[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQ48DMK5672A2CZWJ1YKEY":3,"skill-same:01M6KQ48DMK5672A2CZWJ1YKEY":85},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":13,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6KQ48DMK5672A2CZWJ1YKEY","phoenix-observability",null,"Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-MLOps\u002Fphoenix\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"observability","Phoenix","phoenix observability","running","debugging","when","source","for","orchestra","insights","LLM Applications","platform","time","observability platform","use","open","applications","real-time","OpenTelemetry","Evaluation","monitoring","tracing","2026-11-10 10:12:30",false,"---\nname: phoenix-observability\ndescription: Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Observability, Phoenix, Arize, Tracing, Evaluation, Monitoring, LLM Ops, OpenTelemetry]\ndependencies: [arize-phoenix>=12.0.0]\n---\n\n# Phoenix - AI Observability Platform\n\nOpen-source AI observability and evaluation platform for LLM applications with tracing, evaluation, datasets, experiments, and real-time monitoring.\n\n## When to use Phoenix\n\n**Use Phoenix when:**\n- Debugging LLM application issues with detailed traces\n- Running systematic evaluations on datasets\n- Monitoring production LLM systems in real-time\n- Building experiment pipelines for prompt\u002Fmodel comparison\n- Self-hosted observability without vendor lock-in\n\n**Key features:**\n- **Tracing**: OpenTelemetry-based trace collection for any LLM framework\n- **Evaluation**: LLM-as-judge evaluators for quality assessment\n- **Datasets**: Versioned test sets for regression testing\n- **Experiments**: Compare prompts, models, and configurations\n- **Playground**: Interactive prompt testing with multiple models\n- **Open-source**: Self-hosted with PostgreSQL or SQLite\n\n**Use alternatives instead:**\n- **LangSmith**: Managed platform with LangChain-first integration\n- **Weights & Biases**: Deep learning experiment tracking focus\n- **Arize Cloud**: Managed Phoenix with enterprise features\n- **MLflow**: General ML lifecycle, model registry focus\n\n## Quick start\n\n### Installation\n\n```bash\npip install arize-phoenix\n\n# With specific backends\npip install arize-phoenix[embeddings]  # Embedding analysis\npip install arize-phoenix-otel         # OpenTelemetry config\npip install arize-phoenix-evals        # Evaluation framework\npip install arize-phoenix-client       # Lightweight REST client\n```\n\n### Launch Phoenix server\n\n```python\nimport phoenix as px\n\n# Launch in notebook (ThreadServer mode)\nsession = px.launch_app()\n\n# View UI\nsession.view()  # Embedded iframe\nprint(session.url)  # http:\u002F\u002Flocalhost:6006\n```\n\n### Command-line server (production)\n\n```bash\n# Start Phoenix server\nphoenix serve\n\n# With PostgreSQL\nexport PHOENIX_SQL_DATABASE_URL=\"postgresql:\u002F\u002Fuser:pass@host\u002Fdb\"\nphoenix serve --port 6006\n```\n\n### Basic tracing\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.openai import OpenAIInstrumentor\n\n# Configure OpenTelemetry with Phoenix\ntracer_provider = register(\n    project_name=\"my-llm-app\",\n    endpoint=\"http:\u002F\u002Flocalhost:6006\u002Fv1\u002Ftraces\"\n)\n\n# Instrument OpenAI SDK\nOpenAIInstrumentor().instrument(tracer_provider=tracer_provider)\n\n# All OpenAI calls are now traced\nfrom openai import OpenAI\nclient = OpenAI()\nresponse = client.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\n```\n\n## Core concepts\n\n### Traces and spans\n\nA **trace** represents a complete execution flow, while **spans** are individual operations within that trace.