Dakera vs pgvector

Dakera is a purpose-built AI agent memory engine, while pgvector is a PostgreSQL extension that adds vector similarity search to your existing database. pgvector is the simplest path to vector search if you already run Postgres; Dakera is the complete solution when you need agent memory semantics.

Feature Comparison

Architecture Differences

Dakera

Single Rust binary (~44 MB) that includes everything: HNSW vector indexing, BM25 full-text search, cross-encoder reranking, knowledge graph extraction via GLiNER, memory decay engine, session management, and on-device ONNX inference for embeddings. Exposes REST (port 3300) and gRPC (port 50051). Zero external dependencies.

pgvector

A C extension for PostgreSQL that adds a vector column type and similarity operators (<-> for L2, <=> for cosine, <#> for inner product). Supports IVFFlat and HNSW indexes. Inherits PostgreSQL's ACID guarantees, replication, and ecosystem. You bring your own embeddings — pgvector only stores and searches vectors. Memory semantics must be built in application code.

What You Need to Build on Top of pgvector

To replicate Dakera's memory capabilities using pgvector, you would need to implement:

• Embedding generation pipeline (external model hosting or API calls)
• BM25 ranking + vector fusion logic (RRF or similar)
• Cross-encoder reranking service
• Memory decay logic (cron jobs or triggers)
• Session and namespace management (schema design + access control)
• Knowledge graph extraction and traversal (separate service)
• Entity extraction pipeline (NER model hosting)
• MCP tool protocol implementation

When to Choose

Choose pgvector if:

• You already run PostgreSQL and want to add basic vector search with minimal effort
• You need vector similarity as one feature alongside relational data in the same database
• You want ACID transactions covering both your vectors and relational data
• You have a small-to-medium dataset (pgvector performance is good up to ~10M vectors)
• You prefer SQL-based access and already have PostgreSQL expertise
• You only need simple nearest-neighbor search, not full memory semantics

Choose Dakera if:

• You need a complete AI agent memory system, not just vector storage
• Hybrid retrieval (BM25 + vector + cross-encoder reranking) matters for accuracy
• You need memory decay, sessions, namespaces, and multi-agent isolation out of the box
• You want built-in knowledge graph extraction and traversal
• On-device embedding generation (no external API calls) is important
• You need 83 MCP tools for IDE integration (Claude Desktop, Cursor, Windsurf)
• You want a single binary with everything included vs building custom layers on Postgres