--- title: "helicone vs helm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/helicone-helicone-vs-stanford-crfm-helm" tools: ["helicone-helicone", "stanford-crfm-helm"] --- # helicone vs helm *GraphCanon updated Oct 12, 2026* ## Verdict Pick helicone if helicone is an open-source platform for monitoring and evaluating large language models, offering tools for prompt engineering and management; pick helm if helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. [helicone](https://www.helicone.ai) reports 6.2k GitHub stars, 670 forks, and 155 open issues, last pushed Sep 16, 2026. [helm](https://crfm.stanford.edu/helm) has 2.9k stars, 421 forks, and 112 open issues, last pushed Sep 1, 2026. Figures are from public GitHub metadata via [helicone's repository](https://github.com/Helicone/helicone) and [helm's repository](https://github.com/stanford-crfm/helm). | | [helicone](/tools/helicone-helicone.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Tagline | Open source LLM observability platform | Holistic, reproducible and transparent evaluation of foundation models | | Stars | 6,162 | 2,939 | | Forks | 670 | 421 | | Open issues | 155 | 112 | | Language | TypeScript | Python | | Adopt for | Helicone is an open-source platform for monitoring and evaluating large language models, offering tools for prompt engineering and management. | Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [helicone](/tools/helicone-helicone.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 1d | 41d | | Open issues (now) | 155 | 112 | | Stars delta | +89 (30d) | +37 (30d) | | Open issues delta | -15 (30d) | +8 (30d) | | Full report | [trust report](/tools/helicone-helicone/trust.md) | [trust report](/tools/stanford-crfm-helm/trust.md) | ## Decision facts: helicone - **Adopt for:** Helicone is an open-source platform for monitoring and evaluating large language models, offering tools for prompt engineering and management. ## Decision facts: helm - **Adopt for:** Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. ## Choose when ### Choose helicone if… - helicone is primarily TypeScript; helm is Python. - Tags unique to helicone: agent-monitoring, analytics, gpt, langchain. - helicone ships Docker support for self-hosted deployment. - When you need to monitor and evaluate the performance of large language models in real-time. ### Choose helm if… - helm is primarily Python; helicone is TypeScript. - Tags unique to helm: foundation models, framework, language models. - When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way. ## When NOT to use helicone - If your project requires proprietary or closed-source tools, as Helicone is open-source and can be self-hosted. - When you need a platform that does not require setting up an API key and modifying your codebase to integrate monitoring. - If you are looking for a solution that does not support a wide range of models through a single API, and you prefer platform-specific tools. - For users who do not want to manage their own infrastructure, as Helicone offers self-hosting options which require local setup and maintenance. ## When NOT to use helm - Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. - If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity. ## Common questions ### What is the difference between helicone and helm? helicone: Open source LLM observability platform. helm: Holistic, reproducible and transparent evaluation of foundation models. See the comparison table for live GitHub stats and shared categories. ### When should I choose helicone over helm? Choose helicone over helm when helicone is primarily TypeScript; helm is Python; Tags unique to helicone: agent-monitoring, analytics, gpt, langchain; helicone ships Docker support for self-hosted deployment; When you need to monitor and evaluate the performance of large language models in real-time. ### When should I choose helm over helicone? Choose helm over helicone when helm is primarily Python; helicone is TypeScript; Tags unique to helm: foundation models, framework, language models; When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way. ### When should I avoid helicone? If your project requires proprietary or closed-source tools, as Helicone is open-source and can be self-hosted. When you need a platform that does not require setting up an API key and modifying your codebase to integrate monitoring. If you are looking for a solution that does not support a wide range of models through a single API, and you prefer platform-specific tools. For users who do not want to manage their own infrastructure, as Helicone offers self-hosting options which require local setup and maintenance. ### When should I avoid helm? Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity. ### Is helicone or helm more popular on GitHub? helicone has more GitHub stars (6,162 vs 2,939). Stars measure visibility, not whether either tool fits your constraints. ### Are helicone and helm open source? Yes - both are open-source projects on GitHub (helicone: Apache-2.0, helm: Apache-2.0). ### Where can I find alternatives to helicone or helm? GraphCanon lists graph-backed alternatives at [helicone alternatives](/tools/helicone-helicone/alternatives) and [helm alternatives](/tools/stanford-crfm-helm/alternatives) ([helicone markdown twin](/tools/helicone-helicone/alternatives.md), [helm markdown twin](/tools/stanford-crfm-helm/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/helicone-helicone-vs-stanford-crfm-helm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, helicone or helm? helicone: Very active. helm: Steady. 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 helicone and helm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [helicone trust report](/tools/helicone-helicone/trust); [helm trust report](/tools/stanford-crfm-helm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=helicone-helicone`](/api/graphcanon/graph?tool=helicone-helicone) - 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/_