\n\n```python\nfrom phoenix.otel import register\nfrom opentelemetry import trace\n\n# Setup tracing\ntracer_provider = register(project_name=\"my-app\")\ntracer = trace.get_tracer(__name__)\n\n# Create custom spans\nwith tracer.start_as_current_span(\"process_query\") as span:\n    span.set_attribute(\"input.value\", query)\n\n    # Child spans are automatically nested\n    with tracer.start_as_current_span(\"retrieve_context\"):\n        context = retriever.search(query)\n\n    with tracer.start_as_current_span(\"generate_response\"):\n        response = llm.generate(query, context)\n\n    span.set_attribute(\"output.value\", response)\n```\n\n### Projects\n\nProjects organize related traces:\n\n```python\nimport os\nos.environ[\"PHOENIX_PROJECT_NAME\"] = \"production-chatbot\"\n\n# Or per-trace\nfrom phoenix.otel import register\ntracer_provider = register(project_name=\"experiment-v2\")\n```\n\n## Framework instrumentation\n\n### OpenAI\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.openai import OpenAIInstrumentor\n\ntracer_provider = register()\nOpenAIInstrumentor().instrument(tracer_provider=tracer_provider)\n```\n\n### LangChain\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.langchain import LangChainInstrumentor\n\ntracer_provider = register()\nLangChainInstrumentor().instrument(tracer_provider=tracer_provider)\n\n# All LangChain operations traced\nfrom langchain_openai import ChatOpenAI\nllm = ChatOpenAI(model=\"gpt-4o\")\nresponse = llm.invoke(\"Hello!\")\n```\n\n### LlamaIndex\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n\ntracer_provider = register()\nLlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)\n```\n\n### Anthropic\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.anthropic import AnthropicInstrumentor\n\ntracer_provider = register()\nAnthropicInstrumentor().instrument(tracer_provider=tracer_provider)\n```\n\n## Evaluation framework\n\n### Built-in evaluators\n\n```python\nfrom phoenix.evals import (\n    OpenAIModel,\n    HallucinationEvaluator,\n    RelevanceEvaluator,\n    ToxicityEvaluator,\n    llm_classify\n)\n\n# Setup model for evaluation\neval_model = OpenAIModel(model=\"gpt-4o\")\n\n# Evaluate hallucination\nhallucination_eval = HallucinationEvaluator(eval_model)\nresults = hallucination_eval.evaluate(\n    input=\"What is the capital of France?\",\n    output=\"The capital of France is Paris.\",\n    reference=\"Paris is the capital of France.\"\n)\n```\n\n### Custom evaluators\n\n```python\nfrom phoenix.evals import llm_classify\n\n# Define custom evaluation\ndef evaluate_helpfulness(input_text, output_text):\n    template = \"\"\"\n    Evaluate if the response is helpful for the given question.\n\n    Question: {input}\n    Response: {output}\n\n    Is this response helpful? Answer 'helpful' or 'not_helpful'.\n    \"\"\"\n\n    result = llm_classify(\n        model=eval_model,\n        template=template,\n        input=input_text,\n        output=output_text,\n        rails=[\"helpful\", \"not_helpful\"]\n    )\n    return result\n```\n\n### Run evaluations on dataset\n\n```python\nfrom phoenix import Client\nfrom phoenix.evals import run_evals\n\nclient = Client()\n\n# Get spans to evaluate\nspans_df = client.get_spans_dataframe(\n    project_name=\"my-app\",\n    filter_condition=\"span_kind == 'LLM'\"\n)\n\n# Run evaluations\neval_results = run_evals(\n    dataframe=spans_df,\n    evaluators=[\n        HallucinationEvaluator(eval_model),\n        RelevanceEvaluator(eval_model)\n    ],\n    provide_explanation=True\n)\n\n# Log results back to Phoenix\nclient.log_evaluations(eval_results)\n```\n\n## Datasets and experiments\n\n### Create dataset\n\n```python\nfrom phoenix import Client\n\nclient = Client()\n\n# Create dataset\ndataset = client.create_dataset(\n    name=\"qa-test-set\",\n    description=\"QA evaluation dataset\"\n)\n\n# Add examples\nclient.add_examples_to_dataset(\n    dataset_name=\"qa-test-set\",\n    examples=[\n        {\n            \"input\": {\"question\": \"What is Python?\"},\n            \"output\": {\"answer\": \"A programming language\"}\n        },\n        {\n            \"input\": {\"question\": \"What is ML?