Pydantic Logfire
Python observability with OpenTelemetry traces, metrics, logs, and SQL-queryable telemetry.
- Source repo
- pydantic/logfire
- Stars
- ★ 4.5k
- Last updated
- today
- License
- MIT
- Primary language
- Python
- FA score
- 69/100 · Some gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A Python backend team investigating a slow request can wrap important operations in
logfire.span(...)and inspect the resulting telemetry. - Not a fit if
- Teams needing a fully offline or self-hosted observability platform (backend is closed source; self-hosting needs an enterprise license)
- Users who want an open-source server and won't send data to a vendor cloud
- Teams needing a non-Python SDK (this repo ships only the Python SDK)
- Source review
- 69/100 · Some gaps
What does this agent do, and when should you use it?
Pydantic Logfire is an observability platform from the Pydantic team; this open-source repository contains the Python logfire SDK and its documentation. Developers can emit manual logs and spans or instrument supported Python packages such as FastAPI. The SDK is built around OpenTelemetry and supports traces, metrics, and logs, with export to any OTel-compatible backend. Logfire’s recording and display service—the UI and backend—is closed source, although enterprise licensing permits self-hosting. It is a Python observability product rather than an autonomous AI agent.
After pip install logfire, an application calls logfire.configure() to configure the SDK. It can emit telemetry with logfire.info() and logfire.debug(), and create manual traced scopes with with logfire.span(...). A FastAPI application can be instrumented through logfire.instrument_fastapi(app); the README also directs users to instrument database connectors and HTTP libraries and add a logging handler. The resulting traces, metrics, and logs can be recorded and displayed by the Logfire platform or exported to an OTel-compatible backend, while the platform supports querying data with standard SQL.
- A Python backend team investigating a slow request can wrap important operations in
logfire.span(...)and inspect the resulting telemetry. - A team running FastAPI with Pydantic
BaseModelrequest data can addlogfire.instrument_fastapi(app)to observe application behavior and validation-related data flow. - An engineering team that needs traces, metrics, and logs can adopt a Python SDK built on OpenTelemetry.
- A team with an existing OTel-compatible backend can export data through the Logfire SDK without requiring the Logfire backend as its destination.
- A data or operations team that wants to query observability data with standard SQL or existing BI and database-querying tools can use Logfire’s SQL interface.
How do you install or deploy this agent?
In an environment with Python and pip, run:
pip install logfireThen authenticate with:
logfire authThe supplied material shows the authentication command but does not document the required account, token, or interactive credential flow.
How do you use this agent?
Minimal usage:
import logfirelogfire.configure()
logfire.info('Hello, {name}!', name='world')Use with logfire.span('Asking the user their {question}', question='age'): around an operation to create a span. For FastAPI, create the FastAPI() app, call logfire.configure(), then call logfire.instrument_fastapi(app).
What are this agent's strengths and limitations?
- Built on OpenTelemetry, with documented support for traces, metrics, and logs plus export to any OTel-compatible backend.
- Offers direct Python APIs—
logfire.info(),logfire.debug(), andlogfire.span(...)—for incremental manual instrumentation. - Includes
logfire.instrument_fastapi(app), and the README identifies further instrumentation for database connectors and HTTP libraries. - Supports standard SQL queries, including use with existing BI tools and database-querying libraries.
- The Logfire UI and backend that record and display data are closed source and are not implemented in this repository.
- Self-hosting the Logfire platform requires an enterprise license.
- The supplied material does not specify authentication credentials, configuration parameters, data retention, or failure-handling behavior.
- The open-source repository provides the Python SDK and documentation, not a complete platform-server deployment package.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Pydantic Logfire This agent | 69 · Some gaps | CLIFreemium | ★ 4.5k | today | Python | — |
| OpenInference | 66 · Some gaps | Library / SDKFree | ★ 1.3k | 1d ago | Python | OpenAI API · Claude API |
| DataBuff | 65 · Some gaps | Self-hosted serviceFree + model costs | ★ 723 | 1mo ago | Java | OpenAI API |
| Laminar | 58 · Major gaps | Self-hosted serviceFreemium | ★ 3.5k | 6d ago | TypeScript | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
The documentation explains that the SDK exports telemetry through OpenTelemetry, can disable sending, and separates CLI sign-in from SDK configuration and write credentials; the OAuth examples use authorization code, PKCE, state, and scopes, showing consent boundaries. It says likely passwords and similar values can be scrubbed, but LLM prompts and responses are not scrubbed by default, leaving a meaningful sensitive-data gap. The dependency list sets minimum versions for several known issues, but does not provide full supply-chain audit evidence. Telemetry upload has external data effects, and the supplied material does not show a general rollback or recovery mechanism for SDK operations. Attribution is limited to the repository and copyright notice; publisher identity remains unknown as stipulated.
