{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-pydantic--logfire","slug":"pydantic--logfire","name":"logfire","type":"product","url":"https://logfire.pydantic.dev/docs/","page_url":"https://unfragile.ai/pydantic--logfire","categories":["observability","deployment-infra"],"tags":["agent-observability","ai","ai-observability","ai-tools","evals","fastapi","llm-observability","logging","metrics","observability","openai","opentelemetry","pydantic","pydantic-ai","python","trace"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-pydantic--logfire__cap_0","uri":"capability://automation.workflow.structured.logging.with.context.propagation","name":"structured-logging-with-context-propagation","description":"Provides structured logging via logfire.info(), logfire.debug(), logfire.warning(), logfire.error() methods that automatically capture context and propagate trace IDs across distributed systems using W3C Trace Context standards. Messages support f-string magic for lazy evaluation and automatic JSON serialization of complex objects via Pydantic schema generation, with built-in data scrubbing to redact sensitive fields before export.","intents":["I want to log structured data with automatic context capture without manually threading trace IDs","I need to redact sensitive information from logs before they leave my system","I want lazy-evaluated f-strings in logs to avoid expensive serialization when logs are filtered"],"best_for":["Python backend developers building distributed LLM/agent systems","Teams requiring compliance-aware logging with automatic PII redaction","Developers migrating from unstructured logging to structured observability"],"limitations":["F-string magic requires Python 3.11+ for full AST rewriting support; earlier versions have limited lazy evaluation","Data scrubbing rules must be configured explicitly — no automatic PII detection without custom patterns","Structured logging adds ~5-10ms per log call due to JSON serialization and schema generation overhead"],"requires":["Python 3.8+","logfire package installed","Pydantic 2.0+ for schema generation","OpenTelemetry SDK (bundled with logfire)"],"input_types":["Python objects (dicts, dataclasses, Pydantic models)","f-strings with embedded expressions","primitive types (str, int, float, bool)"],"output_types":["OpenTelemetry LogRecord (OTLP format)","JSON-serialized structured logs","console output (with optional color formatting)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_1","uri":"capability://automation.workflow.distributed.tracing.with.span.context.management","name":"distributed-tracing-with-span-context-management","description":"Implements distributed tracing via context managers (logfire.span()) and decorators (@logfire.instrument()) that automatically create OpenTelemetry spans with parent-child relationships, capturing execution time, attributes, and exceptions. Uses W3C Trace Context headers for cross-service propagation and maintains a thread-local/async-local context stack via OpenTelemetry's context API, enabling automatic trace ID threading without explicit parameter passing.","intents":["I want to trace function execution across multiple services with automatic parent-child span relationships","I need to measure latency and capture exceptions within specific code blocks without boilerplate","I want trace IDs to automatically propagate through async/await and thread boundaries"],"best_for":["Backend teams building microservices or distributed LLM agent systems","Developers debugging performance bottlenecks in async Python code","Teams using FastAPI, Django, or other async frameworks with automatic instrumentation"],"limitations":["Span context is thread-local/async-local only — manual context propagation required for multiprocessing or process pools","Decorator-based instrumentation (@logfire.instrument) adds ~2-5ms overhead per function call due to span creation","Exception capture is automatic but custom exception handling logic must be added manually for non-standard error types"],"requires":["Python 3.8+","logfire package with OpenTelemetry SDK","async/await support for async context propagation (Python 3.7+)"],"input_types":["function definitions (for @logfire.instrument decorator)","code blocks (for logfire.span() context manager)","exception objects (automatically captured)"],"output_types":["OpenTelemetry Span objects (OTLP format)","trace ID strings (W3C Trace Context format)","span attributes (key-value pairs)","span events (for exceptions and custom events)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_10","uri":"capability://automation.workflow.web.framework.middleware.integration","name":"web-framework-middleware-integration","description":"Provides automatic instrumentation for FastAPI, Django, Flask, and Starlette via middleware/decorators that capture HTTP request/response metadata (method, path, status code, latency) as spans. 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Automatically propagates trace context via HTTP headers to downstream services. 