The memory engine for AI agents. A single Rust binary that replaces vector search, full-text search, embeddings, agent memory, knowledge graphs, and more. REST + gRPC APIs, on-device inference, AES-256-GCM encryption at rest, multi-node HA clustering, and 83 MCP tools out of the box.
Public Alpha — Dakera is live. Self-hosted binary, all SDKs, CLI, and MCP server are fully operational and available now. Dakera Cloud (managed hosting, SLA, team monitoring) is coming next — join the waitlist →
What Dakera replaces
Instead of running
Dakera provides
Qdrant · Pinecone · Weaviate
HNSW, IVF, SPFresh, and Flat vector indexes with SIMD-accelerated distances
Elasticsearch · OpenSearch
BM25 full-text search engine with per-namespace indexes
OpenAI / Cohere embeddings API
On-device ONNX inference (MiniLM, BGE, E5) — zero API calls
Redis / Postgres memory layer
Decay-weighted agent memory with sessions, importance scoring, and 6 decay strategies
Neo4j knowledge graph
Entity graph with 4 edge types and cross-agent network visualization
Mem0 / Zep memory services
Import/export from Mem0 and Zep formats; superset feature coverage
Separate NER service
GLiNER zero-shot named entity extraction with multi-provider support
87.6% on LoCoMo — Dakera's memory engine scores 87.6% on the full LoCoMo benchmark (50 sessions, 1,540 questions, May 2026). Read the methodology →
DAKERA