The README, AGENTS.md, and source structure consistently describe a Python SDK and documentation, with the platform server closed source. Configuration docs explain programmatic, environment, and file settings and precedence, and the dependency metadata specifies a Python version range; however, some docs present full-platform capabilities alongside what this SDK itself demonstrates, which can blur boundaries. Configuration docs describe warnings and dropped telemetry when credentials are absent, providing some failure signaling; the supplied material does not establish clear handling for all dependency paths and failure cases.
Documentation covers application developers, platform and security teams, multiple languages, and infrastructure scenarios, though the repository evidence is primarily for Python; other languages rely on separate SDKs or OpenTelemetry. The README states that the server is closed source, configuration docs say SDK setup is not interactive, and skills distinguish application telemetry, infrastructure monitoring, and model gateway tasks, making boundaries relatively clear. Skill triggers are specific, but the sample does not establish consistent rules for every relevant scenario. Environment support includes Python versions and multiple configuration methods, while some platform capabilities require cloud or enterprise deployment.
Repository structure, configuration docs, and FAQ provide clear entry points. The README gives pip installation, SDK-only installation, and basic connection steps, while deeper setup relies on external documentation. Public names such as logfire, logfire-sdk, and logfire-api are explained, though readers must distinguish multiple packages. Examples and FAQ are substantial and include security guidance; the supplied docs identify limits such as unsanitized LLM content and the closed-source server, but do not provide a complete limitations inventory. The license text matches MIT metadata. A changelog-related CI rule and version field are present, but no concrete release history is included. Contribution and vulnerability-reporting paths are shown, while maintenance responsibility is not tied to a specifically verified team or publisher.
SDK examples, OpenTelemetry integrations, SQL querying, and AI observability docs indicate value for inspecting applications and model-call context; the evidence supports observability and debugging, not Agent execution or task completion. OpenTelemetry compatibility and cross-layer traces may add value, but the marginal benefit depends on the user's existing monitoring stack. The docs mention sampling and billing to help control data volume and cost; the supplied evidence does not quantify setup, storage, or operational costs, so cost-benefit support is partial.
The README, configuration guide, platform security guide, and source each support claims about SDK scope, configuration, and telemetry implementation; FAQ claims about product advantages, compliance, and features are largely self-asserted without independent corroboration in this packet. Multiple files corroborate the Python SDK, OpenTelemetry, and closed-server boundary, but cloud capabilities and compliance claims lack independent cross-checking here. Documentation generally distinguishes configuration facts from advice, but marketing comparisons and broad benefit claims are not clearly marked as independently unverified.
- LLM prompts and responses are not scrubbed by default; configure content capture and scrubbing in line with the sensitive-data guidance before exporting telemetry.
- This assessment covers the visible SDK and documentation, not a fully verifiable platform or autonomous Agent. The server is closed source, and several product capabilities are self-reported in the supplied material.
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[](https://followagents.com/en/agents/logfire)<a href="https://followagents.com/en/agents/logfire"><img src="https://followagents.com/badge/logfire.svg" alt="FARS review"></a>Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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Release notes excerpt
* Drop all incoming context when distributed tracing is disabled by @dmontagu in [#2679](https://github.com/pydantic/logfire/pull/2679) * Carry variable overrides and targeting context into thread pools by @dmontagu in [#2680](https://github.com/pydantic/logfire/pull/2680) * Name the setting when LOGFIRE_TRACE_SAMPLE_RATE is not a number by @chrikrah in [#2685](https://github.com/pydantic/logfire/pull/2685) * Record audio responses and legacy function calls from OpenAI chat completions by @pcbeingused333 in [#2636](https://github.com/pydantic/logfire/pull/2636) …View on GitHub ↗ -
Release notes excerpt
* logfire.agent_sandboxes() by @samuelcolvin in [#2658](https://github.com/pydantic/logfire/pull/2658) and [#2672](https://github.com/pydantic/logfire/pull/2672) * fix(sdk): serialize all pathlib.Path variants as plain strings by @RichardoMrMu in [#2669](https://github.com/pydantic/logfire/pull/2669) * Format an empty set as set() in the console exporter by @chrikrah in [#2671](https://github.com/pydantic/logfire/pull/2671) * Keep binary attributes out of the secret scrubber by @Eapp1e in [#2668](https://github.com/pydantic/logfire/pull/2668) …View on GitHub ↗ -
Release notes excerpt
* Automatically instrument FastAPI with logfire run by @alexmojaki in [#2606](https://github.com/pydantic/logfire/pull/2606) * Automatically instrument Starlette apps with logfire run by @alexmojaki in [#2596](https://github.com/pydantic/logfire/pull/2596) * Automatically instrument Flask apps with logfire run by @alexmojaki in [#2594](https://github.com/pydantic/logfire/pull/2594) * Raise when SQLAlchemy instrumentation is skipped by @maaz-bin-hassan in [#2602](https://github.com/pydantic/logfire/pull/2602) …View on GitHub ↗
FAQ
Is this an autonomous AI agent?
Can I use the SDK without the closed-source Logfire backend?
How do I authenticate?
logfire auth after installation. The supplied material does not specify the account, token, or authentication-flow requirements.Can the Logfire platform be self-hosted?
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