---
title: "evidently vs lmnr"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/evidentlyai-evidently-vs-lmnr-ai-lmnr"
tools: ["evidentlyai-evidently", "lmnr-ai-lmnr"]
---

# evidently vs lmnr

Neutral, constraint-first comparison with live GitHub stats.

| | [evidently](/tools/evidentlyai-evidently.md) | [lmnr](/tools/lmnr-ai-lmnr.md) |
| --- | --- | --- |
| Tagline | An open-source ML and LLM observability framework. | Observability platform for AI agents |
| Stars | 7,673 | 3,072 |
| Forks | 874 | 216 |
| Open issues | 285 | 92 |
| Language | Jupyter Notebook | TypeScript |
| Adopt for | Evidently is a robust open-source Python library for evaluating, testing, and monitoring both machine learning (ML) and large language model (LLM) systems. It supports 100+ metrics and can handle diverse data types from | <b><i>lmnr</i></b>: Open-source observability platform designed specifically for AI agents, providing tools like tracing, signal monitoring, evaluations (evals), and custom dashboards in a high-performance rust-based SDK |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | Apache-2.0 |
| Categories | Evaluation & Observability | Evaluation & Observability, Developer Tools |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [evidently](/tools/evidentlyai-evidently.md) | [lmnr](/tools/lmnr-ai-lmnr.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Very active (96%) |
| Days since push | 66d | 0d |
| Open issues (now) | 285 | 92 |
| Full report | [trust report](/tools/evidentlyai-evidently/trust.md) | [trust report](/tools/lmnr-ai-lmnr/trust.md) |

**Typed relationship:** evidently _(alternative)_ lmnr

Both Evidently and lmnr provide observability for AI systems, but they offer different solutions and approaches.

## Decision facts: evidently

- **Adopt for:** Evidently is a robust open-source Python library for evaluating, testing, and monitoring both machine learning (ML) and large language model (LLM) systems. It supports 100+ metrics and can handle diverse data types from

## Decision facts: lmnr

- **Adopt for:** <b><i>lmnr</i></b>: Open-source observability platform designed specifically for AI agents, providing tools like tracing, signal monitoring, evaluations (evals), and custom dashboards in a high-performance rust-based SDK

## Choose when

### Choose evidently if…

- evidently is primarily Jupyter Notebook; lmnr is TypeScript.
- Both Evidently and lmnr provide observability for AI systems, but they offer different solutions and approaches.
- Tags unique to evidently: ml-pipelines, data-science, llm, data-drift.
- When you need comprehensive evaluation capabilities for generative AI tasks such as sentiment analysis, text length checks, or content validation.

### Choose lmnr if…

- lmnr is primarily TypeScript; evidently is Jupyter Notebook.
- Both Evidently and lmnr provide observability for AI systems, but they offer different solutions and approaches.
- Tags unique to lmnr: evaluation, aiops, monitoring, llm-evaluation.
- Also covers Developer Tools.
- lmnr ships Docker support for self-hosted deployment.
- <ul><li>You are working with specific AI frameworks such as Vercel AI SDK, LangChain, or OpenAI where <b>lmnr</b>'s one-line integration can automate the tracing process.</li><br/><li>Your application

## When NOT to use evidently

- If you're working exclusively with non-textual generative AI models (like image generation) as Evidently primarily focuses on text-related metrics.
- Evidently Cloud is available for enhanced features like dataset and user management but comes at an additional cost. For those not interested in subscriptions, the open-source version may suffice, but

## When NOT to use lmnr

- <ul><li>When you need a solution that does not support agent-specific tracing features or does not integrate well with specific AI frameworks like Vercel, LangChain, etc., as lmnr is specialized for.
- <br/><li>If you prefer open-source tools but require a simpler observability setup without the performance optimizations and extensive feature set of lmnr.</li>
- ],

## Common questions

### What is the difference between evidently and lmnr?

evidently: An open-source ML and LLM observability framework.. lmnr: Observability platform for AI agents. See the comparison table for live GitHub stats and shared categories.

### When should I choose evidently over lmnr?

Choose evidently over lmnr when evidently is primarily Jupyter Notebook; lmnr is TypeScript; Both Evidently and lmnr provide observability for AI systems, but they offer different solutions and approaches; Tags unique to evidently: ml-pipelines, data-science, llm, data-drift; When you need comprehensive evaluation capabilities for generative AI tasks such as sentiment analysis, text length checks, or content validation.

### When should I choose lmnr over evidently?

Choose lmnr over evidently when lmnr is primarily TypeScript; evidently is Jupyter Notebook; Both Evidently and lmnr provide observability for AI systems, but they offer different solutions and approaches; Tags unique to lmnr: evaluation, aiops, monitoring, llm-evaluation; Also covers Developer Tools; lmnr ships Docker support for self-hosted deployment; <ul><li>You are working with specific AI frameworks such as Vercel AI SDK, LangChain, or OpenAI where <b>lmnr</b>'s one-line integration can automate the tracing process.</li><br/><li>Your application.

### When should I avoid evidently?

If you're working exclusively with non-textual generative AI models (like image generation) as Evidently primarily focuses on text-related metrics. Evidently Cloud is available for enhanced features like dataset and user management but comes at an additional cost. For those not interested in subscriptions, the open-source version may suffice, but

### When should I avoid lmnr?

<ul><li>When you need a solution that does not support agent-specific tracing features or does not integrate well with specific AI frameworks like Vercel, LangChain, etc., as lmnr is specialized for. <br/><li>If you prefer open-source tools but require a simpler observability setup without the performance optimizations and extensive feature set of lmnr.</li> ],

### Is evidently or lmnr more popular on GitHub?

evidently has more GitHub stars (7,673 vs 3,072). Stars measure visibility, not whether either tool fits your constraints.

### Are evidently and lmnr open source?

Yes - both are open-source projects on GitHub (evidently: Apache-2.0, lmnr: Apache-2.0).

### Where can I find alternatives to evidently or lmnr?

GraphCanon lists graph-backed alternatives at /tools/evidentlyai-evidently/alternatives and /tools/lmnr-ai-lmnr/alternatives (/tools/evidentlyai-evidently/alternatives.md, /tools/lmnr-ai-lmnr/alternatives.md), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at /compare/evidentlyai-evidently-vs-lmnr-ai-lmnr.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, evidently or lmnr?

evidently: Steady. lmnr: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for evidently and lmnr?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: evidently: /tools/evidentlyai-evidently/trust; lmnr: /tools/lmnr-ai-lmnr/trust.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=evidentlyai-evidently`](/api/graphcanon/graph?tool=evidentlyai-evidently)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_