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Dakera vs Zep

Dakera and Zep both target AI agent memory as their core use case. Zep combines vector search with a temporal knowledge graph and offers both open-source and enterprise editions. Dakera takes a single-binary Rust approach with hybrid retrieval and on-device ML inference.

Feature Comparison

FeatureDakeraZep
LanguageRust (single binary)Go + Python services
RetrievalHybrid HNSW + BM25, RRF fusion, cross-encoder rerankingVector search + temporal knowledge graph
Knowledge GraphGLiNER entity extraction, 4 edge types, BFS traversalTemporal knowledge graph with entity/relation extraction
Memory Decay6 strategies (importance, spaced repetition, access-count)Not natively supported
Session ManagementFull sessions with namespaces, multi-agent isolationSession-based memory with user context
EmbeddingOn-device ONNX (MiniLM, BGE, E5)External embedding service (OpenAI, etc.)
RerankingOn-device bge-reranker-base (ONNX)Not built-in
EncryptionAES-256-GCM at restDatabase-level encryption
MCP Integration83 MCP toolsNot available
SDKsPython, TypeScript, Go, RustPython, TypeScript
APIsREST + gRPCREST API
SummarizationVia external LLM (optional)Built-in conversation summarization
Open SourceMIT SDKs, proprietary serverApache 2.0 (CE), proprietary (Enterprise)

Architecture Differences

Dakera

A single Rust binary with zero external dependencies for core operations. Embeddings, reranking, and entity extraction run on-device via ONNX runtime. Storage is self-contained. The architecture prioritizes minimal operational overhead — one process, one port (REST on 3300, gRPC on 50051).

Zep

A multi-service architecture with Go and Python components. Zep uses a temporal knowledge graph that automatically extracts entities and relationships from conversations, maintaining a time-aware view of how facts evolve. It relies on external services for embeddings (typically OpenAI). The enterprise edition adds features like structured data extraction and advanced graph querying.

Deployment Model

AspectDakeraZep
Self-hostedSingle binary (Docker, K8s, systemd)Docker Compose (multiple services)
CloudComing soon (managed hosting)Zep Cloud (managed enterprise)
DependenciesNone (embedded storage + ONNX)PostgreSQL + external embedding API
Resources~200MB RAM baselineHigher (multiple services + PostgreSQL)
ScalingVertical + horizontalHorizontal via service replication

Pricing Comparison

TierDakeraZep
Free/OSSSelf-hosted, unlimitedCommunity Edition (Apache 2.0)
EnterpriseSelf-hosted free (Cloud coming)Zep Cloud (usage-based pricing)
Hidden costsYour infra onlyExternal embedding API costs (OpenAI, etc.)

When to Choose

Choose Zep if:

Choose Dakera if:

Verdict

Dakera offers operational simplicity as a single 44 MB Rust binary with hybrid BM25 + HNSW vector search, cross-encoder reranking, 6 memory decay strategies, and 83 MCP tools for Claude Desktop, Cursor, and Windsurf — scoring 87.6% on the LoCoMo benchmark. Zep brings genuinely strong temporal knowledge graphs for tracking evolving facts over time, plus an Apache 2.0 community edition that appeals to open-source-first teams. Choose Dakera when you need retrieval precision, minimal dependencies, and deep IDE integration. Choose Zep when temporal graph reasoning and open-source licensing are your top priorities.

Try Dakera Free

Single binary, no PostgreSQL, no external API dependencies. Full memory engine running in under 5 minutes.

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