AI OSS tool repo goes archived over night after raising $7.3M Seed

TensorZero is an open-source LLMOps platform that unifies: You can take what you need, adopt incrementally, and complement with other tools. It plays nicely with the OpenAI SDK, OpenTelemetry, and every major LLM provider. TensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and fuels ~1% of global LLM API spend today. Website · Docs · Twitter · Slack · Discord Quick Start (5min) · Deployment Guide · API Reference · Configuration Reference 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/B tests. It dramatically improves the performance of LLM agents across diverse tasks: Integrate with TensorZero once and access every major LLM provider. Anthropic, AWS Bedrock, AWS SageMaker, Azure, DeepSeek, Fireworks, GCP Vertex AI Anthropic, GCP Vertex AI Gemini, Google AI Studio (Gemini API), Groq, Hyperbolic, Mistral, OpenAI, OpenRouter, SGLang, TGI, Together AI, vLLM, and xAI (Grok). Need something else? TensorZero also supports any OpenAI-compatible API (e.g. Ollama). You can use TensorZero with any OpenAI SDK (Python, Node, Go, etc.) or OpenAI-compatible client. from openai import OpenAI # Point the client to the TensorZero Gateway client = OpenAI(base_url="http://localhost:3000/openai/v1", api_key="not-used") response = client.chat.completions.create( # Call any model provider (or TensorZero function) model="tensorzero::model_name::anthropic::claude-sonnet-4-6", messages=[ { "role": "user", "content": "Share a fun fact about TensorZero.", } ], ) See Quick Start for more information. 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. Send production metrics and human feedback to easily optimize your prompts, models, and inference strategies — using the UI or programmatically. Compare prompts, models, and inference strategies using evaluations powered by heuristics and LLM judges. docker compose run --rm evaluations \ --evaluation-name extract_data \ --dataset-name hard_test_cases \ --variant-name gpt_4o \ --concurrency 5 Run ID: 01961de9-c8a4-7c60-ab8d-15491a9708e4 Number of datapoints: 100 ██████████████████████████████████████ 100/100 exact_match: 0.83 ± 0.03 (n=100) semantic_match: 0.98 ± 0.01 (n=100) item_count: 7.15 ± 0.39 (n=100) 🧪 LLM Experimentation Ship with confidence with built-in A/B testing, routing, fallbacks, retries, etc. 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. How is TensorZero different from other LLM frameworks? Yes. Every major programming language is supported. It plays nicely with the OpenAI SDK, OpenTelemetry, and every major LLM provider. Yes. TensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and powers ~1% of the global LLM API spend today. Here's a case study: Automating Code Changelogs at a Large Bank with LLMs TensorZero (LLMOps platform) is 100% self-hosted and open-source. TensorZero Autopilot (automated AI engineer) is a complementary paid product powered by TensorZero. Our 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 and coverage from VentureBeat. We're hiring in NYC. You can adopt TensorZero incrementally. Our Quick Start goes from a vanilla OpenAI wrapper to a production-ready LLM application with observability and fine-tuning in just 5 minutes. Start building today. The Quick Start shows it's easy to set up an LLM application with TensorZero. Using TensorZero at work? Email us at hello@tensorzero.com to set up a Slack or Teams channel with your team (free). We are working on a series of complete runnable examples illustrating TensorZero's data & learning flywheel. Optimizing Data Extraction (NER) with TensorZero This example shows how to use TensorZero to optimize a data extraction pipeline. We demonstrate techniques like fine-tuning and dynamic in-context learning (DICL). 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. Agentic RAG — Multi-Hop Question Answering with LLMs This example shows how to build a multi-hop retrieval agent using TensorZero. The agent iteratively searches Wikipedia to gather information, and decides when it has enough context to answer a complex question. Writing Haikus to Satisfy a Judge with Hidden Preferences This example fine-tunes GPT-4o Mini to generate haikus tailored to a specific taste. You'll see T

AI OSS tool repo goes archived over night after raising $7.3M Seed

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