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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "You can send detailed logging information, known as traces, from your app to see exactly what happened during a run",
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    "id": "arize-phoenix-docs-2",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "score outputs using evaluation tests to identify failures and regressions",
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    "id": "arize-phoenix-docs-3",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "iterate on your prompts using real production examples",
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    "id": "arize-phoenix-docs-4",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "optimize your app with experiments that compare changes on the same inputs",
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    "id": "arize-phoenix-docs-5",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "Phoenix accepts traces over OpenTelemetry (OTLP) and provides auto-instrumentation for popular frameworks (LlamaIndex, LangChain, DSPy, Mastra, Vercel AI SDK)",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "Human annotations — Attach ground truth labels directly in the UI",
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    "id": "arize-phoenix-docs-7",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "Prompt Management — Version, store, and deploy prompts",
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    "id": "arize-phoenix-docs-8",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "Span Replay — Debug by replaying LLM calls with different inputs",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "Prompts in Code — Sync prompts across environments via SDK",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "You can group traces into datasets, rerun them through different versions of your application, and compare evaluation results",
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    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Let your coding agent set up tracing for you: start Phoenix, then run npx -y @arizeai/phoenix-cli setup from your app's root directory.",
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    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Application Latency: Identify and address slow invocations of LLMs, Retrievers, and other components within your application",
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    "id": "arize-phoenix-docs-13",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Retrieved Documents: Inspect the documents retrieved during a Retriever call, including the score and order in which they were returned",
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  },
  {
    "id": "arize-phoenix-docs-14",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Tool Descriptions: View the descriptions and function signatures of the tools your LLM has been given access to",
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  },
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    "id": "arize-phoenix-docs-15",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Projects\n\nOrganize traces into separate projects for different applications",
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  },
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    "id": "arize-phoenix-docs-16",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Sessions\n\nGroup related traces into conversations and user sessions",
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  },
  {
    "id": "arize-phoenix-docs-17",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "Phoenix supports both deterministic code-based evaluators (exact match, regex, custom heuristics) and LLM-as-a-judge evaluators",
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    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "Configure evaluators in the Phoenix UI and attach them to your datasets. Phoenix scores experiment results automatically — no code required.",
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    "id": "arize-phoenix-docs-19",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "Model Agnostic via adapters (for OpenAI, LiteLLM, LangChain, AI SDK, and more) — so you can easily switch judge models",
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  },
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    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "Built-in Explanations — all Phoenix LLM evaluations return explanations by default for better results and richer signals",
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    "id": "arize-phoenix-docs-21",
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    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "This means you can run thousands of evaluations without writing any retry or concurrency logic yourself.",
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    "id": "arize-phoenix-docs-22",
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    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "Use Phoenix's trace viewer to explore evaluation traces, identify systematic biases, and continuously improve evaluator performance.",
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    "id": "arize-phoenix-docs-23",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/prompt-engineering/overview-prompts",
    "excerpt": "Play with prompts, models, invocation parameters and track your progress via tracing and experiments",
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    "id": "arize-phoenix-docs-24",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/prompt-engineering/overview-prompts",
    "excerpt": "Replay the invocation of an LLM. Whether it's an LLM step in an LLM workflow or a router query, you can step into the LLM invocation and see if any modifications to the invocation would have yielded a better outcome.",
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    "url": "https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets",
