Documentation Index
Fetch the complete documentation index at: https://docs.jeanmemory.com/llms.txt
Use this file to discover all available pages before exploring further.
The Jean Memory Python SDK provides a simple, headless interface to our powerful Context API.
Installation
The Golden Path
How to add memory to an AI agent in 5 steps.
Initialize Clients
Create instances of JeanMemoryClient and your LLM client (e.g., OpenAI).
Get User Token
Retrieve the user_token from your frontend (passed via OAuth) to identify the user.
Get Context
Call jean.get_context() with the user’s message. Jean Memory synthesizes the perfect background info.
Engineer Prompt
Inject the retrieved context before the user’s question in your final system prompt.
Call LLM
Send the context-rich prompt to your model for a personalized answer.
Complete Example
import os
from openai import OpenAI
from jeanmemory import JeanMemoryClient
# 1. Initialize
jean = JeanMemoryClient(api_key=os.environ["JEAN_API_KEY"])
openai = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
# 2. Get User Token (from frontend request)
user_token = get_token_from_request()
# 3. Get Context
user_msg = "What were the key takeaways from my last meeting?"
context = jean.get_context(
user_token=user_token,
message=user_msg
).text
# 4. Engineer Prompt
prompt = f"""
Context:
{context}
User Question: {user_msg}
"""
# 5. Call LLM
completion = openai.chat.completions.create(
model="gpt-4-turbo",
messages=[{"role": "user", "content": prompt}]
)
Configuration
Control speed, tools, and formatting.
# Options: "fast", "balanced" (default), "comprehensive"
context = jean.get_context(
user_token=token,
message=msg,
speed="fast"
)
Headless Authentication
For backend-only apps (no frontend user).
# Option 1: Test Mode (Development)
# Passing None creates an automatic test user
jean.get_context(user_token=None, message="Hello")
# Option 2: Manual OAuth (Production)
url = jean.get_auth_url(callback_url="...")
# ... user visits URL ...
token = jean.exchange_code_for_token(code)