{"slug": "ai-oss-tool-repo-goes-archived-over-night-after-raising-7-3m-seed", "title": "AI OSS tool repo goes archived over night after raising $7.3M Seed", "summary": "TensorZero, an open-source LLMOps platform that raised $7.3M in seed funding, archived its repository overnight. The platform provides tools for LLM gateway, observability, evaluation, optimization, and experimentation, and is used by companies from AI startups to Fortune 10 firms.", "body_md": "**TensorZero is an open-source LLMOps platform that unifies:**\n\n**Gateway:** access every LLM provider through a unified API, built for performance (<1ms p99 latency)**Observability:** store inferences and feedback in your database, available programmatically or in the UI**Evaluation:** benchmark individual inferences or end-to-end workflows using heuristics, LLM judges, etc.**Optimization:** collect metrics and human feedback to optimize prompts, models, and inference strategies**Experimentation:** ship with confidence with built-in A/B 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** Website**\n·\n\n**·**\n\n[Docs](https://www.tensorzero.com/docs)**·**[Twitter](https://www.x.com/tensorzero)\n\n**·**\n\n[Slack](https://www.tensorzero.com/slack)\n\n[Discord](https://www.tensorzero.com/discord)**·**\n\n[Quick Start (5min)](https://www.tensorzero.com/docs/quickstart)**·**\n\n[Deployment Guide](https://www.tensorzero.com/docs/deployment/tensorzero-gateway)**·**\n\n[API Reference](https://www.tensorzero.com/docs/gateway/api-reference)\n\n[Configuration Reference](https://www.tensorzero.com/docs/gateway/configuration-reference)## tensorzero-demo.mp4\n\nNote\n\nTensorZero 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.\n\nIt **dramatically improves the performance of LLM agents** across diverse tasks:\n\nIntegrate with TensorZero once and access every major LLM provider.\n\n-\n(API or self-hosted) through a single unified API[Call any LLM](https://www.tensorzero.com/docs/gateway/call-any-llm) - Infer with\n,[tool use](https://www.tensorzero.com/docs/gateway/guides/tool-use),[structured outputs (JSON)](https://www.tensorzero.com/docs/gateway/generate-structured-outputs),[batch](https://www.tensorzero.com/docs/gateway/guides/batch-inference),[embeddings](https://www.tensorzero.com/docs/gateway/generate-embeddings),[multimodal (images, files)](https://www.tensorzero.com/docs/gateway/call-llms-with-image-and-file-inputs), etc.[caching](https://www.tensorzero.com/docs/gateway/guides/inference-caching) -\nto enforce a structured interface between your application and the LLMs[Create prompt templates and schemas](https://www.tensorzero.com/docs/gateway/create-a-prompt-template) - Satisfy extreme throughput and latency needs, thanks to 🦀 Rust:\n[<1ms p99 latency overhead at 10k+ QPS](https://www.tensorzero.com/docs/gateway/benchmarks) -\nwith routing, retries, fallbacks, load balancing, granular timeouts, etc.[Ensure high availability](https://www.tensorzero.com/docs/gateway/guides/retries-fallbacks) -\nand[Track usage and cost](https://www.tensorzero.com/docs/operations/track-usage-and-cost)with granular scopes (e.g. tags)[enforce custom rate limits](https://www.tensorzero.com/docs/operations/enforce-custom-rate-limits) -\nto allow clients to access models without sharing provider API keys[Set up auth for TensorZero](https://www.tensorzero.com/docs/operations/set-up-auth-for-tensorzero)\n\n** Anthropic**,\n\n**,**\n\n[AWS Bedrock](https://www.tensorzero.com/docs/gateway/guides/providers/aws-bedrock)**,**\n\n[AWS SageMaker](https://www.tensorzero.com/docs/gateway/guides/providers/aws-sagemaker)**,**\n\n[Azure](https://www.tensorzero.com/docs/gateway/guides/providers/azure)**,**\n\n[DeepSeek](https://www.tensorzero.com/docs/gateway/guides/providers/deepseek)**,**\n\n[Fireworks](https://www.tensorzero.com/docs/gateway/guides/providers/fireworks)**,**\n\n[GCP Vertex AI Anthropic](https://www.tensorzero.com/docs/gateway/guides/providers/gcp-vertex-ai-anthropic)**,**\n\n[GCP Vertex AI Gemini](https://www.tensorzero.com/docs/gateway/guides/providers/gcp-vertex-ai-gemini)**,**\n\n[Google AI Studio (Gemini