    "excerpt": "Datasets allow you to collect data from production, staging, evaluations, and even manually.",
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    "id": "arize-phoenix-docs-26",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets",
    "excerpt": "Dataset Evaluators serve as test cases that automatically score outputs when running experiments—forming an evaluation harness similar to a unit test suite.",
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  {
    "id": "arize-phoenix-docs-27",
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    "url": "https://arize.com/docs/phoenix/self-hosting",
    "excerpt": "Phoenix is free to self-host with no feature limitations. Your data stays entirely within your infrastructure—nothing is sent to Arize and can be fully air-gapped.",
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    "id": "arize-phoenix-docs-28",
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    "url": "https://arize.com/docs/phoenix/sdk-api-reference",
    "excerpt": "Zero-config tracing — Enable auto_instrument=True to automatically trace AI libraries",
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    "id": "arize-phoenix-docs-29",
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    "url": "https://arize.com/docs/phoenix/sdk-api-reference",
    "excerpt": "Tracing decorators — @tracer.chain, @tracer.tool, and more for manual instrumentation",
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    "id": "arize-phoenix-docs-30",
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    "url": "https://arize.com/docs/phoenix/sdk-api-reference",
    "excerpt": "Spans — Query and analyze traces with powerful filtering capabilities",
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    "id": "arize-phoenix-docs-31",
    "tier": "claimed-docs",
    "url": "https://arize.com/phoenix/",
    "excerpt": "$ uvx arize-phoenix serve",
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  {
    "id": "arize-phoenix-gh-1",
    "tier": "github",
    "url": "https://github.com/Arize-ai/phoenix",
    "excerpt": "Datasets - Create versioned datasets of examples for experimen",
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  {
    "id": "arize-phoenix-docs-32",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Phoenix accepts traces over the OpenTelemetry protocol (OTLP) and supports first-class instrumentation for a variety of frameworks",
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  {
    "id": "arize-phoenix-docs-33",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "Attach ground truth labels directly in the UI",
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    "id": "arize-phoenix-docs-34",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets",
    "excerpt": "Dataset Evaluators serve as test cases that automatically score outputs when running experiments",
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  {
    "id": "arize-phoenix-docs-35",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/prompt-engineering/overview-prompts",
    "excerpt": "you can step into the LLM invocation and see if any modifications to the invocation would have yielded a better outcome.",
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    "id": "arize-phoenix-docs-36",
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    "url": "https://arize.com/docs/phoenix/sdk-api-reference",
    "excerpt": "Phoenix’s Python SDK is modular by design, allowing you to install only what you need.",
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    "id": "arize-phoenix-docs-37",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/self-hosting",
    "excerpt": "Pin to a specific version (e.g., `arizephoenix/phoenix:version-8.0.0`) for production deployments.",
    "fetchedAt": "2026-09-03T23:58:50.217Z"
  },
  {
    "id": "arize-phoenix-docs-38",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix",
    "excerpt": "A trace captures model calls, retrieval, tool use, and custom logic so you can debug behavior and understand where time is spent.",
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    "id": "arize-phoenix-docs-39",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix",
    "excerpt": "You can score traces & spans with LLM-based evaluators, code-based checks, or human labels so you can track performance and identify failures consistently.",
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    "id": "arize-phoenix-docs-40",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "Phoenix helps you iterate on prompts using real examples from your application. You can version prompts, test prompt variants across datasets, and replay calls to see how changes affect outputs before rolling them out.",
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    "id": "arize-phoenix-docs-41",
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    "url": "https://arize.com/docs/phoenix",
    "excerpt": "You can group traces into datasets, rerun them through different versions of your application, and compare evaluation results to confirm whether a change actually improved performance.",
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    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "Every evaluation execution captures the input data, the exact prompts sent to the judge LLM, the model's full reasoning, the final scores, and execution timing.",
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    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Let your coding agent set up tracing for you: start Phoenix, then run `npx -y @arizeai/phoenix-cli setup` from your app's root directory.",
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    "url": "https://arize.com/docs/phoenix/prompt-engineering/overview-prompts",
    "excerpt": "Phoenix offers client SDKs to keep your prompts in sync across different applications and environments.",
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    "id": "arize-phoenix-docs-45",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "start Phoenix, then run `npx -y @arizeai/phoenix-cli setup` from your app’s root directory",
