[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-232":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":8,"totalLinesOfCode":8,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":8,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":8,"rankLanguage":8,"license":8,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":21,"hasPages":21,"topics":23,"createdAt":8,"pushedAt":8,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":15,"starSnapshotCount":15,"syncStatus":16,"lastSyncTime":47,"discoverSource":48},232,"tensorzero","tensorzero\u002Ftensorzero","TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation.",null,"https:\u002F\u002Fgithub.com\u002Ftensorzero\u002Ftensorzero","Rust",11458,836,65,334,0,2,23,100,13,43.77,false,"main",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"ai","artificial-intelligence","deep-learning","gpt","llm","llmops","llms","machine-learning","rust","ml","mlops","anthropic","llama","openai","generative-ai","ai-engineering","python","ml-engineering","large-language-models","genai","2026-06-12 02:00:10","\u003Cp>\u003Cpicture>\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F9d0a93c6-7685-4e57-9737-7cbeb338a218\" alt=\"TensorZero Logo\" width=\"128\" height=\"128\">\u003C\u002Fpicture>\u003C\u002Fp>\n\n# TensorZero\n\n\u003Cp>\u003Cpicture>\u003Cimg src=\"https:\u002F\u002Fwww.tensorzero.com\u002Fgithub-trending-badge.svg\" alt=\"GitHub Trending - #1 Repository Of The Day\">\u003C\u002Fpicture>\u003C\u002Fp>\n\n**TensorZero is an open-source LLMOps platform that unifies:**\n\n- **Gateway:** access every LLM provider through a unified API, built for performance (\u003C1ms p99 latency)\n- **Observability:** store inferences and feedback in your database, available programmatically or in the UI\n- **Evaluation:** benchmark individual inferences or end-to-end workflows using heuristics, LLM judges, etc.\n- **Optimization:** collect metrics and human feedback to optimize prompts, models, and inference strategies\n- **Experimentation:** ship with confidence with built-in A\u002FB testing, routing, fallbacks, retries, etc.\n\nYou can take what you need, adopt incrementally, and complement with other tools.\nIt plays nicely with the **OpenAI SDK**, **OpenTelemetry**, and **every major LLM provider**.\n\nTensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and fuels ~1% of global LLM API spend today.\n\n\u003Cbr>\n\n\u003Cp align=\"center\">\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.tensorzero.com\u002F\" target=\"_blank\">Website\u003C\u002Fa>\u003C\u002Fb>\n  ·\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\" target=\"_blank\">Docs\u003C\u002Fa>\u003C\u002Fb>\n  ·\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.x.com\u002Ftensorzero\" target=\"_blank\">Twitter\u003C\u002Fa>\u003C\u002Fb>\n  ·\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.tensorzero.com\u002Fslack\" target=\"_blank\">Slack\u003C\u002Fa>\u003C\u002Fb>\n  ·\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.tensorzero.com\u002Fdiscord\" target=\"_blank\">Discord\u003C\u002Fa>\u003C\u002Fb>\n  \u003Cbr>\n  \u003Cbr>\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fquickstart\" target=\"_blank\">Quick Start (5min)\u003C\u002Fa>\u003C\u002Fb>\n  ·\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fdeployment\u002Ftensorzero-gateway\" target=\"_blank\">Deployment Guide\u003C\u002Fa>\u003C\u002Fb>\n  ·\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fapi-reference\" target=\"_blank\">API Reference\u003C\u002Fa>\u003C\u002Fb>\n  ·\n  \u003Cb>\u003Ca href=\"https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fconfiguration-reference\" target=\"_blank\">Configuration Reference\u003C\u002Fa>\u003C\u002Fb>\n\u003C\u002Fp>\n\n## Demo\n\n\u003Cvideo src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F04a8466e-27d8-4189-b305-e7cecb6881ee\">\u003C\u002Fvideo>\n\n## Features\n\n> [!NOTE]\n>\n> ### 🆕 TensorZero Autopilot\n>\n> TensorZero Autopilot is an **automated AI engineer** powered by TensorZero that analyzes LLM observability data, sets up evals, optimizes prompts and models, and runs A\u002FB tests.