What can Arize Phoenix do?

opentelemetry-native span ingestion and storage, span-level trace querying and filtering via graphql, span attribute annotation and feedback collection, batch span export and dataset creation from traces, multi-user authentication and role-based access control, kubernetes-native deployment with helm charts and kustomize, automatic llm span instrumentation via python opentelemetry wrapper, evaluation framework with llm-as-judge and custom metrics, experiment tracking and dataset management for prompt/model comparison, retrieval evaluation with embedding-based similarity scoring, rest api with openapi schema for programmatic trace access, interactive playground for prompt testing and iteration, mcp (model context protocol) server for ai agent integration, distributed tracing with automatic parent-child span linking

Arize Phoenix

FrameworkFree

Open-source LLM observability — tracing, evaluation, OpenTelemetry, span analysis.

Open Source

/ 100

14 capabilities

Capabilities14 decomposed

opentelemetry-native span ingestion and storage

Medium confidence

Accepts OpenTelemetry Protocol (OTLP) traces via gRPC server on port 4317, parses span hierarchies with parent-child relationships, and persists them to PostgreSQL or SQLite with automatic schema migrations. Implements the full OTLP specification for trace collection without requiring vendor lock-in or custom instrumentation adapters.

Solves for

Ingest traces from any OpenTelemetry-instrumented Python or Node.js application without custom adaptersStore multi-span traces with full parent-child relationships for distributed tracing analysisRun Phoenix locally or in production with flexible database backends (PostgreSQL for scale, SQLite for dev)

Best for

Teams already using OpenTelemetry instrumentation in their LLM applications

Developers building observability into FastAPI, LangChain, or LlamaIndex applications

Organizations requiring on-premise or self-hosted trace storage without cloud dependencies

Requires

OpenTelemetry SDK for Python (opentelemetry-api >= 1.0) or TypeScript (opentelemetry-api >= 1.4)

PostgreSQL 12+ or SQLite 3.30+ for storage backend

Network access to Phoenix gRPC server on port 4317 (default)

Limitations

gRPC server requires network connectivity; no built-in batching or local buffering for offline scenarios

SQLite backend suitable only for development; PostgreSQL required for production workloads >10K spans/day

No automatic trace sampling — all spans ingested consume storage; requires client-side sampling configuration

What makes it unique

Native OTLP gRPC server with full span hierarchy preservation and dual-database support (PostgreSQL + SQLite) in a single open-source package, eliminating need for separate trace collectors like Jaeger or Tempo

vs alternatives

Simpler than Jaeger for LLM-specific use cases (no complex configuration) and cheaper than Datadog/New Relic (self-hosted, no per-span pricing)

span-level trace querying and filtering via graphql

Medium confidence

Exposes a Strawberry GraphQL API (api/schema.py) that enables complex queries over ingested spans with filters on span name, status, duration, attributes, and parent-child relationships. Supports cursor-based pagination and aggregations (count, latency percentiles) without requiring SQL knowledge, allowing developers to programmatically extract trace subsets for analysis.

Solves for

Query traces by LLM model name, token count, or retrieval source to debug specific application pathsFilter spans by error status or latency thresholds to identify performance bottlenecks in RAG pipelinesRetrieve span hierarchies with full context (parent spans, child spans, attributes) for root-cause analysis

Best for

Data scientists and ML engineers analyzing LLM application behavior programmatically

Backend developers building custom dashboards or alerting on top of Phoenix traces

Teams integrating trace analysis into CI/CD pipelines for automated quality gates

Requires

HTTP access to Phoenix server on port 6006

GraphQL client library (graphql-core for Python, apollo-client for JavaScript) or curl/HTTP client

Understanding of GraphQL query syntax

Limitations

GraphQL queries execute against database directly; no query optimization or caching layer for repeated queries

Complex nested queries (>5 levels deep) may timeout on large datasets (>1M spans); requires manual pagination

No built-in time-series aggregations; requires client-side computation for latency trends over time

What makes it unique

Strawberry GraphQL schema specifically designed for LLM trace patterns (model names, token counts, retrieval metadata) rather than generic span attributes, with built-in support for RAG-specific filters like 'retrieval_source' and 'embedding_model'

vs alternatives

More intuitive than raw SQL queries for non-database engineers, and more flexible than Jaeger's UI-only filtering for programmatic access

span attribute annotation and feedback collection

Medium confidence

Provides APIs and UI for adding human feedback and annotations to spans after they are ingested (e.g., marking a retrieval result as 'relevant' or 'irrelevant', or adding a human score to an LLM response). Feedback is stored separately from spans and linked via span ID, enabling human-in-the-loop evaluation and ground-truth dataset creation from production traces.

Solves for

Collect human feedback on LLM responses in production to create ground-truth labels for model trainingAnnotate retrieval results as relevant/irrelevant to build a labeled dataset for retrieval evaluationMark spans with quality scores (1-5 stars) to track user satisfaction over time

Best for

Teams building labeled datasets from production traces for model fine-tuning

Organizations collecting human feedback on LLM outputs for continuous improvement

Researchers creating ground-truth datasets for RAG or LLM evaluation benchmarks

Requires

Phoenix server with database backend

Span ID to link feedback to spans

Feedback schema definition (e.g., categorical labels, numeric scores)

Limitations

Feedback collection is manual (UI or API); no automatic feedback from user interactions (e.g., thumbs up/down buttons)

Feedback is not versioned; overwriting feedback loses historical annotations

No built-in workflow for feedback review or conflict resolution when multiple annotators disagree

What makes it unique

Feedback is collected directly on Phoenix spans without requiring separate annotation tools or data export, enabling seamless integration of human feedback into trace analysis and dataset creation workflows

vs alternatives

More integrated than external annotation tools (Label Studio, Prodigy) because feedback is stored in the same system as traces; simpler than building custom feedback UIs because Phoenix provides built-in annotation interface

batch span export and dataset creation from traces

Medium confidence

Provides APIs to export spans matching query criteria (e.g., all spans from the last 7 days, or spans with error status) into structured datasets (CSV, JSON, Parquet) for external analysis. Supports filtering, sampling, and transformation (e.g., extracting input/output pairs for fine-tuning datasets) during export.

