--- title: "deepeval vs myscale-telemetry" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-myscale-myscale-telemetry" tools: ["confident-ai-deepeval", "myscale-myscale-telemetry"] --- # deepeval vs myscale-telemetry *GraphCanon updated Sep 20, 2026* ## Verdict Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick myscale-telemetry if myScale Telemetry offers open-source observability specifically for LLM applications in Python, requiring additional tools like Grafana and ClickHouse. [deepeval](https://deepeval.com) reports 18k GitHub stars, 2.0k forks, and 624 open issues, last pushed Sep 18, 2026. [myscale-telemetry](https://pypi.org/project/myscale-telemetry/) has 55 stars, 7 forks, and 4 open issues, last pushed Jan 2, 2025. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [myscale-telemetry's repository](https://github.com/myscale/myscale-telemetry). | | [deepeval](/tools/confident-ai-deepeval.md) | [myscale-telemetry](/tools/myscale-myscale-telemetry.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | Open-source observability for your LLM application | | Stars | 18,342 | 55 | | Forks | 1,953 | 7 | | Open issues | 624 | 4 | | Language | Python | Python | | Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | MyScale Telemetry offers open-source observability specifically for LLM applications in Python, requiring additional tools like Grafana and ClickHouse. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [myscale-telemetry](/tools/myscale-myscale-telemetry.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 616d | | Open issues (now) | 624 | 4 | | Stars delta | +1.1k (30d) | 0 (30d) | | Open issues delta | +220 (30d) | 0 (30d) | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/myscale-myscale-telemetry/trust.md) | ## Shared compatibility - **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [myscale-telemetry](/tools/myscale-myscale-telemetry.md) - Python runtime ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Decision facts: myscale-telemetry - **Adopt for:** MyScale Telemetry offers open-source observability specifically for LLM applications in Python, requiring additional tools like Grafana and ClickHouse. ## Choose when ### Choose deepeval if… - License: deepeval is Apache-2.0, myscale-telemetry is MIT. - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: evaluation, llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose myscale-telemetry if… - License: myscale-telemetry is MIT, deepeval is Apache-2.0. - Tags unique to myscale-telemetry: callback, langchain, llm-observability, monitoring. - - You need to monitor the performance of your large language model (LLM) application and have it integrated with Grafana and ClickHouse data source. ## When NOT to use deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## When NOT to use myscale-telemetry - - If you do not have the necessary infrastructure for Grafana and the ClickHouse data source, making it difficult to integrate MyScale Telemetry into your workflow. - - You are looking for a solution without specific database dependencies; MyScale Telemetry requires a compatible database instance such as MyScaleDB or ClickHouse. ## Common questions ### What is the difference between deepeval and myscale-telemetry? deepeval: LLM Evaluation Framework.. myscale-telemetry: Open-source observability for your LLM application. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over myscale-telemetry? Choose deepeval over myscale-telemetry when License: deepeval is Apache-2.0, myscale-telemetry is MIT; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I choose myscale-telemetry over deepeval? Choose myscale-telemetry over deepeval when License: myscale-telemetry is MIT, deepeval is Apache-2.0; Tags unique to myscale-telemetry: callback, langchain, llm-observability, monitoring; - You need to monitor the performance of your large language model (LLM) application and have it integrated with Grafana and ClickHouse data source. ### When should I avoid deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### When should I avoid myscale-telemetry? - If you do not have the necessary infrastructure for Grafana and the ClickHouse data source, making it difficult to integrate MyScale Telemetry into your workflow. - You are looking for a solution without specific database dependencies; MyScale Telemetry requires a compatible database instance such as MyScaleDB or ClickHouse. ### Is deepeval or myscale-telemetry more popular on GitHub? deepeval has more GitHub stars (18,342 vs 55). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and myscale-telemetry open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, myscale-telemetry: MIT). ### Where can I find alternatives to deepeval or myscale-telemetry? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [myscale-telemetry alternatives](/tools/myscale-myscale-telemetry/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [myscale-telemetry markdown twin](/tools/myscale-myscale-telemetry/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 [this comparison](/compare/confident-ai-deepeval-vs-myscale-myscale-telemetry.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or myscale-telemetry? deepeval: Very active. myscale-telemetry: Dormant. 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 deepeval and myscale-telemetry? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [myscale-telemetry trust report](/tools/myscale-myscale-telemetry/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - 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/_