Memory Spine vs pgvector:
Which AI Agent Memory Solution is Right for You?
An honest, side-by-side comparison of Memory Spine and pgvector (PostgreSQL Extension for Vector Similarity Search). See which tool fits your AI agent memory needs.
Quick Comparison
Feature-by-feature breakdown of Memory Spine vs pgvector.
| Feature | Memory Spine | pgvector |
|---|---|---|
| Purpose | Purpose-built AI agent memory system | Vector similarity search extension for PostgreSQL |
| Protocol | MCP (32 native tools) | Standard PostgreSQL SQL interface |
| Search Speed | Sub-25ms (FTS5 + vector hybrid) | ~30-150ms (index and dataset dependent) |
| Vector Capacity | 160K+ (current), unlimited on Master plan | Millions (PostgreSQL-limited) |
| Pricing | Free (5K) • $19/mo • $49/mo • $99/mo unlimited | Free — open-source PostgreSQL extension |
| Agent Features | Memory pinning, knowledge graphs, conversation tracking, agent handoff, timeline queries, memory consolidation | None — extends SQL with vector operations only |
| Self-Hosted | Yes — SQLite + FTS5, zero dependencies | Yes — requires PostgreSQL installation |
When to Choose What
Both are good tools. The right choice depends on your use case.
⚡ Choose Memory Spine When
- You need persistent AI agent memory with conversation tracking and agent handoff
- You want 32 MCP tools that AI agents call directly — no custom integration
- You need hybrid search (FTS5 keyword + vector semantic) in one system
- You want predictable flat-rate pricing with a generous free tier
- You need memory pinning, knowledge graphs, and timeline queries
- You want zero external dependencies (built on SQLite)
🔨 pgvector Might Be Better When
- You already run PostgreSQL and want to add vector search without another service
- Your application data is already in Postgres and you want co-located vector queries
- You need to join vector similarity results with relational data in standard SQL
- You want the reliability and ecosystem of PostgreSQL (backups, replication, monitoring)
Key Differences Explained
A deeper look at what separates Memory Spine from pgvector.
Architecture
pgvector adds vector operations to an existing PostgreSQL database. Memory Spine is a standalone agent memory system built on SQLite + FTS5 with zero external dependencies.
Setup
pgvector requires a running PostgreSQL instance and the extension installed. Memory Spine runs as a single process — no database server needed.
Agent Memory
pgvector provides vector similarity search via SQL — you write queries. Memory Spine provides 32 MCP tools that AI agents call directly: store, search, pin, tag, consolidate, handoff, timeline queries, and knowledge graph construction.
Search Approach
pgvector uses IVFFlat or HNSW indexes for approximate nearest neighbor search in SQL. Memory Spine combines FTS5 full-text search with vector similarity in a hybrid approach, enabling both keyword and semantic queries through a unified API.
Operational Overhead
pgvector inherits PostgreSQL's operational requirements: backups, vacuuming, connection pooling, memory tuning. Memory Spine on SQLite is operationally simple with minimal resource requirements.
Frequently Asked Questions
Common questions about Memory Spine vs pgvector.
If your AI agents need persistent memory with conversation tracking, agent handoff, and knowledge graphs, choose Memory Spine — it's built for that. If you already run PostgreSQL and just need to add vector similarity search to existing relational queries, pgvector is the pragmatic choice.
For AI agent memory, yes. pgvector is a PostgreSQL extension that adds vector search to SQL queries. Memory Spine is a complete agent memory system with 32 MCP tools, memory pinning, conversation tracking, and agent handoff — features you'd have to build yourself on top of pgvector.
Absolutely. Some architectures use pgvector for application-level vector search (product recommendations, document retrieval) and Memory Spine for AI agent memory (persistent context, conversation state, agent handoff). They serve different layers of your stack.