--- title: "headroom vs RasaGPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/headroomlabs-ai-headroom-vs-paulpierre-rasagpt" tools: ["headroomlabs-ai-headroom", "paulpierre-rasagpt"] --- # headroom vs RasaGPT *GraphCanon updated Sep 20, 2026* ## Verdict Pick headroom if headroom compresses data for LLMs, reducing token usage by 20% for coding agents and 60-95% for JSON, without altering answers. It offers a library, proxy, and MCP server; pick RasaGPT if rasaGPT is a pioneering headless chatbot platform that merges Rasa with technologies like FastAPI, Langchain, and LlamaIndex. [headroom](https://docs.headroomlabs.ai/docs) reports 73k GitHub stars, 5.6k forks, and 671 open issues, last pushed Sep 17, 2026. [RasaGPT](https://rasagpt.dev) has 2.5k stars, 250 forks, and 57 open issues, last pushed Nov 12, 2025. Figures are from public GitHub metadata via [headroom's repository](https://github.com/headroomlabs-ai/headroom) and [RasaGPT's repository](https://github.com/paulpierre/RasaGPT). | | [headroom](/tools/headroomlabs-ai-headroom.md) | [RasaGPT](/tools/paulpierre-rasagpt.md) | | --- | --- | --- | | Tagline | Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. | First headless LLM chatbot platform built on top of Rasa and Langchain | | Stars | 72,850 | 2,463 | | Forks | 5,600 | 250 | | Open issues | 671 | 57 | | Language | Python | Python | | Adopt for | Headroom compresses data for LLMs, reducing token usage by 20% for coding agents and 60-95% for JSON, without altering answers. It offers a library, proxy, and MCP server. | RasaGPT is a pioneering headless chatbot platform that merges Rasa with technologies like FastAPI, Langchain, and LlamaIndex. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | AI Agents, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [headroom](/tools/headroomlabs-ai-headroom.md) | [RasaGPT](/tools/paulpierre-rasagpt.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 299d | | Open issues (now) | 671 | 57 | | Stars delta | +6.4k (30d) | -1 (30d) | | Open issues delta | +183 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/headroomlabs-ai-headroom/trust.md) | [trust report](/tools/paulpierre-rasagpt/trust.md) | ## Shared compatibility - **Python**: [headroom](/tools/headroomlabs-ai-headroom.md) - Python runtime; [RasaGPT](/tools/paulpierre-rasagpt.md) - Python runtime ## Decision facts: headroom - **Requirements:** Requires Docker; Requires Python 3.10+.; ONNX-backed features require AVX2 on x86/x86_64 hosts. - **Adopt for:** Headroom compresses data for LLMs, reducing token usage by 20% for coding agents and 60-95% for JSON, without altering answers. It offers a library, proxy, and MCP server. ## Decision facts: RasaGPT - **Adopt for:** RasaGPT is a pioneering headless chatbot platform that merges Rasa with technologies like FastAPI, Langchain, and LlamaIndex. ## Choose when ### Choose headroom if… - License: headroom is Apache-2.0, RasaGPT is MIT. - Requirements: Requires Docker; Requires Python 3.10+.; ONNX-backed features require AVX2 on x86/x86_64 hosts.. - Tags unique to headroom: agent, anthropic, claude-code, compression. - Also covers Developer Tools, Evaluation & Observability. - When you need to reduce token usage for coding agents by 20% and for JSON by 60-95% without changing the answers. ### Choose RasaGPT if… - License: RasaGPT is MIT, headroom is Apache-2.0. - Tags unique to RasaGPT: chatbot, fastapi, langchain, rasa. - Also covers AI Agents. - When you need a robust framework for developing conversational AI solutions and are already familiar with Rasa's ecosystem. ## When NOT to use headroom - If you are working with environments that do not support Python 3.10+. - When your project does not require token optimization or compression for JSON and coding agents. - If you are working on a platform that does not support the ONNX-backed features, such as some Docker/QEMU setups or older cloud VMs without AVX2. ## When NOT to use RasaGPT - For users preferring platforms that require less technical setup, particularly without needing Docker or specific Python versions. - Avoid if development is focused on non-headless chatbot applications that don't leverage the Rasa framework's architecture. ## Common questions ### What is the difference between headroom and RasaGPT? headroom: Compress tool outputs, logs, files, and RAG chunks before they reach the LLM.. RasaGPT: First headless LLM chatbot platform built on top of Rasa and Langchain. See the comparison table for live GitHub stats and shared categories. ### When should I choose headroom over RasaGPT? Choose headroom over RasaGPT when License: headroom is Apache-2.0, RasaGPT is MIT; Requirements: Requires Docker; Requires Python 3.10+.; ONNX-backed features require AVX2 on x86/x86_64 hosts.; Tags unique to headroom: agent, anthropic, claude-code, compression; Also covers Developer Tools, Evaluation & Observability; When you need to reduce token usage for coding agents by 20% and for JSON by 60-95% without changing the answers. ### When should I choose RasaGPT over headroom? Choose RasaGPT over headroom when License: RasaGPT is MIT, headroom is Apache-2.0; Tags unique to RasaGPT: chatbot, fastapi, langchain, rasa; Also covers AI Agents; When you need a robust framework for developing conversational AI solutions and are already familiar with Rasa's ecosystem. ### When should I avoid headroom? If you are working with environments that do not support Python 3.10+. When your project does not require token optimization or compression for JSON and coding agents. If you are working on a platform that does not support the ONNX-backed features, such as some Docker/QEMU setups or older cloud VMs without AVX2. ### When should I avoid RasaGPT? For users preferring platforms that require less technical setup, particularly without needing Docker or specific Python versions. Avoid if development is focused on non-headless chatbot applications that don't leverage the Rasa framework's architecture. ### Is headroom or RasaGPT more popular on GitHub? headroom has more GitHub stars (72,850 vs 2,463). Stars measure visibility, not whether either tool fits your constraints. ### Are headroom and RasaGPT open source? Yes - both are open-source projects on GitHub (headroom: Apache-2.0, RasaGPT: MIT). ### Where can I find alternatives to headroom or RasaGPT? GraphCanon lists graph-backed alternatives at [headroom alternatives](/tools/headroomlabs-ai-headroom/alternatives) and [RasaGPT alternatives](/tools/paulpierre-rasagpt/alternatives) ([headroom markdown twin](/tools/headroomlabs-ai-headroom/alternatives.md), [RasaGPT markdown twin](/tools/paulpierre-rasagpt/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/headroomlabs-ai-headroom-vs-paulpierre-rasagpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, headroom or RasaGPT? headroom: Very active. RasaGPT: Slowing. 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 headroom and RasaGPT? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [headroom trust report](/tools/headroomlabs-ai-headroom/trust); [RasaGPT trust report](/tools/paulpierre-rasagpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=headroomlabs-ai-headroom`](/api/graphcanon/graph?tool=headroomlabs-ai-headroom) - 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/_