Zep (Graphiti) vs Cognee: AI Agent Memory Compared (2026)
Zep vs Cognee: AI Agent Memory Compared (2026)
Both Zep and Cognee are knowledge graph-centric approaches to agent memory, but they solve different problems. Zep (via its Graphiti engine) models temporal entity relationships — tracking how facts change over time across agent interactions. Cognee extracts structured knowledge from diverse data sources — PDFs, Slack, Notion, images, audio — and builds a queryable knowledge graph from that corpus.
If your agent needs to remember what happened and when things changed, Zep is purpose-built for that. If your agent needs to reason over a large body of existing documents and multimodal data, Cognee's pipeline architecture is a better fit. This guide compares both systems honestly so you can pick the right one. For the broader landscape, see our full comparison of the best AI agent memory systems.
Zep vs Cognee: Quick Comparison
| Zep / Graphiti | Cognee | |
|---|---|---|
| Architecture | Temporal knowledge graph | Knowledge extraction pipeline (ingest/process/store/query) |
| Core strength | Temporal entity tracking, fact validity windows | Multimodal data ingestion, 30+ connectors |
| GitHub stars | ~24K (Graphiti) | ~12K |
| License | Graphiti open source; Zep CE deprecated | Open core |
| Funding | — | €7.5M seed |
| Database | Neo4j, FalkorDB, or Kuzu | SQLite + LanceDB + Kuzu |
| Benchmark | 63.8% on LongMemEval (GPT-4o) | No published score |
| Data sources | Agent interactions (episodes) | 30+ connectors (docs, images, audio, APIs) |
| Multimodal | Text-focused | Text, images, audio transcriptions |
| SDKs | Python, TypeScript, Go | Python only |
| Self-hosting | Via Graphiti + graph DB | Local with SQLite + LanceDB + Kuzu |
| Managed cloud | Yes (Zep Cloud) | Yes (newer) |
| Compliance | SOC2 Type 2, HIPAA | — |
| Pricing | Credit-based (free tier through enterprise) | Open core; managed cloud available |
Agent Memory Architecture: Zep vs Cognee
Zep: Episode-Based Temporal Knowledge Graph
Zep is built around Graphiti, a temporal knowledge graph engine. Every piece of information enters the system as an episode — a conversation turn, an event, a task result. Graphiti decomposes each episode into entities (nodes), relationships (edges), and temporal metadata. The key differentiator is that every fact carries a validity window: a valid_from timestamp, a valid_to timestamp when it's been superseded, and an invalid_at marker when it's been explicitly contradicted.
This means Zep can answer questions like "What was the customer's plan before they upgraded in January?" — not by retrieving chunks that mention the customer, but by traversing the graph and filtering to the correct time window. The underlying graph database (Neo4j, FalkorDB, or Kuzu) handles traversal. Zep Cloud abstracts the infrastructure; self-hosting via raw Graphiti means managing the graph database yourself.
Cognee: Ingest, Process, Store, Query
Cognee takes a pipeline approach. Data flows through four stages:
- Ingest from 30+ connectors — PDFs, Slack messages, Notion pages, images, audio transcriptions, databases
- Process through configurable pipelines that chunk content, extract entities, and resolve relationships
- Store in a local-first stack: SQLite for metadata, LanceDB for vector embeddings, Kuzu for the knowledge graph
- Query using a hybrid of graph traversal and vector similarity
Where Zep is optimized for the agent interaction loop, Cognee is optimized for turning an existing corpus into structured, queryable knowledge. The tagline is "memory in 6 lines of code," and the initial onboarding is genuinely quick for document-based use cases.
Data Source Support
This is the sharpest divergence between the two systems.
Zep is agent-interaction focused. Data enters as episodes — things that happen during agent operation. If your agent has a conversation, completes a task, or observes an event, that becomes an episode that feeds the temporal knowledge graph. This is powerful for capturing operational memory, but it means Zep doesn't natively ingest from document stores, file systems, or third-party APIs.
Cognee has 30+ connectors out of the box. PDFs, Slack, Notion, Google Drive, images, audio files, databases — Cognee can pull from all of them. If you need to build a knowledge graph from an existing corpus of company documents, research papers, or multimodal data, Cognee handles the ingestion heavy lifting without custom integration work.
The practical implication: if your memory system needs to bootstrap from existing data, Cognee has a significant advantage. If your memory system primarily captures what the agent learns through interaction, Zep's episode model is more natural.
Knowledge Graph Approaches
Both systems build knowledge graphs, but the design priorities are different.
Zep is temporal-first. Every entity and relationship in the graph is time-stamped. Facts have validity windows. When a new episode contradicts an existing fact, the old fact gets an invalid_at marker and the new fact takes over with its own valid_from. This creates a layered history of what was true and when — essential for compliance workflows, audit trails, and any use case where understanding change over time matters.
Cognee is extraction-first. The graph is built by mining entities and relationships from multimodal source data. The focus is on breadth of extraction — pulling structured knowledge from unstructured documents, images, and audio — rather than modeling how facts evolve. Entities are stored in Kuzu, and the graph supports traversal queries, but temporal fact supersession isn't the primary design goal.
