Neo4j Agent Memory

Neo4j Labs · Graph-native memory for AI agents

Your agents forget everything. Fix that.

Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j.

Experimental · Community Supported

pip install neo4j-agent-memory

Get Started | GitHub | PyPI

Three Memory Types, One Graph

Most agent memory systems give you a flat context window or a simple vector store. Neo4j Agent Memory gives your agent three distinct memory layers — all connected in a single knowledge graph.

Memory Type	What It Stores	Key Features
Short-Term	Conversations & experiences	Semantic message search, session scoping, metadata filters, LLM-powered summaries
Long-Term	Facts, entities & preferences	POLE+O entity classification, entity deduplication, temporal fact validity, geospatial queries
Reasoning	Tool usage & reasoning traces	Trace similarity search, tool call statistics, message-linked traces, streaming trace recording

Memory Type

What It Stores

Key Features

Short-Term

Conversations & experiences

Semantic message search, session scoping, metadata filters, LLM-powered summaries

Long-Term

Facts, entities & preferences

POLE+O entity classification, entity deduplication, temporal fact validity, geospatial queries

Reasoning

Tool usage & reasoning traces

Trace similarity search, tool call statistics, message-linked traces, streaming trace recording

async with MemoryClient(settings) as memory:
    # Short-term: store conversations
    await memory.short_term.add_message(session_id, "user", "Find Italian restaurants near me")

    # Long-term: build a knowledge graph
    await memory.long_term.add_entity("La Trattoria", "ORGANIZATION", subtype="COMPANY")

    # Reasoning: record how the agent solved problems
    trace = await memory.reasoning.start_trace(session_id, task="Restaurant search")
    await memory.reasoning.record_tool_call(step.id, "search_api", {"query": "Italian"}, results)
    await memory.reasoning.complete_trace(trace.id, outcome="Recommended La Trattoria", success=True)

    # Get unified context for LLM prompts
    context = await memory.get_context("restaurant recommendation", session_id=session_id)

What Makes This Different

Capability	Why It Matters
Graph-Native, Not Bolted On	Built on Neo4j from the ground up. Entities, messages, traces, and preferences are all nodes and relationships — not rows in a table or chunks in a vector store. Query across memory types with a single traversal.
Vector + Graph + Spatial in One	Semantic similarity search, graph traversal, and geospatial queries all in one database. No separate vector DB, no Redis, no Postgres. Find entities by meaning, by relationship, or by location.
Multi-Stage Entity Extraction	Combine spaCy, GLiNER2, GLiREL, and LLM extractors in a configurable pipeline. 8 domain schemas (podcast, news, medical, legal…). Streaming extraction for 100K+ token documents.
Three Memory Layers, Connected	Short-term conversations inform long-term entity extraction. Reasoning traces link back to triggering messages. The agent understands what happened, what it knows, and how it solved things.
Wikipedia Enrichment & Geocoding	Entities are automatically enriched with Wikipedia descriptions, images, and Wikidata IDs. Location entities get coordinates for geospatial queries. All in the background.
Framework Agnostic	Works with LangChain, PydanticAI, LlamaIndex, and CrewAI. Or use the client directly. Your agent framework, your choice — the memory layer stays the same.

Capability

Why It Matters

Graph-Native, Not Bolted On

Built on Neo4j from the ground up. Entities, messages, traces, and preferences are all nodes and relationships — not rows in a table or chunks in a vector store. Query across memory types with a single traversal.

Vector + Graph + Spatial in One

Semantic similarity search, graph traversal, and geospatial queries all in one database. No separate vector DB, no Redis, no Postgres. Find entities by meaning, by relationship, or by location.

Multi-Stage Entity Extraction

Combine spaCy, GLiNER2, GLiREL, and LLM extractors in a configurable pipeline. 8 domain schemas (podcast, news, medical, legal…). Streaming extraction for 100K+ token documents.

Three Memory Layers, Connected

Short-term conversations inform long-term entity extraction. Reasoning traces link back to triggering messages. The agent understands what happened, what it knows, and how it solved things.

Wikipedia Enrichment & Geocoding

Entities are automatically enriched with Wikipedia descriptions, images, and Wikidata IDs. Location entities get coordinates for geospatial queries. All in the background.

Framework Agnostic

Works with LangChain, PydanticAI, LlamaIndex, and CrewAI. Or use the client directly. Your agent framework, your choice — the memory layer stays the same.

Demos

Lenny’s Podcast Memory Explorer (Flagship)

299 podcast episodes transformed into a searchable knowledge graph with a full-stack AI chat agent, interactive graph visualization, geospatial map view, and Wikipedia-enriched entity cards.

