Here's the dirty secret about vector databases: they're terrible at relationships. They'll tell you two documents are similar, but they won't tell you that Alice manages the team that depends on the feature blocking the launch discussed in the meeting that references the API you're redesigning.
That's why knowledge graphs are eating the agent memory stack. Vector stores give you similarity. Graphs give you understanding.
Two platforms are leading the charge in making knowledge graphs accessible for agent memory: Cognee and Graphlit. Both use graphs to give agents structured, relationship-aware memory. But they take fundamentally different approaches to how you build and deploy that memory.
Cognee is built for teams that want control and customization — choose your graph database, define your ontologies, run your own pipelines. Graphlit is built for teams that want turnkey semantic infrastructure — managed graphs, automated enrichment, production-ready from day one.
This comparison will help you understand which philosophy fits your team's needs, resources, and timeline.
TL;DR — Quick Feature Comparison
Understanding the Platforms
What is Cognee?
Cognee is a modular memory engine that gives you the building blocks to construct knowledge graph infrastructure for your AI agents. The core philosophy: you should control how your memory works.
Cognee lets you:
- Choose your graph database (NetworkX for local dev, Neo4j for production, Kuzu for performance, FalkorDB for compliance)
- Define custom ontologies (how entities and relationships are structured)
- Create memory layers scoped to specific agents or domains
- Build pipelines that transform data into graph structures
The platform is backed by published research on optimizing the interface between knowledge graphs and LLMs. Cognee supports 30+ data types — from PDFs and spreadsheets to audio and video — and provides both open-source and managed deployment options.
If you're a team with ML engineering resources and specific requirements around graph modeling, Cognee gives you flexibility and control.
What is Graphlit?
Graphlit is a turnkey semantic memory platform that handles knowledge graph infrastructure for you. We're built for teams that want to ship agents quickly without becoming experts in graph databases, vector stores, or entity extraction pipelines.
When you connect a data source to Graphlit, we:
- Ingest multimodal content (documents, audio, video, feeds, APIs)
- Extract structured entities using Schema.org standards
- Build relationship graphs automatically
- Embed content for hybrid semantic search
- Maintain per-user knowledge graphs for isolation and personalization
Graphlit abstracts the complexity of graph infrastructure behind a unified API. You get production-ready semantic memory without managing databases, scaling pipelines, or debugging embedding models.
If you're a team that wants to focus on agent logic rather than memory infrastructure, Graphlit eliminates the operational overhead.
Graph Database Flexibility vs. Abstraction
Cognee gives you choice in graph databases:
- NetworkX: Lightweight, Python-native, good for development
- Neo4j: Industry-standard graph database with Cypher query language
- Kuzu: Embedded graph database optimized for performance
- FalkorDB: Redis-based graph database for low-latency queries
- Amazon Neptune: Managed graph service with high availability
This flexibility is powerful if you have specific requirements: compliance needs that mandate on-premise Neo4j, performance requirements that favor Kuzu, or existing Neptune infrastructure you want to leverage.
The trade-off: you manage the database. Scaling, backups, query optimization, and operational complexity are your responsibility.
Graphlit abstracts graph infrastructure entirely. We manage the databases, handle scaling, optimize queries, and ensure high availability. You interact through our API — no database expertise required.
This means you can't choose your graph database. If you need that level of control, Cognee wins. But if you want to focus on building agents rather than operating infrastructure, Graphlit's abstraction is liberating.
Ontology and Schema Modeling
Cognee excels at custom ontology generation. You can define exactly how entities and relationships should be structured for your domain:
Custom Ontology Example (Construction Industry):
- Entities: Project, Site, Contractor, Material, Inspector
- Relationships: Project → (located_at) → Site
Contractor → (supplies) → Material
Inspector → (approved) → Project
This is powerful for specialized domains where Schema.org entities don't capture your business logic. Cognee lets you model knowledge graphs that match your mental model exactly.
Graphlit uses Schema.org entities as the foundation — a standardized vocabulary for people, organizations, places, events, creative works, and more. We extract these automatically during ingestion:
Schema.org Entities:
- Person: Alice Chen (CTO)
- Organization: Acme Inc.
- Event: Sprint Planning (Oct 15, 2025)
- CreativeWork: Product Roadmap Q4.pdf
We also support custom taxonomies for domain-specific classification. But our entity model is more standardized than Cognee's fully custom ontologies.
Trade-off: Cognee offers more ontology control, Graphlit offers standardization and faster time-to-value.
Memory Layers and Agent Context
Cognee introduces memory layers — scoped contexts for different agents or use cases:
- Agent-scoped layers (each agent has isolated memory)
- Domain-specific layers (finance layer, legal layer, engineering layer)
- CodeGraph layer (specialized for coding copilots with code structure understanding)
This layering approach lets you build complex multi-agent systems where different agents have different views of knowledge.
