Comparison
Ori-Mnemos vs firecrawl
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.
Markdown twin · Ori-Mnemos alternatives · firecrawl alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | Ori-Mnemos | firecrawl |
|---|---|---|
| Maintenance | Active (20d since push) As of today · github_public_v1 | Very active (0d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No MCP manifest As of today · mcp_manifest | No lockfile As of today · none |
Tagline
- 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. 🔥
Stars
- Ori-Mnemos
- 314
- firecrawl
- 149k
Forks
- Ori-Mnemos
- 28
- firecrawl
- 8.5k
Open issues
- Ori-Mnemos
- 5
- firecrawl
- 395
Language
- Ori-Mnemos
- TypeScript
- firecrawl
- TypeScript
Adopt for
- Ori-Mnemos
- Ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents.
- firecrawl
- FireCrawl is an API-driven toolkit built for conducting scalable searches, scraping tasks, and interactive operations with the web using AI agents.
Persona
- Ori-Mnemos
- -
- firecrawl
- -
Runtime
- Ori-Mnemos
- -
- firecrawl
- -
License
- Ori-Mnemos
- Apache-2.0
- firecrawl
- 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.
Last pushed
- Ori-Mnemos
- Jun 21, 2026
- firecrawl
- Jul 11, 2026
Categories
- Ori-Mnemos
- AI Agents, Data & Retrieval
- firecrawl
- AI Agents, Data & Retrieval
Trust and health
Maintenance
- Ori-Mnemos
- Active (82%)
- firecrawl
- Very active (96%)
Days since push
- Ori-Mnemos
- 20d
- firecrawl
- 0d
Open issues (now)
- Ori-Mnemos
- 5
- firecrawl
- 395
Owner type
- Ori-Mnemos
- User
- firecrawl
- Organization
Security scan
- Ori-Mnemos
- No MCP manifest
- firecrawl
- No lockfile
Full report
- Ori-Mnemos
- Trust report
- firecrawl
- Trust report
Choose Ori-Mnemos if…
- License: Ori-Mnemos is Apache-2.0, firecrawl is AGPL-3.0.
- Tags unique to Ori-Mnemos: markdown, persistent-memory, llm, model-context-protocol.
- Ori-Mnemos ships an MCP server manifest.
- When you need a robust, local-first solution that prioritizes offline capabilities and security.
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.
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: webscraping, scraping, search, crawler.
- 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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (aayoawoyemi/Ori-Mnemos) · observed Jul 11, 2026
- GitHub forks (aayoawoyemi/Ori-Mnemos) · observed Jul 11, 2026
- Last push (aayoawoyemi/Ori-Mnemos) · observed Jun 21, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (firecrawl/firecrawl) · observed Jul 11, 2026
- GitHub forks (firecrawl/firecrawl) · observed Jul 11, 2026
- Last push (firecrawl/firecrawl) · observed Jul 11, 2026
- License file (AGPL-3.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Ori-Mnemos 314 · firecrawl 149k (synced Jul 11, 2026).
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: markdown, persistent-memory, llm, model-context-protocol; 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: webscraping, scraping, search, crawler; 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 and firecrawl alternatives (Ori-Mnemos markdown twin, firecrawl markdown twin), 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 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; firecrawl trust report.