Solves for

Export production traces to create fine-tuning datasets for custom LLM modelsExtract failed spans for root-cause analysis in external tools (Jupyter, Pandas)Create benchmark datasets from historical traces for evaluating new models or prompts

Best for

ML engineers preparing training data from production LLM traces

Data analysts performing exploratory analysis on trace data in Jupyter notebooks

Teams building evaluation benchmarks from historical production data

Requires

Phoenix server with database backend

Query filters to select spans for export

External storage (S3, local filesystem) for exported files

Limitations

Export is one-time snapshot; no automatic incremental exports or streaming

Large exports (>100K spans) may timeout or consume significant memory; requires pagination or sampling

Transformation logic is limited to simple field extraction; complex transformations require post-processing

What makes it unique

Export directly from Phoenix traces without intermediate data warehouse, and supports transformation rules (e.g., extracting input/output pairs) for common fine-tuning dataset formats

vs alternatives

More integrated than manual trace export because filtering and transformation happen in Phoenix; more flexible than fixed-schema exports because users can define custom transformations

multi-user authentication and role-based access control

Medium confidence

Implements authentication and authorization (Authentication & Authorization section in DeepWiki) supporting multiple user types (admin, viewer, editor) with fine-grained permissions on datasets, experiments, and traces. Integrates with OAuth2 or API key authentication for programmatic access, and supports RBAC policies for multi-tenant deployments.

Solves for

Restrict trace visibility to specific teams or projects in a multi-tenant Phoenix deploymentGrant read-only access to stakeholders (product managers, executives) without allowing modificationsEnforce audit trails by tracking which user modified which dataset or experiment

Best for

Enterprise teams deploying Phoenix in multi-tenant environments

Organizations with compliance requirements (SOC2, HIPAA) needing access control and audit logs

Teams sharing a single Phoenix instance across multiple projects or departments

Requires

OAuth2 provider (Okta, Auth0, Google) or API key management system

User database or directory service (LDAP, Active Directory)

Phoenix server configured with authentication backend

Limitations

RBAC is coarse-grained (admin/viewer/editor); no fine-grained field-level permissions

OAuth2 integration requires external identity provider (Okta, Auth0); no built-in user management

API key authentication is basic (no key rotation, expiration, or scoping); requires external key management

What makes it unique

RBAC integrated with Phoenix's GraphQL and REST APIs, allowing fine-grained control over which users can query, modify, or export traces and datasets without separate authorization layer

vs alternatives

More integrated than external authorization services (Auth0, Okta) because permissions are enforced at the API level; simpler than building custom RBAC because Phoenix provides built-in role definitions

kubernetes-native deployment with helm charts and kustomize

Medium confidence

Provides production-ready Kubernetes manifests (kustomize/ directory) and Helm charts for deploying Phoenix server, PostgreSQL, and supporting services as a scalable cluster. Includes configuration for resource limits, health checks, persistent volumes, and horizontal pod autoscaling based on trace ingestion rate.

Solves for

Deploy Phoenix to a Kubernetes cluster with automatic scaling based on trace volumeRun Phoenix in a multi-replica configuration for high availability and fault toleranceIntegrate Phoenix into existing Kubernetes-based observability stacks (Prometheus, Grafana, ELK)

Best for

DevOps teams managing Kubernetes clusters and wanting to deploy Phoenix at scale

Organizations with existing Kubernetes infrastructure and standardized deployment processes

Teams requiring high availability and automatic failover for observability platform

Requires

Kubernetes cluster 1.20+ with kubectl access

Helm 3.0+ or Kustomize 4.0+

PostgreSQL 12+ for production (or managed service like RDS)

Limitations

Kubernetes deployment adds operational complexity; requires Kubernetes expertise and monitoring

Helm charts are opinionated (e.g., PostgreSQL backend required); customization requires chart modification

Persistent volume provisioning depends on cluster storage classes; may require manual configuration

What makes it unique

Kubernetes manifests are version-controlled in the Phoenix repo and tested in CI/CD, ensuring deployment configurations stay in sync with server code; includes Kustomize overlays for dev/staging/prod environments

vs alternatives

More integrated than generic Kubernetes deployments because manifests are Phoenix-specific and tested; simpler than building custom Helm charts because charts are provided and maintained by Arize

automatic llm span instrumentation via python opentelemetry wrapper

Medium confidence

The arize-phoenix-otel package provides auto-instrumentation decorators and context managers that wrap LLM calls (OpenAI, Anthropic, LlamaIndex, LangChain) and automatically emit spans with model name, token counts, latency, and error status. Uses Python's contextvars for automatic parent-child span linking without manual trace ID propagation.

