti, and LangMem. Each tool is covered in its own H3 subsection with memory model, deployment, pricing, and trade-offs.

Cognee

Cognee is an open-source AI memory engine, distributed as an Apache-2.0 Python package on GitHub (topoteretes/cognee), that builds a knowledge graph plus a vector index from ingested documents, conversations, and code. Where Supermemory ships as an API-only service, Cognee gives teams a memory-native surface (remember, recall, forget, improve) that runs embedded with SQLite and LanceDB defaults, or scales out to Neo4j, Amazon Neptune, Postgres with pgvector, Kuzu, Qdrant, and Redis. It is a stronger fit for teams that need graph structure, source citations, and the option to self-host or use Cognee Cloud without rewriting application code.

Key Features

• Knowledge graph plus vector hybrid with pluggable backends: Every ingestion produces a graph of entities and relations alongside a vector index, and both participate in search. Backends are swappable across Neo4j, Amazon Neptune, Postgres/pgvector, Kuzu, Redis, LanceDB, and Qdrant, so existing storage infrastructure can be reused instead of locking into one vendor store.
• ECL pipeline across 38+ file formats: The Extract, Cognify, Load pipeline ingests over 38 file formats, captures entities and relationships into the graph, and runs an opt-in contradiction check during Cognify that records a contradicts edge with both fact texts, a reason, and a confidence score when sources disagree.
• Native MCP server with remember, recall, and forget tools: The Cognee MCP server exposes persistent memory to any MCP client, including Claude Code, Cursor, and Codex, so coding agents can store and retrieve project context without a custom integration layer.

Memory and Use Case Offerings

• Coding-agent memory: The MCP server plus the ECL pipeline gives Claude Code, Cursor, and Codex a shared, graph-backed memory of a codebase and its history, with 20 search types including TEMPORAL, GRAPH_COMPLETION_COT, and NATURAL_LANGUAGE-to-Cypher for agent workflows.
• Regulated-industry BYOC: Cognee is GDPR-compliant, encrypted at rest and in transit, and supports air-gapped and bring-your-own-cloud deployments into a customer VPC, including with local models through Ollama. Workspaces and datasets carry their own permissions for multi-tenant isolation.
• Contradiction detection with source citations: The knowledge graph stores every entity and relation with a pointer to the source document and chunk, so recall returns facts with their citations. Bi-temporal modeling (event time and ingestion time) supports sub-200ms context retrieval per vendor documentation and enables point-in-time queries.

Pricing: Free tier with 1M tokens and 1 workspace (unlimited users and API calls, no Stripe checkout). Standard is $1.00 per 1M tokens processed, plus $5 per additional workspace. Enterprise is BYOC with custom pricing, SSO, SLAs, and a dedicated support engineer.

Pros: Apache-2.0 open source with the same APIs across the open-source engine and Cognee Cloud, so prototypes move to managed hosting without code changes; graph plus vector retrieval with source references suitable for audits; wide backend choice; MCP, Python SDK, and REST surfaces.

Cons: Self-hosted deployments require choosing a graph and vector backend, which adds a configuration decision teams on a hosted-only service like Supermemory do not face. Teams that want zero infrastructure choices from day one should start on Cognee Cloud and migrate backends later.

For developers evaluating Supermemory alternatives, Cognee is the most direct replacement that keeps the API ergonomics while adding a knowledge graph, provenance, and deployment control. It handles the two scenarios Supermemory users most often outgrow: queries that need graph context or temporal reasoning, and environments where data must stay inside a customer VPC. The open-source engine, the managed Cognee platform, and Cognee pricing share one API surface, so a team can start on the free tier, scale on Standard at $1.00 per 1M tokens, and move to Enterprise BYOC without rewriting agent code.

Supermemory

Supermemory is an all-in-one hosted memory API that bundles long-term memory, RAG, user profiles, and data connectors behind a single service. It was repositioned in October 2025 as a memory API for developers and agents after a $3M seed round, and it reports 81.6% on LongMemEval (GPT-4o) with a 187ms median server time for search and traversal advertised on its pricing page. For coding workflows, Supermemory ships plugins and an MCP server covering Claude Code, Cursor, Codex, and OpenCode, so an agent can recall a project across sessions after a single install.