API)](https://www.tensorzero.com/docs/gateway/guides/providers/google-ai-studio-gemini)**,**\n\n[Groq](https://www.tensorzero.com/docs/gateway/guides/providers/groq)**,**\n\n[Hyperbolic](https://www.tensorzero.com/docs/gateway/guides/providers/hyperbolic)**,**\n\n[Mistral](https://www.tensorzero.com/docs/gateway/guides/providers/mistral)**,**\n\n[OpenAI](https://www.tensorzero.com/docs/gateway/guides/providers/openai)**,**\n\n[OpenRouter](https://www.tensorzero.com/docs/gateway/guides/providers/openrouter)**,**\n\n[SGLang](https://www.tensorzero.com/docs/gateway/guides/providers/sglang)**,**\n\n[TGI](https://www.tensorzero.com/docs/gateway/guides/providers/tgi)**,**\n\n[Together AI](https://www.tensorzero.com/docs/gateway/guides/providers/together)**, and**\n\n[vLLM](https://www.tensorzero.com/docs/gateway/guides/providers/vllm)**.**\n\n[xAI (Grok)](https://www.tensorzero.com/docs/gateway/guides/providers/xai)Need something else? TensorZero also supports ** any OpenAI-compatible API (e.g. Ollama)**.\n\nYou can use TensorZero with any OpenAI SDK (Python, Node, Go, etc.) or OpenAI-compatible client.\n\n(one Docker container).[Deploy the TensorZero Gateway](https://www.tensorzero.com/docs/deployment/tensorzero-gateway)- Update the\n`base_url`\n\nand`model`\n\nin your OpenAI-compatible client. - Run inference:\n\n``` python\nfrom openai import OpenAI\n\n# Point the client to the TensorZero Gateway\nclient = OpenAI(base_url=\"http://localhost:3000/openai/v1\", 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** for more information.\n\nZoom 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- Store inferences and\nin your own database[feedback (metrics, human edits, etc.)](https://www.tensorzero.com/docs/gateway/guides/metrics-feedback) - Dive into individual inferences or high-level aggregate patterns using the TensorZero UI or programmatically\n-\nfor optimization, evaluation, and other workflows[Build datasets](https://www.tensorzero.com/docs/gateway/api-reference/datasets-datapoints) - Replay historical inferences with new prompts, models, inference strategies, etc.\n-\nand[Export OpenTelemetry traces (OTLP)](https://www.tensorzero.com/docs/operations/export-opentelemetry-traces)to your favorite application observability tools[export Prometheus metrics](https://www.tensorzero.com/docs/operations/export-prometheus-metrics) - Soon: AI-assisted debugging and root cause analysis; AI-assisted data labeling\n\nSend production metrics and human feedback to easily optimize your prompts, models, and inference strategies — using the UI or programmatically.\n\n- Optimize your models with\n, RLHF, and other techniques[supervised fine-tuning](https://www.tensorzero.com/docs/optimization/supervised-fine-tuning-sft) - Optimize your prompts with automated prompt engineering algorithms like\n[GEPA](https://www.tensorzero.com/docs/optimization/gepa) - Optimize your\nwith[inference strategy](https://www.tensorzero.com/docs/gateway/guides/inference-time-optimizations), best/mixture-of-N sampling, etc.[dynamic in-context learning](https://www.tensorzero.com/docs/optimization/dynamic-in-context-learning-dicl) - 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\nCompare prompts, models, and inference strategies using evaluations powered by heuristics and LLM judges.\n\n-\nwith[Evaluate individual inferences](https://www.tensorzero.com/docs/evaluations/inference-evaluations/tutorial)*inference evaluations*powered by heuristics or LLM judges (≈ unit tests for LLMs) -\nwith[Evaluate end-to-end workflows](https://www.tensorzero.com/docs/evaluations/workflow-evaluations/tutorial)*workflow evaluations*with complete flexibility (≈ integration tests for LLMs) - Optimize LLM judges just like any other TensorZero function to align them to human preferences\n- Soon: more built-in evaluators; headless evaluations\n\nEvaluation » UI |\nEvaluation » CLI |\n|\n\n```\ndocker compose run --rm evaluations \\\n  --evaluation-name extract_data \\\n  --dataset-name hard_test_cases \\\n  --variant-name gpt_4o \\\n  --concurrency 5\nRun ID: 01961de9-c8a4-7c60-ab8d-15491a9708e4\nNumber of datapoints: 100\n██████████████████████████████████████ 100/100\nexact_match: 0.83 ± 0.03 (n=100)\nsemantic_match: 0.98 ± 0.01 (n=100)\nitem_count: 7.15 ± 0.39 (n=100)\n```\n\n |\n\nShip with confidence with built-in A/B testing, routing, fallbacks, retries, etc.