Solves for

Instrument LLM applications with zero changes to business logic using decorators or context managersAutomatically capture token counts and model names from LLM API responses for cost and performance trackingTrace nested LLM calls (e.g., LangChain agent → multiple LLM calls) with automatic parent-child relationships

Best for

Python developers using LangChain, LlamaIndex, or direct OpenAI/Anthropic SDK calls

Teams wanting observability without rewriting application code

Rapid prototyping scenarios where minimal instrumentation overhead is critical

Requires

Python 3.9+

arize-phoenix-otel package installed (pip install arize-phoenix-otel)

OpenTelemetry SDK (opentelemetry-api, opentelemetry-sdk)

Limitations

Python-only; no auto-instrumentation for Node.js LLM libraries (requires manual TypeScript client)

Decorator approach requires application code to import and apply decorators; not fully transparent like Java agents

Token count extraction depends on LLM API response format; custom models or local LLMs may not expose token counts

What makes it unique

Specialized auto-instrumentation for LLM APIs (not generic HTTP tracing) that extracts model names and token counts from API responses and embeds them as span attributes, enabling cost and performance analysis without custom parsing

vs alternatives

Simpler than manual OpenTelemetry instrumentation and more LLM-aware than generic Python auto-instrumentation libraries like opentelemetry-instrumentation-requests

evaluation framework with llm-as-judge and custom metrics

Medium confidence

The arize-phoenix-evals package provides a pluggable evaluation system that runs LLM-based judges (using OpenAI, Anthropic, or local models) to score span outputs against criteria (relevance, hallucination, toxicity). Supports custom Python evaluation functions, batch evaluation over datasets, and integration with experiment tracking for A/B testing LLM prompts or models.

Solves for

Evaluate RAG retrieval quality by scoring relevance of retrieved documents against queries using an LLM judgeDetect hallucinations in LLM responses by comparing generated text against source documentsRun batch evaluations over historical traces to establish baseline quality metrics for regression detection

Best for

ML teams building quality gates for LLM applications (e.g., reject responses below relevance threshold)

Researchers comparing LLM models or prompt variations using automated evaluation

Production systems requiring continuous quality monitoring via periodic batch evaluations

Requires

arize-phoenix-evals package (pip install arize-phoenix-evals)

API key for LLM judge (OpenAI, Anthropic, or local model endpoint)

Spans with populated 'output' and 'metadata' attributes for evaluation context

Limitations

LLM-as-judge evaluations are non-deterministic and may vary across runs; requires multiple runs for statistical significance

Evaluation latency scales with number of spans; batch evaluation of 10K spans may take 30+ minutes with rate-limited APIs

Custom evaluation functions require Python code; no low-code UI for defining evaluation logic

What makes it unique

Integrated LLM-as-judge evaluation tightly coupled with trace data (no separate evaluation dataset needed) and experiment tracking, allowing direct comparison of evaluation scores across different LLM models or prompts tested in production

vs alternatives

More integrated than standalone evaluation frameworks (Ragas, DeepEval) because evaluations run directly on Phoenix traces without data export; more flexible than rule-based metrics because judges can reason about semantic quality

experiment tracking and dataset management for prompt/model comparison

Medium confidence

Provides a dataset and experiment system (Datasets & Experiments feature) that allows users to create versioned datasets of test cases, run experiments comparing different LLM prompts or models against those datasets, and track evaluation results over time. Integrates with the evaluation framework to automatically score experiment runs and surface performance deltas.

Solves for

Create a golden dataset of Q&A pairs and run A/B tests comparing two prompt variations on the same datasetTrack performance metrics (accuracy, latency, cost) across multiple LLM model versions to identify regressionsVersion control datasets and experiments for reproducibility and audit trails in regulated environments

Best for

Product teams iterating on LLM prompts and needing structured A/B testing

ML engineers managing multiple LLM model versions in production

Compliance-heavy organizations requiring audit trails of model changes and performance

Requires

Phoenix server with database backend

Dataset defined as list of test cases (input, expected output, context)

Evaluation function to score experiment runs

Limitations

Experiment runs are manual (via API or UI); no built-in scheduling for continuous A/B testing

Dataset versioning is basic (snapshots); no branching or merging workflows for collaborative dataset curation

No statistical significance testing; requires external tools (scipy, statsmodels) to determine if deltas are meaningful

What makes it unique

Experiments are tied directly to Phoenix traces and evaluations, enabling automatic evaluation of experiment runs without manual data export; datasets are versioned and queryable via GraphQL, allowing reproducible comparisons across time

vs alternatives

More integrated than MLflow (which requires manual trace export) and simpler than Weights & Biases (no complex project setup) for LLM-specific A/B testing

retrieval evaluation with embedding-based similarity scoring

Medium confidence

Specialized evaluation module for RAG systems that scores retrieval quality by computing embedding similarity between queries and retrieved documents, and between retrieved documents and ground-truth relevant documents. Supports multiple embedding models (OpenAI, Cohere, local) and metrics (cosine similarity, NDCG, MRR) for ranking evaluation.

