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Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1566,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fphoenix-observability\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"observability","Phoenix","phoenix observability","running","debugging","when","source","for","insights","LLM Applications","platform","time","observability platform","use","ops","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.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: ml-inference-and-ops\n  maintainer: Kalaris Labs\n  tags: Observability, Phoenix, Arize, Tracing, Evaluation, Monitoring, LLM Ops, OpenTelemetry\n  dependencies: 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\n## Related skills\n\n- `langsmith-observability`: LLM observability platform for tracing, evaluation, and monitoring.\n- `chroma`: Open-source embedding database for AI applications.\n- `speculative-decoding`: Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques.\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\n## Related skills\n\n- `langsmith-observability`: LLM observability platform for tracing, evaluation, and monitoring.\n- `chroma`: Open-source embedding database for AI applications.\n- `speculative-decoding`: Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques.\n",{"name":5,"license":15,"metadata":46,"description":7},{"tags":47,"version":48,"category":49,"maintainer":50,"dependencies":51},"Observability, Phoenix, Arize, Tracing, Evaluation, Monitoring, LLM Ops, OpenTelemetry","1.0.0","ml-inference-and-ops","Kalaris Labs","arize-phoenix>=12.0.0","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fphoenix-observability",483,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-13 02:16:32",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill phoenix-observability",{"tool":68,"command":69},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill phoenix-observability",{"tool":71,"command":72},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill phoenix-observability","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fphoenix-observability\u002FSKILL.md",true,[76,82],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",15277,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fphoenix-observability\u002Freferences\u002Fadvanced-usage.md",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Ftroubleshooting.md",10944,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fphoenix-observability\u002Freferences\u002Ftroubleshooting.md",[87,96,103,110,135,160],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":95},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":95},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":95},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":41},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[117,118,119,120,121,122,123,124,125,126,23,24,127,128,129,130,131,132,133,134],"search","deja search","never","error","ready","memory","re","decide","fix","design","you","done","reviewer","io","change","cli","merge","past",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":141,"publishedAt":41},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[142,143,144,130,145,146,147,148,149,150,151,152,153,154,122,155,156,157,158,159],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":177,"publishedAt":41},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[166,171,174],{"categoryNo":167,"categoryName":168,"taxonomyType":169,"taxonomyTypeDesc":170},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":172,"categoryName":173,"taxonomyType":169,"taxonomyTypeDesc":170},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":175,"categoryName":176,"taxonomyType":169,"taxonomyTypeDesc":170},"01JBSEEDCAT000000000000010","后端与接口",[162,178,179,180,181,182,183,156,184,123,185,186,187,188,24,189,190,191,192,193],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]