pydantic/logfire
AI observability platform for production LLM and agent systems. observed · 2026-08-28
Health v2 · maintenance only
87/100
- Activity 99
- Release rhythm 86
- Longevity 61
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 3.0
- age_days: 862
- days_rel: 13
- days_push: 7
- n_releases_24m: 135
Adoption not part of the score
4441 stars · 282 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Pydantic Logfire is an AI observability platform built on OpenTelemetry that traces LLM calls, agent runs, and application telemetry in one correlated timeline, queryable with SQL. It includes evaluation tooling (datasets, experiments, live evals, human review) and a Python SDK with integrations for Pydantic AI, OpenAI, Anthropic, FastAPI, and other OTel-instrumented stacks.
Use cases
- trace llm calls and agent runs in production
- monitor ai application quality with evals
- debug slow agent tool calls and model requests
- observe python app traces logs and metrics with opentelemetry
- run offline and online evaluations of llm systems
- review and annotate agent runs by hand
- query telemetry data with sql
- instrument openai or anthropic sdk calls
When to choose
- you want unified observability for both your backend and LLM/agent stack in one trace
- you use Python, Pydantic, Pydantic AI, or FastAPI and want first-class instrumentation
- you need production evals, live monitoring, and human review of agent outputs
- you prefer OpenTelemetry compatibility and SQL-based querying of telemetry
When to avoid
- you need a fully self-hosted open-source observability stack with no hosted component (the server is proprietary; only the SDK is MIT)
- your stack is non-Python and you want deep language-specific insights
- you need a free unlimited-volume solution for very high telemetry volumes beyond 10M records/month
- you want a vendor-neutral tool you can point at any existing OTel backend without a Logfire account
Facets
service · maturity active
monitoring tracing logging alerting analytics llm-inference agent-framework rag sdk mcp monitoring large-language-models developer-tools data-visualization python rust cloud self-hosted cross-platform opentelemetry llm-observability agent-observability evals pydantic-ai fastapi openai anthropic sql-querying hosted-platform freemium ai-agents retrieval-augmented-generation nodejs web-server docker
10 sources
- readme: https://github.com/pydantic/logfire · fetched 2026-08-28 · 1983205fe553
- homepage: https://pydantic.dev/logfire/ · fetched 2026-08-29 · 146d1f7c7a27
- site_page: https://pydantic.dev/docs/logfire/evaluate/evals · fetched 2026-08-29 · 2d2ab3260f82
- site_page: https://pydantic.dev/docs/logfire/evaluate/datasets-and-experiments · fetched 2026-08-29 · 2d2ab3260f82
- site_page: https://pydantic.dev/docs/logfire/evaluate/human-review · fetched 2026-08-29 · c1d3fbb0535c
- site_page: https://pydantic.dev/docs/logfire/evaluate/live-evals · fetched 2026-08-29 · 1af42e47ff52
- site_page: https://pydantic.dev/docs/logfire/integrations/llms/pydanticai · fetched 2026-08-29 · 8b503d3197ec
- site_page: https://pydantic.dev/docs/logfire/integrations/llms/openai · fetched 2026-08-29 · b86a70412188
- site_page: https://pydantic.dev/docs/logfire/integrations/llms/anthropic · fetched 2026-08-29 · 2bb264194064
- registry_pypi: https://pypi.org/pypi/logfire/json · fetched 2026-08-29 · 33099b5f17c1
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| pydantic/logfire | main | 87 |
For agents
markdown · JSON · MCP: product_card(name="pydantic/logfire")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem