rememhq 0.1.15

Reasoning memory layer for AI agents — Python SDK

remem Python SDK

The official Python SDK for remem — the reasoning memory layer for AI agents.

Remem provides a persistent, queryable memory system that uses LLM-powered reasoning for importance scoring, contradiction detection, knowledge graph construction, and session consolidation. This SDK offers a strongly-typed, fully asynchronous interface over the remem REST API using httpx and pydantic.

Key Features

• Fully Asynchronous: Built natively on asyncio and httpx.
• Type-Safe Models: Robust validation and autocompletion powered by Pydantic.
• LLM-Powered Reasoning: Effortlessly invoke semantic recall, importance scoring, and consolidation.
• Knowledge Graph: Native support for storing and querying relationship triples.

Installation

Install the package via pip or your preferred package manager (e.g., uv, poetry):

pip install rememhq

Quick Start

1. Start the remem API Server

Before using the SDK, start the rememhq-api server from the remem repository root:

cargo run -p rememhq-api -- --project default

By default, the server listens on http://localhost:7474.

2. Initialize the Client

import asyncio
from rememhq import Memory


async def main():
    # Initialize the memory client
    memory = Memory(
        base_url="http://localhost:7474",
        project="my-agent",
        reasoning_model="claude-sonnet-4-6",  # Configure the underlying reasoning engine
    )

    # Store a memory
    store_resp = await memory.store(content="The user prefers Python for ML tasks.", tags=["preferences", "ml"])
    print(f"Stored memory ID: {store_resp.id}")

    # Recall memories using semantic reasoning
    results = await memory.recall(query="What language does the user prefer for machine learning?", limit=5)

    for result in results:
        print(f"Content: {result.content}")
        print(f"Reasoning trace: {result.reasoning}")


if __name__ == "__main__":
    asyncio.run(main())

Configuration Options

You can configure the SDK using constructor parameters or environment variables:

memory = Memory(
    base_url="http://localhost:7474",
    project="my-agent",
    reasoning_model="gpt-4o",
    timeout=30.0,
)

Environment Variables:

• REMEM_API_URL — API server URL (default: http://localhost:7474)
• REMEM_PROJECT — Target project namespace (default: default)
• REMEM_REASONING_MODEL — Reasoning model (default: claude-sonnet-4-6)
• REMEM_TIMEOUT — Request timeout in seconds (default: 30)

API Reference

Memory Methods

• store(content: str, tags: list[str] = None, importance: float = None) -> StoreResponse: Store a new memory.
• recall(query: str, limit: int = 8) -> list[RecallResult]: Retrieve contextually relevant memories utilizing LLM evaluation.
• search(query: str, limit: int = 10) -> list[SearchResult]: Execute rapid full-text/vector search.
• update(memory_id: str, content: str) -> UpdateResponse: Modify existing memory content.
• forget(memory_id: str) -> ForgetResponse: Delete a specific memory item.
• consolidate(session_id: str) -> ConsolidateResponse: Transform short-term session logs into long-term durable facts.

Development

To develop the SDK locally:

cd sdk/python

# Install dependencies (requires Python 3.11+)
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Format and Lint
black rememhq/ tests/
ruff check rememhq/ tests/

Contributing

We welcome contributions! Please review our Contributing Guide for details on submitting pull requests, reporting issues, and suggesting enhancements.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.