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Configuration via environment variables or logfire.configure() controls which modules/functions are instrumented.","intents":["I want to add observability to my entire codebase without manually adding @logfire.instrument() decorators","I need automatic tracing of database queries, HTTP requests, and LLM API calls without modifying third-party libraries","I want to enable/disable instrumentation at runtime via configuration without code changes"],"best_for":["Teams onboarding observability to existing large codebases","Developers building LLM agents using OpenAI, Anthropic, or LangChain without modifying those libraries","FastAPI/Django teams wanting zero-instrumentation-code observability"],"limitations":["AST rewriting only works for code imported after logfire.install_auto_tracing() is called — existing imports are not retroactively instrumented","AST rewriting adds ~50-100ms startup overhead and increases memory usage by ~5-10% due to AST parsing and bytecode modification","Some dynamic code patterns (exec(), eval(), importlib.import_module() with runtime paths) may not be instrumented correctly","Framework-specific instrumentation requires explicit plugin registration — not all libraries are supported out-of-the-box"],"requires":["Python 3.8+","logfire package with AST rewriting support","install_auto_tracing() called before importing instrumented modules","Framework-specific plugins installed (e.g., logfire[fastapi], logfire[sqlalchemy])"],"input_types":["Python module names (as strings)","configuration dictionaries or environment variables","function/class names to instrument (optional filtering)"],"output_types":["modified bytecode (transparent to user)","OpenTelemetry spans (created automatically)","trace logs (for instrumentation events)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_3","uri":"capability://automation.workflow.llm.provider.instrumentation.with.token.counting","name":"llm-provider-instrumentation-with-token-counting","description":"Provides native integrations for OpenAI, Anthropic, LangChain, and Pydantic AI that automatically instrument LLM API calls, capturing prompts, completions, model names, and token counts without code changes. Uses provider-specific APIs (OpenAI's usage field, Anthropic's usage object, LangChain's callbacks) to extract token metrics and logs them as span attributes and metrics. Supports streaming responses with automatic token estimation.","intents":["I want to track LLM API costs and token usage automatically without parsing responses manually","I need to capture prompts and completions in traces for debugging agent behavior","I want to monitor which models are being used and their latency without modifying my LLM client code"],"best_for":["Teams building LLM agents using OpenAI, Anthropic, or LangChain","Developers needing cost tracking and token usage analytics for LLM applications","Pydantic AI users wanting built-in observability for agent systems"],"limitations":["Token counting for streaming responses is estimated, not exact — actual token counts only available after response completion","Prompt/completion capture requires explicit configuration to avoid logging sensitive data (API keys, PII in prompts)","Anthropic integration requires anthropic>=0.7.0; older versions not supported","LangChain integration depends on LangChain's callback system — custom LLM implementations may not be instrumented"],"requires":["Python 3.8+","logfire package","Provider SDK installed (openai, anthropic, langchain, pydantic-ai)","Valid API credentials for the LLM provider"],"input_types":["LLM API requests (prompts, model names, parameters)","streaming response chunks","LangChain agent/chain execution"],"output_types":["OpenTelemetry spans with LLM-specific attributes (model, tokens, latency)","metrics (token counts, API call counts, latency histograms)","structured logs with prompts/completions (if enabled)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_4","uri":"capability://data.processing.analysis.metrics.collection.with.custom.instruments","name":"metrics-collection-with-custom-instruments","description":"Exposes OpenTelemetry Meter API via logfire.metrics() to create custom metrics (counters, histograms, gauges, up-down counters) with attributes and aggregation. Metrics are batched and exported via OTLP alongside traces and logs. Supports both synchronous and asynchronous (observable) instruments for pull-based metrics like memory usage or queue depth.","intents":["I want to track custom business metrics (e.g., tokens per request, agent decision counts) alongside traces","I need to export metrics to Prometheus, Grafana, or other observability backends","I want to measure system-level metrics (memory, CPU, queue depth) without external monitoring tools"],"best_for":["Teams building LLM agents needing custom business metrics (token usage, decision counts)","Developers integrating with Prometheus or Grafana for metrics visualization","Backend teams wanting unified observability (traces + metrics + logs) in a single platform"],"limitations":["Metrics are aggregated and exported in batches (default 60s) — real-time metrics require custom exporters","Observable (async) instruments require callback functions that may block metric export if they're slow","Cardinality explosion risk if high-cardinality attributes (e.g., user IDs, request IDs) are used in metrics","No built-in alerting on metrics — requires external monitoring system (Prometheus AlertManager, etc.)"],"requires":["Python 3.8+","logfire package with OpenTelemetry Metrics SDK","OTLP exporter configured (included by default)"],"input_types":["metric names (strings)","metric values (int, float)","attributes (key-value pairs for metric dimensions)","callback functions (for observable instruments)"],"output_types":["OpenTelemetry MetricData (OTLP format)","Prometheus-compatible metrics (if using Prometheus exporter)","time-series data (for Grafana/Datadog visualization)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_5","uri":"capability://data.processing.analysis.pydantic.model.validation.tracing","name":"pydantic-model-validation-tracing","description":"Integrates with Pydantic's plugin system to automatically trace model