Episodes loaded	Agent tools	Memory types
299	19	3

Episodes loaded

Agent tools

Memory types

299

Interactive graph visualization (Neo4j NVL)
Geospatial map with marker clusters & heatmaps
Wikipedia-enriched entity cards with images
SSE streaming with tool call visualization
Automatic preference learning from conversation
FastAPI + PydanticAI + Next.js + Chakra UI

Try Live Demo | View on GitHub

Full-Stack Chat Agent

A news research assistant that uses all three memory types with an interactive memory graph visualization. Double-click nodes to expand neighbors and explore the knowledge graph.

Memory graph visualization with expansion
Conversation-scoped graph filtering
Dual Neo4j databases (memory + news)

View on GitHub

Domain Schema Examples

8 specialized extraction schemas for different domains. See how GLiNER2 domain schemas improve entity extraction accuracy with type descriptions and confidence tuning.

Schemas: Podcast · News · Scientific · Business · Medical · Legal · Entertainment · POLE+O

View on GitHub

Quick Start

pip install neo4j-agent-memory[openai]

import asyncio
from pydantic import SecretStr
from neo4j_agent_memory import MemoryClient, MemorySettings

async def main():
    settings = MemorySettings(
        neo4j={"uri": "bolt://localhost:7687", "password": SecretStr("password")}
    )

    async with MemoryClient(settings) as memory:
        # Store a conversation
        await memory.short_term.add_message("user-123", "user", "I love Italian food!")

        # Build knowledge automatically
        await memory.long_term.add_preference("food", "Loves Italian cuisine", confidence=0.9)

        # Get combined context for your LLM
        context = await memory.get_context("restaurant recommendation", session_id="user-123")
        print(context)

asyncio.run(main())

Entity Extraction Pipeline

Combine extractors in a configurable pipeline. 5 merge strategies. 8 domain schemas. Streaming extraction for 100K+ token documents.

Stage	Purpose	Best for
spaCy	Fast rule-based & statistical NER	High throughput, no API cost
GLiNER2	Zero-shot NER with domain schemas	Accuracy without LLM cost
GLiREL	Relation extraction between entities	Graph edge extraction
LLM fallback	High-accuracy extraction via API	Complex or ambiguous text

Stage

Purpose

Best for

spaCy

Fast rule-based & statistical NER

High throughput, no API cost

GLiNER2

Zero-shot NER with domain schemas

Accuracy without LLM cost

GLiREL

Relation extraction between entities

Graph edge extraction

LLM fallback

High-accuracy extraction via API

Complex or ambiguous text

Framework Integrations

Framework	Integration	Guide
LangChain	Memory & retriever	LangChain guide
PydanticAI	Deps & trace recording	PydanticAI guide
LlamaIndex	Memory nodes	LlamaIndex guide
CrewAI	Crew memory	CrewAI guide
AWS Strands	Agent tools	Strands guide
Google ADK	MemoryService	ADK tutorial

Framework

Integration

Guide

LangChain

Memory & retriever

LangChain guide

PydanticAI

Deps & trace recording

PydanticAI guide

LlamaIndex

Memory nodes

LlamaIndex guide

CrewAI

Crew memory

CrewAI guide

AWS Strands

Agent tools

Strands guide

Google ADK

MemoryService

ADK tutorial

Architecture

Documentation

Section	Purpose	Start here
Tutorials	Learn by building complete examples	Build your first memory-enabled agent
How-To Guides	Practical recipes for common tasks	Configure entity extraction
Reference	Technical specifications and API docs	Configuration reference
Explanation	Concepts and architecture	Understanding memory types

Section

Purpose

Start here

Tutorials

Learn by building complete examples

Build your first memory-enabled agent

How-To Guides

Practical recipes for common tasks

Configure entity extraction

Reference

Technical specifications and API docs

Configuration reference

Explanation

Concepts and architecture

Understanding memory types

Quick Links

Guide	Description
Build Your First Agent	Installation, setup, and your first memory-enabled agent
Understanding Memory Types	Deep dive into short-term, long-term, and reasoning memory
Configure Entity Extraction	Domain schemas, multi-stage pipelines, and custom extractors
Configuration Reference	Complete configuration options and environment variables
Framework Integrations	Using with PydanticAI, LangChain, LlamaIndex, and CrewAI

Guide

Description

Build Your First Agent

Installation, setup, and your first memory-enabled agent

Understanding Memory Types

Deep dive into short-term, long-term, and reasoning memory

Configure Entity Extraction

Domain schemas, multi-stage pipelines, and custom extractors

Configuration Reference

Complete configuration options and environment variables

Framework Integrations

Using with PydanticAI, LangChain, LlamaIndex, and CrewAI