--- title: "haystack vs LLMStack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepset-ai-haystack-vs-trypromptly-llmstack" tools: ["deepset-ai-haystack", "trypromptly-llmstack"] --- # haystack vs LLMStack *GraphCanon updated Aug 16, 2026* ## Verdict Pick haystack if haystack is an open-source AI orchestration framework for building context-engineered LLM applications; pick LLMStack if lLMStack is a no-code framework designed for building LLM Agents and complex workflows with data integration, suitable for teams that want to leverage AI without deep coding expertise. [haystack](https://haystack.deepset.ai) reports 26k GitHub stars, 3.0k forks, and 108 open issues, last pushed Aug 1, 2026. [LLMStack](https://llmstack.trypromptly.com) has 2.3k stars, 347 forks, and 23 open issues, last pushed Dec 11, 2024. Figures are from public GitHub metadata via [haystack's repository](https://github.com/deepset-ai/haystack) and [LLMStack's repository](https://github.com/trypromptly/LLMStack). | | [haystack](/tools/deepset-ai-haystack.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Tagline | Open-source AI orchestration framework for building context-engineered LLM applications. | No-code multi-agent framework to build LLM Agents, workflows and applications with your data | | Stars | 26,073 | 2,309 | | Forks | 2,972 | 347 | | Open issues | 108 | 23 | | Language | Python | Python | | Adopt for | Haystack is an open-source AI orchestration framework for building context-engineered LLM applications. | LLMStack is a no-code framework designed for building LLM Agents and complex workflows with data integration, suitable for teams that want to leverage AI without deep coding expertise. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | AI Agents, Data & Retrieval, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [haystack](/tools/deepset-ai-haystack.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 612d | | Open issues (now) | 108 | 23 | | Stars delta | Unknown | +2 (30d) | | Open issues delta | Unknown | -1 (30d) | | Full report | [trust report](/tools/deepset-ai-haystack/trust.md) | [trust report](/tools/trypromptly-llmstack/trust.md) | ## Shared compatibility - **Python**: [haystack](/tools/deepset-ai-haystack.md) - Python runtime; [LLMStack](/tools/trypromptly-llmstack.md) - Python runtime ## Decision facts: haystack - **Pricing:** freemium - Free and open-source under the Apache-2.0 license, but users have to manage their own infrastructure and resources. - **Requirements:** Min 4 GB RAM; Requires Docker - **Adopt for:** Haystack is an open-source AI orchestration framework for building context-engineered LLM applications. - **License detail:** Apache-2.0 ## Decision facts: LLMStack - **Adopt for:** LLMStack is a no-code framework designed for building LLM Agents and complex workflows with data integration, suitable for teams that want to leverage AI without deep coding expertise. ## Choose when ### Choose haystack if… - License: haystack is Apache-2.0, LLMStack is Other. - Pricing: Free and open-source under the Apache-2.0 license, but users have to manage their own infrastructure and resources.. - Requirements: Min 4 GB RAM; Requires Docker. - Tags unique to haystack: agent, ai, gemini, gpt-4. - Also covers Data & Retrieval. - You need explicit control over retrieval, routing, memory, and generation within your LLM application pipelines. ### Choose LLMStack if… - License: LLMStack is Other, haystack is Apache-2.0. - Tags unique to LLMStack: ai-agents-framework, llm-agents, llm-chain, no-code-ai. - Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language models. ## When NOT to use haystack - You require integration with specific proprietary tools or frameworks not supported by Haystack. - Your development team is not familiar with Python-based technologies, since Haystack primarily supports Python-based workflows. - You are looking for a completely managed service rather than an open-source framework that requires more hands-on configuration and customization. ## When NOT to use LLMStack - Avoid using LLMStack if your project necessitates heavy customization or fine-tuning at the coding level, as it may limit flexibility compared to code-based alternatives. - Not recommended for teams with robust software engineering capabilities and a need for detailed control over underlying AI model architectures. ## Common questions ### What is the difference between haystack and LLMStack? haystack: Open-source AI orchestration framework for building context-engineered LLM applications.. LLMStack: No-code multi-agent framework to build LLM Agents, workflows and applications with your data. See the comparison table for live GitHub stats and shared categories. ### When should I choose haystack over LLMStack? Choose haystack over LLMStack when License: haystack is Apache-2.0, LLMStack is Other; Pricing: Free and open-source under the Apache-2.0 license, but users have to manage their own infrastructure and resources.; Requirements: Min 4 GB RAM; Requires Docker; Tags unique to haystack: agent, ai, gemini, gpt-4; Also covers Data & Retrieval; You need explicit control over retrieval, routing, memory, and generation within your LLM application pipelines. ### When should I choose LLMStack over haystack? Choose LLMStack over haystack when License: LLMStack is Other, haystack is Apache-2.0; Tags unique to LLMStack: ai-agents-framework, llm-agents, llm-chain, no-code-ai; Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language models. ### When should I avoid haystack? You require integration with specific proprietary tools or frameworks not supported by Haystack. Your development team is not familiar with Python-based technologies, since Haystack primarily supports Python-based workflows. You are looking for a completely managed service rather than an open-source framework that requires more hands-on configuration and customization. ### When should I avoid LLMStack? Avoid using LLMStack if your project necessitates heavy customization or fine-tuning at the coding level, as it may limit flexibility compared to code-based alternatives. Not recommended for teams with robust software engineering capabilities and a need for detailed control over underlying AI model architectures. ### Is haystack or LLMStack more popular on GitHub? haystack has more GitHub stars (26,073 vs 2,309). Stars measure visibility, not whether either tool fits your constraints. ### Are haystack and LLMStack open source? Yes - both are open-source projects on GitHub (haystack: Apache-2.0, LLMStack: Other). ### Where can I find alternatives to haystack or LLMStack? GraphCanon lists graph-backed alternatives at [haystack alternatives](/tools/deepset-ai-haystack/alternatives) and [LLMStack alternatives](/tools/trypromptly-llmstack/alternatives) ([haystack markdown twin](/tools/deepset-ai-haystack/alternatives.md), [LLMStack markdown twin](/tools/trypromptly-llmstack/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-trypromptly-llmstack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, haystack or LLMStack? haystack: Very active. LLMStack: 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 haystack and LLMStack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [haystack trust report](/tools/deepset-ai-haystack/trust); [LLMStack trust report](/tools/trypromptly-llmstack/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/_