Solves for

Evaluate RAG retrieval quality by measuring how well retrieved documents match the query intent using embeddingsDetect when retrieval degrades (e.g., after vector DB index update) by comparing embedding similarity scoresRank retrieval strategies (BM25 vs semantic search vs hybrid) using NDCG or MRR metrics on a test dataset

Best for

RAG system builders optimizing retrieval pipelines (vector DB, embedding model, reranking)

Teams monitoring retrieval quality in production and needing automated degradation alerts

Researchers comparing retrieval strategies with standardized ranking metrics

Requires

arize-phoenix-evals package with retrieval evaluation module

Embedding model API key (OpenAI, Cohere) or local embedding endpoint

Spans with 'retrieved_documents' and 'ground_truth_documents' attributes

Limitations

Embedding-based evaluation requires ground-truth relevance labels; no automatic label generation

Similarity scores are relative (0-1) and model-dependent; scores from different embedding models are not comparable

NDCG and MRR require ranked lists of documents; cannot evaluate single-document retrieval scenarios

What makes it unique

Embedding-based retrieval evaluation integrated directly with trace data, allowing automatic evaluation of retrieval spans without separate ground-truth dataset; supports multiple embedding models and ranking metrics in a single framework

vs alternatives

More comprehensive than simple cosine similarity (includes NDCG, MRR) and more integrated than standalone RAG evaluation tools (Ragas) because it operates on Phoenix traces directly

rest api with openapi schema for programmatic trace access

Medium confidence

Exposes a REST API (api/routes.py) with OpenAPI/Swagger documentation that mirrors GraphQL capabilities, enabling non-GraphQL clients (curl, Postman, REST-only frameworks) to query spans, create datasets, and trigger evaluations. Provides JSON request/response format with standard HTTP status codes and error messages.

Solves for

Query traces from shell scripts or CI/CD pipelines using curl without GraphQL client librariesIntegrate Phoenix with monitoring systems (Prometheus, Grafana) that expect REST APIsBuild custom dashboards in tools that don't support GraphQL (e.g., Metabase, Superset)

Best for

DevOps engineers integrating Phoenix into existing monitoring stacks

Teams using REST-only tools (Postman, Insomnia) for API exploration

Shell script automation for trace extraction and analysis

Requires

HTTP client (curl, requests library, Postman)

HTTP access to Phoenix server on port 6006

OpenAPI schema documentation (available at /openapi.json)

Limitations

REST API is less efficient than GraphQL for complex queries (multiple round-trips required for nested data)

No built-in request batching; querying 100 spans requires 100 separate HTTP requests

OpenAPI schema is auto-generated and may not document all filter options; GraphQL schema is more discoverable

What makes it unique

REST API auto-generated from GraphQL schema using Strawberry's REST support, ensuring consistency between GraphQL and REST interfaces without manual synchronization

vs alternatives

More discoverable than GraphQL for REST-only teams, and simpler than Jaeger's REST API because it's auto-generated from a single schema definition

interactive playground for prompt testing and iteration

Medium confidence

Web-based playground interface (Playground System in frontend) that allows users to write and test LLM prompts directly against live data, with support for variable substitution, model selection, and response comparison. Integrates with Phoenix traces to use real historical data as test inputs, enabling prompt iteration without leaving the platform.

Solves for

Test prompt variations against real user queries from production traces without writing codeCompare responses from different LLM models on the same prompt to identify best-performing modelIterate on prompt templates with variable substitution (e.g., {{context}}, {{query}}) and see results in real-time

Best for

Non-technical product managers and content creators optimizing LLM prompts

Prompt engineers rapidly iterating on templates before committing to code

Teams wanting a low-code interface for prompt A/B testing

Requires

Web browser with JavaScript enabled

Access to Phoenix web UI on port 6006

LLM API key (OpenAI, Anthropic) configured in Phoenix settings

Limitations

Playground is UI-only; no programmatic API for automated prompt testing

Variable substitution is basic (string templating); no support for complex logic or conditionals

Response comparison is manual (side-by-side viewing); no automated scoring or ranking of responses

What makes it unique

Playground is integrated with Phoenix traces, allowing users to select real historical queries as test inputs without manual copy-paste; supports variable substitution and model comparison in a single interface

vs alternatives

More integrated than standalone prompt testing tools (PromptFoo, LangSmith) because it uses real production data from traces; simpler than code-based prompt testing because no Python/JavaScript required

mcp (model context protocol) server for ai agent integration

Medium confidence

Exposes Phoenix as an MCP server (js/packages/phoenix-mcp) that allows Claude, ChatGPT, and other AI agents to query traces, run evaluations, and manage datasets through natural language. Implements MCP resource and tool protocols, enabling agents to autonomously analyze LLM application performance and suggest optimizations.

Solves for

Ask Claude to analyze traces and identify performance bottlenecks using natural language queriesHave an AI agent automatically run evaluations on new traces and report quality regressionsEnable agents to suggest prompt improvements based on historical trace analysis

Best for

Teams using Claude or ChatGPT for autonomous LLM application analysis

Developers building AI agents that need to introspect LLM application behavior

Organizations wanting natural language interfaces to observability data

Requires

Phoenix MCP server package (js/packages/phoenix-mcp)

Node.js 18+ runtime

Claude API key or local Claude instance

Limitations

MCP server is read-only for most operations; no write support for modifying traces or datasets

Agent responses are non-deterministic and may hallucinate or misinterpret trace data

MCP protocol is still evolving; compatibility with new Claude versions not guaranteed

What makes it unique

MCP server implementation allows AI agents to autonomously query and analyze Phoenix traces using natural language, enabling agents to discover performance issues without human prompting or manual data extraction

vs alternatives

More flexible than REST API for agent integration because agents can use natural language instead of structured queries; more integrated than external agent tools because MCP server runs in-process with Phoenix

distributed tracing with automatic parent-child span linking

Medium confidence

Implements OpenTelemetry context propagation using Python contextvars and JavaScript async context to automatically link parent and child spans across function calls and async boundaries. Traces are reconstructed as hierarchical trees in the database, enabling visualization of full request flows through multi-step LLM applications (e.g., agent → tool call → LLM call → retrieval).

