shivnathtathe

shivnathtathe / opensmith

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The open-source, local-first alternative to LangSmith. No cloud. No setup.

shivnathtathe.com anthropic claude-code langsmith-alternative llm local-ai
11
2
100% credibility
Found May 08, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OpenSmith is an open-source tool for locally tracing and visualizing steps, performance, token usage, and costs in Python AI language model pipelines without cloud services.

How It Works

1
📖 Discover OpenSmith

You learn about a simple local tool that tracks your AI experiments without sending anything online.

2
🛠️ Add it easily

You install it on your computer with one quick command, no accounts needed.

3
✨ Start tracking

You add a tiny marker to your AI steps, and it quietly watches everything they do.

4
▶️ Run your AI

You use your AI programs exactly as usual, and it captures the details behind the scenes.

5
📊 See quick summaries

Summaries of speed, results, and costs pop up right on your screen as tasks finish.

6
🌐 Open your dashboard

You launch a private webpage on your computer to explore full details visually.

✅ Master your AI

You now clearly see every step, timing, and cost of your AI work, all safe and local.

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Star Growth

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AI-Generated Review

What is opensmith?

opensmith is a Python-based, open-source GitHub project that provides local-first LLM tracing as a self-hosted alternative to cloud-dependent LangSmith. It lets you monitor pipelines with decorators, context managers, or zero-code autopatching for backends like OpenAI, Anthropic, and vector stores such as Qdrant or ChromaDB—all stored in a local SQLite database. Run `opensmith ui` for a dashboard at localhost:7823, or use CLI commands like `traces`, `stats`, and `clear` for quick insights without any setup beyond `pip install opensmith`.

Why is it gaining traction?

Unlike LangSmith's cloud requirements and LangChain focus, opensmith runs fully offline with no accounts or data leaks, making it a privacy-first open-source tool for any Python LLM code. Autopatch hooks popular libraries automatically, console mode prints traces live, and it tracks latency, tokens, and costs out of the box. Developers grab it for instant observability in local experiments, dodging vendor lock-in.

Who should use this?

Python devs building RAG pipelines, agents, or chat apps with OpenAI/Anthropic who need quick debugging without cloud bills. Ideal for solo prototypers or teams evaluating open-source GitHub tools before scaling to hosted services. Skip if you're deep in LangChain and okay with remote tracing.

Verdict

Try opensmith for local LLM dev—it's a solid, no-fuss alternative with clean docs and MIT license, but at 11 stars and 1.0% credibility, it's alpha-stage with limited testing. Great starter for self-hosted tracing; watch for production hardening.

(198 words)

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