memex
A production-grade persistent memory service for AI agents. Agents forget everything between sessions by default — memex fixes that. It stores, retrieves, and ranks conversation memory using semantic search with recency decay, so agents surface what's relevant and recent, not just what's semantically closest.
POST /v1/memories → store a memory, embed it, persist to Postgres
POST /v1/memories/search → retrieve top-k memories ranked by similarity + recency
DELETE /v1/memories/{id} → forget a specific memory
GET /v1/memories/count → how many memories does this agent/user have
GET /health → liveness + DB connectivity check
GET /metrics → Prometheus metrics
Architecture
caller (agent / app)
│
▼
FastAPI (async)
│
┌────┴────┐
│ │
embeddings asyncpg pool (min=5, max=20)
(fastembed │
ONNX, ▼
local) PostgreSQL 16
pgvector extension
ivfflat index (cosine)
Write path: content → fastembed ONNX inference (local, ~12 ms CPU, BAAI/bge-small-en-v1.5) → INSERT with 384-dim vector → return memory ID.
Read path: query → embed → pgvector cosine search (top_k × 3 candidates) → re-rank with recency decay in Python → return top_k results with scores.
Design decisions
1. Recency decay on top of semantic search
Pure vector similarity returns the most semantically similar memories, not the most useful ones. A fact from 90 days ago that's a 0.95 similarity match is often less useful than a 0.80 match from yesterday.
Score formula:
score = α × cosine_similarity + (1 − α) × exp(−λ × age_days)
Where λ = ln(2) / half_life_days (default: 30 days, so a 30-day-old memory has 50% recency weight).
α is configurable per request (default 0.7). Task-focused agents use higher α (semantic dominates). Conversational agents use lower α (recency matters more).
2. Fetch 3× candidates, re-rank in Python
The pgvector query returns top_k × 3 candidates sorted by pure similarity. Python re-ranks with the decay formula and slices to top_k. This prevents recency decay from starving high-similarity older memories — they're still in the candidate pool.
At 10× scale (>1M memories per agent): push the scoring into a Postgres function using pg_proc to eliminate the Python re-ranking round-trip.
3. asyncpg + explicit pool sizing over SQLAlchemy async
SQLAlchemy adds ORM overhead on every query. The hot retrieval path — embed, query, re-rank — needs to be tight. asyncpg gives direct control over pool min/max (same instinct as tuning HikariCP in Java). pgvector queries require raw SQL for the <=> operator anyway.
Pool defaults: min=5, max=20. Right-size for a single-instance deployment. Override via DB_MAX_POOL_SIZE env var.
4. Rate limiting in Postgres, not Redis
Sliding window counter via upsert. One fewer dependency. Correct under concurrent requests (transactional upsert). At 10× scale with distributed deployments: replace with Redis INCR + EXPIRE — atomic operations, no lock contention.
5. ivfflat index, not HNSW
ivfflat has lower build cost and lower memory footprint — the right tradeoff at small-to-medium scale (<1M vectors). lists=100 works well up to ~1M rows. At 10× scale: switch to HNSW (m=16, ef_construction=64) for better recall at the cost of higher memory and build time.
Running locally
Prerequisites: Docker and Docker Compose. No API keys required — the entire stack runs locally.
git clone https://github.com/ayushagrawal288/memex
cd memex
docker compose up
The API is live at http://localhost:8000. Interactive docs at http://localhost:8000/docs.
API reference
Store a memory
curl -X POST http://localhost:8000/v1/memories \
-H "Content-Type: application/json" \
-d '{
"agent_id": "my-agent",
"user_id": "user-123",
"content": "User prefers concise responses and dislikes verbose explanations.",
"memory_type": "semantic",
"importance": 1.2
}'
{
"id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
"agent_id": "my-agent",
"user_id": "user-123",
"content": "User prefers concise responses and dislikes verbose explanations.",
"importance": 1.2,
"memory_type": "semantic",
"created_at": "2026-05-26T10:30:00Z",
"score": null
}
Search memories
curl -X POST http://localhost:8000/v1/memories/search \
-H "Content-Type: application/json" \
-d '{
"agent_id": "my-agent",
"user_id": "user-123",
"query": "how does this user like to communicate",
"top_k": 5,
"alpha": 0.7
}'
{
"results": [
{
"id": "3fa85f64-...",
"content": "User prefers concise responses and dislikes verbose explanations.",
"memory_type": "semantic",
"created_at": "2026-05-26T10:30:00Z",
"score": 0.8921
}
],
"query": "how does this user like to communicate",
"total": 1
}
Memory types
Load test results
Run on a MacBook M-series, Docker Desktop, single Postgres instance:
locust -f scripts/load_test.py --host=http://localhost:8000 \
--headless -u 50 -r 10 -t 60s
Realistic load (50 users, 100–300 ms think time — models actual agent traffic):
Saturation test (500 users, minimal think time — finds the throughput ceiling):
Run on MacBook M-series, Docker Desktop (4 CPUs), 4 uvicorn workers, 16 threads/worker.
