memex
README.md
# memex
[](https://github.com/ayushagrawal288/memex/releases)
[](https://www.python.org)
[](https://fastapi.tiangolo.com)
[](https://modelcontextprotocol.io)
[](LICENSE)
[](https://glama.ai/mcp/servers/ayushagrawal288/memex)
[](https://glama.ai/mcp/servers/ayushagrawal288/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.
```bash
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
```bash
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
}'
```
```json
{
"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
```bash
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
}'
```
```json
{
"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
| Type | Use for |
|---|---|
| `episodic` | Specific events, past conversations |
| `semantic` | Facts, preferences, general knowledge |
| `procedural` | Workflows, how-to instructions |
---
## Load test results
Run on a MacBook M-series, Docker Desktop, single Postgres instance:
```bash
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):
| Endpoint | RPS | p50 (ms) | p95 (ms) | p99 (ms) | Error rate |
|---|---|---|---|---|---|
| POST /v1/memories (write) | 27 | 160 | 270 | 330 | 0% |
| POST /v1/memories/search | 83 | 110 | 200 | 250 | 0% |
| Aggregated | **113** | 120 | 230 | 300 | **0%** |
**Saturation test** (500 users, minimal think time — finds the throughput ceiling):
| Endpoint | RPS (plateau) | p50 (ms) | p99 (ms) | Error rate |
|---|---|---|---|---|
| POST /v1/memories (write) | 28 | 3,900 | 6,100 | **0%** |
| POST /v1/memories/search | 91 | 3,600 | 5,800 | **0%** |
| Aggregated | **~120** | 3,700 | 5,900 | **0%** |
> 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:**
| Approach | Expected gain | Complexity |
|---|---|---|
| Embedding cache (Redis, key = SHA256 of text) | 2–3× (40–60% hit rate on repeated agent queries) | Low |
| Horizontal scaling (N replicas behind a load balancer) | N× linear | Medium |
| GPU inference (swap ONNX runtime → CUDA) | 10–50× | Medium |
| Voyage-3 API (offload to Anthropic's inference fleet) | Scales to thousands of RPS, limited by API quota | Low code change |
---
## 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:
| Service | URL | Credentials |
|---|---|---|
| REST API docs | http://localhost:8000/docs | — |
| MCP server | http://localhost:8001/mcp/ | — |
| Prometheus | http://localhost:9090 | — |
| Grafana | http://localhost:3000 | admin / admin |
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](https://modelcontextprotocol.io) 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:**
| Tool | Description |
|---|---|
| `store_memory` | Embed + persist a memory (type, importance configurable) |
| `search_memories` | Semantic + recency ranked retrieval with configurable alpha |
| `delete_memory` | Forget a specific memory by UUID |
| `count_memories` | How many memories an agent/user pair has |
### Connect from Claude Desktop
Add to `~/.config/claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"memex": {
"type": "streamable-http",
"url": "http://localhost:8001/mcp/"
}
}
}
```
### Connect from Claude Code
```bash
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:
| Var | Default | Description |
|---|---|---|
| `SUMMARIZATION_ENABLED` | `true` | Toggle the background job |
| `SUMMARIZATION_THRESHOLD` | `100` | Episodic count to trigger per pair |
| `SUMMARIZATION_BATCH_SIZE` | `50` | Oldest N memories to condense per run |
| `SUMMARIZATION_INTERVAL_SECONDS` | `300` | How often the job wakes up |
---
## What's next
- [x] **Memory summarisation** — background job to condense old episodic memories (local extractive algorithm, zero API calls) when count exceeds threshold
- [x] **Prometheus + Grafana** — p50/p99 latency dashboards, embedding API call duration, pool saturation
- [x] **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 `importance` score into ranking formula alongside similarity and recency
---
## Tech stack
| Layer | Choice | Why |
|---|---|---|
| API | FastAPI + uvicorn | Async-first, fast, excellent OpenAPI generation |
| Embeddings | fastembed ONNX (`BAAI/bge-small-en-v1.5`) | Local, zero API calls, ~12 ms CPU inference, 384-dim |
| Database | PostgreSQL 16 + pgvector | Relational + vector in one system, no extra infra |
| Vector index | ivfflat | Lower build cost than HNSW at this scale |
| Pool | asyncpg | Direct control, zero ORM overhead |
| Summariser | Pure Python extractive | Jaccard dedup + TF scoring, zero ML deps, ~1 ms |
| Retry | tenacity | Jitter-based backoff on transient errors |
| Metrics | Prometheus + prometheus-fastapi-instrumentator | Standard observability |
| Load testing | Locust | Python-native, realistic user simulation |
TDQS
A4.1/5.0
Scored across 4 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: storing, searching, counting, and deleting memories. No two tools overlap in functionality, so an agent can easily select the correct one.
Naming Consistency5/5
All tool names follow a verb_noun pattern in snake_case (store_memory, search_memories, count_memories, delete_memory). The slight plural/singular variation is negligible and does not break consistency.
Tool Count5/5
Four tools is an appropriate number for a focused memory server. Each tool serves a core operation without unnecessary bloat or missing essentials.
Completeness4/5
The set covers create, read (search and count), and delete operations for memories. An update tool is missing, but that is often handled by delete+store. The lack of a direct get-by-ID tool is a minor gap.
Maintenance
ActivityInactive
ResponsivenessNo issues