\n\n-\nto ship with confidence and identify the best prompts and models for your use cases.[Run adaptive A/B tests](https://www.tensorzero.com/docs/experimentation/run-adaptive-ab-tests) - Enforce principled experiments in complex workflows, including support for multi-turn LLM systems, sequential testing, and more.\n\nBuild 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- Build simple applications or massive deployments with GitOps-friendly orchestration\n-\nwith built-in escape hatches, programmatic-first usage, direct database access, and more[Extend TensorZero](https://www.tensorzero.com/docs/operations/extend-tensorzero) - Integrate with third-party tools: specialized observability and evaluations, model providers, agent orchestration frameworks, etc.\n- Iterate quickly by experimenting with prompts interactively using the Playground UI\n\n**How is TensorZero different from other LLM frameworks?**\n\n- TensorZero enables you to optimize complex LLM applications based on production metrics and human feedback.\n- TensorZero supports the needs of industrial-grade LLM applications: low latency, high throughput, type safety, self-hosted, GitOps, customizability, etc.\n- 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. TensorZero 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://www.tensorzero.com/blog/case-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** and\n\n**. We're**\n\n[coverage from VentureBeat](https://venturebeat.com/ai/tensorzero-nabs-7-3m-seed-to-solve-the-messy-world-of-enterprise-llm-development/)**.**\n\n[hiring in NYC](https://www.tensorzero.com/jobs)**How do I get started?**\n\nYou 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.\n\n**Start building today.**\nThe ** Quick Start** shows it's easy to set up an LLM application with TensorZero.\n\n**Questions?**\nAsk us on ** Slack** or\n\n**.**\n\n[Discord](https://www.tensorzero.com/discord)**Using TensorZero at work?**\nEmail us at ** hello@tensorzero.com** to set up a Slack or Teams channel with your team (free).\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]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.\n\n[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.\n\n[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 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.\n\n[Image Data Extraction — Multimodal (Vision) Fine-tuning]This example shows how to fine-tune multimodal models (VLMs) like GPT-4o to improve their performance on vision-language tasks. 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]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\nWe write about LLM engineering on the ** TensorZero Blog**.\nHere are some of our favorite posts:\n\n[Bandits in your LLM Gateway: Improve LLM Applications Faster with Adaptive Experimentation (A/B Testing)](https://www.tensorzero.com/blog/bandits-in-your-llm-gateway/)[Is OpenAI's Reinforcement Fine-Tuning (RFT) Worth It?](https://www.tensorzero.com/blog/is-openai-reinforcement-fine-tuning-rft-worth-it/)[Distillation with Programmatic Data Curation: Smarter LLMs, 5-30x Cheaper Inference](https://www.tensorzero.com/blog/distillation-programmatic-data-curation-smarter-llms-5-30x-cheaper-inference/)[From NER to Agents: Does Automated Prompt Engineering Scale to Complex Tasks?](https://www.tensorzero.com/blog/from-ner-to-agents-does-automated-prompt-engineering-scale-to-complex-tasks/)", "url": "https://wpnews.pro/news/ai-oss-tool-repo-goes-archived-over-night-after-raising-7-3m-seed", "canonical_source": "https://github.com/tensorzero/tensorzero", "published_at": "2026-06-13 12:10:47+00:00", "updated_at": "2026-06-13 12:50:51.045734+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "mlops", "developer-tools", "ai-startups"], "entities": ["TensorZero", "OpenAI", "Anthropic", "AWS Bedrock", "Azure", "DeepSeek", "Fireworks", "GCP Vertex AI"], "alternates": {"html": "https://wpnews.pro/news/ai-oss-tool-repo-goes-archived-over-night-after-raising-7-3m-seed", "markdown": "https://wpnews.pro/news/ai-oss-tool-repo-goes-archived-over-night-after-raising-7-3m-seed.md", "text": "https://wpnews.pro/news/ai-oss-tool-repo-goes-archived-over-night-after-raising-7-3m-seed.txt", "jsonld": "https://wpnews.pro/news/ai-oss-tool-repo-goes-archived-over-night-after-raising-7-3m-seed.jsonld"}}