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Build Your First Agent with Cortex Memory

This guide builds a simple question-answering agent that remembers facts across conversations.

Prerequisites

  • Cortex server running at localhost:9090 (see Quick Start)
  • Python 3.10+
  • An OpenAI API key

Install

pip install cortex-memory-client openai

The Agent

import os
from openai import OpenAI
from cortex_memory import Cortex

cx = Cortex("localhost:9090")
llm = OpenAI()

AGENT_ID = "my-assistant"

def chat(user_message: str) -> str:
    # Get agent briefing — everything relevant the agent should know
    briefing = cx.briefing(AGENT_ID)

    response = llm.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": f"You are a helpful assistant.\n\n{briefing}"},
            {"role": "user", "content": user_message},
        ]
    )
    reply = response.choices[0].message.content

    # Store the interaction as an event node
    cx.store(
        kind="event",
        title=user_message[:80],
        body=f"User: {user_message}\nAssistant: {reply}",
        source_agent=AGENT_ID,
        importance=0.5,
    )

    return reply

# Run a simple REPL
while True:
    msg = input("You: ")
    if msg.lower() in ("exit", "quit"):
        break
    print(f"Assistant: {chat(msg)}")

What's Happening

  1. At the start of each turn, cx.briefing(AGENT_ID) generates a tailored context document from the knowledge graph — recent events, patterns, goals, and any relevant facts.
  2. After the turn, the conversation is stored as an event node. The auto-linker runs in the background and will wire related events together.
  3. Over time, the graph builds up structured memory that the agent draws on automatically.

Next Steps