OpenCrab
Ontology-first Graph RAG platform that turns messy knowledge into reusable packs
OpenCrab is the right call when knowledge is a product you want to package and sell, not just a database to query. Its ontology packs and MCP integration deliver auditable answers, but the learning curve is real. Skip it if you want zero-config vector search or a public REST API.
Verified 4d ago · liveness 63/100 · cite: rightaichoice.com/tools/opencrab
- Researchers building private evidence graphs from papers and reports
- Domain experts packaging expertise into sellable ontology packs via marketplace
- AI builders grounding agents with MCP-connected knowledge (Claude, GPT)
- Analysts requiring auditable, sourced answers with graph RAG
- Users who need a simple vector database without ontology modeling
- Teams requiring a public REST API for custom integrations
- Non-technical users who prefer zero-configuration AI chat tools
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Skip OpenCrab if you need zero-config vector search, a public REST API, or if you're non-technical and want a simple AI chat tool without ontology overhead.
Private storage is only available on the Pro tier ($10/mo); the free tier is public-only, so you can't keep knowledge confidential.
OpenCrab's pricing ($0 Free, $10/mo Pro, $30/mo Expert) is affordable for individual researchers and small teams. Compared to enterprise graph RAG platforms that charge hundreds per month, it's budget-friendly. However, for simple vector RAG, cheaper or free options like RAGFlow may suffice.
In short
OpenCrab — Ontology-first Graph RAG platform that turns messy knowledge into reusable packs. Best for Researchers building private evidence graphs from papers and reports, Domain experts packaging expertise into sellable ontology packs via marketplace, AI builders grounding agents with MCP-connected knowledge (Claude, GPT). Free to start; paid plans from $10/mo.
What people actually say about OpenCrab — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
3 mentions across 2 sources (Hacker News, Product Hunt) · researched Jul 2, 2026.
- +Innovative ontology-centric approach for structuring unstructured knowledge into reusable packs.
- +Supports diverse document types: PDF, HWP, GitHub, expert notes.
- +Integrates with MCP for AI workspaces like Claude Desktop.
- +Evidence graph construction with auditing for grounded answers.
- +Offers free tier for getting started without upfront cost.
- −Virtually no independent user reviews exist to validate performance.
- −Hacker News post questions authenticity and suggests AI-generated marketing.
- −Community engagement is extremely low—only 2 upvotes on Product Hunt.
- −No transparency on pricing details or hidden costs in feedback.
- −Learning curve likely steep due to complex ontology structure.
- • No transparent pricing disclosed for paid tiers—potential for unexpected costs.
- • Marketplace monetization may involve unknown revenue share.
Viability Score
How well maintained and how widely used is OpenCrab? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Document ingestion from PDF, HWP, GitHub, and expert notes
- OCR and CLIP-based parsing for scanned PDFs and images
- Nine-space MetaOntology extraction (Subject, Evidence, Claim, Policy, Lever)
- Graph RAG querying with evidence-grounded, auditable answers
- MCP integration for Claude Desktop, GPT, and custom agents
- CrabSkill workflow with validator and playbook
- Ontology pack creation with manifest, nodes, edges, and evidence
- Pack publishing and licensing through the marketplace
- Reverse ingest from connected MCP tools
- Mobile apps for iOS and Android
- Webapp, desktop client, and MCP runtime panels
- Bilingual support for Korean and English
- Agent deployment to Codex and Claude Code
About OpenCrab
OpenCrab is an ontology-first Graph RAG platform for teams that treat knowledge as a product. It ingests documents from PDF, HWP, GitHub, and expert notes, parses them with OCR and CLIP, and structures them into ontology packs composed of nodes, edges, and evidence. This approach makes every answer auditable—each response can be traced back to its source material, which is a big deal for research and compliance-heavy work. The workflow is clear: ingest data, parse it into chunks, structure it into packs, host them in a workspace, package them as skills, and deploy them to agents. You can query the graph through the webapp, desktop client, or MCP (Model Context Protocol), meaning the same knowledge can feed Claude Desktop, GPT, or custom agents. Recent updates added mobile apps for iOS and Android, plus the CrabSkill workflow with a validator and playbook for consistent pack building. OpenCrab targets three core groups: researchers and analysts building private evidence graphs, domain experts packaging expertise into sellable packs via the marketplace, and AI builders who want MCP-connected knowledge for grounded agents. The platform is bilingual (Korean and English) and operated by AgentKorea. Compared to simpler RAG tools like RAGFlow or Danswer, OpenCrab's ontology modeling adds upfront complexity but delivers reusable, auditable knowledge products. If you need turnkey vector search, it might be overkill—but for structured, sellable knowledge packs and MCP-native integration, it fills a unique niche.