Solves for

Trace a user request through an LLM agent that makes multiple tool calls and LLM invocations, seeing the full call treeIdentify which step in a multi-step LLM pipeline is causing latency by examining span durations in the trace treeCorrelate errors in downstream services (retrieval, embedding) with upstream LLM calls that triggered them

Best for

Teams building complex LLM agents with multiple steps and tool calls

Developers debugging multi-service LLM applications (LLM + vector DB + API calls)

Organizations needing end-to-end visibility into request flows

Requires

OpenTelemetry SDK with context propagation support

Instrumentation at each function/service boundary to create child spans

Trace ID propagation headers for cross-service tracing (HTTP headers, message metadata)

Limitations

Context propagation requires explicit instrumentation at async boundaries; automatic propagation is not guaranteed across all async patterns

Trace tree reconstruction assumes correct parent-child span IDs; malformed trace IDs result in orphaned spans

Large trace trees (>1000 spans) may be slow to render in the UI; requires pagination or filtering

What makes it unique

Automatic parent-child span linking via contextvars (Python) and async context (JavaScript) without requiring manual trace ID propagation in application code, reducing instrumentation boilerplate

vs alternatives

Simpler than Jaeger's manual trace ID propagation because context is automatically threaded through async calls; more reliable than implicit correlation because parent-child relationships are explicit in span data

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with Arize Phoenix, ranked by overlap. Discovered automatically through the match graph.

Prompt32

phoenix

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feedback and annotation capture on spanstrace querying and filtering via graphql apiopentelemetry trace ingestion via grpc otlp protocolfrontend visualization of trace execution flows

4 shared capabilities

Framework56

OpenLLMetry

OpenTelemetry-based LLM observability with automatic instrumentation.

custom span processor framework for extensible telemetry pipelinesstreaming response handling with span lifecycle managementprivacy-aware data redaction and pii filtering

3 shared capabilities

MCP Server31

MCP Server for OpenTelemetry

Hey HN, Gal, Nir and Doron here.Over the past 2 years, we've helped teams debug everything from prompt issues to production outages.We kept running into the same problem: Jumping between our IDEs and our observability dashboards. So, we built an open-source MCP server that connects any OpenTel

span attribute schema discovery and validationtrace-aware context injection for claude conversations

2 shared capabilities

Framework24

trulens-eval

Backwards-compatibility package for API of trulens_eval<1.0.0 using API of trulens-*>=1.0.0.

opentelemetry-based application instrumentation with decorator-driven span generationframework-specific application wrapping with semantic span kinds

2 shared capabilities

Framework58

Opik

LLM evaluation and tracing platform — automated metrics, prompt management, CI/CD integration.

distributed trace collection and span aggregation with multi-framework integrationframework-agnostic tracing via opentelemetry integration

2 shared capabilities

Framework56

TruLens

LLM app instrumentation and evaluation with feedback functions.

custom instrumentation via @instrument decorator with span type taxonomyopentelemetry-based application instrumentation with automatic span generation

2 shared capabilities

Best For

✓Teams already using OpenTelemetry instrumentation in their LLM applications
✓Developers building observability into FastAPI, LangChain, or LlamaIndex applications
✓Organizations requiring on-premise or self-hosted trace storage without cloud dependencies
✓Data scientists and ML engineers analyzing LLM application behavior programmatically
✓Backend developers building custom dashboards or alerting on top of Phoenix traces
✓Teams integrating trace analysis into CI/CD pipelines for automated quality gates
✓Teams building labeled datasets from production traces for model fine-tuning
✓Organizations collecting human feedback on LLM outputs for continuous improvement

Known Limitations

⚠gRPC server requires network connectivity; no built-in batching or local buffering for offline scenarios
⚠SQLite backend suitable only for development; PostgreSQL required for production workloads >10K spans/day
⚠No automatic trace sampling — all spans ingested consume storage; requires client-side sampling configuration
⚠GraphQL queries execute against database directly; no query optimization or caching layer for repeated queries
⚠Complex nested queries (>5 levels deep) may timeout on large datasets (>1M spans); requires manual pagination
⚠No built-in time-series aggregations; requires client-side computation for latency trends over time

Requirements

OpenTelemetry SDK for Python (opentelemetry-api >= 1.0) or TypeScript (opentelemetry-api >= 1.4)PostgreSQL 12+ or SQLite 3.30+ for storage backendNetwork access to Phoenix gRPC server on port 4317 (default)HTTP access to Phoenix server on port 6006GraphQL client library (graphql-core for Python, apollo-client for JavaScript) or curl/HTTP clientUnderstanding of GraphQL query syntaxPhoenix server with database backendSpan ID to link feedback to spans

Input / Output

Accepts: OTLP ExportTraceServiceRequest (protobuf), Span attributes (key-value pairs, strings, numbers, booleans), Span events and links with timestamps, GraphQL query strings with filters (span name, status, attribute predicates), Pagination cursors and limits, Span ID, Feedback value (text, numeric score, categorical label), Annotator metadata (user ID, timestamp), Span query filters (time range, status, attributes), Export format (CSV, JSON, Parquet), Transformation rules (field selection, renaming), User credentials (OAuth2 tokens, API keys), Role assignments (admin, viewer, editor), Resource permissions (dataset ID, experiment ID), Helm values.yaml or Kustomize patches for configuration, Container image registry (Docker Hub, ECR, GCR), PostgreSQL connection string, Python function calls to LLM APIs (OpenAI, Anthropic, etc.), Decorator or context manager wrapping, Span objects with input, output, and context attributes, Evaluation criteria as natural language prompts or Python functions, Dataset of test cases (query, expected output, context), Dataset: list of dicts with 'input', 'expected_output', 'context' keys, Experiment config: model name, prompt template, hyperparameters, Evaluation criteria, Query text, Retrieved document texts (list), Ground-truth relevant document texts (list), Embedding model selection (OpenAI, Cohere, local), HTTP GET/POST requests with JSON payloads, Query parameters for filtering (span_name, status, limit, offset), Prompt template text with {{variable}} placeholders, Model selection (GPT-4, Claude, etc.), Test inputs from Phoenix traces or manual text entry, Natural language queries from AI agents, MCP resource requests (trace IDs, dataset names), MCP tool calls (run_evaluation, query_spans), Parent span ID and trace ID from context, Child span creation with parent span ID reference