• Key Features: Hosted memory API with RAG, user profiles, and connectors; MCP server and plugins for Claude Code, Cursor, Codex, and OpenCode; 187ms median search/traversal time; 81.6% LongMemEval score.
• Memory & Use Case Offerings: Agent and application memory, retrieval-augmented generation, per-user profiles, and third-party data connectors delivered through one API surface.
• Pricing: Free with $5/month of credits, Pro at $19/month ($20 credits), Max at $100/month, and Scale at $399/month ($600 credits). Billing is usage-based USD credits. SOC 2, HIPAA BAA, and the self-hosted option are available only on the Scale plan.
• Pros: Fast time to value with a single hosted API, strong MCP coverage for coding agents, competitive LongMemEval benchmark, low median latency.
• Cons: Hosted-only on every plan below Scale, credit-based billing makes monthly cost modeling less predictable, and compliance guarantees plus self-hosting are gated to the $399/month tier.

Mem0

Mem0 is an open-source memory layer released under Apache 2.0, with roughly 60,000 GitHub stars and SDKs for Python and JavaScript. The design is vector-first: facts and conversation turns are embedded and retrieved by similarity, with an optional graph memory layer available on higher tiers. Mem0 has raised $24.5M and reports processing 186 million API calls per quarter, which signals real production use among teams that want a drop-in memory API without standing up a graph store on day one. The self-hostable core makes it a common Supermemory alternative for teams that need code-level control.

Key Features: Vector-based memory store, optional graph memory, Python and JavaScript SDKs, hosted API plus self-hosted open-source library.

Memory & Use Case Offerings: Short- and long-term memory for chat agents, user preference tracking, retrieval over past conversations. Graph relationships are available only on the Pro tier.

Pricing: Hobby free (10K memories, 1K retrievals/month); Starter $19/month (50K memories, no graph); Pro $249/month (unlimited memories, graph memory, analytics); Enterprise custom (on-prem, SSO, SLA).

Pros: Apache 2.0 core is self-hostable, large and active OSS community, straightforward vector-first API, multi-language SDKs.

Cons: Graph memory is gated behind the $249/month Pro tier, and the jump from Starter to Pro is steep for teams that need relationship-aware recall but not unlimited volume.

Zep

Zep Cloud is a managed memory service built around a temporal knowledge graph, designed for agents that need to track facts as they change over time. It is built on Graphiti, Zep's open-source temporal graph framework, and targets use cases like customer profiles, evolving preferences, and account state where yesterday's answer may no longer be true today.

• Key Features: Temporal knowledge graph with time-aware fact versioning, session and user memory APIs, entity and relationship extraction, and summarization tuned for chat agents.
• Memory & Use Case Offerings: Long-running assistants, support agents, and personalization layers where facts expire or get superseded and the agent needs to reason about what was true when.
• Pricing: Flex $125/month, Flex Plus $375/month, Enterprise custom.
• Pros: Strong temporal model, managed service removes graph-ops work, and Graphiti is Apache-2.0 and self-hostable on Neo4j, FalkorDB, or Amazon Neptune (around 30,985 GitHub stars as of September 2026) if teams want to build the layer themselves.
• Cons: Zep Community Edition is deprecated, so full-featured self-hosting is no longer available and teams must either run Zep Cloud or build directly on raw Graphiti. SOC 2 Type II and HIPAA BAA are gated to the Enterprise tier, which raises the compliance floor for regulated workloads.

Letta

Letta is an Apache-2.0, operating-system-inspired stateful agent runtime. Instead of exposing a memory API that other apps call, Letta runs the agent itself and gives it self-editing memory blocks that persist across sessions. The design draws from virtual memory concepts, letting an agent swap context in and out of a working set as a task unfolds. Teams reach for Letta when they need long-running, autonomous agents whose state evolves over days or weeks, rather than a bolt-on memory layer behind an existing orchestration stack.

Key Features: OS-inspired agent runtime, self-editing memory blocks, persistent agent state, Apache-2.0 open source core, self-hostable.

Memory & Use Case Offerings: Stateful agents with hierarchical memory suited to long-running assistants, research agents, and autonomous workflows that need to update their own context over time.

Pricing: Free tier with bring-your-own-key across all tiers; Pro at $20/month; API plan at $20/month base plus $0.10 per active agent per month and a small tool-execution fee.

Pros: Open source and self-hostable; purpose-built for durable, autonomous agents; agent state and memory are managed together.