\"},\n            \"output\": {\"answer\": \"Machine learning\"}\n        }\n    ]\n)\n```\n\n### Run experiment\n\n```python\nfrom phoenix import Client\nfrom phoenix.experiments import run_experiment\n\nclient = Client()\n\ndef my_model(input_data):\n    \"\"\"Your model function.\"\"\"\n    question = input_data[\"question\"]\n    return {\"answer\": generate_answer(question)}\n\ndef accuracy_evaluator(input_data, output, expected):\n    \"\"\"Custom evaluator.\"\"\"\n    return {\n        \"score\": 1.0 if expected[\"answer\"].lower() in output[\"answer\"].lower() else 0.0,\n        \"label\": \"correct\" if expected[\"answer\"].lower() in output[\"answer\"].lower() else \"incorrect\"\n    }\n\n# Run experiment\nresults = run_experiment(\n    dataset_name=\"qa-test-set\",\n    task=my_model,\n    evaluators=[accuracy_evaluator],\n    experiment_name=\"baseline-v1\"\n)\n\nprint(f\"Average accuracy: {results.aggregate_metrics['accuracy']}\")\n```\n\n## Client API\n\n### Query traces and spans\n\n```python\nfrom phoenix import Client\n\nclient = Client(endpoint=\"http:\u002F\u002Flocalhost:6006\")\n\n# Get spans as DataFrame\nspans_df = client.get_spans_dataframe(\n    project_name=\"my-app\",\n    filter_condition=\"span_kind == 'LLM'\",\n    limit=1000\n)\n\n# Get specific span\nspan = client.get_span(span_id=\"abc123\")\n\n# Get trace\ntrace = client.get_trace(trace_id=\"xyz789\")\n```\n\n### Log feedback\n\n```python\nfrom phoenix import Client\n\nclient = Client()\n\n# Log user feedback\nclient.log_annotation(\n    span_id=\"abc123\",\n    name=\"user_rating\",\n    annotator_kind=\"HUMAN\",\n    score=0.8,\n    label=\"helpful\",\n    metadata={\"comment\": \"Good response\"}\n)\n```\n\n### Export data\n\n```python\n# Export to pandas\ndf = client.get_spans_dataframe(project_name=\"my-app\")\n\n# Export traces\ntraces = client.list_traces(project_name=\"my-app\")\n```\n\n## Production deployment\n\n### Docker\n\n```bash\ndocker run -p 6006:6006 arizephoenix\u002Fphoenix:latest\n```\n\n### With PostgreSQL\n\n```bash\n# Set database URL\nexport PHOENIX_SQL_DATABASE_URL=\"postgresql:\u002F\u002Fuser:pass@host:5432\u002Fphoenix\"\n\n# Start server\nphoenix serve --host 0.0.0.0 --port 6006\n```\n\n### Environment variables\n\n| Variable | Description | Default |\n|----------|-------------|---------|\n| `PHOENIX_PORT` | HTTP server port | `6006` |\n| `PHOENIX_HOST` | Server bind address | `127.0.0.1` |\n| `PHOENIX_GRPC_PORT` | gRPC\u002FOTLP port | `4317` |\n| `PHOENIX_SQL_DATABASE_URL` | Database connection | SQLite temp |\n| `PHOENIX_WORKING_DIR` | Data storage directory | OS temp |\n| `PHOENIX_ENABLE_AUTH` | Enable authentication | `false` |\n| `PHOENIX_SECRET` | JWT signing secret | Required if auth enabled |\n\n### With authentication\n\n```bash\nexport PHOENIX_ENABLE_AUTH=true\nexport PHOENIX_SECRET=\"your-secret-key-min-32-chars\"\nexport PHOENIX_ADMIN_SECRET=\"admin-bootstrap-token\"\n\nphoenix serve\n```\n\n## Best practices\n\n1. **Use projects**: Separate traces by environment (dev\u002Fstaging\u002Fprod)\n2. **Add metadata**: Include user IDs, session IDs for debugging\n3. **Evaluate regularly**: Run automated evaluations in CI\u002FCD\n4. **Version datasets**: Track test set changes over time\n5. **Monitor costs**: Track token usage via Phoenix dashboards\n6. **Self-host**: Use PostgreSQL for production deployments\n\n## Common issues\n\n**Traces not appearing:**\n```python\nfrom phoenix.otel import register\n\n# Verify endpoint\ntracer_provider = register(\n    project_name=\"my-app\",\n    endpoint=\"http:\u002F\u002Flocalhost:6006\u002Fv1\u002Ftraces\"  # Correct endpoint\n)\n\n# Force flush\nfrom opentelemetry import trace\ntrace.get_tracer_provider().force_flush()\n```\n\n**High memory in notebook:**\n```python\n# Close session when done\nsession = px.launch_app()\n# ... do work ...\nsession.close()\npx.close_app()\n```\n\n**Database connection issues:**\n```bash\n# Verify PostgreSQL connection\npsql $PHOENIX_SQL_DATABASE_URL -c \"SELECT 1\"\n\n# Check Phoenix logs\nphoenix serve --log-level debug\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Custom evaluators, experiments, production setup\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, performance\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.arize.com\u002Fphoenix\n- **Repository**: https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix\n- **Docker Hub**: https:\u002F\u002Fhub.docker.com\u002Fr\u002Farizephoenix\u002Fphoenix\n- **Version**: 12.0.0+\n- **License**: Apache 2.0\n","# Phoenix - AI Observability Platform\n\nOpen-source AI observability and evaluation platform for LLM applications with tracing, evaluation, datasets, experiments, and real-time monitoring.\n\n## When to use Phoenix\n\n**Use Phoenix when:**\n- Debugging LLM application issues with detailed traces\n- Running systematic evaluations on datasets\n- Monitoring production LLM systems in real-time\n- Building experiment pipelines for prompt\u002Fmodel comparison\n- Self-hosted observability without vendor lock-in\n\n**Key features:**\n- **Tracing**: OpenTelemetry-based trace collection for any LLM framework\n- **Evaluation**: LLM-as-judge evaluators for quality assessment\n- **Datasets**: Versioned test sets for regression testing\n- **Experiments**: Compare prompts, models, and configurations\n- **Playground**: Interactive prompt testing with multiple models\n- **Open-source**: Self-hosted with PostgreSQL or SQLite\n\n**Use alternatives instead:**\n- **LangSmith**: Managed platform with LangChain-first integration\n- **Weights & Biases**: Deep learning experiment tracking focus\n- **Arize Cloud**: Managed Phoenix with enterprise features\n- **MLflow**: General ML lifecycle, model registry focus\n\n## Quick start\n\n### Installation\n\n```bash\npip install arize-phoenix\n\n# With specific backends\npip install arize-phoenix[embeddings]  # Embedding analysis\npip install arize-phoenix-otel         # OpenTelemetry config\npip install arize-phoenix-evals        # Evaluation framework\npip install arize-phoenix-client       # Lightweight REST client\n```\n\n### Launch Phoenix server\n\n```python\nimport phoenix as px\n\n# Launch in notebook (ThreadServer mode)\nsession = px.launch_app()\n\n# View UI\nsession.view()  # Embedded iframe\nprint(session.url)  # http:\u002F\u002Flocalhost:6006\n```\n\n### Command-line server (production)\n\n```bash\n# Start Phoenix server\nphoenix serve\n\n# With PostgreSQL\nexport PHOENIX_SQL_DATABASE_URL=\"postgresql:\u002F\u002Fuser:pass@host\u002Fdb\"\nphoenix serve --port 6006\n```\n\n### Basic tracing\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.openai import OpenAIInstrumentor\n\n# Configure OpenTelemetry with Phoenix\ntracer_provider = register(\n    project_name=\"my-llm-app\",\n    endpoint=\"http:\u002F\u002Flocalhost:6006\u002Fv1\u002Ftraces\"\n)\n\n# Instrument OpenAI SDK\nOpenAIInstrumentor().instrument(tracer_provider=tracer_provider)\n\n# All OpenAI calls are now traced\nfrom openai import OpenAI\nclient = OpenAI()\nresponse = client.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\n```\n\n## Core concepts\n\n### Traces and spans\n\nA **trace** represents a complete execution flow, while **spans** are individual operations within that trace.\n\n```python\nfrom phoenix.otel import register\nfrom opentelemetry import trace\n\n# Setup tracing\ntracer_provider = register(project_name=\"my-app\")\ntracer = trace.get_tracer(__name__)\n\n# Create custom spans\nwith tracer.start_as_current_span(\"process_query\") as span:\n    span.set_attribute(\"input.value\", query)\n\n    # Child spans are automatically nested\n    with tracer.start_as_current_span(\"retrieve_context\"):\n        context = retriever.search(query)\n\n    with tracer.start_as_current_span(\"generate_response\"):\n        response = llm.generate(query, context)\n\n    span.set_attribute(\"output.value\", response)\n```\n\n### Projects\n\nProjects