\n>\n> It **dramatically improves the performance of LLM agents** across diverse tasks:\n>\n> \u003Cimg width=\"600\" alt=\"Bar chart showing baseline vs. optimized scores across diverse LLM tasks\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Faa474fe3-b55a-48aa-9f0d-e7c2f8e32ccd\" \u002F>\n> \u003Cbr>\n>\n> **[Learn more →](https:\u002F\u002Fwww.tensorzero.com\u002Fblog\u002Fautomated-ai-engineer\u002F)**  **[Schedule a demo →](https:\u002F\u002Fwww.tensorzero.com\u002Fschedule-demo)**\n\n### 🌐 LLM Gateway\n\n> **Integrate with TensorZero once and access every major LLM provider.**\n\n- [x] **[Call any LLM](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fcall-any-llm)** (API or self-hosted) through a single unified API\n- [x] Infer with **[tool use](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Ftool-use)**, **[structured outputs (JSON)](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fgenerate-structured-outputs)**, **[batch](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fbatch-inference)**, **[embeddings](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fgenerate-embeddings)**, **[multimodal (images, files)](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fcall-llms-with-image-and-file-inputs)**, **[caching](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Finference-caching)**, etc.\n- [x] **[Create prompt templates and schemas](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fcreate-a-prompt-template)** to enforce a structured interface between your application and the LLMs\n- [x] Satisfy extreme throughput and latency needs, thanks to 🦀 Rust: **[\u003C1ms p99 latency overhead at 10k+ QPS](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fbenchmarks)**\n- [x] **[Ensure high availability](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fretries-fallbacks)** with routing, retries, fallbacks, load balancing, granular timeouts, etc.\n- [x] **[Track usage and cost](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foperations\u002Ftrack-usage-and-cost)** and **[enforce custom rate limits](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foperations\u002Fenforce-custom-rate-limits)** with granular scopes (e.g. tags)\n- [x] **[Set up auth for TensorZero](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foperations\u002Fset-up-auth-for-tensorzero)** to allow clients to access models without sharing provider API keys\n\n#### Supported Model Providers\n\n**[Anthropic](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fanthropic)**,\n**[AWS Bedrock](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Faws-bedrock)**,\n**[AWS SageMaker](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Faws-sagemaker)**,\n**[Azure](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fazure)**,\n**[DeepSeek](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fdeepseek)**,\n**[Fireworks](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Ffireworks)**,\n**[GCP Vertex AI Anthropic](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fgcp-vertex-ai-anthropic)**,\n**[GCP Vertex AI Gemini](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fgcp-vertex-ai-gemini)**,\n**[Google AI Studio (Gemini API)](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fgoogle-ai-studio-gemini)**,\n**[Groq](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fgroq)**,\n**[Hyperbolic](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fhyperbolic)**,\n**[Mistral](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fmistral)**,\n**[OpenAI](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fopenai)**,\n**[OpenRouter](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fopenrouter)**,\n**[SGLang](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fsglang)**,\n**[TGI](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Ftgi)**,\n**[Together AI](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Ftogether)**,\n**[vLLM](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fvllm)**, and\n**[xAI (Grok)](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fxai)**.\n\nNeed something else? TensorZero also supports **[any OpenAI-compatible API (e.g. Ollama)](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fproviders\u002Fopenai-compatible)**.\n\n#### Usage Example\n\nYou can use TensorZero with any OpenAI SDK (Python, Node, Go, etc.) or OpenAI-compatible client.\n\n1. **[Deploy the TensorZero Gateway](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fdeployment\u002Ftensorzero-gateway)** (one Docker container).\n2. Update the `base_url` and `model` in your OpenAI-compatible client.\n3. Run inference:\n\n```python\nfrom openai import OpenAI\n\n# Point the client to the TensorZero Gateway\nclient = OpenAI(base_url=\"http:\u002F\u002Flocalhost:3000\u002Fopenai\u002Fv1\", api_key=\"not-used\")\n\nresponse = client.chat.completions.create(\n    # Call any model provider (or TensorZero function)\n    model=\"tensorzero::model_name::anthropic::claude-sonnet-4-6\",\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"Share a fun fact about TensorZero.\",\n        }\n    ],\n)\n```\n\nSee **[Quick Start](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fquickstart)** for more information.\n\n### 🔍 LLM Observability\n\n> **Zoom in to debug individual API calls, or zoom out to monitor metrics across models and prompts over time — all using the open-source TensorZero UI.**\n\n- [x] Store inferences and **[feedback (metrics, human edits, etc.)](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Fmetrics-feedback)** in your own database\n- [x] Dive into individual inferences or high-level aggregate patterns using the TensorZero UI or programmatically\n- [x] **[Build datasets](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fapi-reference\u002Fdatasets-datapoints)** for optimization, evaluation, and other workflows\n- [x] Replay historical inferences with new prompts, models, inference strategies, etc.\n- [x] **[Export OpenTelemetry traces (OTLP)](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foperations\u002Fexport-opentelemetry-traces)** and **[export Prometheus metrics](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foperations\u002Fexport-prometheus-metrics)** to your favorite application observability tools\n- [ ] Soon: AI-assisted debugging and root cause analysis; AI-assisted data labeling\n\n### 📈 LLM Optimization\n\n> **Send production metrics and human feedback to easily optimize your prompts, models, and inference strategies — using the UI or programmatically.**\n\n- [x] Optimize your models with **[supervised fine-tuning](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foptimization\u002Fsupervised-fine-tuning-sft)**, RLHF, and other techniques\n- [x] Optimize your prompts with automated prompt engineering algorithms like **[GEPA](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foptimization\u002Fgepa)**\n- [x] Optimize your **[inference strategy](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fgateway\u002Fguides\u002Finference-time-optimizations)** with **[dynamic in-context learning](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foptimization\u002Fdynamic-in-context-learning-dicl)**, best\u002Fmixture-of-N sampling, etc.\n- [x] Enable a feedback loop for your LLMs: a data & learning flywheel turning production data into smarter, faster, and cheaper models\n- [ ] Soon: synthetic data generation\n\n### 📊 LLM Evaluation\n\n> **Compare prompts, models, and inference strategies using evaluations powered by heuristics and LLM judges.**\n\n- [x] **[Evaluate individual inferences](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fevaluations\u002Finference-evaluations\u002Ftutorial)** with _inference evaluations_ powered by heuristics or LLM judges (≈ unit tests for LLMs)\n- [x] **[Evaluate end-to-end workflows](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fevaluations\u002Fworkflow-evaluations\u002Ftutorial)** with _workflow evaluations_ with complete flexibility (≈ integration tests for LLMs)\n- [x] Optimize LLM judges just like any other TensorZero function to align them to human preferences\n- [ ] Soon: more built-in evaluators; headless evaluations\n\n\u003Ctable>\n  \u003Ctr>\u003C\u002Ftr> \u003C!