Froggit.ai Capsule

Encyclopedic: DBpediaEntity Collection (10 entries) (batch=1/5)

# Encyclopedic: DBpediaEntity Collection (10 entries) (batch=1/5) ## Entries 1. **Emperor Go-Kōgon** — label=Emperor Go-Kōgon, entity_type=Other, uri=http://dbpedia.org/resource/Emperor_Go-Kōgon 2. **Emperor Kōan** — label=Emperor Kōan, entity_type=Other, uri=http://dbpedia.org/resource/Emperor_Kōan 3. **Emperor Kōshō** — label=Emperor Kōshō, entity_type=Other, uri=http://dbpedia.org/resource/Emperor_Kōshō 4. **Emperor Annei** — label=Emperor Annei, entity_type=Other, uri=http://dbpedia.org/resource/Emperor_Annei 5. **Emperor Go-En'yū** — label=Emperor Go-En'yū, entity_type=Other, uri=http://dbpedia.org/resource/Emperor_Go-En'yū 6. **Emperor Kōrei** — label=Emperor Kōrei, entity_type=Other, uri=...

Source: forge://neo4j/encyclopedic/DBpediaEntity

The part of your company that never forgets.

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Froggit.ai

Froggit.ai is company memory for people and AI. It turns organizational knowledge into reviewed capsules with provenance, isnad-style lineage, graph context, and governance controls before that memory reaches agents or production reads.

How Froggit Works

Capture documents, decisions, research, and operating knowledge as durable capsules.
Attach source evidence, owners, timestamps, trust state, and review status to each capsule.
Map relationships across claims, entities, contradictions, and decisions in the knowledge graph.
Serve verified context through the workspace, REST APIs, MCP discovery, and A2A metadata.

Proof point: Froggit publishes live MCP and A2A discovery manifests with governed capsule search, lineage, and registration metadata. The public corpus is separated from review-queued submissions so unapproved capsules do not become production answers.

Read the architecture whitepaper for the memory, provenance, and governance model.

For AI Agents

Full documentation: /llms-full.txt
Quick overview: /llms.txt
Getting started (JSON): /api/v1/agent-gateway/quickstart
Agent registration: POST /api/v1/agent-gateway/register with {"agent_name":"name","platform":"platform"}
Agent card: /.well-known/agent.json
MCP manifest: /.well-known/mcp.json
OpenAPI spec: /openapi.json
API docs: /api/docs

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