organize related traces:\n\n```python\nimport os\nos.environ[\"PHOENIX_PROJECT_NAME\"] = \"production-chatbot\"\n\n# Or per-trace\nfrom phoenix.otel import register\ntracer_provider = register(project_name=\"experiment-v2\")\n```\n\n## Framework instrumentation\n\n### OpenAI\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.openai import OpenAIInstrumentor\n\ntracer_provider = register()\nOpenAIInstrumentor().instrument(tracer_provider=tracer_provider)\n```\n\n### LangChain\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.langchain import LangChainInstrumentor\n\ntracer_provider = register()\nLangChainInstrumentor().instrument(tracer_provider=tracer_provider)\n\n# All LangChain operations traced\nfrom langchain_openai import ChatOpenAI\nllm = ChatOpenAI(model=\"gpt-4o\")\nresponse = llm.invoke(\"Hello!\")\n```\n\n### LlamaIndex\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n\ntracer_provider = register()\nLlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)\n```\n\n### Anthropic\n\n```python\nfrom phoenix.otel import register\nfrom openinference.instrumentation.anthropic import AnthropicInstrumentor\n\ntracer_provider = register()\nAnthropicInstrumentor().instrument(tracer_provider=tracer_provider)\n```\n\n## Evaluation framework\n\n### Built-in evaluators\n\n```python\nfrom phoenix.evals import (\n    OpenAIModel,\n    HallucinationEvaluator,\n    RelevanceEvaluator,\n    ToxicityEvaluator,\n    llm_classify\n)\n\n# Setup model for evaluation\neval_model = OpenAIModel(model=\"gpt-4o\")\n\n# Evaluate hallucination\nhallucination_eval = HallucinationEvaluator(eval_model)\nresults = hallucination_eval.evaluate(\n    input=\"What is the capital of France?\",\n    output=\"The capital of France is Paris.\",\n    reference=\"Paris is the capital of France.\"\n)\n```\n\n### Custom evaluators\n\n```python\nfrom phoenix.evals import llm_classify\n\n# Define custom evaluation\ndef evaluate_helpfulness(input_text, output_text):\n    template = \"\"\"\n    Evaluate if the response is helpful for the given question.\n\n    Question: {input}\n    Response: {output}\n\n    Is this response helpful? Answer 'helpful' or 'not_helpful'.\n    \"\"\"\n\n    result = llm_classify(\n        model=eval_model,\n        template=template,\n        input=input_text,\n        output=output_text,\n        rails=[\"helpful\", \"not_helpful\"]\n    )\n    return result\n```\n\n### Run evaluations on dataset\n\n```python\nfrom phoenix import Client\nfrom phoenix.evals import run_evals\n\nclient = Client()\n\n# Get spans to evaluate\nspans_df = client.get_spans_dataframe(\n    project_name=\"my-app\",\n    filter_condition=\"span_kind == 'LLM'\"\n)\n\n# Run evaluations\neval_results = run_evals(\n    dataframe=spans_df,\n    evaluators=[\n        HallucinationEvaluator(eval_model),\n        RelevanceEvaluator(eval_model)\n    ],\n    provide_explanation=True\n)\n\n# Log results back to Phoenix\nclient.log_evaluations(eval_results)\n```\n\n## Datasets and experiments\n\n### Create dataset\n\n```python\nfrom phoenix import Client\n\nclient = Client()\n\n# Create dataset\ndataset = client.create_dataset(\n    name=\"qa-test-set\",\n    description=\"QA evaluation dataset\"\n)\n\n# Add examples\nclient.add_examples_to_dataset(\n    dataset_name=\"qa-test-set\",\n    examples=[\n        {\n            \"input\": {\"question\": \"What is Python?\"},\n            \"output\": {\"answer\": \"A programming language\"}\n        },\n        {\n            \"input\": {\"question\": \"What is ML?\"},\n            \"output\": {\"answer\": \"Machine learning\"}\n        }\n    ]\n)\n```\n\n### Run experiment\n\n```python\nfrom phoenix import Client\nfrom phoenix.experiments import run_experiment\n\nclient = Client()\n\ndef my_model(input_data):\n    \"\"\"Your model function.\"\"\"\n    question = input_data[\"question\"]\n    return {\"answer\": generate_answer(question)}\n\ndef accuracy_evaluator(input_data, output, expected):\n    \"\"\"Custom evaluator.