Backend Usage

Learn how to use the MIRIX backend directly through Python code for maximum flexibility and control over your personal assistant.

Getting Started

Initialize the Agent

First, create and initialize your MIRIX agent:

from mirix.agent import AgentWrapper

# Initialize agent with configuration
agent = AgentWrapper("./configs/mirix.yaml")

Configuration File

Make sure your mirix.yaml configuration file and .env file are properly set up before initializing the agent.

Basic Operations

Sending Messages

Simple Text Messages

# Send basic text information
agent.send_message(
    message="The moon now has a president.",
    memorizing=True,
    force_absorb_content=True
)

MIRIX can process text, images, and voice recordings together:

# Send information with images and voice
agent.send_message(
    message="I'm working on a new project about machine learning.",
    image_uris=["/path/to/screenshot1.png", "/path/to/screenshot2.png"],
    memorizing=True,
    force_absorb_content=True
)

For complex multi-modal conversations:

# Multi-modal message format
agent.send_message(
    message=[
        {'type': 'text', 'text': "The moon now has a president. This is how she looks like:"},
        {'type': 'image', 'image_url': "base64_encoded_image"}
    ],
    image_uris=["/path/to/image_1", "/path/to/image_2"],
    memorizing=True,
    force_absorb_content=True
)

Conversational Queries

# Ask questions about your activities
response = agent.send_message("What was I working on yesterday?")
print("MIRIX:", response)

# Get specific information
response = agent.send_message("Show me documents about PostgreSQL")
print("MIRIX:", response)

Understanding Parameters

Memory Management Control

The memorizing parameter controls where messages are routed:

Value	Destination	Purpose
`True`	Meta-memory-manager	Store information for long-term recall and knowledge building
`False`	Chat agent	Direct conversation without memory storage

# Store information for future reference
agent.send_message(
    message="Project meeting notes - new feature requirements discussed",
    memorizing=True,
    force_absorb_content=True
)

# Direct chat without memory storage
agent.send_message(
    message="What's the weather like today?",
    memorizing=False
)

Content Absorption Control

The force_absorb_content parameter controls when information is processed:

Value	Behavior	Use Case
`True`	Process immediately	Important information, real-time updates
`False`	Batch processing (after 20 images)	Regular browsing, background activities

# Immediate processing for important content
agent.send_message(
    message="Critical meeting notes from client call",
    memorizing=True,
    force_absorb_content=True
)

# Batch processing for regular activities
agent.send_message(
    message="Browsing documentation",
    memorizing=True,
    force_absorb_content=False
)

Input Types and Formats

Message Parameter

Can be either a string or a structured list:

# Simple string
message = "Working on the project documentation"

# Structured multi-modal format
message = [
    {'type': 'text', 'text': "Here's the latest design mockup:"},
    {'type': 'image', 'image_url': "data:image/png;base64,iVBORw0KGgoAAAANS..."}
]

The images included here are in the user query. When the agent is generating responses, it will respond to the multi-modal query.

Image Handling

These are background images that will be treated as screenshots, the agent will not respond to these images, but rather extract information from them and save into memory.

# Use local file paths
image_uris = [
    "/Users/username/Screenshots/screenshot1.png",
    "/Users/username/Screenshots/screenshot2.png",
]

Voice Files

Under Robustness Test, Coming Soon.

Complete Workflow Examples

Example 1: Daily Work Session

from mirix.agent import AgentWrapper

# Initialize
agent = AgentWrapper("./configs/mirix.yaml")

# Morning briefing
agent.send_message(
    message="Starting work day. Today's priorities: finish documentation, review code, team meeting at 2 PM",
    memorizing=True,
    force_absorb_content=True,
)

# Work activities (batch processing)
agent.send_message(
    message="Working on API documentation",
    image_uris=["/screenshots/vscode_api_docs.png"],
    memorizing=True,
    force_absorb_content=False
)

agent.send_message(
    message="Code review session with team",
    image_uris=["/screenshots/github_pr.png"],
    memorizing=True,
    force_absorb_content=False
)

# Important meeting (immediate processing all existing accumulated messages)
agent.send_message(
    message="Team meeting - discussed Q4 roadmap, new feature priorities",
    voice_files=["base64_meeting_recording"],
    memorizing=True,
    force_absorb_content=True
)

# End of day query
response = agent.send_message("Summarize what I accomplished today")
print("Today's Summary:", response)

Example 2: Research Session

# Research topic introduction
agent.send_message(
    message="Researching machine learning optimization techniques",
    memorizing=True,
    force_absorb_content=True
)

# Reading and capturing research materials
research_screenshots = [
    "/screenshots/arxiv_paper1.png",
    "/screenshots/arxiv_paper2.png",
    "/screenshots/github_implementation.png"
]

agent.send_message(
    message="Reading papers on gradient descent optimization",
    image_uris=research_screenshots,
    memorizing=True,
    force_absorb_content=False
)

# Taking notes
agent.send_message(
    message=[
        {'type': 'text', 'text': "Key insight from the research:"},
        {'type': 'text', 'text': "Adam optimizer performs better than SGD for sparse gradients"}
    ],
    memorizing=True,
    force_absorb_content=True
)

# Query research findings
findings = agent.send_message("What are the key points about optimization techniques I discovered?")
print("Research Findings:", findings)

What's Next?

Learn about performance optimization and advanced topics:

Performance →