{
  "schema_version": "openagent.agent_resource_packet.v1",
  "id": "res_ragflow",
  "slug": "ragflow",
  "name": "ragflow",
  "canonical_url": "https://www.openagent.bot/memory-systems/ragflow",
  "category": "memory-systems",
  "resource_type": "memory_system",
  "summary": "RAGFlow is an open-source RAG engine that goes beyond simple vector search by combining deep document understanding, layout analysis, and agent-based orchestration. It processes complex documents (PDFs, images, tables) with layout-aware parsing, then uses agent capabilities to route, filter, and augment retrieval results — creating a production-ready context layer for LLM applications.",
  "capabilities": [
    "rag",
    "workflow",
    "memory"
  ],
  "constraints": [
    "open-source"
  ],
  "scenarios": [
    "personal-memory"
  ],
  "deployment_modes": [
    "cloud"
  ],
  "interfaces": [
    "repo"
  ],
  "integrations": [
    "memory systems"
  ],
  "permission_surface": [
    "memory"
  ],
  "risk_level": "low",
  "source_confidence": "high",
  "recommended_workflows": [
    "Memory or RAG workflow"
  ],
  "avoid_when": [
    "Simple vector search use cases where basic chunking and embedding are sufficient",
    "Teams that prefer to assemble RAG pipelines from individual components rather than using an integrated platform"
  ],
  "primary_actions": [
    "Inspect repository",
    "Open Homepage",
    "Inspect repository"
  ],
  "evidence_urls": [
    "https://github.com/infiniflow/ragflow",
    "https://ragflow.io",
    "https://github.com/infiniflow/ragflow/blob/main/README.md"
  ],
  "last_verified_at": "2026-06-03",
  "machine_readable": {
    "json_url": "https://www.openagent.bot/memory-systems/ragflow.json",
    "markdown_url": "https://www.openagent.bot/memory-systems/ragflow.md",
    "agent_json_url": "https://www.openagent.bot/memory-systems/ragflow.agent.json"
  }
}