DAKERADakera 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
| Feature | Dakera | Zep |
|---|---|---|
| Language | Rust (single binary) | Go + Python services |
| Retrieval | Hybrid HNSW + BM25, RRF fusion, cross-encoder reranking | Vector search + temporal knowledge graph |
| Knowledge Graph | GLiNER entity extraction, 4 edge types, BFS traversal | Temporal knowledge graph with entity/relation extraction |
| Memory Decay | 6 strategies (importance, spaced repetition, access-count) | Not natively supported |
| Session Management | Full sessions with namespaces, multi-agent isolation | Session-based memory with user context |
| Embedding | On-device ONNX (MiniLM, BGE, E5) | External embedding service (OpenAI, etc.) |
| Reranking | On-device bge-reranker-base (ONNX) | Not built-in |
| Encryption | AES-256-GCM at rest | Database-level encryption |
| MCP Integration | 83 MCP tools | Not available |
| SDKs | Python, TypeScript, Go, Rust | Python, TypeScript |
| APIs | REST + gRPC | REST API |
| Summarization | Via external LLM (optional) | Built-in conversation summarization |
| Open Source | MIT SDKs, proprietary server | Apache 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
| Aspect | Dakera | Zep |
|---|---|---|
| Self-hosted | Single binary (Docker, K8s, systemd) | Docker Compose (multiple services) |
| Cloud | Coming soon (managed hosting) | Zep Cloud (managed enterprise) |
| Dependencies | None (embedded storage + ONNX) | PostgreSQL + external embedding API |
| Resources | ~200MB RAM baseline | Higher (multiple services + PostgreSQL) |
| Scaling | Vertical + horizontal | Horizontal via service replication |
Pricing Comparison
| Tier | Dakera | Zep |
|---|---|---|
| Free/OSS | Self-hosted, unlimited | Community Edition (Apache 2.0) |
| Enterprise | Self-hosted free (Cloud coming) | Zep Cloud (usage-based pricing) |
| Hidden costs | Your infra only | External embedding API costs (OpenAI, etc.) |
When to Choose
Choose Zep if:
- You want a fully open-source memory server (Apache 2.0 community edition)
- Temporal knowledge graphs are central to your use case (tracking how facts change over time)
- You already use PostgreSQL and want tight integration
- Built-in conversation summarization without extra LLM calls is valuable
- You prefer a managed cloud option with enterprise support
Choose Dakera if:
- You need hybrid retrieval (BM25 + vector + reranking) for highest recall
- On-device inference matters — no external API calls for embeddings or reranking
- You want minimal operational complexity (single binary, no PostgreSQL dependency)
- Memory decay with 6 strategies is important for your agent's behavior
- You need MCP integration for IDE-based AI workflows
- gRPC is needed for high-throughput, low-latency access
- You need Go or Rust SDKs alongside Python/TypeScript
Verdict
Zep and Dakera are the closest competitors in this space — both are memory-first solutions with knowledge graph capabilities. Zep's temporal graph is genuinely strong for tracking evolving facts, and its Apache 2.0 community edition is a real advantage for open-source purists. Dakera wins on operational simplicity (single binary vs multi-service), retrieval depth (hybrid + reranking), and IDE integration (83 MCP tools). Choose based on whether you value temporal graph richness (Zep) or retrieval precision with minimal dependencies (Dakera).
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Single binary, no PostgreSQL, no external API dependencies. Full memory engine running in under 5 minutes.
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