Remembr

Remembr gives AI agents durable, searchable memory with a simple store, search, and delete workflow, session-aware context, and a self-hosted stack you can run locally in minutes.

Quick Start

git clone https://github.com/ai-emart/remembr.git
cd remembr
cp .env.example .env
python -c "import secrets; print(secrets.token_hex(32))"
# paste the generated value into SECRET_KEY in .env
bash scripts/docker-init.sh
curl http://localhost:8000/api/v1/health

The Docker bootstrap script starts the full local stack: PostgreSQL, Redis, PgBouncer, Ollama (for embeddings), the API server, worker, and migrations. EMBEDDING_PROVIDER=ollama is the default inside Docker Compose; sentence_transformers is the default for bare pip installs.

See QUICKSTART.md for the full self-hosted walkthrough.

Install The SDKs

pip install remembr
npm install @remembr/sdk

Python Example

import asyncio

from remembr import RemembrClient


async def main() -> None:
    async with RemembrClient(
        api_key="your-api-key",
        base_url="http://localhost:8000/api/v1",
    ) as client:
        session = await client.create_session(
            metadata={"user": "demo", "context": "support"}
        )

        await client.store(
            content="User prefers email notifications on Fridays",
            role="user",
            session_id=session.session_id,
            tags=["preference", "notification"],
        )

        results = await client.search(
            query="When should I send notifications?",
            session_id=session.session_id,
            limit=5,
            search_mode="hybrid",
            weights={"semantic": 0.6, "keyword": 0.3, "recency": 0.1},
        )

        for memory in results.results:
            print(f"[{memory.role}] {memory.content} (score: {memory.score:.3f})")


asyncio.run(main())

TypeScript Example

import { RemembrClient } from '@remembr/sdk';

async function main() {
  const client = new RemembrClient({
    apiKey: process.env.REMEMBR_API_KEY!,
    baseUrl: 'http://localhost:8000/api/v1'
  });

  const session = await client.createSession({
    user: 'demo',
    context: 'support'
  });

  await client.store({
    content: 'User prefers dark mode interface',
    role: 'user',
    sessionId: session.session_id,
    tags: ['preference', 'ui']
  });

  const results = await client.search({
    query: 'What are the user UI preferences?',
    sessionId: session.session_id,
    limit: 5,
    searchMode: 'hybrid',
    weights: { semantic: 0.6, keyword: 0.3, recency: 0.1 }
  });

  results.results.forEach((memory) => {
    console.log(`[${memory.role}] ${memory.content} (score: ${memory.score})`);
  });
}

main();

Environment Variables

Variable	Purpose	Required	Default
DATABASE_URL	PostgreSQL connection string	Yes	postgresql://postgres:postgres@localhost:5432/remembr
REDIS_URL	Redis connection string	Yes	redis://localhost:6379
SECRET_KEY	JWT signing secret	Yes	None
EMBEDDING_PROVIDER	Embedding backend: sentence_transformers, jina, ollama, or openai	Yes	sentence_transformers
SENTENCE_TRANSFORMERS_MODEL	Local sentence-transformers model name	No	all-MiniLM-L6-v2
JINA_API_KEY	Jina API key when EMBEDDING_PROVIDER=jina	No	None
JINA_EMBEDDING_MODEL	Jina embedding model	No	jina-embeddings-v3
OLLAMA_BASE_URL	Ollama base URL when EMBEDDING_PROVIDER=ollama	No	http://localhost:11434
OLLAMA_EMBEDDING_MODEL	Ollama embedding model	No	nomic-embed-text
OPENAI_API_KEY	OpenAI API key when EMBEDDING_PROVIDER=openai	No	None
OPENAI_EMBEDDING_MODEL	OpenAI embedding model	No	text-embedding-3-small
OTEL_ENABLED	Enable OpenTelemetry export	No	false
OTEL_EXPORTER_ENDPOINT	OTLP exporter endpoint	No	None

Docs

Full documentation: https://ai-emart.github.io/remembr/

• Framework quickstarts
• API reference
• Self-hosting guide
• Python SDK README
• TypeScript SDK README // test mindragent hook