Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. It provides:
- Tracing - Trace your LLM application's runtime using OpenTelemetry-based instrumentation.
- Evaluation - Leverage LLMs to benchmark your application's performance using response and retrieval evals.
- Datasets - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.
- Experiments - Track and evaluate changes to prompts, LLMs, and retrieval.
Phoenix is vendor and language agnostic with out-of-the-box support for popular frameworks (🦙LlamaIndex, 🦜⛓LangChain, Haystack, 🧩DSPy) and LLM providers (OpenAI, Bedrock, and more). For details on auto-instrumentation, check out the OpenInference project.
Phoenix runs practically anywhere, including your Jupyter notebook, local machine, containerized deployment, or in the cloud.
Install Phoenix via pip or conda
pip install arize-phoenixPhoenix container images are available via Docker Hub and can be deployed using Docker or Kubernetes.
Join our community to connect with thousands of AI builders.
- 🌍 Join our Slack community.
- 📚 Read our documentation.
- 💡 Ask questions and provide feedback in the #phoenix-support channel.
- 🌟 Leave a star on our GitHub.
- 🐞 Report bugs with GitHub Issues.
- 𝕏 Follow us on 𝕏.
- 💌️ Sign up for our mailing list.
- 🗺️ Check out our roadmap to see where we're heading next.
See the migration guide for a list of breaking changes.
Copyright 2023 Arize AI, Inc. All Rights Reserved.
Portions of this code are patent protected by one or more U.S. Patents. See IP_NOTICE.
This software is licensed under the terms of the Elastic License 2.0 (ELv2). See LICENSE.