-- flip highlight order -->\n  \u003Ctr>\n    \u003Ctd width=\"50%\" align=\"center\" valign=\"middle\">\u003Cb>Evaluation » UI\u003C\u002Fb>\u003C\u002Ftd>\n    \u003Ctd width=\"50%\" align=\"center\" valign=\"middle\">\u003Cb>Evaluation » CLI\u003C\u002Fb>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd width=\"50%\" align=\"center\" valign=\"middle\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Ff4bf54e3-1b63-46c8-be12-2eaabf615699\">\u003C\u002Ftd>\n    \u003Ctd width=\"50%\" align=\"left\" valign=\"middle\">\n\u003Cpre>\u003Ccode class=\"language-bash\">docker compose run --rm evaluations \\\n  --evaluation-name extract_data \\\n  --dataset-name hard_test_cases \\\n  --variant-name gpt_4o \\\n  --concurrency 5\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cpre>\u003Ccode class=\"language-bash\">Run ID: 01961de9-c8a4-7c60-ab8d-15491a9708e4\nNumber of datapoints: 100\n██████████████████████████████████████ 100\u002F100\nexact_match: 0.83 ± 0.03 (n=100)\nsemantic_match: 0.98 ± 0.01 (n=100)\nitem_count: 7.15 ± 0.39 (n=100)\u003C\u002Fcode>\u003C\u002Fpre>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🧪 LLM Experimentation\n\n> **Ship with confidence with built-in A\u002FB testing, routing, fallbacks, retries, etc.**\n\n- [x] **[Run adaptive A\u002FB tests](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fexperimentation\u002Frun-adaptive-ab-tests)** to ship with confidence and identify the best prompts and models for your use cases.\n- [x] Enforce principled experiments in complex workflows, including support for multi-turn LLM systems, sequential testing, and more.\n\n### & more!\n\n> **Build with an open-source stack well-suited for prototypes but designed from the ground up to support the most complex LLM applications and deployments.**\n\n- [x] Build simple applications or massive deployments with GitOps-friendly orchestration\n- [x] **[Extend TensorZero](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Foperations\u002Fextend-tensorzero)** with built-in escape hatches, programmatic-first usage, direct database access, and more\n- [x] Integrate with third-party tools: specialized observability and evaluations, model providers, agent orchestration frameworks, etc.\n- [x] Iterate quickly by experimenting with prompts interactively using the Playground UI\n\n## Frequently Asked Questions\n\n**How is TensorZero different from other LLM frameworks?**\n\n1. TensorZero enables you to optimize complex LLM applications based on production metrics and human feedback.\n2. TensorZero supports the needs of industrial-grade LLM applications: low latency, high throughput, type safety, self-hosted, GitOps, customizability, etc.\n3. TensorZero unifies the entire LLMOps stack, creating compounding benefits. For example, LLM evaluations can be used for fine-tuning models alongside AI judges.\n\n**Can I use TensorZero with \\_\\_\\_?**\n\nYes.\nEvery major programming language is supported.\nIt plays nicely with the **OpenAI SDK**, **OpenTelemetry**, and **every major LLM provider**.\n\n**Is TensorZero production-ready?**\n\nYes.\nTensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and powers ~1% of the global LLM API spend today.\n\nHere's a case study: **[Automating Code Changelogs at a Large Bank with LLMs](https:\u002F\u002Fwww.tensorzero.com\u002Fblog\u002Fcase-study-automating-code-changelogs-at-a-large-bank-with-llms)**\n\n**How much does TensorZero cost?**\n\nTensorZero (LLMOps platform) is 100% self-hosted and open-source.\n\nTensorZero Autopilot (automated AI engineer) is a complementary paid product powered by TensorZero.\n\n**Who is building TensorZero?**\n\nOur technical team includes a former Rust compiler maintainer, machine learning researchers (Stanford, CMU, Oxford, Columbia) with thousands of citations, and the chief product officer of a decacorn startup. We're backed by the same investors as leading open-source projects (e.g. ClickHouse, CockroachDB) and AI labs (e.g. OpenAI, Anthropic). See our **[$7.3M seed round announcement](https:\u002F\u002Fwww.tensorzero.com\u002Fblog\u002Ftensorzero-raises-7-3m-seed-round-to-build-an-open-source-stack-for-industrial-grade-llm-applications\u002F)** and **[coverage from VentureBeat](https:\u002F\u002Fventurebeat.com\u002Fai\u002Ftensorzero-nabs-7-3m-seed-to-solve-the-messy-world-of-enterprise-llm-development\u002F)**. We're **[hiring in NYC](https:\u002F\u002Fwww.tensorzero.com\u002Fjobs)**.\n\n**How do I get started?