\"\"\"\n    return {\n        \"score\": 1.0 if expected[\"answer\"].lower() in output[\"answer\"].lower() else 0.0,\n        \"label\": \"correct\" if expected[\"answer\"].lower() in output[\"answer\"].lower() else \"incorrect\"\n    }\n\n# Run experiment\nresults = run_experiment(\n    dataset_name=\"qa-test-set\",\n    task=my_model,\n    evaluators=[accuracy_evaluator],\n    experiment_name=\"baseline-v1\"\n)\n\nprint(f\"Average accuracy: {results.aggregate_metrics['accuracy']}\")\n```\n\n## Client API\n\n### Query traces and spans\n\n```python\nfrom phoenix import Client\n\nclient = Client(endpoint=\"http:\u002F\u002Flocalhost:6006\")\n\n# Get spans as DataFrame\nspans_df = client.get_spans_dataframe(\n    project_name=\"my-app\",\n    filter_condition=\"span_kind == 'LLM'\",\n    limit=1000\n)\n\n# Get specific span\nspan = client.get_span(span_id=\"abc123\")\n\n# Get trace\ntrace = client.get_trace(trace_id=\"xyz789\")\n```\n\n### Log feedback\n\n```python\nfrom phoenix import Client\n\nclient = Client()\n\n# Log user feedback\nclient.log_annotation(\n    span_id=\"abc123\",\n    name=\"user_rating\",\n    annotator_kind=\"HUMAN\",\n    score=0.8,\n    label=\"helpful\",\n    metadata={\"comment\": \"Good response\"}\n)\n```\n\n### Export data\n\n```python\n# Export to pandas\ndf = client.get_spans_dataframe(project_name=\"my-app\")\n\n# Export traces\ntraces = client.list_traces(project_name=\"my-app\")\n```\n\n## Production deployment\n\n### Docker\n\n```bash\ndocker run -p 6006:6006 arizephoenix\u002Fphoenix:latest\n```\n\n### With PostgreSQL\n\n```bash\n# Set database URL\nexport PHOENIX_SQL_DATABASE_URL=\"postgresql:\u002F\u002Fuser:pass@host:5432\u002Fphoenix\"\n\n# Start server\nphoenix serve --host 0.0.0.0 --port 6006\n```\n\n### Environment variables\n\n| Variable | Description | Default |\n|----------|-------------|---------|\n| `PHOENIX_PORT` | HTTP server port | `6006` |\n| `PHOENIX_HOST` | Server bind address | `127.0.0.1` |\n| `PHOENIX_GRPC_PORT` | gRPC\u002FOTLP port | `4317` |\n| `PHOENIX_SQL_DATABASE_URL` | Database connection | SQLite temp |\n| `PHOENIX_WORKING_DIR` | Data storage directory | OS temp |\n| `PHOENIX_ENABLE_AUTH` | Enable authentication | `false` |\n| `PHOENIX_SECRET` | JWT signing secret | Required if auth enabled |\n\n### With authentication\n\n```bash\nexport PHOENIX_ENABLE_AUTH=true\nexport PHOENIX_SECRET=\"your-secret-key-min-32-chars\"\nexport PHOENIX_ADMIN_SECRET=\"admin-bootstrap-token\"\n\nphoenix serve\n```\n\n## Best practices\n\n1. **Use projects**: Separate traces by environment (dev\u002Fstaging\u002Fprod)\n2. **Add metadata**: Include user IDs, session IDs for debugging\n3. **Evaluate regularly**: Run automated evaluations in CI\u002FCD\n4. **Version datasets**: Track test set changes over time\n5. **Monitor costs**: Track token usage via Phoenix dashboards\n6. **Self-host**: Use PostgreSQL for production deployments\n\n## Common issues\n\n**Traces not appearing:**\n```python\nfrom phoenix.otel import register\n\n# Verify endpoint\ntracer_provider = register(\n    project_name=\"my-app\",\n    endpoint=\"http:\u002F\u002Flocalhost:6006\u002Fv1\u002Ftraces\"  # Correct endpoint\n)\n\n# Force flush\nfrom opentelemetry import trace\ntrace.get_tracer_provider().force_flush()\n```\n\n**High memory in notebook:**\n```python\n# Close session when done\nsession = px.launch_app()\n# ... do work ...