Produces: Persisted span records in database, Span hierarchies queryable via GraphQL/REST APIs, Span objects with attributes, events, links, and parent/child references, Aggregated metrics (count, min/max/p50/p99 latency), Feedback records linked to spans, Feedback statistics (distribution of labels, average scores), Exported dataset files (CSV, JSON, Parquet), Dataset metadata (row count, schema), Access tokens (JWT, OAuth2 bearer tokens), Audit logs (user ID, action, resource, timestamp), Kubernetes Deployment, Service, StatefulSet, ConfigMap resources, Prometheus metrics for monitoring, OTLP spans with model name, token counts, latency, error status, Automatic parent-child span relationships via context propagation, Evaluation scores (0-1 or categorical: pass/fail), Evaluation explanations from LLM judges, Aggregated metrics (pass rate, average score) per experiment, Experiment run results: scores per test case, aggregated metrics, Performance comparison: delta in accuracy, latency, cost between runs, Embedding similarity scores (0-1), Ranking metrics: NDCG, MRR, precision@k, Per-document relevance scores, JSON response with span objects, HTTP status codes (200, 400, 404, 500), LLM response text, Token count and latency, Side-by-side comparison of responses from different models, Trace data in JSON format, Evaluation results, Natural language summaries from agent, Hierarchical span tree with parent-child relationships, Trace visualization showing call flow and timing

UnfragileRank

Adoption70%(30% weight)

Quality90%(20% weight)

Ecosystem50%(15% weight)

Match Graph25%(30% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Framework

14 capabilities

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About

Open-source observability for LLM applications. Tracing, evaluation, and dataset management. Features span-level analysis, retrieval evaluation, and experiment tracking. Works with OpenTelemetry. By Arize AI.

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Capabilities14 decomposed

opentelemetry-native span ingestion and storage

Medium confidence

Solves for

Best for

Teams already using OpenTelemetry instrumentation in their LLM applications

Developers building observability into FastAPI, LangChain, or LlamaIndex applications

Organizations requiring on-premise or self-hosted trace storage without cloud dependencies

Requires

OpenTelemetry SDK for Python (opentelemetry-api >= 1.0) or TypeScript (opentelemetry-api >= 1.4)

PostgreSQL 12+ or SQLite 3.30+ for storage backend

Network access to Phoenix gRPC server on port 4317 (default)

Limitations

gRPC server requires network connectivity; no built-in batching or local buffering for offline scenarios

SQLite backend suitable only for development; PostgreSQL required for production workloads >10K spans/day

No automatic trace sampling — all spans ingested consume storage; requires client-side sampling configuration

What makes it unique

vs alternatives

Simpler than Jaeger for LLM-specific use cases (no complex configuration) and cheaper than Datadog/New Relic (self-hosted, no per-span pricing)

span-level trace querying and filtering via graphql

Medium confidence

Solves for

Best for

Data scientists and ML engineers analyzing LLM application behavior programmatically

Backend developers building custom dashboards or alerting on top of Phoenix traces

Teams integrating trace analysis into CI/CD pipelines for automated quality gates

Requires

HTTP access to Phoenix server on port 6006

GraphQL client library (graphql-core for Python, apollo-client for JavaScript) or curl/HTTP client

Understanding of GraphQL query syntax

Limitations

GraphQL queries execute against database directly; no query optimization or caching layer for repeated queries

Complex nested queries (>5 levels deep) may timeout on large datasets (>1M spans); requires manual pagination

No built-in time-series aggregations; requires client-side computation for latency trends over time

What makes it unique

vs alternatives

More intuitive than raw SQL queries for non-database engineers, and more flexible than Jaeger's UI-only filtering for programmatic access

span attribute annotation and feedback collection

Medium confidence

Solves for

Best for

Teams building labeled datasets from production traces for model fine-tuning

Organizations collecting human feedback on LLM outputs for continuous improvement

Researchers creating ground-truth datasets for RAG or LLM evaluation benchmarks

Requires

Phoenix server with database backend

Span ID to link feedback to spans

Feedback schema definition (e.g., categorical labels, numeric scores)

Limitations

Feedback collection is manual (UI or API); no automatic feedback from user interactions (e.g., thumbs up/down buttons)

Feedback is not versioned; overwriting feedback loses historical annotations

No built-in workflow for feedback review or conflict resolution when multiple annotators disagree

What makes it unique

vs alternatives

batch span export and dataset creation from traces

Medium confidence

Solves for

Best for

ML engineers preparing training data from production LLM traces

Data analysts performing exploratory analysis on trace data in Jupyter notebooks

Teams building evaluation benchmarks from historical production data

Requires

Phoenix server with database backend

Query filters to select spans for export

External storage (S3, local filesystem) for exported files

Limitations

Export is one-time snapshot; no automatic incremental exports or streaming

Large exports (>100K spans) may timeout or consume significant memory; requires pagination or sampling

