--- title: "Ori-Mnemos vs firecrawl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aayoawoyemi-ori-mnemos-vs-firecrawl-firecrawl" tools: ["aayoawoyemi-ori-mnemos", "firecrawl-firecrawl"] --- # Ori-Mnemos vs firecrawl *GraphCanon updated Jul 11, 2026* ## Verdict Pick Ori-Mnemos if ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents; pick firecrawl if fireCrawl is an API-driven toolkit built for conducting scalable searches, scraping tasks, and interactive operations with the web using AI agents. [Ori-Mnemos](https://orimnemos.com.) reports 314 GitHub stars, 28 forks, and 5 open issues, last pushed Jun 21, 2026. [firecrawl](https://firecrawl.dev) has 149k stars, 8.5k forks, and 395 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [Ori-Mnemos's repository](https://github.com/aayoawoyemi/Ori-Mnemos) and [firecrawl's repository](https://github.com/firecrawl/firecrawl). | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [firecrawl](/tools/firecrawl-firecrawl.md) | | --- | --- | --- | | Tagline | Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). | The API to search, scrape, and interact with the web at scale. 🔥 | | Stars | 314 | 149,109 | | Forks | 28 | 8,524 | | Open issues | 5 | 395 | | Language | TypeScript | TypeScript | | Adopt for | Ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents. | FireCrawl is an API-driven toolkit built for conducting scalable searches, scraping tasks, and interactive operations with the web using AI agents. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | AGPL-3.0 license requires that any changes to FireCrawl's source code also be made available as free software when the adapted version is used. | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [firecrawl](/tools/firecrawl-firecrawl.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 20d | 0d | | Open issues (now) | 5 | 395 | | Owner type | User | Organization | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/aayoawoyemi-ori-mnemos/trust.md) | [trust report](/tools/firecrawl-firecrawl/trust.md) | ## Decision facts: Ori-Mnemos - **Adopt for:** Ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents. ## Decision facts: firecrawl - **Hosting:** self hosted - FireCrawl can be deployed on your infrastructure, giving you complete control over where and how the API interacts with web data. - **Requirements:** Min 4 GB RAM; Requires Docker - **Adopt for:** FireCrawl is an API-driven toolkit built for conducting scalable searches, scraping tasks, and interactive operations with the web using AI agents. - **License detail:** AGPL-3.0 license requires that any changes to FireCrawl's source code also be made available as free software when the adapted version is used. ## Choose when ### Choose Ori-Mnemos if… - License: Ori-Mnemos is Apache-2.0, firecrawl is AGPL-3.0. - Tags unique to Ori-Mnemos: agent-memory, knowledge-graph, llm, local-first. - Ori-Mnemos ships an MCP server manifest. - When you need a robust, local-first solution that prioritizes offline capabilities and security. ### Choose firecrawl if… - License: firecrawl is AGPL-3.0, Ori-Mnemos is Apache-2.0. - FireCrawl can be deployed on your infrastructure, giving you complete control over where and how the API interacts with web data. - Requirements: Min 4 GB RAM; Requires Docker. - Tags unique to firecrawl: crawler, scraping, search, web-data-extraction. - When you need to automate complex web interactions that require understanding context or content from multiple sources, leveraging its AI agent capabilities. ## When NOT to use Ori-Mnemos - When real-time synchronization across devices or cloud integration is a non-negotiable requirement for your application. - If you are looking for a memory system that leverages distributed databases for scalable access patterns; Ori-Mnemos focuses on local storage using SQLite. - In environments where complex, multi-node architectures and high availability requirements demand more than a single point of data persistence. ## When NOT to use firecrawl - For lightweight scraping tasks where minimal data extraction is sufficient and speed is of utmost importance without the need for advanced AI analysis. - If you require open-source components under a license other than AGPL-3.0, as this license may impose certain restrictions on derivative works. ## Common questions ### What is the difference between Ori-Mnemos and firecrawl? Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. firecrawl: The API to search, scrape, and interact with the web at scale. 🔥. See the comparison table for live GitHub stats and shared categories. ### When should I choose Ori-Mnemos over firecrawl? Choose Ori-Mnemos over firecrawl when License: Ori-Mnemos is Apache-2.0, firecrawl is AGPL-3.0; Tags unique to Ori-Mnemos: agent-memory, knowledge-graph, llm, local-first; Ori-Mnemos ships an MCP server manifest; When you need a robust, local-first solution that prioritizes offline capabilities and security. ### When should I choose firecrawl over Ori-Mnemos? Choose firecrawl over Ori-Mnemos when License: firecrawl is AGPL-3.0, Ori-Mnemos is Apache-2.0; FireCrawl can be deployed on your infrastructure, giving you complete control over where and how the API interacts with web data; Requirements: Min 4 GB RAM; Requires Docker; Tags unique to firecrawl: crawler, scraping, search, web-data-extraction; When you need to automate complex web interactions that require understanding context or content from multiple sources, leveraging its AI agent capabilities. ### When should I avoid Ori-Mnemos? When real-time synchronization across devices or cloud integration is a non-negotiable requirement for your application. If you are looking for a memory system that leverages distributed databases for scalable access patterns; Ori-Mnemos focuses on local storage using SQLite. In environments where complex, multi-node architectures and high availability requirements demand more than a single point of data persistence. ### When should I avoid firecrawl? For lightweight scraping tasks where minimal data extraction is sufficient and speed is of utmost importance without the need for advanced AI analysis. If you require open-source components under a license other than AGPL-3.0, as this license may impose certain restrictions on derivative works. ### Is Ori-Mnemos or firecrawl more popular on GitHub? firecrawl has more GitHub stars (149,109 vs 314). Stars measure visibility, not whether either tool fits your constraints. ### Are Ori-Mnemos and firecrawl open source? Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, firecrawl: AGPL-3.0). ### Where can I find alternatives to Ori-Mnemos or firecrawl? GraphCanon lists graph-backed alternatives at [Ori-Mnemos alternatives](/tools/aayoawoyemi-ori-mnemos/alternatives) and [firecrawl alternatives](/tools/firecrawl-firecrawl/alternatives) ([Ori-Mnemos markdown twin](/tools/aayoawoyemi-ori-mnemos/alternatives.md), [firecrawl markdown twin](/tools/firecrawl-firecrawl/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/aayoawoyemi-ori-mnemos-vs-firecrawl-firecrawl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Ori-Mnemos or firecrawl? Ori-Mnemos: Active. firecrawl: Very active. 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 Ori-Mnemos and firecrawl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Ori-Mnemos trust report](/tools/aayoawoyemi-ori-mnemos/trust); [firecrawl trust report](/tools/firecrawl-firecrawl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aayoawoyemi-ori-mnemos`](/api/graphcanon/graph?tool=aayoawoyemi-ori-mnemos) - 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/_