Lilypad
Open-source OpenTelemetry LLM observability for Python devs
A solid choice for Python devs already running an OTel backend who want lightweight, open-source LLM tracing. Skip it if you need a managed UI or advanced experiment tracking—Langfuse is a better fit.
- Python developers building LLM apps needing OTel-based observability
- Teams already using an OTel backend who want to add LLM-specific tracing
- Projects requiring vendor-neutral, open-source instrumentation without lock-in
- Users of Mirascope framework wanting integrated tracing and versioning
- Teams wanting a fully managed, hosted observability platform with a UI
- Non-Python stacks (Python-only library)
- Projects needing advanced prompt versioning or experiment tracking
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Skip Lilypad if you prefer a fully managed observability platform with a UI and don't want to operate your own OpenTelemetry backend.
Lilypad is free and open-source (MIT), making it cost-effective for any team already running an OpenTelemetry backend. Unlike managed solutions like Langfuse or LangSmith, there are no per-seat or per-event fees, but you must cover your own infrastructure costs for the backend.
In short
Lilypad — Open-source OpenTelemetry LLM observability for Python devs. Best for Python developers building LLM apps needing OTel-based observability, Teams already using an OTel backend who want to add LLM-specific tracing, Projects requiring vendor-neutral, open-source instrumentation without lock-in. Free to use.
Viability Score
How likely is Lilypad to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- OpenTelemetry-based distributed tracing
- @ops.trace decorator for automatic function tracing
- @ops.version decorator for function versioning
- ops.session() context manager to group traces
- ops.span() for explicit span creation
- Automatic LLM call instrumentation
- Context propagation across services
- OTLP protocol support
- Console exporter for development debugging
- Configurable tracer provider
- Batch span processing
- Custom span attributes and events
- Python-first API with type hints
- Mirascope ecosystem integration
- Open-source (MIT license)
About Lilypad
Lilypad (part of Mirascope) is an open-source observability module for LLM applications built on OpenTelemetry. It provides distributed tracing, function versioning, and session management for Python developers who need production-grade LLM observability without vendor lock-in. Core features include the @ops.trace decorator for automatic function tracing, ops.session() to group related traces, @ops.version to track prompt versions, and auto-instrumentation of LLM calls. Traces can be exported to any OTel-compatible backend such as Langfuse, Jaeger, Zipkin, Grafana Tempo, or Datadog. Unlike managed solutions like Langfuse, Lilypad gives you vendor-neutral, open-source instrumentation but requires you to bring your own OTel backend—no hosted UI is provided. It integrates tightly with Mirascope and is designed for Python developers comfortable managing their own observability infrastructure.
Behind the Verdict
Lilypad does one thing well: it gives Python developers OpenTelemetry-native LLM tracing without forcing a proprietary backend. The @ops.trace decorator and ops.session() context manager feel nearly invisible in existing Mirascope codebases. We'd reach for this when we're already invested in the Mirascope ecosystem and have an OTel pipeline running. Where it bites: there's no hosted UI, no advanced prompt versioning, and Python-only support. If you're not using Mirascope or don't want to manage an OTel backend, Langfuse or Datadog LLM Observability offer more out of the box. For teams that value open-source and vendor neutrality, Lilypad delivers exactly what it promises—no more, no less.
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Real-world workflow fit
Concrete scenarios for the personas Lilypad actually fits — and what changes day-one when you adopt it.
You have a Mirascope-based LLM app in production and need to trace individual LLM calls to debug unexpected responses.
Outcome: Add the @ops.trace decorator to your function; within minutes, traces appear in your OTel console or backend for analysis.
Your team wants to monitor LLM latency and error rates across services without committing to a proprietary vendor.
Outcome: Configure Lilypad with your existing Jaeger or Grafana Tempo backend; auto-instrument all LLM calls and propagate context across microservices.
You're versioning prompts in a collaborative project and need to track which version produced which output.
Outcome: Use @ops.version to tag each prompt iteration; group related traces with ops.session() to compare performance across versions.
Use Cases
- Trace LLM function execution to debug and optimize responses.
- Version control and iterate on LLM prompts collaboratively.
- Group related LLM interactions into sessions for analysis.
- Automatically instrument LLM calls for observability.
- Propagate tracing context across distributed services.
Limitations
- Lilypad is an open-source OpenTelemetry-based observability tool designed Python-first for LLM applications.
- It requires integration with the Mirascope ecosystem and a compatible OTel backend (e.g., Jaeger or Grafana Tempo), which you must deploy and manage yourself.
- The tool focuses on tracing and versioning without providing a hosted UI, prompt management, or evaluation capabilities.
as of 2026-06-30
Where the pricing makes sense
The company stage and team size where Lilypad's pricing actually pencils out — and where peers do it cheaper.
Lilypad is free and open-source (MIT), making it cost-effective for any team already running an OpenTelemetry backend. Unlike managed solutions like Langfuse or LangSmith, there are no per-seat or per-event fees, but you must cover your own infrastructure costs for the backend.
Setup time & first value
How long it actually takes to get something useful out of Lilypad — broken out by persona, not the marketing-page minute.
For a Mirascope user: add the decorator and configure a tracer provider in under 10 minutes. For new adopters: budget 1–2 hours to set up an OTel backend (e.g., Jaeger via Docker) and integrate Lilypad into your Python project.
Switching to or from Lilypad
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From custom logging: replace print/logging statements with @ops.trace and ops.session for structured traces.
- →From Langfuse managed: if you want open-source, export your existing OTel traces to your own backend and switch to Lilypad's decorators.
- ↗To Langfuse: if you need a managed UI, export your Lilypad traces via OTLP to Langfuse's backend.
- ↗To LangSmith: if you need experiment tracking, migrate by replacing @ops.trace with LangSmith's run instrumentation.
Integrations
Resources & Guides
- Documentationlilypad.so
Configuration · Lilypad
Full product docs from lilypad.so
- Documentationlilypad.so
Tracing · Lilypad
Full product docs from lilypad.so
- Documentationlilypad.so
Sessions · Lilypad
Full product docs from lilypad.so
- Documentationlilypad.so
Spans · Lilypad
Full product docs from lilypad.so
- Documentationlilypad.so
Versioning · Lilypad
Full product docs from lilypad.so
- Documentationlilypad.so
Instrumentation · Lilypad
Full product docs from lilypad.so
- Documentationlilypad.so
Context Propagation · Lilypad
Full product docs from lilypad.so
Official links
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