Graphlit uses per-user knowledge graphs for isolation, combined with collections for organizing content:
User: alice@acme.com
Collections:
- Engineering Docs (team-wide)
- Alice's Meeting Notes (personal)
- Customer Conversations (filtered)
Our model is simpler than Cognee's layering but covers most production use cases. If you need sophisticated multi-layer memory architectures, Cognee provides more granular control.
Data Ingestion and Pipeline Architecture
Both platforms support 30+ data types, but their ingestion philosophies differ:
Cognee provides a pipeline framework where you define how data flows through:
- Data ingestion (source connectors)
- Transformation (custom processing logic)
- Graph construction (entity and relationship extraction)
- Storage (write to chosen graph + vector databases)
This gives you full control over the pipeline but requires more setup. You can optimize for your specific needs — run custom NLP models, apply domain-specific rules, or integrate proprietary data sources.
Graphlit provides managed connectors with automatic processing:
- Connect Slack, GitHub, Jira, RSS feeds, etc.
- Graphlit handles polling, webhook management, and sync
- Entities, relationships, and embeddings are extracted automatically
- No pipeline code needed — configure once, ingest continuously
Cognee is better for teams with custom processing requirements. Graphlit is better for teams that want to connect data sources in minutes without writing pipeline code.
Search and Retrieval Patterns
Cognee offers graph-native search with flexibility:
- Traverse relationships using graph queries
- Combine vector similarity with graph context
- Run custom Cypher or graph-specific query languages (depending on database choice)
- Build retrieval logic that matches your ontology
This is powerful for complex queries: "Find all projects managed by Alice where at least two contractors are behind schedule and the inspector hasn't approved in 30 days."
Graphlit provides unified hybrid search:
- Vector semantic search (find conceptually similar content)
- Keyword search with BM25 ranking
- Graph-aware context expansion (traverse entity relationships)
- Entity filters ("content where Alice discussed pricing")
- Metadata filters (time, type, source, collection)
Our search is more opinionated — we've optimized the retrieval patterns for most agent use cases. You don't write graph queries; you use filters and semantic search that leverage the graph underneath.
Trade-off: Cognee offers more query flexibility, Graphlit offers faster development with pre-optimized search.
Deployment and Operational Overhead
Cognee offers three deployment models:
- Open-source: Self-host with full control (free)
- On-premise: Deployed in your infrastructure with support ($3,500/month)
- Cognee Cloud: Managed service in beta ($25/month during beta)
The open-source option is powerful for teams with DevOps resources. But you're responsible for:
- Managing graph and vector databases
- Scaling ingestion pipelines
- Handling model inference
- Monitoring and debugging
The on-prem enterprise option includes support and architecture review.
Graphlit is cloud-native only — no self-hosting. We handle all infrastructure, scaling, and operations. This means:
- No database management
- No infrastructure scaling concerns
- Predictable performance and uptime
If you need on-premise deployment (for regulatory or security reasons), Cognee is the choice. If you want zero operational overhead, Graphlit wins.
Pricing and Total Cost of Ownership
Cognee:
- Free (open-source, self-hosted)
- $3,500/month (on-premise subscription with support)
- $25/month (cloud beta pricing)
The free option is attractive, but factor in:
- Infrastructure costs (databases, compute, storage)
- Engineering time (setup, maintenance, optimization)
- Operational overhead (monitoring, debugging, scaling)
Graphlit:
- Free tier with 100 credits ($0/month)
- Paid plans from $49/month with usage-based pricing
- All infrastructure included
- No hidden database or compute costs
For total cost of ownership, Cognee's free option can be more expensive than Graphlit's managed service once you account for engineering time and infrastructure.
Use Cases: When to Choose Each
Choose Cognee if:
- You need custom ontologies and domain-specific graph modeling
- You have ML/DevOps resources to manage infrastructure
- You require on-premise deployment for compliance or security
- You want to choose specific graph and vector databases
- You need fine-grained control over ingestion pipelines and memory layers
- You're building research projects or specialized systems
Choose Graphlit if:
- You want turnkey semantic infrastructure without operational overhead
- You need 30+ connectors working out of the box
- You prefer standardized Schema.org entities with custom taxonomies
- You're building production agents and want to ship quickly
- Your team wants to focus on agent logic, not memory infrastructure
Final Verdict: Control vs. Convenience
Cognee and Graphlit both recognize that knowledge graphs are essential for intelligent agent memory. The difference is philosophy:
Cognee gives you control. Choose your databases, define your ontologies, build your pipelines. This flexibility is powerful for teams with specific requirements and engineering resources.
Graphlit gives you infrastructure. We handle the complexity of graph databases, vector stores, entity extraction, and scaling. You focus on building agents that use semantic memory.
For teams with ML engineers who want to optimize every layer of the memory stack, Cognee is an excellent choice. For teams that want production-ready semantic infrastructure without becoming experts in graph databases, Graphlit removes the friction.
Both platforms represent the cutting edge of knowledge graph memory. The choice comes down to your team's priorities: customization (Cognee) or velocity (Graphlit).
Explore more:
Graphs don't just store memory — they model reality. Choose the platform that matches your team's ambitions.