KnowledgeMay 8, 2026·4 min read

Phoenix Tracing Quickstart — OpenInference Tracer Setup

Phoenix instruments OpenAI, Anthropic, LangChain, LlamaIndex, CrewAI via OpenInference. Local UI or Arize cloud. No per-call code changes.

Arize AI
Arize AI · Community
Agent ready

Safe staging for this asset

This asset is staged first. The copied prompt tells the agent to inspect the staged files and ask before activating scripts, MCP config, or global config.

Stage only · 27/100Policy: stage
Agent surface
Any MCP/CLI agent
Kind
Knowledge
Install
Stage only
Trust
Trust: Community
Entrypoint
Asset
Safe staging command
npx -y tokrepo@latest install 0ba1d7ad-c101-4f54-be76-388d98ddaf40 --target codex

Stages files first; activation requires review of the staged README and plan.

Intro

Phoenix is the open-source observability companion to Arize AX — drop the OpenInference tracer in once and every OpenAI / Anthropic / LangChain / LlamaIndex / CrewAI / DSPy call gets a trace span automatically with prompts, completions, latency, and token cost. View traces in the local Phoenix UI (port 6006) or send them to Arize cloud. Best for: debugging multi-step agents, finding which retrieval step poisoned the answer, comparing prompt versions side-by-side. Works with: any Python LLM stack via OpenInference instrumentation. Setup time: 2 minutes.

Install + start local Phoenix

pip install arize-phoenix openinference-instrumentation-openai openinference-instrumentation-langchain
phoenix serve  # starts UI on http://localhost:6006

Auto-instrument OpenAI

from phoenix.otel import register
from openinference.instrumentation.openai import OpenAIInstrumentor

tracer_provider = register(project_name="my-rag-app", endpoint="http://localhost:6006/v1/traces")
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

# That's it. Now every OpenAI call traces automatically:
from openai import OpenAI
client = OpenAI()
client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain quantum entanglement"}],
)
# Open localhost:6006 — your trace appears with prompt, completion, latency, cost.

LangChain + LlamaIndex

from openinference.instrumentation.langchain import LangChainInstrumentor
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor

LangChainInstrumentor().instrument(tracer_provider=tracer_provider)
LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)

Send to Arize cloud instead

tracer_provider = register(
    project_name="my-rag-app",
    endpoint="https://otlp.arize.com/v1/traces",
    headers={"api_key": ARIZE_API_KEY, "space_id": ARIZE_SPACE_ID},
)

Trace span attributes (OpenInference standard)

Attribute Example
llm.model_name gpt-4o
llm.token_count.prompt 847
llm.token_count.completion 213
llm.input_messages.0.message.content full prompt text
output.value model output
retrieval.documents.*.document.content chunks fetched in RAG

FAQ

Q: Phoenix vs Langfuse vs LangSmith? A: Phoenix is OpenInference-native — vendor-neutral OTel attributes that any backend can read. Langfuse has stronger prompt management and self-host story. LangSmith is best if you live in LangChain. Phoenix is the choice when you want OTel and may switch backends.

Q: Does Phoenix need a database? A: Local mode uses SQLite under ~/.phoenix. Production self-host swaps to Postgres via PHOENIX_SQL_DATABASE_URL. Arize cloud handles persistence for you. SQLite is fine for solo dev with <10K traces.

Q: Can I see traces from a notebook? A: Yes — phoenix.launch_app() opens the UI inline as a Jupyter widget or new tab. Combine with phoenix.evals to run LLM-as-judge evals and view them next to traces.

Quick Use

  1. pip install arize-phoenix openinference-instrumentation-openai
  2. phoenix serve (or use Arize cloud endpoint)
  3. OpenAIInstrumentor().instrument() — every call now traces

Intro

Phoenix is the open-source observability companion to Arize AX — drop the OpenInference tracer in once and every OpenAI / Anthropic / LangChain / LlamaIndex / CrewAI / DSPy call gets a trace span automatically with prompts, completions, latency, and token cost. View traces in the local Phoenix UI (port 6006) or send them to Arize cloud. Best for: debugging multi-step agents, finding which retrieval step poisoned the answer, comparing prompt versions side-by-side. Works with: any Python LLM stack via OpenInference instrumentation. Setup time: 2 minutes.

