# pydantic/logfire

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4,034 stars · 207 forks · Python · mit

## Links

- GitHub: https://github.com/pydantic/logfire
- Homepage: https://logfire.pydantic.dev/docs/
- awesome-repositories: https://awesome-repositories.com/repository/pydantic-logfire.md

## Topics

`agent-observability` `ai` `ai-observability` `ai-tools` `evals` `fastapi` `llm-observability` `logging` `metrics` `observability` `openai` `opentelemetry` `pydantic` `pydantic-ai` `python` `trace`

## Description

Logfire is an OpenTelemetry observability platform and Python application monitoring tool. It provides a suite of tools for collecting, storing, and querying spans, logs, and metrics to monitor application performance and execution.

The platform features a specialized monitor for Pydantic data validation, tracking data flow and validation outcomes in real time. It also includes a telemetry analysis tool that uses standard SQL to query observability data and connect to business intelligence tools.

The system provides automatic instrumentation for Python libraries and frameworks, allowing for the collection of telemetry without manual code changes. Its capability surface covers application execution tracing, structured log serialization, and validation-aware telemetry to link data outcomes directly to execution traces.

## Tags

### System Administration & Monitoring

- [Application Observability](https://awesome-repositories.com/f/system-administration-monitoring/application-observability.md) — Provides a comprehensive framework for monitoring, tracing, and debugging the execution flow of Python applications.
- [Automatic Framework Instrumentation](https://awesome-repositories.com/f/system-administration-monitoring/automatic-framework-instrumentation.md) — Provides automatic telemetry collection for popular Python libraries and frameworks via predefined plugins.
- [Automatic Tracing Instrumentation](https://awesome-repositories.com/f/system-administration-monitoring/automatic-tracing-instrumentation.md) — Automatically instruments Python packages to capture execution details and performance metrics without manual code changes. ([source](https://cdn.jsdelivr.net/gh/pydantic/logfire@main/README.md))
- [Validation Outcome Analytics](https://awesome-repositories.com/f/system-administration-monitoring/data-pattern-monitoring/validation-outcome-analytics.md) — Generates analytics based on data flow and resulting outcomes from validation models. ([source](https://cdn.jsdelivr.net/gh/pydantic/logfire@main/README.md))
- [Execution Tracing](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/execution-tracing-analysis/execution-tracing.md) — Records sequences of operations and function calls to trace application behavior and analyze performance. ([source](https://cdn.jsdelivr.net/gh/pydantic/logfire@main/README.md))
- [OpenTelemetry Standard Integrations](https://awesome-repositories.com/f/system-administration-monitoring/observability-instrumentation/opentelemetry-standard-integrations.md) — Implements instrumentation based on vendor-neutral OpenTelemetry standards to ensure compatibility across monitoring tools.
- [Observability Platforms](https://awesome-repositories.com/f/system-administration-monitoring/observability-instrumentation/opentelemetry-standard-integrations/observability-platforms.md) — Acts as a complete backend platform for the collection and analysis of OpenTelemetry spans, logs, and metrics.
- [Python Language Runtimes](https://awesome-repositories.com/f/system-administration-monitoring/observability-instrumentation/runtime-language-instrumentation/python-language-runtimes.md) — Automatically instruments the Python runtime to capture traces and metrics without requiring manual code additions.

### Part of an Awesome List

- [Application Monitoring Suites](https://awesome-repositories.com/f/awesome-lists/devtools/crash-and-monitoring/application-monitoring-suites.md) — Ships a comprehensive suite for tracing execution and monitoring data flow within Python applications.

### Data & Databases

- [Validation Model Monitoring](https://awesome-repositories.com/f/data-databases/data-validation/validation-model-monitoring.md) — Tracks the flow of data through Pydantic validation models in real time to identify data quality issues.
- [Execution Span Hierarchies](https://awesome-repositories.com/f/data-databases/data-visualization/hierarchical-performance-visualizers/execution-span-hierarchies.md) — Tracks function call lifecycles using nested timed spans to visualize execution hierarchies and identify latency.
- [Unified Observability SQL Querying](https://awesome-repositories.com/f/data-databases/distributed-storage/sql-query-interfaces/unified-observability-sql-querying.md) — Allows querying metrics, logs, and traces together using standard SQL for deep telemetry analysis and business intelligence.
- [Structured Log Serializers](https://awesome-repositories.com/f/data-databases/data-serialization-formats/json-serialization/json-message-serializers/structured-log-serializers.md) — Serializes application events into machine-readable formats like JSON to enable efficient indexing and filtering.

### Testing & Quality Assurance

- [Pydantic Validation Monitoring](https://awesome-repositories.com/f/testing-quality-assurance/static-analysis-rules/pydantic-validation-monitoring.md) — Provides a specialized monitoring system to track real-time validation outcomes and data flow through Pydantic models.
- [Validation-Aware Telemetry](https://awesome-repositories.com/f/testing-quality-assurance/validation-verification/input-validation/validation-aware-telemetry.md) — Links data validation outcomes directly to execution traces to monitor how specific input values affect application behavior.