Welcome to xpuls.ai 👋

MLMonitor - Automatic Instrumentation for ML Frameworks

Roadmap 🚀

💡 If support of any framework/feature is useful for you, please feel free to reach out to us via Discord or Github Discussions

🔗 Installation

1. Install from PyPI

pip install xpuls-mlmonitor

🧩 Usage Example

from xpuls.mlmonitor.langchain.instrument import LangchainTelemetry
import os

# Enable this for advance tracking with our xpuls-ml platform
os.environ["XPULSAI_TRACING_ENABLED"] = "true"

# Add default labels that will be added to all captured metrics
default_labels = {"service": "ml-project-service", "k8s_cluster": "app0", "namespace": "dev", "agent_name": "fallback_value"}

# Enable the auto-telemetry
LangchainTelemetry(
  default_labels=default_labels,
  xpuls_host_url="http://app.xpuls.ai" # Optional param, required when XPULSAI_TRACING is enabled
).auto_instrument()

## [Optional] Override labels for scope of decorator [Useful if you have multiple scopes where you need to override the default label values]
@TelemetryOverrideLabels(agent_name="chat_agent_alpha")
def get_response_using_agent_alpha(prompt, query):
    agent = initialize_agent(llm=chat_model,
                             verbose=True,
                             agent=CONVERSATIONAL_REACT_DESCRIPTION,
                             memory=memory)

    res = agent.run(f"{prompt}. \n Query: {query}")

ℹ️ Complete Usage Guides

• Langchain Framework + Grafana Template

🧾 License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.

📢 Contributing

We welcome contributions to MLMonitor! If you're interested in contributing.

If you encounter any issues or have feature requests, please file an issue on our GitHub repository.

💬 Get in touch

👉 Join our Discord community!

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