Python SDK Quickstart

Installation

Install the core Python package via pip. You can also specify optional vector backend dependencies:

Terminal

# Core embedded package
pip install negentro-memory

# Or install with optional database drivers (pgvector, redis, qdrant)
pip install "negentro-memory[pgvector,redis]"

Quickstart Code

Initialize the memory engine and start storing conversational context in under five lines of code:

quickstart.py

from negentro import Memory

# 1. Initialize embedded memory
m = Memory()

# 2. Add memories for a specific user
m.add(
    "User prefers dark mode UI and works primarily with React and TypeScript.",
    user_id="usr_9918",
    metadata={"category": "preferences"}
)

# 3. Retrieve relevant context
results = m.search(
    query="What frontend framework does the user like?",
    user_id="usr_9918",
    limit=3
)

for mem in results["results"]:
    print(f"Score: {mem['score']} | Content: {mem['memory']}")

Core Memory Operations

The Python SDK provides complete CRUD operations with support for temporal audits and user boundaries:

Method	Parameters	Description
m.add(data, user_id=…)	content, user_id, metadata, agent_id	Extracts facts from natural language text and persists them.
m.search(query, user_id=…)	query, user_id, limit, threshold	Runs hybrid vector + keyword retrieval with Reciprocal Rank Fusion.
m.get_all(user_id=…)	user_id, agent_id, limit	Returns all active memories for the scoped identity.
m.update(memory_id, data)	memory_id, new_content	Explicitly updates a memory and records a new bitemporal revision.
m.delete(memory_id)	memory_id	Deletes a memory record and purges vector index entries.
m.history(memory_id)	memory_id	Returns the complete audit log of changes made to this memory.

LangChain & CrewAI Integration

Use Negentro as the persistent memory provider for popular agent orchestration frameworks:

agent_integration.py

from langchain_openai import ChatOpenAI
from negentro.integrations.langchain import NegentroMemory

# Plug Negentro directly into LangChain agents
memory = NegentroMemory(user_id="alex_204")
llm = ChatOpenAI(model="gpt-4o")

# Memory automatically syncs after each turn
context = memory.load_memory_variables({"prompt": "What did we discuss earlier?"})

Getting Started

Configuration & Architecture

Installation

Quickstart Code

Core Memory Operations

LangChain & CrewAI Integration

​Installation

​Quickstart Code

​Core Memory Operations

​LangChain & CrewAI Integration

Installation

Quickstart Code

Core Memory Operations

LangChain & CrewAI Integration