4 instrumentors shipped today thanks to our cracked OSS community.

🤖 AG2: multi-agent conversations, group chats, and tool calls ⚡ Together AI: fast inference across OSS models 🧠 Cohere: enterprise chat, search, and RAG 🦙 Ollama: local models galore

https://t.co/cPWI96qGyz

Integrations - Phoenix

Source: https://arize.com/docs/phoenix/integrations Phoenix integrates with the leading AI frameworks, LLM providers, and tools to provide seamless observability, evaluation, and debugging for your AI applications. Whether you’re building with Python, TypeScript, or Java, Phoenix has you covered.

Integration Types

Phoenix offers several types of integrations to support your AI development workflow:

Integrate Phoenix with AI coding assistants to debug and analyze your LLM applications directly from your development environment.

Coding Agents

Trace your sessions with a coding agent — turns, tool calls, and token costs — in Phoenix, no application code changes required. Install thecoding-harness-tracingtoolkit and pick your agent:

Tracing Integrations

Phoenix captures detailed traces from your AI applications, giving you visibility into every step of your LLM pipeline.

By Language

LLM Providers

Phoenix provides native tracing support for all major LLM providers:

Platforms

Integrate Phoenix with AI development platforms and infrastructure:

Eval Model Integrations

Phoenix’s evaluation library (phoenix\-evals) can use any LLM provider to power evaluations. These models score, classify, and analyze your traces.

Eval Library Integrations

Use external evaluation libraries alongside Phoenix to get the best of both worlds:

Span Processors

Normalize and convert data from other instrumentation libraries by adding span processors that unify traces to the OpenInference format:

Sandboxes

Run Phoenixcode evaluatorsin hosted sandbox providers for kernel-level isolation, runtime dependency installation, and opt-in outbound network access. Configure providers fromSettings → Sandboxes.