Embeddings: local ONNX (BAAI/bge-small-en-v1.5) — zero external API calls, zero cost.
Why the ceiling is ~120 RPS:
Every write and every search requires one ONNX inference (~10–15 ms on CPU). With 4 Docker CPUs: 4 cores / 12 ms ≈ 333 embeddings/s theoretical max. After Python overhead, DB queries, and asyncio scheduling: ~120 RPS actual.
Path to higher throughput:
Project structure
memex/
├── app/
│ ├── main.py # REST API — FastAPI, lifespan, router registration
│ ├── mcp_server.py # MCP server — single-worker FastAPI on port 8001
│ ├── core/
│ │ └── config.py # All settings, loaded from env
│ ├── db/
│ │ └── pool.py # asyncpg pool, migrations
│ ├── models/
│ │ └── schemas.py # Pydantic request/response models
│ ├── services/
│ │ ├── embeddings.py # fastembed ONNX inference (local, zero API calls)
│ │ ├── local_summarizer.py # Extractive summariser — Jaccard dedup + TF scoring
│ │ ├── memory.py # Core write/search/scoring logic
│ │ ├── metrics.py # Prometheus metric definitions
│ │ ├── summarizer.py # Background summarisation job
│ │ └── rate_limit.py # Sliding window rate limiter
│ └── api/routes/
│ ├── memories.py # Memory endpoints
│ ├── health.py # Health + readiness
│ └── mcp_tools.py # MCP tool definitions (store, search, delete, count)
├── scripts/
│ └── load_test.py # Locust load test
├── docker-compose.yml
├── Dockerfile
└── requirements.txt
Observability
docker compose up starts Prometheus and Grafana alongside the API:
The Grafana dashboard is provisioned automatically. Panels:
- HTTP request rate + latency p50/p99 — from
prometheus-fastapi-instrumentator - Embedding API latency p50/p99 — per-attempt histogram by operation (
embed/embed_batch) - Memory operations/s — create, search, delete throughput
- DB pool utilisation — active vs idle connections (update interval: 15 s)
- Summariser activity — memories condensed per hour, run outcomes
- Embedding errors/min — by operation and error type
Custom metrics are in app/services/metrics.py and exposed on /metrics alongside the standard FastAPI instrumentator metrics.
MCP endpoint
memex exposes itself as an MCP server so any MCP-aware agent (Claude Desktop, Claude Code, custom agents) can store and retrieve memories without custom HTTP integration.
Transport: Streamable HTTP (MCP 2024-11-05 spec). Single-worker process on port 8001 — session state is in-process, so a separate service avoids sticky-session complexity while keeping the REST API's multi-worker throughput.
Tools:
Connect from Claude Desktop
Add to ~/.config/claude/claude_desktop_config.json:
{
"mcpServers": {
"memex": {
"type": "streamable-http",
"url": "http://localhost:8001/mcp/"
}
}
}
Connect from Claude Code
claude mcp add --transport http memex http://localhost:8001/mcp/
Design: why a separate service
The MCP Streamable HTTP transport is session-stateful — initialize, tools/list, and tools/call must all reach the same server process. The REST API runs 4 uvicorn workers with round-robin routing; routing different MCP requests to different workers breaks session state.
Running a dedicated single-worker MCP service on port 8001 avoids sticky-session infrastructure (nginx ip_hash, Redis session store) while keeping the REST API fully multi-worker.
Memory summarisation
Runs as a background asyncio task on a configurable interval (default: every 5 minutes). Finds any (agent_id, user_id) pair where episodic memory count exceeds a threshold, condenses the oldest batch into a single semantic memory, then deletes the originals. Fully local — no LLM API calls.
How it summarises: Pure Python extractive algorithm. Sentences are deduplicated by Jaccard similarity (≥ 0.7 threshold), scored by word frequency (TF), and the top-N are returned in original order. ~1 ms per summarisation, zero dependencies beyond the standard library.
Why episodic-only: Episodic memories are conversation events with natural time-based obsolescence. Semantic and procedural memories encode facts and skills — silently condensing them risks precision loss; they age out via recency decay instead.
Concurrency safety: Uses pg_try_advisory_xact_lock keyed on hashtext(agent_id|user_id). The lock is held only during the DB write transaction, not during the embedding call.
Tune via env vars:
What's next
- Memory summarisation — background job to condense old episodic memories (local extractive algorithm, zero API calls) when count exceeds threshold
- Prometheus + Grafana — p50/p99 latency dashboards, embedding API call duration, pool saturation
- MCP-compatible endpoint — Streamable HTTP server on port 8001; 4 tools (store, search, delete, count); connects to Claude Desktop and Claude Code
- HNSW index option — flag to switch from ivfflat to HNSW for deployments with >1M vectors
- Importance-weighted retrieval — factor
importancescore into ranking formula alongside similarity and recency