--- title: "haystack vs helm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepset-ai-haystack-vs-stanford-crfm-helm" tools: ["deepset-ai-haystack", "stanford-crfm-helm"] --- # haystack vs helm *GraphCanon updated Oct 12, 2026* ## Verdict Pick haystack if haystack is an open-source AI orchestration framework for building scalable AI applications, including agents, RAG systems, and conversational systems, with explicit control over retrieval, routing, memory, and generaton; 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. [haystack](https://haystack.deepset.ai) reports 27k GitHub stars, 3.1k forks, and 157 open issues, last pushed Sep 18, 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 [haystack's repository](https://github.com/deepset-ai/haystack) and [helm's repository](https://github.com/stanford-crfm/helm). | | [haystack](/tools/deepset-ai-haystack.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Tagline | Open-source AI orchestration framework for building context-engineered, production-ready LLM applications | Holistic, reproducible and transparent evaluation of foundation models | | Stars | 26,536 | 2,939 | | Forks | 3,144 | 421 | | Open issues | 157 | 112 | | Language | Python | Python | | Adopt for | Haystack is an open-source AI orchestration framework for building scalable AI applications, including agents, RAG systems, and conversational systems, with explicit control over retrieval, routing, memory, and generaton | 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 | AI Agents, Data & Retrieval, LLM Frameworks | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [haystack](/tools/deepset-ai-haystack.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 41d | | Open issues (now) | 157 | 112 | | Stars delta | +463 (30d) | +37 (30d) | | Open issues delta | +49 (30d) | +8 (30d) | | Full report | [trust report](/tools/deepset-ai-haystack/trust.md) | [trust report](/tools/stanford-crfm-helm/trust.md) | ## Shared compatibility - **Python**: [haystack](/tools/deepset-ai-haystack.md) - Python runtime; [helm](/tools/stanford-crfm-helm.md) - Python runtime ## Decision facts: haystack - **Requirements:** Requires Docker; Supports multiple installation methods, including Docker images - **Adopt for:** Haystack is an open-source AI orchestration framework for building scalable AI applications, including agents, RAG systems, and conversational systems, with explicit control over retrieval, routing, memory, and generaton - **License detail:** Apache-2.0 ## 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 haystack if… - Requirements: Requires Docker; Supports multiple installation methods, including Docker images. - Tags unique to haystack: agent-framework, agentic-ai, agentic-rag, agents. - Also covers AI Agents, Data & Retrieval, LLM Frameworks. - When you need explicit control over retrieval, routing, memory, and generation processes in your AI applications ### Choose helm if… - Tags unique to helm: evaluation, foundation models, language models. - Also covers Evaluation & Observability. - 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 haystack - If you require a framework that integrates tightly with a specific set of pre-defined models and does not offer the flexibility to customize retrieval and generation processes - When your project does not require the scalability and explicit control over AI processes that Haystack provides, and a simpler, more streamlined solution is preferred ## 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 haystack and helm? haystack: Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. helm: Holistic, reproducible and transparent evaluation of foundation models. See the comparison table for live GitHub stats and shared categories. ### When should I choose haystack over helm? Choose haystack over helm when Requirements: Requires Docker; Supports multiple installation methods, including Docker images; Tags unique to haystack: agent-framework, agentic-ai, agentic-rag, agents; Also covers AI Agents, Data & Retrieval, LLM Frameworks; When you need explicit control over retrieval, routing, memory, and generation processes in your AI applications. ### When should I choose helm over haystack? Choose helm over haystack when Tags unique to helm: evaluation, foundation models, language models; Also covers Evaluation & Observability; 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 haystack? If you require a framework that integrates tightly with a specific set of pre-defined models and does not offer the flexibility to customize retrieval and generation processes When your project does not require the scalability and explicit control over AI processes that Haystack provides, and a simpler, more streamlined solution is preferred ### 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 haystack or helm more popular on GitHub? haystack has more GitHub stars (26,536 vs 2,939). Stars measure visibility, not whether either tool fits your constraints. ### Are haystack and helm open source? Yes - both are open-source projects on GitHub (haystack: Apache-2.0, helm: Apache-2.0). ### Where can I find alternatives to haystack or helm? GraphCanon lists graph-backed alternatives at [haystack alternatives](/tools/deepset-ai-haystack/alternatives) and [helm alternatives](/tools/stanford-crfm-helm/alternatives) ([haystack markdown twin](/tools/deepset-ai-haystack/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/deepset-ai-haystack-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, haystack or helm? haystack: 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 haystack and helm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [haystack trust report](/tools/deepset-ai-haystack/trust); [helm trust report](/tools/stanford-crfm-helm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepset-ai-haystack`](/api/graphcanon/graph?tool=deepset-ai-haystack) - 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/_