\nsession.close()\npx.close_app()\n```\n\n**Database connection issues:**\n```bash\n# Verify PostgreSQL connection\npsql $PHOENIX_SQL_DATABASE_URL -c \"SELECT 1\"\n\n# Check Phoenix logs\nphoenix serve --log-level debug\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Custom evaluators, experiments, production setup\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, performance\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.arize.com\u002Fphoenix\n- **Repository**: https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix\n- **Docker Hub**: https:\u002F\u002Fhub.docker.com\u002Fr\u002Farizephoenix\u002Fphoenix\n- **Version**: 12.0.0+\n- **License**: Apache 2.0\n",{"name":5,"tags":45,"author":51,"license":14,"version":52,"description":7,"dependencies":53},[46,19,47,48,37,49,50,36],"Observability","Arize","Tracing","Monitoring","LLM Ops","Orchestra Research","1.0.0",[54],"arize-phoenix>=12.0.0","main","f19b254e8975d0bfddb394686706106930d7964d","AI-MLOps\u002Fphoenix","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-11 18:19:10",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add endman100\u002Fskill-router-nexus --skill phoenix-observability",{"tool":67,"command":68},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill phoenix-observability",{"tool":70,"command":71},"pnpm","pnpm dlx skills add endman100\u002Fskill-router-nexus --skill phoenix-observability","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-MLOps\u002Fphoenix\u002FSKILL.md",true,[75,81],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",15277,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-MLOps\u002Fphoenix\u002Freferences\u002Fadvanced-usage.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Ftroubleshooting.md",10944,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-MLOps\u002Fphoenix\u002Freferences\u002Ftroubleshooting.md",[86,110,145,167,176,199],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":90,"sourceHtmlUrl":91,"categories":92,"keywords":98,"publishedAt":40},"01M6MPCT5BXVER8HQZTW3QE9AS","create-master","基于佛教经典文献，生成特定高僧大德的 AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[93],{"categoryNo":94,"categoryName":95,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[99,100,101,102,103,104,105,106,107,108,109],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":114,"sourceHtmlUrl":115,"categories":116,"keywords":128,"publishedAt":40},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style analysis.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FSKILL.md",[117,122,125],{"categoryNo":118,"categoryName":119,"taxonomyType":120,"taxonomyTypeDesc":121},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":123,"categoryName":124,"taxonomyType":120,"taxonomyTypeDesc":121},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":126,"categoryName":127,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000036","内容创作者",[129,130,131,132,133,134,135,136,137,138,32,139,140,102,141,142,24,25,143,144],"writing dna skill","DNA","writing","strategy","english","logic","visual","structure","chinese","reusable","style","distill","analysis","frame","at","complete",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":149,"sourceHtmlUrl":150,"categories":151,"keywords":154,"publishedAt":40},"01M6M428TTCGQ4ME3KY99VN037","stop-slop","Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[152,153],{"categoryNo":118,"categoryName":119,"taxonomyType":120,"taxonomyTypeDesc":121},{"categoryNo":126,"categoryName":127,"taxonomyType":96,"taxonomyTypeDesc":97},[155,156,157,158,23,159,160,161,162,32,163,164,165,25,166,131],"slop","stop slop","stop","drafting","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":171,"sourceHtmlUrl":172,"categories":173,"keywords":174,"publishedAt":40},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[169,175],"文本",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":183,"publishedAt":40},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[178,184,185,186,187,188,32,189,107,24,190,191,192,193,109,33,194,195,196,23,197,198,155],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","saas","open source","product","describe","ai slop",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":207,"publishedAt":40},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[206],{"categoryNo":118,"categoryName":119,"taxonomyType":120,"taxonomyTypeDesc":121},[208,209,210,107,106,105,131,102,175,211,155,157],"humanizer","zh","humanizer zh","翻译"]