Neither approach is wrong. They reflect different bets about what makes agent memory useful — and both go well beyond traditional RAG architectures: Zep bets on temporal depth; Cognee bets on source breadth.
Retrieval Quality: Zep vs Cognee Benchmarks
Zep scores 63.8% on LongMemEval (using GPT-4o), the most widely cited benchmark for long-term agent memory as documented in "Memory in the Age of AI Agents". Retrieval works by traversing the temporal graph — following entity relationships, filtering by time windows, and returning facts that match the query's temporal and semantic constraints.
Cognee has no published LongMemEval score as of early 2026. Retrieval combines graph traversal with vector similarity search, which covers semantic and structural queries well. However, without a benchmark comparison, it's harder to assess retrieval quality directly against other systems.
The retrieval approaches themselves reflect the architectural differences. Zep's graph traversal excels at multi-hop temporal queries ("What changed between Q2 and Q3?"). Cognee's hybrid graph-plus-vector approach works well for grounding queries in extracted knowledge ("What does the documentation say about X?"). Different query patterns, different strengths.
Self-Hosting Agent Memory: Zep vs Cognee
Both systems can run locally, but the operational burden differs.
Zep: The Zep Community Edition has been deprecated. To self-host, you run Graphiti directly and manage your own graph database — Neo4j, FalkorDB, or Kuzu. This is a real operational commitment. Neo4j in particular requires meaningful infrastructure. Python is the primary language for self-hosted Graphiti deployments.
Cognee: Runs locally with a lighter stack — SQLite for metadata, LanceDB for vectors, Kuzu for the graph. No heavyweight external database required. The local-first design means you can get started on a laptop without provisioning infrastructure. Python only for self-hosting.
Both are Python-only for self-hosted deployments. If your stack is TypeScript or Go, you'll need Zep Cloud (which offers Python, TypeScript, and Go SDKs) or build a wrapper around Cognee's Python API.
Agent Memory Pricing: Zep vs Cognee
Zep uses credit-based pricing on Zep Cloud:
- Free: 1,000 credits to start
- Flex: $25/month for 20,000 credits
- Enterprise: Custom pricing with SOC2 Type 2 and HIPAA compliance
Cognee follows an open-core model. The core framework is free and self-hostable. A managed cloud offering is available but newer, and public pricing details are still emerging. The €7.5M seed round suggests the managed cloud will be the primary revenue path.
For teams that need compliance certifications (SOC2, HIPAA), Zep Cloud is the more mature managed option today.
When to Choose Zep
Pick Zep when your primary need is temporal agent interaction memory:
- Your agent needs to track how entities and relationships change over time
- Fact validity windows and supersession are core requirements (compliance, audit trails, evolving customer relationships)
- You want a managed cloud with SOC2 Type 2 and HIPAA compliance
- You need SDKs beyond Python (TypeScript, Go available on Zep Cloud)
- Temporal depth matters more than data source breadth
See also: Zep alternatives.
When to Choose Cognee
Pick Cognee when your primary need is building knowledge from diverse existing data:
- You need to ingest from 30+ sources — documents, Slack, Notion, images, audio
- Your agent's value comes from grounding responses in a structured knowledge graph extracted from a corpus
- You want a lightweight local stack without managing Neo4j
- Multimodal ingestion (text, images, audio) is a requirement
- You're comfortable with Python-only SDKs
See also: Cognee alternatives.
Worth Considering: Hindsight
Both Zep and Cognee are strong on their specific graph approaches, but each is narrower on retrieval breadth. Zep goes deep on temporal graph traversal. Cognee goes wide on data ingestion. Neither runs multiple retrieval strategies in parallel to cover different query types.
Hindsight takes a different approach by combining graph traversal with semantic search, BM25 keyword matching, and temporal filtering — four strategies that run in parallel and merge results. This multi-strategy design scores 91.4% on LongMemEval, compared to Zep's 63.8%. The storage layer is embedded PostgreSQL — simpler operationally than either Zep's graph database requirement or Cognee's three-database local stack. Hindsight also offers Python, TypeScript, and Go SDKs with an MCP-first integration model.
If you've evaluated both Zep and Cognee and found that neither's retrieval approach fully covers your query patterns, Hindsight is worth a look. Detailed comparisons: Hindsight vs Zep and Hindsight vs Cognee.
Verdict: Zep vs Cognee for Agent Memory
Zep and Cognee are both knowledge-graph-centric, but they optimize for different sides of the agent memory problem. Zep is the strongest choice for temporal interaction memory — tracking how facts change across agent conversations and events. Cognee is the strongest choice for knowledge extraction — turning a diverse corpus of documents and multimodal data into structured, queryable knowledge.
Choose based on where your data lives and what your agent needs to remember. If memory comes from agent interactions and temporal reasoning matters, go with Zep. If memory comes from existing documents and diverse data sources, go with Cognee. If retrieval breadth across multiple strategies matters most, consider Hindsight as a third option.
Further reading:
- What Is Agent Memory? — foundational concepts and how persistent memory works
- Agent Memory vs RAG — key architectural differences explained
- Best AI Agent Memory Systems in 2026 — comparison of all 8 major frameworks
- Zep Alternatives — other options if Zep isn't the right fit
- Cognee Alternatives — other options if Cognee isn't the right fit