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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.

Description

Llama Stack is an innovative modular framework aimed at simplifying the creation of applications that utilize Meta's Llama language models. It features a client-server architecture with adaptable configurations, giving developers the ability to combine various providers for essential components like inference, memory, agents, telemetry, and evaluations. This framework comes with pre-configured distributions optimized for a range of deployment scenarios, facilitating smooth transitions from local development to live production settings. Developers can engage with the Llama Stack server through client SDKs that support numerous programming languages, including Python, Node.js, Swift, and Kotlin. In addition, comprehensive documentation and sample applications are made available to help users efficiently construct and deploy applications based on the Llama framework. The combination of these resources aims to empower developers to build robust, scalable applications with ease.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

APIFuzzer Yes 
Amazon Bedrock Yes 
Arize AI Yes 
Codestral Yes 
Codestral Mamba Yes 
Conda Yes 
CrewAI Yes 
Databricks Yes 
GitHub Yes 
Haystack Yes 
Mathstral Yes 
Ministral 3B Yes 
Ministral 8B Yes 
Mistral AI Yes 
Mistral Large Yes 
Mistral Small Yes 
Mixtral 8x22B Yes 
Mixtral 8x7B Yes 
Python Yes 
Vercel Yes 

Integrations

APIFuzzer No 
Amazon Bedrock No 
Arize AI No 
Codestral No 
Codestral Mamba No 
Conda No 
CrewAI No 
Databricks No 
GitHub No 
Haystack No 
Mathstral No 
Ministral 3B No 
Ministral 8B No 
Mistral AI No 
Mistral Large No 
Mistral Small No 
Mixtral 8x22B No 
Mixtral 8x7B No 
Python No 
Vercel No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Arize AI

Country

United States

Website

docs.arize.com/phoenix

Vendor Details

Company Name

Meta

Founded

2004

Country

United States

Website

github.com/meta-llama/llama-stack

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