echograph-rag 0.2.0

Official Python SDK & Local-First RAG Memory Engine for AI Agents

EchoGraph Python SDK

Official Python Client for the EchoGraph Cognitive Memory Engine. Easily connect long-term episodic memory to your LLMs (OpenAI, Gemini, Claude, LangChain, or custom agents).

🚀 1-Step Zero-Config Setup

Users can set up everything with two simple commands:

pip install echograph
echograph setup

That's it! echograph setup automatically detects the operating system (macOS, Windows, or Linux), locates Claude Desktop's configuration file, and registers the EchoGraph Local SQLite RAG MCP Server.

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🛠️ CLI Commands

Command	Description
echograph setup	Automatically configures Claude Desktop MCP server (zero manual JSON editing)
echograph list	Lists all local SQLite RAG memories
echograph store "text"	Stores a new memory into local SQLite DB
echograph search "query"	Performs semantic RAG vector search locally
echograph sync	Uploads local SQLite data to cloud server
echograph pull	Downloads cloud memories onto local device

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1. Local-First Mode (Offline SQLite Database)

Stores and retrieves RAG memories locally at ~/.echograph/echograph_local.db with sub-millisecond response times. Zero server required!

from echograph import EchoGraphClient

# Initialize local-first client (default)
client = EchoGraphClient(local_first=True)

# Store memory locally in SQLite
client.store("User prefers dark mode UI and SQLite database", type="preference")

# Search local RAG context
context = client.format_context("database preferences")
print(context)

2. On-Demand Cloud Sync ("Upload to Cloud")

Uploads all local SQLite memories to the EchoGraph cloud server when requested by user or sync routine:

client.authenticate("[e-mail]", "your_password")

# Upload all un-synced local memories to cloud
sync_res = client.upload_to_cloud()
print(sync_res)

# Pull synced cloud memories onto a new device
pull_res = client.pull_from_cloud()
print(pull_res)