Behind the Verdict
Most RAG tools give you a chatbot bolted onto a vector store. OpenCrab goes further: it treats knowledge as a structured, reusable asset. The ontology packs—with nodes, edges, and evidence—let you trace every answer back to a source, which is huge if you need to audit AI outputs. The MCP integration is the real differentiator. Instead of building custom connectors, you point Claude Desktop, GPT, or any MCP client at the same knowledge graph. That one endpoint approach saves time when you have several AI tools in play. But there's a catch: ontology modeling isn't for everyone. If you just want a quick Q&A bot over your PDFs, the extra structuring overhead will feel like a chore. You're better off with something turnkey like RAGFlow or Danswer. Also, there's no public REST API documented. It's MCP or nothing for custom integrations. If your stack depends on direct API calls, that's a blocker. The marketplace for selling packs is a nice touch for domain experts, but it depends on there being buyers. On mobile, the new iOS and Android apps mean you can check your packs on the go, but expect a companion experience rather than a full editing suite. Overall, OpenCrab earns its keep in research, compliance, and expertise monetization. We'd reach for it when auditability matters more than speed-to-setup. In practice, teams that already think in knowledge graphs will feel at home; teams that just want answers won't.
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Real-world workflow fit
Concrete scenarios for the personas OpenCrab actually fits — and what changes day-one when you adopt it.
Starting a literature review with dozens of PDFs and needing evidence-grounded answers.
Outcome: Within an hour, ingest PDFs, extract ontology packs, and query via Graph RAG to get answers with traceable sources, saving days of manual reading.
Building a custom agent that needs domain-specific knowledge from proprietary documents.
Outcome: In an afternoon, ingest documents into OpenCrab, structure ontology packs, and connect to Claude Desktop via MCP, providing the agent with grounded, auditable knowledge.
Wanting to monetize expertise by packaging it into sellable ontology packs.
Outcome: Over a few days, use CrabSkill workflow to create a validated pack, publish to the marketplace on the Expert tier, and start licensing it to others.
Use Cases
- Ingest research papers and build a private evidence graph for grounded answers.
- Package domain expertise into ontology packs and publish to the marketplace.
- Connect Claude Desktop via MCP to query structured knowledge packs.
- Create a content planning ontology for marketing and brand analysis.
- Synthesize YouTube channel scripts using ontology-driven graph RAG.
Models Under the Hood
as of 2026-08-22
Limitations
- The free tier only allows public pack access with no private storage.
- No public API is available, limiting programmatic integration.
- The platform is currently operated by a small team (AgentKorea) and support is via email only.
- Expert tier requires manual review for pack publishing.
as of 2026-08-12
Verification history
We have re-verified OpenCrab 5 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published OpenCrab tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Casual users, students, or anyone curious about Graph RAG who wants to explore public ontology packs without commitment.
What this tier adds
Free entry point with public pack access, basic graph search, and external AI tool lookup; no private storage.
Pro
$10/mo
Ideal for
Individual researchers, analysts, or small teams needing a private workspace for sensitive documents and personal knowledge graphs.
What this tier adds
Adds private document ingest, personal graph RAG workspace, and isolated ontology storage on top of Free.
Expert
$30/mo
Ideal for
Domain experts, consultants, and knowledge entrepreneurs who want to package and sell ontology packs through the marketplace.
What this tier adds
Adds ontology pack publishing workflow and reverse ingest from MCP tools, enabling commercialization.
Where the pricing makes sense
The company stage and team size where OpenCrab's pricing actually pencils out — and where peers do it cheaper.
OpenCrab's pricing ($0 Free, $10/mo Pro, $30/mo Expert) is affordable for individual researchers and small teams. Compared to enterprise graph RAG platforms that charge hundreds per month, it's budget-friendly. However, for simple vector RAG, cheaper or free options like RAGFlow may suffice.
Setup time & first value
How long it actually takes to get something useful out of OpenCrab — broken out by persona, not the marketing-page minute.
Research: ~1 hour to ingest documents and query. AI builder: ~2-3 hours to set up MCP connection and test. Domain expert: ~2-3 days to create and publish a pack due to manual review.
Switching to or from OpenCrab
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From RAGFlow: Export your documents and re-ingest into OpenCrab; restructure into ontology packs for richer context.
- →From Danswer: Similar document import; ontology modeling adds a structure layer on top of existing data.
- →From Notion: Export content as PDF or use GitHub integration to import notes directly.
- ↗To a vector DB: You can export your packs and import into a vector store, but you'll lose the ontology structure.
- ↗To a custom MCP server: Build your own MCP server wrapping the knowledge graph, though you lose the GUI.
- ↗To a spreadsheet: For simple data, export nodes and edges as CSV for analysis.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with OpenCrab
Common stack mates teams adopt alongside OpenCrab, with the specific reason each pairing earns its keep.
GraphRAG
Open-source knowledge-graph RAG that maps entities and communities to answer complex, cross-document questions.
Iris.ai
AI knowledge foundation for regulated enterprises — turning complex data into trusted, auditable intelligence.
Wisedocs
AI medical record review platform that turns claims documents into decisions
Featured Head-to-Head Comparisons
Opencrab vs Praktika
Praktika and OpenCrab serve completely different needs. Choose Praktika for conversational language practice with AI tutors; choose OpenCrab for structuring messy knowledge into ontology packs for AI agents. They are not direct competitors.
Opencrab vs Screenplayiq
If you're a screenwriter or studio exec needing script marketability analysis and box office predictions, ScreenplayIQ is the clear choice with its beat sheets, heatmaps, and comparative market data. But if you're a researcher or AI builder who needs to turn messy documents into structured, auditable knowledge graphs with MCP integration (Claude/GPT), OpenCrab is far more powerful. Choose based on your domain: narrative finance vs. ontology-driven knowledge packaging.
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