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Cover image for Local LLM Observability for Developers — Introducing DoCoreAI (Free)
Saji John Miranda
Saji John Miranda

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Local LLM Observability for Developers — Introducing DoCoreAI (Free)

#ai #python #llm #observability

Most LLM debugging tools only work in the cloud.

That means your prompts, responses, latencies, and costs get routed through external systems — which makes it hard to see what your model is really doing inside your own environment.

So we built something different.

🔥 Meet DoCoreAI — Local Observability for GenAI Apps

DoCoreAI runs locally, right beside your Python application, and gives developers instant visibility into:

  • 🔹 Token usage
  • 🔹 Latency & bottlenecks
  • 🔹 Cost per prompt
  • 🔹 Temperature behavior
  • 🔹 Model drift
  • 🔹 Response variations

All without sending any data to a cloud server.

Your prompts stay on your machine.

You get production-grade observability without cloud dashboards, lock-in, or complex setup.

🚀 Why Local Observability Matters

Cloud-based LLM monitoring tools (Langfuse, PromptLayer, etc.) are great, but they don’t always show:

✔ Real latency inside your environment

✔ Real behavior drift with your data

✔ Real cost impact inside your infra

✔ How temperature affects output in your pipeline

Local-first debugging gives you the truth.

If you're building:

  • AI agents
  • RAG applications
  • customer support tools
  • automation workflows
  • prompt pipelines
  • multi-model switching systems

…you need visibility before deployment.

⚡ Install & Run (It’s Just 3 Commands) 

pip install docoreai
docoreai start
docoreai stop
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This starts a local collector that observes your LLM calls (OpenAI, Anthropic, Gemini, etc.) and displays charts in your browser.

Free for up to 10 prompts.
Login to see full dashboards.

📊 What You’ll See

When DoCoreAI is running alongside your app, you’ll get:

🧠 Token usage breakdown

Which prompts are consuming the most?

⏱ Latency visualization

Where are you losing time?

📉 Operational Cost

Which prompts are bleeding your token budget?

🔥 Temperature behavior graph

How does temperature affect accuracy or creativity?

🌡 Model drift indicators

Detect inconsistencies early.

Most developers are surprised by how much inefficiency becomes obvious once they visualize real-world usage.

🔒 Privacy First: Nothing Leaves Your Machine

This is one of the biggest differences between DoCoreAI and cloud-based monitoring:

✔ All your prompts stay local
✔ All metrics are generated locally
✔ No prompt data is transmitted
✔ No vendor lock-in

If you’re working with sensitive data, this matters.

🎁 Free Tier for Individual Developers

You get:

  • 10 prompts fully visualized

  • local collector

  • latency + token + cost + drift charts

  • temperature evaluator

  • developer playground behavior (via your own app)

Perfect for:

  • debugging

  • optimizing

  • comparing models

  • understanding behavior differences

🧑‍💻 Try It Yourself

pip install docoreai
docoreai start
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💬 Feedback Wanted

We built DoCoreAI because we wanted an easier way to debug GenAI applications without sending prompts to external systems.

If you try it, I’d love to hear:

  • What metrics matter most?

  • What should we add next?

  • Would you use a local developer playground?

  • Should we open-source parts of the collector?

  • Comment below — I’ll respond to everyone.

⭐ Final Thoughts

LLMs are powerful, but debugging them is still painful.
Local observability gives developers the visibility they need to build faster, cheaper, and more reliable AI systems.

If you're tired of guessing what your model is doing, give DoCoreAI a try.

Happy prompting! 🚀

🔗 Register → https://docoreai.com/register

🔐 Generate Token → https://docoreai.com/generate-token

📘 Docs → https://docoreai.com/docs

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