DEV Community: Fex Beck

How to give Claude Code persistent memory with 51 MCP tools

Fex Beck — Wed, 08 Apr 2026 20:40:47 +0000

The Problem

Claude Code uses MEMORY.md files for persistence. 200-line limit, no search, no validation, no multi-agent support. After months of fighting this, I built BrainDB.

Setup (2 minutes)

Clone, configure, start:

git clone https://github.com/beckfexx/BrainDB.git
cd BrainDB && cp .env.example .env
bun install && bun run start

Add to your MCP config (Claude Code settings):

{

  "mcpServers": {

    "braindb": {

      "command": "bun",

      "args": ["run", "src/mcp-client.ts"],

      "env": { "BRAINDB_URL": "http://localhost:3197" }

    }

  }

}

What you get (51 tools)

Smart Context — Claude loads relevant memories based on CWD and git branch.

Decisions that stick — Authoritative memories that supersede conflicting old ones.

Session Handovers — Pass context to the next session seamlessly.

Contradiction Detection — Flags conflicting stored facts automatically.

Nightly Self-Learning — Validates memories against web sources overnight.

MEMORY.md vs BrainDB

Feature	MEMORY.md	BrainDB
Search	None	FTS5 + embeddings
Capacity	200 lines	Unlimited
Validation	None	Nightly fact-checking
Multi-agent	File conflicts	Claims + handovers
Contradictions	Manual	Auto-detected

The 51 Tools grouped

Memory (15): remember, recall, forget, decide, context, validate, learn...
Search (5): semantic-search, hybrid-search, embeddings...
Agents (8): heartbeat, claim, release, handover, inbox...
Graph (10): entities, relations, contradictions...
System (8): health, stats, backup, capabilities...
Inception (5): dream, decay, inception findings...

Every tool Claude Code needs for real persistent memory. No more MEMORY.md limitations.

GitHub: beckfexx/BrainDB — AGPL-3.0, TypeScript.

Why SQLite+FTS5 beats Vector DBs for AI Agent Memory

Fex Beck — Wed, 08 Apr 2026 20:40:09 +0000

The conventional wisdom is wrong

Everyone says you need a vector database for AI memory. Pinecone, Weaviate, Qdrant. They all need a server, an API key, and a monthly bill.

I went a different way: SQLite + FTS5. One file. Zero dependencies. Better results.

How it works

BrainDB stores 4,300+ memories in a single SQLite file with three search modes:

1. Full-text search (FTS5) — Sub-millisecond keyword search with BM25 ranking.

2. Embedding similarity — 384-dim vectors stored as BLOBs, cosine similarity computed in TypeScript.

3. Hybrid search — Reciprocal Rank Fusion combines both for best-of-both-worlds retrieval.

Custom relevance scoring

SQLite custom functions run type-aware ranking inside the database:

Decisions get +0.3 boost (authoritative)
Issues get -0.1 (often resolved)
Superseded memories return 0
Exponential time decay with type-specific half-lives

The numbers

Metric	SQLite+FTS5	Pinecone Free
Latency	<1ms	50-200ms
Setup	0 minutes	15 minutes
Cost	$0	$0-70/mo
Backup	cp file.db backup.db	API call
Offline	Yes	No

For most AI agent memory use cases, SQLite is the right choice.

Try it: github.com/beckfexx/BrainDB — AGPL-3.0, TypeScript, Bun.

I built an AI memory that fact-checks itself while you sleep

Fex Beck — Wed, 08 Apr 2026 19:45:46 +0000

The Problem

AI agents forget everything between sessions. Claude Code uses MEMORY.md files — a 200-line limit, no search, no validation. After months of manually maintaining memory files, I built something better.

What is BrainDB?

BrainDB is a local-first AI memory system built on SQLite. No cloud, no vector database, no subscriptions. One SQLite file, 110 REST endpoints, 51 MCP tools.

Why SQLite instead of Pinecone/Weaviate?

SQLite with FTS5 gives you sub-millisecond full-text search with BM25 ranking. Combined with embedding vectors (stored as BLOBs), you get hybrid search — keyword precision + semantic understanding. For <100k memories, this beats any managed vector DB in latency and simplicity.

The killer features nobody else has

1. Inception — Nightly fact-checking

Every night, BrainDB picks high-importance memories, generates search queries, searches the web via SearXNG, and fact-checks them with an LLM. Last run found 26 outdated facts automatically.

[inception] Processing: "PostgreSQL default port is 5433"
[inception] Web search: 3 results confirm port 5432
[inception] Result: CONTRADICTION — stored fact is wrong

2. autoDream — Knowledge consolidation

While you sleep, BrainDB merges duplicate memories, archives stale ones, and adjusts importance scores. Like defragmenting your AI's brain.

3. Multi-agent coordination

Multiple AI agents can share the same memory with pessimistic locking (claims), context handovers, heartbeat monitoring, and a signal protocol. No conflicts, no data races.

Architecture

MCP Client (Claude/Cursor) → MCP Server → REST API → SQLite+FTS5
                                              ↓
                                    Ollama (local embeddings)

7 route modules: Memory, Search, Agents, Graph, Orchestrator, System, Inception.

Relevance Scoring

Custom SQLite function with type-aware ranking:

Decisions get +0.3 boost (authoritative)
Issues get -0.1 (often resolved)
Superseded memories return 0 (replaced by newer version)
Exponential time decay with type-specific half-lives

Quick Start

git clone https://github.com/beckfexx/BrainDB.git
cd BrainDB && cp .env.example .env
bun install && bun run start
# → http://localhost:3197/health

Or with Docker:

docker compose up -d

Numbers

4,300+ memories in production
110 REST endpoints across 7 domain modules
51 MCP tools for Claude Code / Cursor
0 TypeScript errors
AGPL-3.0 — free for self-hosting

Compared to alternatives

Feature	BrainDB	MemPalace	LangChain Memory
Hybrid search (FTS5+embeddings)	✅	❌	❌
Self-learning (Inception)	✅	❌	❌
Multi-agent coordination	✅	❌	⚠️ Basic
Local-first (no cloud)	✅	✅	❌
Knowledge graph	✅	❌	❌
Production-tested	✅ 4.3k memories	Demo only	Framework

GitHub: beckfexx/BrainDB

Feedback welcome. This is a solo project — every star, issue, or PR helps.