Transformation logic is limited to simple field extraction; complex transformations require post-processing

What makes it unique

Export directly from Phoenix traces without intermediate data warehouse, and supports transformation rules (e.g., extracting input/output pairs) for common fine-tuning dataset formats

vs alternatives

More integrated than manual trace export because filtering and transformation happen in Phoenix; more flexible than fixed-schema exports because users can define custom transformations

multi-user authentication and role-based access control

Medium confidence

Solves for

Best for

Enterprise teams deploying Phoenix in multi-tenant environments

Organizations with compliance requirements (SOC2, HIPAA) needing access control and audit logs

Teams sharing a single Phoenix instance across multiple projects or departments

Requires

OAuth2 provider (Okta, Auth0, Google) or API key management system

User database or directory service (LDAP, Active Directory)

Phoenix server configured with authentication backend

Limitations

RBAC is coarse-grained (admin/viewer/editor); no fine-grained field-level permissions

OAuth2 integration requires external identity provider (Okta, Auth0); no built-in user management

API key authentication is basic (no key rotation, expiration, or scoping); requires external key management

What makes it unique

RBAC integrated with Phoenix's GraphQL and REST APIs, allowing fine-grained control over which users can query, modify, or export traces and datasets without separate authorization layer

vs alternatives

kubernetes-native deployment with helm charts and kustomize

Medium confidence

Solves for

Best for

DevOps teams managing Kubernetes clusters and wanting to deploy Phoenix at scale

Organizations with existing Kubernetes infrastructure and standardized deployment processes

Teams requiring high availability and automatic failover for observability platform

Requires

Kubernetes cluster 1.20+ with kubectl access

Helm 3.0+ or Kustomize 4.0+

PostgreSQL 12+ for production (or managed service like RDS)

Limitations

Kubernetes deployment adds operational complexity; requires Kubernetes expertise and monitoring

Helm charts are opinionated (e.g., PostgreSQL backend required); customization requires chart modification

Persistent volume provisioning depends on cluster storage classes; may require manual configuration

What makes it unique

vs alternatives

More integrated than generic Kubernetes deployments because manifests are Phoenix-specific and tested; simpler than building custom Helm charts because charts are provided and maintained by Arize

automatic llm span instrumentation via python opentelemetry wrapper

Medium confidence

Solves for

Best for

Python developers using LangChain, LlamaIndex, or direct OpenAI/Anthropic SDK calls

Teams wanting observability without rewriting application code

Rapid prototyping scenarios where minimal instrumentation overhead is critical

Requires

Python 3.9+

arize-phoenix-otel package installed (pip install arize-phoenix-otel)

OpenTelemetry SDK (opentelemetry-api, opentelemetry-sdk)

Limitations

Python-only; no auto-instrumentation for Node.js LLM libraries (requires manual TypeScript client)

Decorator approach requires application code to import and apply decorators; not fully transparent like Java agents

Token count extraction depends on LLM API response format; custom models or local LLMs may not expose token counts

What makes it unique

vs alternatives

Simpler than manual OpenTelemetry instrumentation and more LLM-aware than generic Python auto-instrumentation libraries like opentelemetry-instrumentation-requests

evaluation framework with llm-as-judge and custom metrics

Medium confidence

Solves for

Best for

ML teams building quality gates for LLM applications (e.g., reject responses below relevance threshold)

Researchers comparing LLM models or prompt variations using automated evaluation

Production systems requiring continuous quality monitoring via periodic batch evaluations

Requires

arize-phoenix-evals package (pip install arize-phoenix-evals)

API key for LLM judge (OpenAI, Anthropic, or local model endpoint)

Spans with populated 'output' and 'metadata' attributes for evaluation context

Limitations

LLM-as-judge evaluations are non-deterministic and may vary across runs; requires multiple runs for statistical significance

Evaluation latency scales with number of spans; batch evaluation of 10K spans may take 30+ minutes with rate-limited APIs

Custom evaluation functions require Python code; no low-code UI for defining evaluation logic

What makes it unique

vs alternatives

experiment tracking and dataset management for prompt/model comparison

Medium confidence

Solves for

Best for

Product teams iterating on LLM prompts and needing structured A/B testing

ML engineers managing multiple LLM model versions in production

Compliance-heavy organizations requiring audit trails of model changes and performance

Requires

Phoenix server with database backend

Dataset defined as list of test cases (input, expected output, context)

Evaluation function to score experiment runs

Limitations

Experiment runs are manual (via API or UI); no built-in scheduling for continuous A/B testing

Dataset versioning is basic (snapshots); no branching or merging workflows for collaborative dataset curation

No statistical significance testing; requires external tools (scipy, statsmodels) to determine if deltas are meaningful

What makes it unique

vs alternatives

More integrated than MLflow (which requires manual trace export) and simpler than Weights & Biases (no complex project setup) for LLM-specific A/B testing

retrieval evaluation with embedding-based similarity scoring

Medium confidence

Solves for

Best for

RAG system builders optimizing retrieval pipelines (vector DB, embedding model, reranking)

Teams monitoring retrieval quality in production and needing automated degradation alerts

Researchers comparing retrieval strategies with standardized ranking metrics

Requires

arize-phoenix-evals package with retrieval evaluation module

Embedding model API key (OpenAI, Cohere) or local embedding endpoint

Spans with 'retrieved_documents' and 'ground_truth_documents' attributes

Limitations

Embedding-based evaluation requires ground-truth relevance labels; no automatic label generation

