base on TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models. <img src="https://github.com/user-attachments/assets/47d67430-386d-4675-82ad-d4734d3262d9" width=128 height=128> # 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 <p align="center"> <b><a href="https://www.tensorzero.com/" target="_blank">Website</a></b> · <b><a href="https://www.tensorzero.com/docs" target="_blank">Docs</a></b> · <b><a href="https://www.x.com/tensorzero" target="_blank">Twitter</a></b> · <b><a href="https://www.tensorzero.com/slack" target="_blank">Slack</a></b> · <b><a href="https://www.tensorzero.com/discord" target="_blank">Discord</a></b> <br> <br> <b><a href="https://www.tensorzero.com/docs/gateway/quickstart" target="_blank">Quick Start (5min)</a></b> · <b><a href="https://www.tensorzero.com/docs/gateway/tutorial" target="_blank">Comprehensive Tutorial</a></b> · <b><a href="https://www.tensorzero.com/docs/gateway/deployment" target="_blank">Deployment Guide</a></b> · <b><a href="https://www.tensorzero.com/docs/gateway/api-reference" target="_blank">API Reference</a></b> · <b><a href="https://www.tensorzero.com/docs/gateway/deployment" target="_blank">Configuration Reference</a></b> </p> ## 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 <br> <p align="center" > <a href="https://www.tensorzero.com/docs"> <picture> <source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/34a92c18-242e-4d76-a99c-861283de68a6"> <source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/e8bc699b-6378-4c2a-9cc1-6d189025e270"> <img alt="TensorZero Flywheel" src="https://github.com/user-attachments/assets/34a92c18-242e-4d76-a99c-861283de68a6" width=720> </picture> </a> </p> <br> 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 &lt;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!_ ", Assign "at most 3 tags" to the expected json: {"id":"12213","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"