Install + start local Phoenix

pip install arize-phoenix openinference-instrumentation-openai openinference-instrumentation-langchain
phoenix serve  # starts UI on http://localhost:6006

Auto-instrument OpenAI

from phoenix.otel import register
from openinference.instrumentation.openai import OpenAIInstrumentor

tracer_provider = register(project_name="my-rag-app", endpoint="http://localhost:6006/v1/traces")
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

# That's it. Now every OpenAI call traces automatically:
from openai import OpenAI
client = OpenAI()
client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain quantum entanglement"}],
)
# Open localhost:6006 — your trace appears with prompt, completion, latency, cost.

LangChain + LlamaIndex

from openinference.instrumentation.langchain import LangChainInstrumentor
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor

LangChainInstrumentor().instrument(tracer_provider=tracer_provider)
LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)

Send to Arize cloud instead

tracer_provider = register(
    project_name="my-rag-app",
    endpoint="https://otlp.arize.com/v1/traces",
    headers={"api_key": ARIZE_API_KEY, "space_id": ARIZE_SPACE_ID},
)

Trace span attributes (OpenInference standard)

Attribute Example
llm.model_name gpt-4o
llm.token_count.prompt 847
llm.token_count.completion 213
llm.input_messages.0.message.content full prompt text
output.value model output
retrieval.documents.*.document.content chunks fetched in RAG

FAQ

Q: Phoenix vs Langfuse vs LangSmith? A: Phoenix is OpenInference-native — vendor-neutral OTel attributes that any backend can read. Langfuse has stronger prompt management and self-host story. LangSmith is best if you live in LangChain. Phoenix is the choice when you want OTel and may switch backends.

Q: Does Phoenix need a database? A: Local mode uses SQLite under ~/.phoenix. Production self-host swaps to Postgres via PHOENIX_SQL_DATABASE_URL. Arize cloud handles persistence for you. SQLite is fine for solo dev with <10K traces.

Q: Can I see traces from a notebook? A: Yes — phoenix.launch_app() opens the UI inline as a Jupyter widget or new tab. Combine with phoenix.evals to run LLM-as-judge evals and view them next to traces.

Source & Thanks

Built by Arize AI. Licensed under Apache-2.0.

Arize-ai/phoenix — ⭐ 4,500+

🙏

Source & Thanks

Built by Arize AI. Licensed under Apache-2.0.

Arize-ai/phoenix — ⭐ 4,500+

Discussion

Sign in to join the discussion.
No comments yet. Be the first to share your thoughts.

Related Assets

GroqCloud Quickstart — 250 tokens/sec OpenAI-Compat API

GroqCloud runs Llama 3.3 70B at 250+ tok/sec on LPU silicon. OpenAI-compatible API. Free tier, sub-second TTFT, ideal for streaming.

Knowledge
Groq

Weave — Trace and Debug LLM Apps

Weave adds tracing to LLM apps with `@weave.op`. Install `weave`, call `weave.init()`, then track inputs/outputs across API calls and validation steps.

Knowledge
Agent Toolkit

xAI Grok API Quickstart — OpenAI-Compatible Frontier Model

xAI Grok API is OpenAI-compatible at api.x.ai/v1. Swap base URL + key, keep the SDK. Grok-3, Grok-2 Vision, 1M-token context.

Knowledge
xAI

Memori — Agent-Native Memory Infrastructure

Memori is an Apache-2.0 memory layer that captures what agents do (not just say) and plugs into existing stacks via Python/Node SDKs and a cloud option.

Knowledge
AI Open Source
◈Home🔍Search👤Me