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    "id": "arize-phoenix-docs-46",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Gain a detailed breakdown of token usage for your LLM calls, allowing you to identify and optimize the most expensive LLM invocations.",
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    "id": "arize-phoenix-docs-47",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Inspect the documents retrieved during a Retriever call, including the score and order in which they were returned",
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  },
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    "id": "arize-phoenix-docs-48",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Organize traces into separate projects for different applications",
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    "id": "arize-phoenix-docs-49",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/tracing/llm-traces",
    "excerpt": "Group related traces into conversations and user sessions",
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    "id": "arize-phoenix-docs-50",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "Phoenix supports both deterministic code-based evaluators (exact match, regex, custom heuristics) and LLM-as-a-judge evaluators, where a second model scores the output against a rubric.",
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    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/evaluation/llm-evals",
    "excerpt": "Every evaluation execution captures the input data, the exact prompts sent to the judge LLM, the model’s full reasoning, the final scores, and execution timing.",
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    "id": "arize-phoenix-docs-52",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/prompt-engineering/overview-prompts",
    "excerpt": "Prompt Playground - Play with prompts, models, invocation parameters and track your progress via tracing and experiments",
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  },
  {
    "id": "arize-phoenix-docs-53",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/prompt-engineering/overview-prompts",
    "excerpt": "Span Replay - Replay the invocation of an LLM... you can step into the LLM invocation and see if any modifications to the invocation would have yielded a better outcome.",
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    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/prompt-engineering/overview-prompts",
    "excerpt": "Prompts in Code - Phoenix offers client SDKs to keep your prompts in sync across different applications and environments.",
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  },
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    "id": "arize-phoenix-docs-55",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets",
    "excerpt": "Dataset Evaluators serve as **test cases** that automatically score outputs when running experiments—forming an evaluation harness similar to a unit test suite.",
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  },
  {
    "id": "arize-phoenix-docs-56",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/self-hosting",
    "excerpt": "Phoenix is **free to self-host** with no feature limitations. Your data stays entirely within your infrastructure—nothing is sent to Arize and can be fully air-gapped.",
    "fetchedAt": "2026-09-04T01:11:04.145Z"
  },
  {
    "id": "arize-phoenix-docs-57",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/sdk-api-reference",
    "excerpt": "Zero-config tracing — Enable `auto_instrument=True` to automatically trace AI libraries",
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  },
  {
    "id": "arize-phoenix-docs-58",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix/integrations/remote-mcp",
    "excerpt": "Point any MCP-compatible client (Claude Code, Cursor, VS Code, and others) at your Phoenix instance’s `/mcp` endpoint and it can search, query, and operate on your projects, traces, datasets, experiments, prompts, and annotations",
    "fetchedAt": "2026-09-04T01:11:04.145Z"
  },
  {
    "id": "arize-phoenix-docs-59",
    "tier": "claimed-docs",
    "url": "https://arize.com/docs/phoenix",
    "excerpt": "Phoenix is built by Arize AI and the open-source community. It is built on top of OpenTelemetry and is powered by OpenInference instrumentation.",
    "fetchedAt": "2026-09-04T01:11:04.145Z"
  },
  {
    "id": "arize-phoenix-comm-1",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=37765954",
    "excerpt": "Been using it for a while, def recommend anyone building with LLMs to try it out",
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  },
  {
    "id": "arize-phoenix-probe-1",
    "tier": "probe",
    "url": "https://arize.com/llms.txt",
    "excerpt": "PROBE llms.txt: HTTP 200 at https://arize.com/llms.txt # Arize — AI & Agent Engineering\n\n> Arize is the AI engineering platform for teams building and improving AI agents and",
    "fetchedAt": "2026-09-04T01:10:37.543Z"
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  {
    "id": "arize-phoenix-probe-2",
    "tier": "probe",
    "url": "https://arize.com/docs/phoenix.md",
    "excerpt": "PROBE docs-md: HTTP 200 at https://arize.com/docs/phoenix.md > ## Documentation Index\n> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt\n>",
    "fetchedAt": "2026-09-04T01:10:37.543Z"
  },
  {
    "id": "arize-phoenix-probe-3",
    "tier": "probe",
    "url": "https://arize.com/openapi.json",
    "excerpt": "PROBE openapi: all candidate paths 404 (https://arize.com/openapi.json, https://arize.com/swagger.json, https://arize.com/api/openapi.json, https://arize.com/.well-known/openapi.json)",
    "fetchedAt": "2026-09-04T01:10:37.543Z"
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  {
    "id": "arize-phoenix-probe-4",
    "tier": "probe",
    "url": "https://arize.com/docs/phoenix/integrations/remote-mcp",
    "excerpt": "official MCP server documented at https://arize.com/docs/phoenix/integrations/remote-mcp",
    "fetchedAt": "2026-09-04T01:10:37.543Z"
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]