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https://github.com/tensorzero/tensorzero
TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models.
https://github.com/tensorzero/tensorzero
ai ai-engineering anthropic artificial-intelligence deep-learning genai generative-ai gpt large-language-models llama llm llmops llms machine-learning ml ml-engineering mlops openai python rust
Last synced: 2 days ago
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TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models.
- Host: GitHub
- URL: https://github.com/tensorzero/tensorzero
- Owner: tensorzero
- License: apache-2.0
- Created: 2024-07-16T21:00:53.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-02T23:29:06.000Z (9 days ago)
- Last Synced: 2025-01-03T05:02:50.286Z (9 days ago)
- Topics: ai, ai-engineering, anthropic, artificial-intelligence, deep-learning, genai, generative-ai, gpt, large-language-models, llama, llm, llmops, llms, machine-learning, ml, ml-engineering, mlops, openai, python, rust
- Language: Rust
- Homepage: https://tensorzero.com
- Size: 14.5 MB
- Stars: 1,653
- Watchers: 18
- Forks: 76
- Open Issues: 83
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
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README
# TensorZero
**TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models.**
1. Integrate our model gateway
2. Send metrics or feedback
3. Optimize prompts, models, and inference strategies
4. Watch your LLMs improve over time
It provides a **data & learning flywheel for LLMs** by unifying:
- [x] **Inference:** one API for all LLMs, with <1ms P99 overhead
- [x] **Observability:** inference & feedback → your database
- [x] **Optimization:** from prompts to fine-tuning and RL (& even 🍓? **[→](https://www.tensorzero.com/docs/gateway/guides/inference-time-optimizations)**)
- [x] **Experimentation:** built-in A/B testing, routing, fallbacks
Website
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Docs
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Twitter
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Slack
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Discord
Quick Start (5min)
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Comprehensive Tutorial
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Deployment Guide
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API Reference
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Configuration Reference
## Demo
> [!NOTE]
>
> **Watch LLMs get better at data extraction in real time with TensorZero!**
>
> **[Dynamic in-context learning (DICL)](https://www.tensorzero.com/docs/gateway/guides/inference-time-optimizations#dynamic-in-context-learning-dicl)** is a powerful inference-time optimization available out of the box with TensorZero.
> It enhances LLM performance by automatically incorporating relevant historical examples into the prompt, without the need for model fine-tuning.
https://github.com/user-attachments/assets/4df1022e-886e-48c2-8f79-6af3cdad79cb
## Overview
1. The **[TensorZero Gateway](https://www.tensorzero.com/docs/gateway/)** is a high-performance model gateway written in Rust 🦀 that provides a unified API interface for all major LLM providers, allowing for seamless cross-platform integration and fallbacks.
2. It handles structured schema-based inference with <1ms P99 latency overhead (see **[Benchmarks](https://www.tensorzero.com/docs/gateway/benchmarks)**) and built-in observability, experimentation, and **[inference-time optimizations](https://www.tensorzero.com/docs/gateway/guides/inference-time-optimizations)**.
3. It also collects downstream metrics and feedback associated with these inferences, with first-class support for multi-step LLM systems.
4. Everything is stored in a ClickHouse data warehouse that you control for real-time, scalable, and developer-friendly analytics.
5. Over time, **[TensorZero Recipes](https://www.tensorzero.com/docs/recipes)** leverage this structured dataset to optimize your prompts and models: run pre-built recipes for common workflows like fine-tuning, or create your own with complete flexibility using any language and platform.
6. Finally, the gateway's experimentation features and GitOps orchestration enable you to iterate and deploy with confidence, be it a single LLM or thousands of LLMs.
Our goal is to help engineers build, manage, and optimize the next generation of LLM applications: systems that learn from real-world experience.
Read more about our **[Vision & Roadmap](https://www.tensorzero.com/docs/vision-roadmap/)**.
## Get Started
**Start building today.**
The **[Quick Start](https://www.tensorzero.com/docs/gateway/quickstart)** shows it's easy to set up an LLM application with TensorZero.
If you want to dive deeper, the **[Tutorial](https://www.tensorzero.com/docs/gateway/tutorial)** teaches how to build a simple chatbot, an email copilot, a weather RAG system, and a structured data extraction pipeline.
**Questions?**
Ask us on **[Slack](https://www.tensorzero.com/slack)** or **[Discord](https://www.tensorzero.com/discord)**.
**Using TensorZero at work?**
Email us at **[[email protected]](mailto:[email protected])** to set up a Slack or Teams channel with your team (free).
**Work with us.**
We're **[hiring in NYC](https://www.tensorzero.com/jobs)**.
We'd also welcome **[open-source contributions](https://github.com/tensorzero/tensorzero/blob/main/CONTRIBUTING.md)**!
## Examples
We are working on a series of **complete runnable examples** illustrating TensorZero's data & learning flywheel.
> **[Writing Haikus to Satisfy a Judge with Hidden Preferences](https://github.com/tensorzero/tensorzero/tree/main/examples/haiku-hidden-preferences)**
>
> This example fine-tunes GPT-4o Mini to generate haikus tailored to a specific taste.
> You'll see TensorZero's "data flywheel in a box" in action: better variants leads to better data, and better data leads to better variants.
> You'll see progress by fine-tuning the LLM multiple times.
> **[Improving Data Extraction (NER) by Fine-Tuning a Llama 3 Model](https://github.com/tensorzero/tensorzero/tree/main/examples/ner-fine-tuning)**
>
> This example shows that an optimized Llama 3.1 8B model can be trained to outperform GPT-4o on a Named Entity Recognition (NER) task using a small amount of training data, and served by Fireworks at a fraction of the cost and latency.
> **[Improving LLM Chess Ability with Best-of-N Sampling](https://github.com/tensorzero/tensorzero/tree/main/examples/chess-puzzles-best-of-n-sampling/)**
>
> This example showcases how best-of-N sampling can significantly enhance an LLM's chess-playing abilities by selecting the most promising moves from multiple generated options.
> **[Improving Data Extraction (NER) with Dynamic In-Context Learning](https://github.com/tensorzero/tensorzero/tree/main/examples/ner-dicl)**
>
> This example demonstrates how Dynamic In-Context Learning (DICL) can enhance Named Entity Recognition (NER) performance by leveraging relevant historical examples to improve data extraction accuracy and consistency without having to fine-tune a model.
> **[Improving Math Reasoning with a Custom Recipe for Automated Prompt Engineering (DSPy)](https://github.com/tensorzero/tensorzero/tree/main/examples/gsm8k-custom-recipe-dspy)**
>
> TensorZero provides a number of pre-built optimization recipes covering common LLM engineering workflows.
> But you can also easily create your own recipes and workflows!
> This example shows how to optimize a TensorZero function using an arbitrary tool — here, DSPy.
_& many more on the way!_