Similarity scores are relative (0-1) and model-dependent; scores from different embedding models are not comparable

NDCG and MRR require ranked lists of documents; cannot evaluate single-document retrieval scenarios

What makes it unique

vs alternatives

More comprehensive than simple cosine similarity (includes NDCG, MRR) and more integrated than standalone RAG evaluation tools (Ragas) because it operates on Phoenix traces directly

rest api with openapi schema for programmatic trace access

Medium confidence

Solves for

Best for

DevOps engineers integrating Phoenix into existing monitoring stacks

Teams using REST-only tools (Postman, Insomnia) for API exploration

Shell script automation for trace extraction and analysis

Requires

HTTP client (curl, requests library, Postman)

HTTP access to Phoenix server on port 6006

OpenAPI schema documentation (available at /openapi.json)

Limitations

REST API is less efficient than GraphQL for complex queries (multiple round-trips required for nested data)

No built-in request batching; querying 100 spans requires 100 separate HTTP requests

OpenAPI schema is auto-generated and may not document all filter options; GraphQL schema is more discoverable

What makes it unique

REST API auto-generated from GraphQL schema using Strawberry's REST support, ensuring consistency between GraphQL and REST interfaces without manual synchronization

vs alternatives

More discoverable than GraphQL for REST-only teams, and simpler than Jaeger's REST API because it's auto-generated from a single schema definition

interactive playground for prompt testing and iteration

Medium confidence

Solves for

Best for

Non-technical product managers and content creators optimizing LLM prompts

Prompt engineers rapidly iterating on templates before committing to code

Teams wanting a low-code interface for prompt A/B testing

Requires

Web browser with JavaScript enabled

Access to Phoenix web UI on port 6006

LLM API key (OpenAI, Anthropic) configured in Phoenix settings

Limitations

Playground is UI-only; no programmatic API for automated prompt testing

Variable substitution is basic (string templating); no support for complex logic or conditionals

Response comparison is manual (side-by-side viewing); no automated scoring or ranking of responses

What makes it unique

vs alternatives

mcp (model context protocol) server for ai agent integration

Medium confidence

Solves for

Best for

Teams using Claude or ChatGPT for autonomous LLM application analysis

Developers building AI agents that need to introspect LLM application behavior

Organizations wanting natural language interfaces to observability data

Requires

Phoenix MCP server package (js/packages/phoenix-mcp)

Node.js 18+ runtime

Claude API key or local Claude instance

Limitations

MCP server is read-only for most operations; no write support for modifying traces or datasets

Agent responses are non-deterministic and may hallucinate or misinterpret trace data

MCP protocol is still evolving; compatibility with new Claude versions not guaranteed

What makes it unique

vs alternatives

distributed tracing with automatic parent-child span linking

Medium confidence

Solves for

Best for

Teams building complex LLM agents with multiple steps and tool calls

Developers debugging multi-service LLM applications (LLM + vector DB + API calls)

Organizations needing end-to-end visibility into request flows

Requires

OpenTelemetry SDK with context propagation support

Instrumentation at each function/service boundary to create child spans

Trace ID propagation headers for cross-service tracing (HTTP headers, message metadata)

Limitations

Context propagation requires explicit instrumentation at async boundaries; automatic propagation is not guaranteed across all async patterns

Trace tree reconstruction assumes correct parent-child span IDs; malformed trace IDs result in orphaned spans

Large trace trees (>1000 spans) may be slow to render in the UI; requires pagination or filtering

What makes it unique

Automatic parent-child span linking via contextvars (Python) and async context (JavaScript) without requiring manual trace ID propagation in application code, reducing instrumentation boilerplate

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Arize Phoenix

Capabilities14 decomposed

opentelemetry-native span ingestion and storage

span-level trace querying and filtering via graphql

span attribute annotation and feedback collection

batch span export and dataset creation from traces

multi-user authentication and role-based access control

kubernetes-native deployment with helm charts and kustomize

automatic llm span instrumentation via python opentelemetry wrapper

evaluation framework with llm-as-judge and custom metrics

experiment tracking and dataset management for prompt/model comparison

retrieval evaluation with embedding-based similarity scoring

rest api with openapi schema for programmatic trace access

interactive playground for prompt testing and iteration

mcp (model context protocol) server for ai agent integration

distributed tracing with automatic parent-child span linking

Related Artifactssharing capabilities

phoenix

OpenLLMetry

MCP Server for OpenTelemetry

trulens-eval

Opik

TruLens

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Arize Phoenix

Are you the builder of Arize Phoenix?

Get the weekly brief

Data Sources

Arize Phoenix

Capabilities14 decomposed

opentelemetry-native span ingestion and storage

span-level trace querying and filtering via graphql

span attribute annotation and feedback collection

batch span export and dataset creation from traces

multi-user authentication and role-based access control

kubernetes-native deployment with helm charts and kustomize

automatic llm span instrumentation via python opentelemetry wrapper

evaluation framework with llm-as-judge and custom metrics

experiment tracking and dataset management for prompt/model comparison

retrieval evaluation with embedding-based similarity scoring

rest api with openapi schema for programmatic trace access

interactive playground for prompt testing and iteration

mcp (model context protocol) server for ai agent integration

distributed tracing with automatic parent-child span linking

Related Artifactssharing capabilities

phoenix

OpenLLMetry

MCP Server for OpenTelemetry

trulens-eval

Opik

TruLens

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Arize Phoenix

Are you the builder of Arize Phoenix?

Get the weekly brief

Data Sources