validation, capturing validation errors, field values, and schema information as span attributes. Uses Pydantic's before_validator and after_validator hooks to create child spans for custom validators. Supports both Pydantic v1 and v2 with automatic version detection.","intents":["I want to trace Pydantic model validation to debug validation failures in LLM agent outputs","I need to capture which fields failed validation and why without manual instrumentation","I want to measure validation latency for performance optimization"],"best_for":["Teams using Pydantic for LLM output parsing and validation","Developers debugging validation failures in agent systems","Backend teams needing detailed observability of data validation pipelines"],"limitations":["Validation tracing only captures top-level model validation — nested model validation requires explicit instrumentation","Custom validators must be decorated with @logfire.instrument() to be traced; automatic instrumentation via AST rewriting may not work for all validator patterns","Pydantic v1 support is limited — v2 provides richer validation context and error information"],"requires":["Python 3.8+","logfire package","Pydantic 1.10+ (v2 recommended for full features)","Models defined using Pydantic BaseModel"],"input_types":["Pydantic BaseModel instances","raw data (dicts, JSON) being validated","custom validator functions"],"output_types":["OpenTelemetry spans with validation metadata","validation error logs (with field names and error messages)","span attributes (model name, field names, validation status)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_6","uri":"capability://automation.workflow.multi.backend.export.with.otlp.protocol","name":"multi-backend-export-with-otlp-protocol","description":"Exports all telemetry data (traces, logs, metrics) via OpenTelemetry Protocol (OTLP) to any OTLP-compatible backend (Logfire platform, Jaeger, Grafana Loki, Datadog, New Relic, etc.). Supports both gRPC and HTTP transports with configurable batch size, timeout, and retry logic. Includes built-in console exporter for local development and testing.","intents":["I want to export observability data to multiple backends (Logfire + Datadog, Jaeger + Prometheus, etc.)","I need to switch observability backends without code changes","I want to run observability locally for development without external services"],"best_for":["Teams using multiple observability platforms (e.g., Logfire for logs, Prometheus for metrics)","Developers building vendor-agnostic observability into their applications","Backend teams wanting local observability for development/testing"],"limitations":["OTLP export adds ~10-20ms latency per batch (default batch size 512 spans) due to network I/O","Retry logic uses exponential backoff (max 5 retries) — failed exports are dropped after retries exhausted","gRPC transport requires grpcio package; HTTP transport is pure Python but slightly slower","Console exporter is for development only — not suitable for production due to unbuffered output"],"requires":["Python 3.8+","logfire package with OTLP exporter","OTLP-compatible backend (Logfire, Jaeger, Grafana, Datadog, etc.)","Network connectivity to export endpoint","grpcio (optional, for gRPC transport)"],"input_types":["OpenTelemetry spans, logs, metrics (internal format)","OTLP endpoint URL (string)","export configuration (batch size, timeout, headers)"],"output_types":["OTLP ProtoBuf messages (gRPC or HTTP)","console output (for console exporter)","structured telemetry data in backend-specific format"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_7","uri":"capability://data.processing.analysis.sampling.and.filtering.with.configurable.rules","name":"sampling-and-filtering-with-configurable-rules","description":"Provides sampling configuration to reduce telemetry volume by filtering spans/logs based on rules (e.g., sample 10% of traces, drop all logs below WARNING level, exclude specific modules). Sampling is applied at the processor level before export, reducing network I/O and storage costs. Supports both probabilistic sampling (e.g., 1 in 100) and deterministic sampling (e.g., based on trace ID).","intents":["I want to reduce observability costs by sampling high-volume traces (e.g., health checks, background jobs)","I need to filter out noisy logs while keeping important traces","I want to sample traces deterministically so related spans are always kept together"],"best_for":["Teams with high-volume applications needing cost optimization","Developers debugging specific user sessions (deterministic sampling by user ID)","Backend teams wanting to reduce observability noise from health checks and background jobs"],"limitations":["Sampling is applied at processor level — sampled-out spans are still created in memory, adding CPU overhead","Deterministic sampling requires consistent sampling key (e.g., user ID) — inconsistent keys may result in incomplete traces","Sampling rules are static per process — dynamic sampling (e.g., based on error rate) requires custom processor implementation","Sampled-out metrics are still aggregated — metric sampling is not supported"],"requires":["Python 3.8+","logfire package","sampling configuration via LogfireConfig or environment variables"],"input_types":["sampling rate (float, 0.0-1.0)","sampling rules (module names, log levels, span names)","sampling key (for deterministic sampling)"],"output_types":["filtered spans/logs (reduced volume)","sampling metadata (sampling rate, decision)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_8","uri":"capability://automation.workflow.testing.utilities.with.deterministic.exporters","name":"testing-utilities-with-deterministic-exporters","description":"Provides