**\n\nYou can adopt TensorZero incrementally. Our **[Quick Start](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fquickstart)** goes from a vanilla OpenAI wrapper to a production-ready LLM application with observability and fine-tuning in just 5 minutes.\n\n## Get Started\n\n**Start building today.**\nThe **[Quick Start](https:\u002F\u002Fwww.tensorzero.com\u002Fdocs\u002Fquickstart)** shows it's easy to set up an LLM application with TensorZero.\n\n**Questions?**\nAsk us on **[Slack](https:\u002F\u002Fwww.tensorzero.com\u002Fslack)** or **[Discord](https:\u002F\u002Fwww.tensorzero.com\u002Fdiscord)**.\n\n**Using TensorZero at work?**\nEmail us at **[hello@tensorzero.com](mailto:hello@tensorzero.com)** to set up a Slack or Teams channel with your team (free).\n\n## Examples\n\nWe are working on a series of **complete runnable examples** illustrating TensorZero's data & learning flywheel.\n\n> **[Optimizing Data Extraction (NER) with TensorZero](https:\u002F\u002Fgithub.com\u002Ftensorzero\u002Ftensorzero\u002Ftree\u002Fmain\u002Fexamples\u002Fdata-extraction-ner)**\n>\n> This example shows how to use TensorZero to optimize a data extraction pipeline.\n> We demonstrate techniques like fine-tuning and dynamic in-context learning (DICL).\n> In the end, an optimized GPT-4o Mini model outperforms GPT-4o on this task — at a fraction of the cost and latency — using a small amount of training data.\n\n> **[Agentic RAG — Multi-Hop Question Answering with LLMs](https:\u002F\u002Fgithub.com\u002Ftensorzero\u002Ftensorzero\u002Ftree\u002Fmain\u002Fexamples\u002Frag-retrieval-augmented-generation\u002Fsimple-agentic-rag\u002F)**\n>\n> This example shows how to build a multi-hop retrieval agent using TensorZero.\n> The agent iteratively searches Wikipedia to gather information, and decides when it has enough context to answer a complex question.\n\n> **[Writing Haikus to Satisfy a Judge with Hidden Preferences](https:\u002F\u002Fgithub.com\u002Ftensorzero\u002Ftensorzero\u002Ftree\u002Fmain\u002Fexamples\u002Fhaiku-hidden-preferences)**\n>\n> This example fine-tunes GPT-4o Mini to generate haikus tailored to a specific taste.\n> 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.\n> You'll see progress by fine-tuning the LLM multiple times.\n\n> **[Image Data Extraction — Multimodal (Vision) Fine-tuning](https:\u002F\u002Fgithub.com\u002Ftensorzero\u002Ftensorzero\u002Ftree\u002Fmain\u002Fexamples\u002Fmultimodal-vision-finetuning)**\n>\n> This example shows how to fine-tune multimodal models (VLMs) like GPT-4o to improve their performance on vision-language tasks.\n> Specifically, we'll build a system that categorizes document images (screenshots of computer science research papers).\n\n> **[Improving LLM Chess Ability with Best-of-N Sampling](https:\u002F\u002Fgithub.com\u002Ftensorzero\u002Ftensorzero\u002Ftree\u002Fmain\u002Fexamples\u002Fchess-puzzles\u002F)**\n>\n> 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.\n\n## Blog Posts\n\nWe write about LLM engineering on the **[TensorZero Blog](https:\u002F\u002Fwww.tensorzero.com\u002Fblog)**.\nHere are some of our favorite posts:\n\n- **[Bandits in your LLM Gateway: Improve LLM Applications Faster with Adaptive Experimentation (A\u002FB Testing)](https:\u002F\u002Fwww.tensorzero.com\u002Fblog\u002Fbandits-in-your-llm-gateway\u002F)**\n- **[Is OpenAI's Reinforcement Fine-Tuning (RFT) Worth It?](https:\u002F\u002Fwww.tensorzero.com\u002Fblog\u002Fis-openai-reinforcement-fine-tuning-rft-worth-it\u002F)**\n- **[Distillation with Programmatic Data Curation: Smarter LLMs, 5-30x Cheaper Inference](https:\u002F\u002Fwww.tensorzero.com\u002Fblog\u002Fdistillation-programmatic-data-curation-smarter-llms-5-30x-cheaper-inference\u002F)**\n- **[From NER to Agents: Does Automated Prompt Engineering Scale to Complex Tasks?](https:\u002F\u002Fwww.tensorzero.com\u002Fblog\u002Ffrom-ner-to-agents-does-automated-prompt-engineering-scale-to-complex-tasks\u002F)**\n","TensorZero 是一个开源的LLMOps平台，集成了大语言模型（LLM）网关、可观测性、评估、优化和实验等功能。其核心功能包括通过统一API访问所有LLM提供商（性能达到\u003C1ms p99延迟）、存储推理与反馈数据、使用启发式方法及LLM评判进行基准测试、收集指标和人类反馈以优化提示词、模型及推理策略，以及内置A\u002FB测试等。该平台采用Rust语言开发，支持OpenAI SDK、OpenTelemetry，并兼容各大主流LLM供应商。TensorZero适用于从前沿AI初创公司到财富十强企业等多种规模组织，在需要高效管理和优化LLM应用的场景中尤为适用。","2026-06-11 02:31:43","trending"]