Naboo vs Cognee
Cognee is open-source agent memory - what your agent has seen. Naboo is a Reasoning Layer - what your company has decided. Different categories, different jobs.
The thesis in one paragraph
Cognee is a widely adopted open-source memory library for AI agents - a Python SDK that gives an agent a knowledge graph over facts you ingest plus episodic memory across runs. It's the right tool when you're building an agent and need it to remember things. Naboo is a Reasoning Layer for the enterprise: a Decision Graph with decisions, owners, triggers, blockers, and evidence as first-class nodes - backed by deep ETL across code, tickets, PRs, Slack, and internal services, and shipped end-to-end by a Forward Deployed Agent. Different primitive, different buyer, different depth. Many enterprise stacks run both.
Side by side
Category
Naboo
Reasoning Layer (returns the decision chain)
Cognee
Open-source agent memory library (stores what the agent has seen)
Primary abstraction
Naboo
Decision Graph - decisions, owners, triggers, blockers as first-class nodes
Cognee
Knowledge graph over ingested facts + episodic memory of agent runs
What the agent gets back
Naboo
Structured chain of decisions with owners and evidence, joined across systems
Cognee
Retrieved facts + past agent context relevant to the query
Enterprise ETL depth
Naboo
Live joins across code / tickets / PRs / Slack / internal services, encoded by a Forward Deployed Agent
Cognee
You wire up your own pipelines against the Cognee Python SDK
Permission model
Naboo
Native RBAC at retrieval, mirrored from source ACLs
Cognee
Application-level - whatever you build around the library
Deployment
Naboo
On-prem or VPC, zero data egress, enterprise-grade
Cognee
Self-hosted (open-source) or Cognee Cloud
Buyer
Naboo
Enterprise R&D and Platform leaders
Cognee
Developers adding memory to their own agent stacks
Time to value
Naboo
2-4 weeks - Decision Graph shipped end-to-end by the Forward Deployed Agent
Cognee
Hours to install the library; weeks to months to reach production depth
Open source?
Naboo
Decision Graph spec is open; engine is proprietary
Cognee
Yes - Apache 2.0 core, cloud is proprietary
Compose with each other?
Naboo
Yes - Naboo can serve as the enterprise substrate a Cognee-augmented agent queries
Cognee
Yes - Cognee handles agent-side memory while Naboo grounds enterprise context
| Feature | Naboo | Cognee |
|---|---|---|
| Category | Reasoning Layer (returns the decision chain) | Open-source agent memory library (stores what the agent has seen) |
| Primary abstraction | Decision Graph - decisions, owners, triggers, blockers as first-class nodes | Knowledge graph over ingested facts + episodic memory of agent runs |
| What the agent gets back | Structured chain of decisions with owners and evidence, joined across systems | Retrieved facts + past agent context relevant to the query |
| Enterprise ETL depth | Live joins across code / tickets / PRs / Slack / internal services, encoded by a Forward Deployed Agent | You wire up your own pipelines against the Cognee Python SDK |
| Permission model | Native RBAC at retrieval, mirrored from source ACLs | Application-level - whatever you build around the library |
| Deployment | On-prem or VPC, zero data egress, enterprise-grade | Self-hosted (open-source) or Cognee Cloud |
| Buyer | Enterprise R&D and Platform leaders | Developers adding memory to their own agent stacks |
| Time to value | 2-4 weeks - Decision Graph shipped end-to-end by the Forward Deployed Agent | Hours to install the library; weeks to months to reach production depth |
| Open source? | Decision Graph spec is open; engine is proprietary | Yes - Apache 2.0 core, cloud is proprietary |
| Compose with each other? | Yes - Naboo can serve as the enterprise substrate a Cognee-augmented agent queries | Yes - Cognee handles agent-side memory while Naboo grounds enterprise context |
FAQ
Is Cognee a Naboo competitor?
Adjacent, not the same category. Cognee is a widely adopted open-source agent memory library - it lets a developer give an agent a knowledge graph over ingested facts plus episodic memory across runs. Naboo is a Reasoning Layer for the enterprise: a Decision Graph with owners, triggers, blockers, and evidence, backed by deep ETL across code, tickets, PRs, Slack, and internal services. Cognee is what you reach for when you want your agent to remember things. Naboo is what you reach for when your agent needs to know what your company already decided.
Isn't a knowledge graph a knowledge graph?
Not really. Cognee's graph is what the agent has been shown - facts, entities, and relationships extracted from documents you ingest. Naboo's Decision Graph is what your company decides and ships - decisions as nodes, with owners, triggers, blockers, and evidence pulled live from source systems. One is a memory substrate for an agent's inputs. The other is a queryable model of your organization's operating state. Different primitives, different jobs.