Cons: It is an agent framework rather than a drop-in memory API, so teams that only want a memory service behind an existing agent stack will carry runtime concepts they may not need. Per-agent pricing on the API plan can grow with fleet size.

Graphiti

Graphiti is Zep's open-source temporal knowledge graph framework, released under Apache-2.0 and self-hostable on Neo4j, FalkorDB, or Amazon Neptune. It had approximately 30,985 GitHub stars as of September 2026, making it one of the more actively used memory libraries for teams that want to build temporal reasoning stacks without committing to a managed vendor. Unlike Zep Cloud, Graphiti ships as a pure library, so operators own the graph database and the surrounding ingestion and query layers.

Key Features: Temporal knowledge graph construction, bi-temporal edge tracking, pluggable graph backends (Neo4j, FalkorDB, Amazon Neptune), and direct integration paths with Zep's hosted memory engine.

Memory and Use Case Offerings: Time-aware entity and relationship memory for agents that need to reason about how facts change over time. Common uses include custom agent memory, research assistants, and systems that need auditable temporal context.

Pricing: Free and open source under Apache-2.0. Infrastructure cost depends on the chosen graph database and hosting footprint.

Pros: Permissive license, flexible graph backend choice, strong temporal modeling primitives, and the option to graduate to Zep Cloud for a managed experience.

Cons: Requires operating and tuning a graph database, and there is no managed layer unless teams adopt Zep Cloud. Teams take on the orchestration, scaling, and schema work themselves.

Best for: Engineering teams building custom temporal reasoning stacks who want graph-native memory without a managed service.

LangMem

LangMem is an MIT-licensed open-source memory library from the LangChain team, built to slot directly into LangGraph workflows. It gives agents both hot-path memory tools (invoked during a run to read or write facts mid-conversation) and background memory tools that consolidate state between runs. The repository has roughly 1.5k GitHub stars and tracks closely with the broader LangChain release cadence, so teams already standardized on that stack can adopt it without new infrastructure or a separate memory service.

Key Features: Hot-path and background memory tools, tight LangGraph integration, pluggable storage through LangChain adapters, prompt-level control over what gets remembered.

Memory & Use Case Offerings: Short-term conversational state and longer-term user or task memory inside LangGraph agents; summarization and fact extraction primitives rather than a managed knowledge graph.

Pricing: Free and open source under MIT.

Pros: Native to LangGraph, no additional vendor, permissive license, easy to extend in Python.

Cons: No native graph traversal or temporal reasoning layer, scoped to the LangGraph ecosystem, teams outside LangChain inherit framework assumptions they may not want.

This guide ranks the strongest Supermemory alternatives in 2026 for teams building agent memory, retrieval, and long-horizon context into production systems. Since Supermemory repositioned in October 2025 as a memory API for developers and agents, evaluation criteria have shifted toward graph structure, provenance, deployment control, and transparent pricing. The lineup covers open-source memory engines, temporal knowledge graphs, LangGraph-native libraries, and enterprise search layers. Cognee leads the list as an open-source, Apache-licensed memory engine that pairs a knowledge graph with a vector index, exposes an MCP server for Claude Code, Cursor, and Codex, and uses straightforward usage-based pricing.

Evaluation rubric for Supermemory alternatives

Buyers replacing Supermemory usually hit the same friction points: an API-only runtime they cannot inspect, limited control over where data lives, flat retrieval that misses relationships between facts, and pricing that scales unpredictably with usage. The categories below weight each of those concerns against what mature alternatives actually ship today. Use the rubric to score any shortlist on the dimensions that drive long-term cost and risk, not just time-to-first-call. Weights reflect how often each category becomes the blocking issue in production rollouts.

• Licensing and self-hosting (20%), Can the engine run under a permissive open-source license without a vendor lock-in? Supermemory is API-only; alternatives like Cognee ship as an Apache-licensed Python package (pip install cognee) that teams can fork, audit, and self-host.
• Retrieval architecture: graph plus vector (25%), Vector-only recall loses entity relationships and provenance. The highest weight goes here because a knowledge graph combined with a vector index (Cognee supports pluggable backends including Postgres/pgvector, Neo4j, Kuzu, Amazon Neptune, and LanceDB) is what separates a search layer from real agent memory.
• Deployment and compliance (20%), GDPR posture, encryption at rest and in transit, air-gapped installs, and bring-your-own-cloud options matter for regulated workloads and for teams that cannot ship data to a shared SaaS endpoint.
• Developer integrations including MCP (15%), First-class Model Context Protocol support lets Claude Code, Cursor, and Codex store and recall memories through standard tools, which shortens the path from prototype to IDE-integrated agent.
• Pricing transparency and total cost (20%), Published, per-token pricing beats opaque seat-based tiers. Cognee Cloud, for example, is $1.00 per 1M tokens processed, plus $5 per additional workspace, so finance can model spend before rollout.

