Skip to main content

Mirix Client - Lightweight Python client for Mirix server

Project description

Mirix Logo

MIRIX - Multi-Agent Personal Assistant with an Advanced Memory System

Your personal AI that builds memory through screen observation and natural conversation

| 🌐 Website | 📚 Documentation | 📄 Paper | 💬 Discord


Key Features 🔥

  • Multi-Agent Memory System: Six specialized memory components (Core, Episodic, Semantic, Procedural, Resource, Knowledge Vault) managed by dedicated agents
  • Screen Activity Tracking: Continuous visual data capture and intelligent consolidation into structured memories
  • Privacy-First Design: All long-term data stored locally with user-controlled privacy settings
  • Advanced Search: PostgreSQL-native BM25 full-text search with vector similarity support
  • Multi-Modal Input: Text, images, voice, and screen captures processed seamlessly

Quick Start

Step 1: Backend & Dashboard (Docker):

docker compose up -d --pull always

Step 2: Create an API key in the dashboard (http://localhost:5173) and set as the environmental variable MIRIX_API_KEY.

Step 3: Client (Python, mirix-client, https://pypi.org/project/mirix-client/):

pip install mirix-client

Now you are ready to go! See the example below:

from mirix import MirixClient

client = MirixClient(
    api_key="your-api-key",
    base_url="http://localhost:8531",
)

client.initialize_meta_agent(
    config={
        "llm_config": {
            "model": "gemini-2.0-flash",
            "model_endpoint_type": "google_ai",
            "api_key": "your-api-key-here",
            "model_endpoint": "https://generativelanguage.googleapis.com",
            "context_window": 1_000_000,
        },
        "embedding_config": {
            "embedding_model": "text-embedding-004",
            "embedding_endpoint_type": "google_ai",
            "api_key": "your-api-key-here",
            "embedding_endpoint": "https://generativelanguage.googleapis.com",
            "embedding_dim": 768,
        },
        "meta_agent_config": {
            "agents": [
                {
                    "core_memory_agent": {
                        "blocks": [
                            {"label": "human", "value": ""},
                            {"label": "persona", "value": "I am a helpful assistant."},
                        ]
                    }
                },
                "resource_memory_agent",
                "semantic_memory_agent",
                "episodic_memory_agent",
                "procedural_memory_agent",
                "knowledge_vault_memory_agent",
            ],
        },
    }
)

client.add(
    user_id="demo-user",
    messages=[
        {"role": "user", "content": [{"type": "text", "text": "The moon now has a president."}]},
        {"role": "assistant", "content": [{"type": "text", "text": "Noted."}]},
    ],
)

memories = client.retrieve_with_conversation(
    user_id="demo-user",
    messages=[
        {"role": "user", "content": [{"type": "text", "text": "What did we discuss on MirixDB in last 4 days?"}]},
    ],
    limit=5,
)
print(memories)

For more API examples, see samples/run_client.py.

License

Mirix is released under the Apache License 2.0. See the LICENSE file for more details.

Contact

For questions, suggestions, or issues, please open an issue on the GitHub repository or contact us at founders@mirix.io

Join Our Community

Connect with other Mirix users, share your thoughts, and get support:

💬 Discord Community

Join our Discord server for real-time discussions, support, and community updates: https://discord.gg/S6CeHNrJ

🎯 Weekly Discussion Sessions

We host weekly discussion sessions where you can:

  • Discuss issues and bugs
  • Share ideas about future directions
  • Get general consultations and support
  • Connect with the development team and community

📅 Schedule: Friday nights, 8-9 PM PST
🔗 Zoom Link: https://ucsd.zoom.us/j/96278791276

📱 WeChat Group

You can add the account ari_asm so that I can add you to the group chat.

Acknowledgement

We would like to thank Letta for open-sourcing their framework, which served as the foundation for the memory system in this project.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jl_ecms_client-0.19.47.tar.gz (91.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

jl_ecms_client-0.19.47-py3-none-any.whl (111.3 kB view details)

Uploaded Python 3

File details

Details for the file jl_ecms_client-0.19.47.tar.gz.

File metadata

  • Download URL: jl_ecms_client-0.19.47.tar.gz
  • Upload date:
  • Size: 91.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for jl_ecms_client-0.19.47.tar.gz
Algorithm Hash digest
SHA256 f88cbe45931f4e936b64dad8687f9e84b095cc9608627c2cecddf87c577a32c6
MD5 eab725a6cb8cd7a206b5fbee28d35637
BLAKE2b-256 ee11a65b8cb4b025f1b3b09fb07650f0c6005da579ead2fd03d44037e85998c0

See more details on using hashes here.

File details

Details for the file jl_ecms_client-0.19.47-py3-none-any.whl.

File metadata

File hashes

Hashes for jl_ecms_client-0.19.47-py3-none-any.whl
Algorithm Hash digest
SHA256 bbeaccc9ca2583de332ab398914efb6a7fc8601f1846a57bd0327a405fc83c8d
MD5 348f72d0652ab4265f4651da2e987e2e
BLAKE2b-256 a6c107cb071cf0372977cd3bc79eac9b357aab9852f758eb9fbd43efaea039f2

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page