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 | 14 core MCP tools (86+ available via profiles) | 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
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 14 core MCP tools (86+ available via profiles) for Claude Desktop, Cursor, and Windsurf — scoring 88.2% 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.
Frequently Asked Questions
How does Zep's temporal knowledge graph differ from Dakera's knowledge graph?
Zep's graph tracks how facts change over time — it can represent that "user's favorite language was Python in January but switched to Rust in March." Dakera's knowledge graph extracts entities and relationships from stored memories using GLiNER (on-device NER) with 4 edge types and BFS traversal, but does not natively version facts over time. If tracking factual evolution is critical, Zep's temporal approach is more specialized.
Does Dakera require PostgreSQL like Zep?
No. Dakera uses embedded storage (redb + tantivy) — zero external database dependencies. Zep requires a running PostgreSQL instance for its core data layer plus external embedding API access. Dakera's single-binary architecture means one process to deploy and monitor, while Zep requires orchestrating multiple services (Go server, Python workers, PostgreSQL, embedding API).
Which has better IDE integration for AI coding assistants?
Dakera provides 14 core MCP tools (86+ available via profiles) purpose-built for integration with Claude Desktop, Cursor, Windsurf, and other MCP-compatible environments. Zep's integration is primarily through its Python/TypeScript SDKs within agent frameworks. If you're building memory into an AI coding assistant or IDE plugin, Dakera's MCP tooling provides native, granular memory control without writing custom integration code.
Can Dakera track how facts change over time like Zep?
Not natively with the same temporal graph semantics. Dakera stores memories with timestamps and importance scores, and its decay strategies model memory fading, but it doesn't maintain a versioned history of how specific facts evolve. You can approximate temporal awareness by storing timestamped memories and querying by time range, but Zep's temporal graph is more purpose-built for fact evolution tracking.
How do the open-source licenses compare?
Zep Community Edition is Apache 2.0 — fully open-source with no restrictions. Dakera's SDKs are MIT-licensed, while the server has a commercial license. If a permissive open-source license for the core engine is a hard requirement (for compliance or philosophical reasons), Zep's Apache 2.0 community edition is more permissive. Both can be self-hosted without cloud vendor lock-in.
Try Dakera Free
Single binary, no PostgreSQL, no external API dependencies. Full memory engine running in under 5 minutes.
Get Started