When is Cognee the right choice?
When you're building an agent product yourself and need long-term memory across sessions, entity extraction from arbitrary text, or a graph memory abstraction inside your Python stack. It's a developer library - fast to install, no enterprise deployment overhead. Great for prototyping, chat products, personal assistants, and startups shipping their own agent.
When is Naboo the right choice?
When you have enterprise systems - Jira, GitHub, Slack, internal services, code, tickets - and your AI agents need to reason across them the way your engineers do. When the answer to a business question isn't a document but a chain of decisions nobody wrote down. When you need on-prem or VPC deployment, permission mirroring from source ACLs, and a Forward Deployed Agent who encodes the ETL joins your team knows by heart. That's Naboo's job, not Cognee's.
Can I run both?
Yes. A common architecture: Cognee (or a similar memory library) handles per-agent memory - what the agent remembers across turns and sessions - and Naboo provides the enterprise substrate the agent queries whenever it needs to know your company's state. The two solve different sides of the same problem: memory of the conversation vs. knowledge of the organization.
What about traction? Cognee has 70+ companies live.
That's true - Cognee has strong open-source adoption among developer teams. Naboo's traction is a different shape: fewer, larger deployments at Fortune 500 R&D orgs where the customer needs on-prem, permission mirroring, and a Decision Graph that reflects their private definitions of shipped, blocked, and owned. Different distribution, different buyer, different depth of engagement.
Related reading
Reasoning Layer for Enterprise AI Agents
Definition, architecture, and the two tiers - Topic Graph and Decision Graph.
Read moreDefinitionWhat is a Decision Graph for AI Agents?
Decisions as first-class nodes - owners, triggers, blockers, evidence. The primitive AI agents need to act.
Read moreHow-toHow to Build a Decision Graph
Seven concrete steps from elicitation to a queryable graph. Two to four weeks via Forward Deployed Agent.
Read moreCFO briefHow to Reduce LLM Token Costs
Don't meter the waste, cut the cause. Reasoning Layer vs observability and caching, compared.
Read moreGuideImprove AI Agent Accuracy
Accuracy is upstream of evals. Four causes of enterprise AI inaccuracy and how a Reasoning Layer fixes them.
Read moreArchitectureConnect Enterprise Data Sources
Live joins vs stale copies. Warehouse, ETL, knowledge graphs, and Reasoning Layer compared.
Read moreGuideOvercome GenAI Hallucinations
Hallucinations are a context-handoff problem, not a model problem. Four causes, one upstream fix.
Read moreROIHow Naboo Saves Cost
Five places Naboo cuts cost in enterprise AI deployments. Four-minute explainer video.
Read moreHubCompare Naboo
Every category enterprise AI buyers weigh against the Reasoning Layer - in one place.
Read moreComparisonNaboo vs Helicone
Reasoning Layer cuts the cause; Helicone measures the waste. Composable.
Read moreComparisonNaboo vs Langfuse
Different layers. Langfuse versions + traces; Naboo grounds the agent.
Read moreComparisonNaboo vs LlamaIndex
RAG framework vs Reasoning Layer. When to use each.
Read moreComparisonNaboo vs LangChain
Orchestration vs substrate. Compose them.
Read moreComparisonNaboo vs Hyperspell
Cloud 'Company Brain' API vs enterprise Reasoning Layer with on-prem, RBAC, and FDA.
Read moreComparisonNaboo vs Modern Relay
Git-style graph DB primitive vs a complete Reasoning Layer shipped end-to-end.
Read moreBackgroundWhy retrieval was the wrong foundation
How enterprise AI agents got built on RAG, why it falls short, and what a reasoning layer fixes.
Read moreComparisonNaboo vs RAG
Retrieval vs reasoning - head-to-head benchmarks, architecture, and when to use each.
Read moreComparisonNaboo vs Glean
Enterprise search vs reasoning layer - when each fits.
Read moreConceptAI Search vs Reasoning Layer
Search returns links; the reasoning layer returns the chain. When to use which.
Read moreCategoryAgent Memory vs Reasoning Layer
Memory recalls what the agent saw. A Reasoning Layer returns what the company decided. Different primitives, different jobs.
Read moreCase studyGlobal-E case study
How Global-E (NASDAQ: GLBE) gave AI agents secure access to customer data.
Read moreComparisonCompare alternatives
Naboo vs other enterprise AI agent infrastructure platforms.
Read moreMemory is not knowledge of the enterprise.
If your agent needs to know what your company already decided - not just what it saw last turn - talk to us. We'll ship the Decision Graph in 2-4 weeks.