dspy-phoenix
Arize Phoenix — Open-Source LLM Observability for DSPy
Guide the user through setting up Arize Phoenix for DSPy tracing, visualization, and evaluation.
What is Arize Phoenix
Phoenix is an open-source LLM observability platform that runs locally or in the cloud. It provides a trace viewer, evaluation tools, and dataset management — all with DSPy auto-instrumentation via the OpenInference plugin.
- Local mode:
px.launch_app()starts a UI athttp://localhost:6006— no account needed - Cloud mode: Hosted on the Arize platform
- Open source: github.com/Arize-ai/phoenix
What gets traced
| Component | Details captured |
|---|---|
| LM calls | Prompts, responses, token counts, latency |
| Retrievals | Queries, passages, relevance scores |
| Module executions | Input/output per module step |
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Switch AI providers or models without breaking things. Use when you want to switch from OpenAI to Anthropic, try a cheaper model, stop depending on one vendor, compare models side-by-side, a model update broke your outputs, you need vendor diversification, or you want to migrate to a local model. Also use when your prompt broke after a model update, prompts that work for GPT-4 do not work for Claude or Llama, or you need to do a model migration. Covers DSPy model portability with provider config, re-optimization, model comparison, and multi-model pipelines. Also used for migrate from OpenAI to Anthropic, GPT to Claude migration, try Llama instead of GPT, model comparison framework, multi-provider AI setup, avoid vendor lock-in for AI, prompts break when switching models, model-agnostic AI code.
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Pull structured data from messy text using AI. Use when parsing invoices, extracting fields from emails, scraping entities from articles, converting unstructured text to JSON, extracting contact info, parsing resumes, reading forms, pulling data from transcripts (VTT, LiveKit, Recall), extracting fields from Langfuse traces, or any task where messy text goes in and clean structured data comes out. Also use when emails are messy and lack structure, or structured data extraction from unstructured content is unreliable., extract entities from text, parse PDF with AI, structured extraction from unstructured text, OCR plus AI extraction, convert email to structured data, pull fields from documents automatically, AI data entry automation, invoice parsing, resume parsing with AI, medical record extraction.
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