--- title: "REST vs raptor" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fasterdecoding-rest-vs-parthsarthi03-raptor" tools: ["fasterdecoding-rest", "parthsarthi03-raptor"] --- # REST vs raptor *GraphCanon updated Jul 12, 2026* ## Verdict Pick REST if rEST is a retrieval-based speculative decoding tool implemented in C, designed for use cases that demand efficiency and fine-grained control over inference processes through its distinctive approach; pick raptor if a specialized tool with a unique recursive abstractive processing approach for retrieval-augmented generation. [REST](https://github.com/FasterDecoding/REST) reports 220 GitHub stars, 17 forks, and 15 open issues, last pushed Mar 5, 2026. [raptor](https://arxiv.org/abs/2401.18059) has 1.7k stars, 231 forks, and 45 open issues, last pushed Sep 3, 2024. Figures are from public GitHub metadata via [REST's repository](https://github.com/FasterDecoding/REST) and [raptor's repository](https://github.com/parthsarthi03/raptor). | | [REST](/tools/fasterdecoding-rest.md) | [raptor](/tools/parthsarthi03-raptor.md) | | --- | --- | --- | | Tagline | REST: Retrieval-Based Speculative Decoding | The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval | | Stars | 220 | 1,723 | | Forks | 17 | 231 | | Open issues | 15 | 45 | | Language | C | Python | | Adopt for | REST is a retrieval-based speculative decoding tool implemented in C, designed for use cases that demand efficiency and fine-grained control over inference processes through its distinctive approach. | A specialized tool with a unique recursive abstractive processing approach for retrieval-augmented generation | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Released under the permissive MIT license, allowing free integration into various projects with attribution | | Categories | Data & Retrieval, Inference & Serving | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [REST](/tools/fasterdecoding-rest.md) | [raptor](/tools/parthsarthi03-raptor.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 128d | 676d | | Open issues (now) | 15 | 45 | | Owner type | Organization | User | | Security scan | 2 low (2 low) | No lockfile | | Full report | [trust report](/tools/fasterdecoding-rest/trust.md) | [trust report](/tools/parthsarthi03-raptor/trust.md) | ## Decision facts: REST - **Adopt for:** REST is a retrieval-based speculative decoding tool implemented in C, designed for use cases that demand efficiency and fine-grained control over inference processes through its distinctive approach. ## Decision facts: raptor - **Requirements:** Min 4 GB RAM; Requires Python 3.8 or higher; Dependencies listed in requirements.txt must be installed - **Adopt for:** A specialized tool with a unique recursive abstractive processing approach for retrieval-augmented generation - **License detail:** Released under the permissive MIT license, allowing free integration into various projects with attribution ## Choose when ### Choose REST if… - REST is primarily C; raptor is Python. - License: REST is Apache-2.0, raptor is MIT. - Tags unique to REST: llm-inference, speculative-decoding. - Also covers Data & Retrieval, Inference & Serving. - - When you need high performance and are willing to work with the C language for customization and optimization. ### Choose raptor if… - raptor is primarily Python; REST is C. - License: raptor is MIT, REST is Apache-2.0. - Requirements: Min 4 GB RAM; Requires Python 3.8 or higher; Dependencies listed in requirements.txt must be installed. - Tags unique to raptor: agents, clustering, framework, language-model. - Also covers AI Agents, LLM Frameworks, Vector Databases. - When you need an innovative approach to retrieve and generate content recursively using tree-organized structures. ## When NOT to use REST - - Avoid if your team lacks proficiency in C programming as this may lead to an overhead in developing and maintaining the tool. - - Not recommended for projects where flexibility with commonly used high-level languages like Python is essential, as REST primarily relies on lower-level language capabilities. ## When NOT to use raptor - Avoid RAPTOR if your project requires simple or straightforward retrieval methods without advanced recursive abstractive processes. - Not recommended if rapid iteration is prioritized over deep abstraction; RAPTOR's complex approach might slow down quick testing cycles. - If you're constrained by the MIT License terms, which offer limited protection against patent claims from contributors, use caution. ## Common questions ### What is the difference between REST and raptor? REST: REST: Retrieval-Based Speculative Decoding. raptor: The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval. See the comparison table for live GitHub stats and shared categories. ### When should I choose REST over raptor? Choose REST over raptor when REST is primarily C; raptor is Python; License: REST is Apache-2.0, raptor is MIT; Tags unique to REST: llm-inference, speculative-decoding; Also covers Data & Retrieval, Inference & Serving; - When you need high performance and are willing to work with the C language for customization and optimization. ### When should I choose raptor over REST? Choose raptor over REST when raptor is primarily Python; REST is C; License: raptor is MIT, REST is Apache-2.0; Requirements: Min 4 GB RAM; Requires Python 3.8 or higher; Dependencies listed in requirements.txt must be installed; Tags unique to raptor: agents, clustering, framework, language-model; Also covers AI Agents, LLM Frameworks, Vector Databases; When you need an innovative approach to retrieve and generate content recursively using tree-organized structures. ### When should I avoid REST? - Avoid if your team lacks proficiency in C programming as this may lead to an overhead in developing and maintaining the tool. - Not recommended for projects where flexibility with commonly used high-level languages like Python is essential, as REST primarily relies on lower-level language capabilities. ### When should I avoid raptor? Avoid RAPTOR if your project requires simple or straightforward retrieval methods without advanced recursive abstractive processes. Not recommended if rapid iteration is prioritized over deep abstraction; RAPTOR's complex approach might slow down quick testing cycles. If you're constrained by the MIT License terms, which offer limited protection against patent claims from contributors, use caution. ### Is REST or raptor more popular on GitHub? raptor has more GitHub stars (1,723 vs 220). Stars measure visibility, not whether either tool fits your constraints. ### Are REST and raptor open source? Yes - both are open-source projects on GitHub (REST: Apache-2.0, raptor: MIT). ### Where can I find alternatives to REST or raptor? GraphCanon lists graph-backed alternatives at [REST alternatives](/tools/fasterdecoding-rest/alternatives) and [raptor alternatives](/tools/parthsarthi03-raptor/alternatives) ([REST markdown twin](/tools/fasterdecoding-rest/alternatives.md), [raptor markdown twin](/tools/parthsarthi03-raptor/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/fasterdecoding-rest-vs-parthsarthi03-raptor.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, REST or raptor? REST: Slowing. raptor: 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 REST and raptor? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [REST trust report](/tools/fasterdecoding-rest/trust); [raptor trust report](/tools/parthsarthi03-raptor/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fasterdecoding-rest`](/api/graphcanon/graph?tool=fasterdecoding-rest) - 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/_