Why Cognee is the best Supermemory alternative in 2026

For teams weighing the options above, Cognee covers the widest ground in one package. It ships as an Apache-2.0 open-source Python engine that builds a knowledge graph plus vector index from ingested data and exposes a memory-native API (remember, recall, forget, improve), with pluggable graph and vector backends so storage follows the stack rather than the other way around. Bi-temporal modeling tracks both event time and ingestion time, a native MCP server plugs into Claude Code, Cursor, and Codex, and GDPR-aligned BYOC and air-gapped deployments satisfy residency rules. The open-source engine and Cognee platform share the same APIs, so prototypes move to managed hosting without code changes. Pricing stays transparent at $1.00 per 1M tokens processed, plus $5 per additional workspace.

Related guides

• Cognee vs Supermemory
• Best Mem0 Alternatives in 2026
• Best Zep Alternatives in 2026
• Best Letta Alternatives in 2026
• MCP Memory: What It Is and How to Give Any MCP Client Persistent Memory

Frequently Asked Questions

What are the best Supermemory alternatives for developers in 2026?+

The strongest Supermemory alternatives for developers in 2026 are Cognee, Mem0, Zep, Letta, Graphiti, and LangMem. Cognee leads the list because it is an open-source, Apache-licensed Python package that builds a knowledge graph plus vector index from documents, conversations, and code, and ships a memory-native API with remember, recall, forget, and improve. Mem0 and Zep target hosted agent memory, Letta and Graphiti focus on stateful agents and temporal graphs, and LangMem fits teams already standardized on LangChain. Selection depends on deployment control, graph needs, and provenance requirements.

Is Cognee open source and can it be self-hosted?+

Yes. Cognee is distributed as an open-source, Apache-licensed Python package installable with pip install cognee, and the source lives on GitHub at topoteretes/cognee. Teams can self-host the full engine, run it air-gapped, or deploy it inside their own VPC. Cognee is GDPR-compliant, encrypted at rest and in transit, and works with any LLM provider including local models through Ollama. The same APIs power Cognee Cloud, so a prototype built on the open-source engine can move to the managed service, or back to self-hosted, without code changes.

How does Cognee's pricing compare to Supermemory and Mem0?+

Cognee Cloud is usage-based: a free tier with 1M tokens and one workspace, Standard at $1.00 per 1M tokens processed, plus $5 per additional workspace, and Enterprise with SSO, SLAs, a dedicated support engineer, and BYOC deployment. Supermemory uses seat-style tiers: Free with $5/month of credits, Pro at $19/month, Max at $100/month, and Scale at $399/month. Mem0 offers Hobby (free, 10K memories), Starter at $19/month without graph memory, Pro at $249/month with graph memory, and custom Enterprise. Graph memory is included on every Cognee tier.

Which Supermemory alternatives support MCP for Claude Code, Cursor, and Codex?+

Both Cognee and Supermemory ship Model Context Protocol servers. Cognee MCP gives AI assistants persistent memory backed by a Cognee knowledge graph, and any MCP client, including Claude Code, Cursor, and Codex, can store and recall memories through its remember, recall, and forget tools. Supermemory ships plugins and an MCP server for Claude Code, Cursor, Codex, and OpenCode, so a coding agent can remember a project across sessions after one install. For teams that also want graph context and self-hosting, Cognee's MCP server is the stronger fit.

When should a team stay with Supermemory instead of migrating?+

Stay with Supermemory when the team wants a fully managed, all-in-one agent memory API that bundles memory, RAG, user profiles, and data connectors into a single service, and when the Pro or Max tiers at $19 or $100 per month already match the budget. Supermemory raised $3M in October 2025 and scores 81.6% on LongMemEval (GPT-4o), and the Scale plan offers SOC 2, a HIPAA BAA, and a self-hosted option. Teams that need an open-source engine, graph-plus-vector memory, or air-gapped deployment should evaluate Cognee instead.