test fixtures (logfire_exporter, logfire_config) and deterministic exporters that capture telemetry data in memory for assertion-based testing. Enables developers to write tests that verify spans, logs, and metrics were created with expected attributes without external observability backends. Includes helpers for filtering and querying captured telemetry.","intents":["I want to write unit tests that verify my code creates the correct spans and logs","I need to test observability instrumentation without setting up external backends","I want to assert on span attributes, log messages, and metrics in my tests"],"best_for":["Backend developers writing unit tests for instrumented code","Teams testing observability instrumentation as part of CI/CD","Developers verifying LLM agent tracing without external observability platforms"],"limitations":["In-memory exporter stores all telemetry — high-volume tests may consume significant memory","Deterministic testing requires careful setup of test fixtures — order of assertions matters if spans are created concurrently","No built-in support for async test fixtures — requires pytest-asyncio or similar for async code"],"requires":["Python 3.8+","logfire package with testing utilities","pytest (recommended test framework)","pytest-asyncio (for async code testing)"],"input_types":["test code that creates spans/logs","expected span/log attributes (for assertions)"],"output_types":["captured spans/logs (in-memory list)","assertion results (pass/fail)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pydantic--logfire__cap_9","uri":"capability://automation.workflow.configuration.management.with.environment.variables","name":"configuration-management-with-environment-variables","description":"Provides LogfireConfig class and configure() function for centralized configuration of all observability settings (API token, project name, sampling rate, export endpoint, etc.) via Python code or environment variables. Supports configuration priority (environment variables > code config > defaults) and lazy initialization to defer credential loading until first use.","intents":["I want to configure observability via environment variables for containerized deployments","I need different configurations for dev/staging/production without code changes","I want to defer credential loading until runtime to avoid startup delays"],"best_for":["DevOps teams deploying containerized applications","Developers managing multiple environments (dev/staging/prod)","Teams using CI/CD pipelines with environment-specific configuration"],"limitations":["Configuration is immutable after first use — reconfiguring logfire at runtime requires manual span processor updates","Environment variable parsing is strict — invalid values cause startup errors rather than fallback to defaults","Lazy initialization means credential errors are not caught until first log/span is created"],"requires":["Python 3.8+","logfire package","environment variables (optional, for non-code configuration)"],"input_types":["LogfireConfig object (Python)","environment variables (LOGFIRE_TOKEN, LOGFIRE_PROJECT_NAME, etc.)","configuration dictionaries"],"output_types":["configured Logfire instance","validation errors (if configuration is invalid)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","logfire package installed","Pydantic 2.0+ for schema generation","OpenTelemetry SDK (bundled with logfire)","logfire package with OpenTelemetry SDK","async/await support for async context propagation (Python 3.7+)","logfire package","FastAPI 0.95+, Django 3.2+, Flask 2.0+, or Starlette 0.20+","SQLAlchemy 1.4+, asyncpg 0.20+, psycopg 2.8+, or compatible driver","httpx 0.20+, requests 2.25+, or aiohttp 3.7+"],"failure_modes":["F-string magic requires Python 3.11+ for full AST rewriting support; earlier versions have limited lazy evaluation","Data scrubbing rules must be configured explicitly — no automatic PII detection without custom patterns","Structured logging adds ~5-10ms per log call due to JSON serialization and schema generation overhead","Span context is thread-local/async-local only — manual context propagation required for multiprocessing or process pools","Decorator-based instrumentation (@logfire.instrument) adds ~2-5ms overhead per function call due to span creation","Exception capture is automatic but custom exception handling logic must be added manually for non-standard error types","Middleware adds ~5-10ms overhead per request due to span creation and context management","Request body capture is disabled by default to avoid logging sensitive data — must be explicitly enabled","Trace context propagation requires compatible downstream services — non-instrumented services will not propagate trace IDs","SQL parameter capture may expose sensitive data (passwords, API keys) — requires explicit redaction rules","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.30506401122446825,"quality":0.35,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:16.561Z","last_commit":"2026-05-01T17:53:16Z"},"community":{"stars":4209,"forks":230,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pydantic--logfire","compare_url":"https://unfragile.ai/compare?artifact=pydantic--logfire"}},"signature":"C2qV/G6CBLyQAQY9ujKsYv9xRljselKqI5KtUiquOcNtD5shZFjF8XD3NnknmdPepm3MOmv29VCN4K0vlZEdCg==","signedAt":"2026-06-21T20:54:26.728Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pydantic--logfire","artifact":"https://unfragile.ai/pydantic--logfire","verify":"https://unfragile.ai/api/v1/verify?slug=pydantic--logfire","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}