DAKERADakera vs Cognee
Both Dakera and Cognee aim to give AI agents persistent memory, but they take different architectural paths: Dakera is a self-hosted Rust binary with hybrid retrieval, while Cognee is a Python framework focused on building knowledge graphs from unstructured data using LLM-powered extraction.
Feature Comparison
| Feature | Dakera | Cognee |
|---|---|---|
| Language | Rust (single ~44 MB binary) | Python framework |
| Deployment | Self-hosted (Docker, K8s, systemd) | Self-hosted (Python package, Docker) |
| Retrieval | Hybrid HNSW + BM25 with RRF fusion + cross-encoder reranking | Graph traversal + vector similarity |
| Benchmark | 88.2% LoCoMo (1540 questions) | No published LoCoMo score |
| Knowledge Graph | GLiNER entity extraction (on-device ONNX), 4 edge types, BFS traversal | LLM-powered extraction, Neo4j/NetworkX, rich ontologies |
| LLM Dependency | None for core operations (on-device inference) | Required for entity extraction and graph construction |
| Memory Decay | 6 strategies (exponential, linear, logarithmic, step, periodic, custom) | Not built-in |
| Encryption | AES-256-GCM at rest | Application-level (user implements) |
| Sessions | Full session management with namespaces | Pipeline-based processing |
| MCP Tools | 14 core tools (86+ available via profiles) for Claude Desktop, Cursor, Windsurf | No MCP integration |
| On-device Inference | ONNX (MiniLM, BGE, E5 + reranker) | Relies on external LLM APIs |
| SDKs | Python, TypeScript, Go, Rust | Python |
| APIs | REST + gRPC | Python API (library) |
| Open Source | MIT SDKs, proprietary server binary | Apache 2.0 |
Architecture Differences
Dakera
Single Rust binary that runs entirely on your infrastructure. Embedding generation, reranking, and knowledge graph extraction all happen on-device via ONNX runtime. No external API calls required for core memory operations. Data never leaves your network. Hybrid retrieval combines BM25 full-text with HNSW vector search through Reciprocal Rank Fusion, then applies cross-encoder reranking for precision.
Cognee
Python framework that builds knowledge graphs from unstructured data. Uses LLM calls (OpenAI, Anthropic, or local models) to extract entities, relationships, and concepts from text, then stores them in Neo4j or NetworkX graph structures. Cognee excels at building rich, interconnected knowledge representations but requires LLM API calls for each ingestion step, adding latency and cost. Retrieval traverses the graph to find relevant context.
Knowledge Graph Approach
| Aspect | Dakera | Cognee |
|---|---|---|
| Entity Extraction | GLiNER (on-device, ONNX, no LLM needed) | LLM-powered (requires API calls) |
| Graph Storage | Built-in (embedded graph with BFS) | Neo4j or NetworkX |
| Edge Types | 4 predefined types | Custom ontology-based relations |
| Cost per Ingestion | $0 (on-device compute only) | LLM API cost per extraction |
| Latency | Milliseconds (local inference) | Seconds (LLM round-trip) |
When to Choose
Choose Cognee if:
- You need rich, LLM-quality entity and relationship extraction with custom ontologies
- Your use case is knowledge-graph-first and you want deep graph querying capabilities
- You already use Neo4j and want to integrate memory into your existing graph infrastructure
- You prefer a Python-native library you can embed directly in your application
- You value open-source (Apache 2.0) with full code access
Choose Dakera if:
- You need hybrid retrieval (BM25 + vector + reranking) beyond just graph traversal
- You want zero LLM dependency for memory operations (no API costs, no latency)
- You need 14 core MCP tools (86+ available via profiles) for direct IDE integration (Claude Desktop, Cursor, Windsurf)
- Memory decay with 6 configurable strategies is important for your use case
- You need AES-256-GCM encryption at rest with scoped API keys
- You require SDKs in multiple languages (Python, TypeScript, Go, Rust)
- You want predictable, sub-millisecond retrieval without LLM round-trips
Verdict
Dakera provides a complete memory engine — hybrid BM25 + HNSW vector search with cross-encoder reranking, knowledge graphs with on-device GLiNER entity extraction, 6 memory decay strategies, and SDKs in Python, TypeScript, Go, and Rust — all in a self-hosted 44 MB binary scoring 88.2% on LoCoMo with zero per-operation LLM costs. Cognee excels at building rich, LLM-powered knowledge graphs with deep entity extraction and reasoning — genuinely strong when you need high-quality graph construction and already have Neo4j infrastructure in place. Choose Dakera when you need a self-contained memory engine with predictable costs, hybrid retrieval, and multi-language SDK support. Choose Cognee when LLM-quality knowledge graph construction is your primary goal and you can accommodate the per-call API costs and external dependencies.
Frequently Asked Questions
Why does Dakera use on-device entity extraction instead of LLM calls like Cognee?
Cost and latency predictability. Cognee's LLM-powered extraction produces semantically richer relationships but costs $0.01-0.10+ per extraction call and adds 500ms-2s latency. Dakera's GLiNER model runs on-device in milliseconds at zero marginal cost. For workloads with thousands of daily memory operations, Dakera's approach saves significant API costs while maintaining useful entity extraction quality.
Can Cognee's LLM-powered extraction produce better knowledge graphs than Dakera?
Generally yes — LLMs can infer implicit relationships, resolve coreferences, and generate custom ontology-based edge types that a local NER model cannot. Cognee's graphs are semantically richer. The trade-off is cost and speed: Cognee's quality comes at LLM API expense and network latency. If knowledge graph quality is your primary optimization target and you have budget for per-call LLM costs, Cognee's approach is stronger.
Does Dakera require Neo4j like Cognee?
No. Dakera's knowledge graph is built-in — stored alongside the memory index in the same binary with no external graph database required. Cognee uses Neo4j or NetworkX for graph storage, adding infrastructure complexity. Dakera's built-in graph is simpler (4 predefined edge types, BFS traversal) but requires zero additional dependencies or operational overhead.
What are the ongoing costs of using Cognee vs Dakera for 10,000 memories/day?
Dakera: effectively $0 beyond server infrastructure (all inference is on-device). Cognee: at minimum $100-1000+/month in LLM API costs for entity extraction on 10,000 daily operations (varies by model and content length). Cognee also needs external embedding API calls unless configured for local models. For high-volume workloads, the cost differential is the primary deciding factor.
Can I use Cognee for graph construction and Dakera for retrieval?
Not directly — they're separate systems with different storage backends. However, you could use Cognee to build a high-quality knowledge graph offline, export the entities and relationships, then import the structured data into Dakera for fast retrieval. This hybrid approach gets Cognee's graph quality with Dakera's retrieval speed and operational simplicity.
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Self-hosted, single binary, no API keys required. Run it on your own infrastructure in under 5 minutes.
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