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-0.19.8.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-0.19.8-py3-none-any.whl (111.3 kB view details)

Uploaded Python 3

File details

Details for the file jl_ecms_client-0.19.8.tar.gz.

File metadata

  • Download URL: jl_ecms_client-0.19.8.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-0.19.8.tar.gz
Algorithm Hash digest
SHA256 0a6018eaa1eab09fe9f42bf7162b80823a711d9af2c15e3243913ac60ebd3ecd
MD5 c87be026835afaba409c6d0f655c5905
BLAKE2b-256 bcdbd47d4db31ee7faa4ab9559cd0541c8ca8bb4f6ac8deb259eb5dc05245619

See more details on using hashes here.

File details

Details for the file jl_ecms_client-0.19.8-py3-none-any.whl.

File metadata

File hashes

Hashes for jl_ecms_client-0.19.8-py3-none-any.whl
Algorithm Hash digest
SHA256 12e8000cec37e650cddc7e2cd9a139fde93f6b2a91a357ba0c304ecfd9e35e95
MD5 876959058f9e43871d4a211b42086275
BLAKE2b-256 dc5e411b7fe8392f2fac0f2f38c53934c56133fdc5f93661dc4b30d0411844e1

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