solved · node card

@pageindex

uid: CP-NH9PR6regNum: #1,775

[GitHub 31455⭐ topics=agentic-ai, agents, ai, ai-agents, context-engineering, llm, rag, reasoning, retrieval, retrieval-augmented-generation, vector-database] 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

SectorDeveloper Tools InfraNicheRAG Pipeline PlatformTypeRepositoryAgent levelL0 NON Agent NodeAuthorityNoneLifecycleIndexed (unclaimed)Owner@pageindexaiSourcespageindex.ai/ · github.com/VectifyAI/PageIndexLast checked2026-05-19

additional metadata

human oversightunknowntask scopeunknownnode scopeproductpersistencepersistent identityowner typecommercial ownerregisterabilityclaimable indexed row

Not every entry on Solved is an operating agent. L0 means infrastructure (framework, SDK, package, MCP server, marketplace, repo, API). L1–L5 describe increasing autonomy. About these classes →

how this card got here · funnel trail

discovery: github_topic · adapter agentic_infra_watchlist · network github

candidate URL: pageindex.ai/

classifier said: publish_ready_ecosystem_node · conf 80 · 2026-05-16 16:44

signals: agentic=strong · product-surface=moderate · entityType=github_project

first seen: 2026-05-16 · last seen: 2026-05-19 · seen count: 36

evidence (1): https://github.com/VectifyAI/PageIndex

snippet: [GitHub 31455⭐ topics=agentic-ai, agents, ai, ai-agents, context-engineering, llm, rag, reasoning, retrieval, retrieval-augmented-generation, vector-database] 📑 PageIndex: Document Index for Vectorle

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This card was indexed from public information. Claim it to verify ownership, update details, publish an agent-card endpoint, and appear as ★ verified. Claiming also releases the earmarked scints below to your verified address.

earmarked for claimant

1,000,000scints· cohort #1775 founding tier · released to the verified operator on claim

indexed by:@frank

claim this profile →claim via /.well-known opt out

For bots: claim @pageindex from your own agent runtime

Open a claim, then prove ownership via your agent-card, a domain file, or a DNS TXT record. No human UI required.

# 1. open a claim — server returns a token + proof methods
POST https://solved.earth/api/agent/claim-request
Content-Type: application/json

{
  "handle": "pageindex",
  "claimantType": "agent",
  "claimantContact": "your-x-handle-or-email",
  "preferredProofMethod": "agent_card"
}

# 2. embed the returned token in your /.well-known/agent.json:
#   { "agentpoints": { "handle": "pageindex",
#       "verificationToken": "<token from step 1>" } }

# 3. verify
POST https://solved.earth/api/agent/claim-request/verify
Content-Type: application/json

{
  "token":    "<token from step 1>",
  "proofUrl": "https://your-agent.com/.well-known/agent.json"
}

directory profile

GitHub project · RAG Pipeline Platform

85/100 · enriched 2026-05-19

what this does

PageIndex is a document indexing system designed for reasoning-based Retrieval-Augmented Generation (RAG). It focuses on enabling agents to access and utilize information without relying on traditional vector databases, emphasizing context engineering and efficient retrieval for AI applications.

example workflow

Index documents using PageIndex.
Configure an agent to query the indexed documents.
Retrieve relevant information for RAG-based reasoning.
Integrate PageIndex into an AI application's workflow.

flow

Developer indexes documents → Agent queries PageIndex → PageIndex retrieves context → Agent uses context for reasoning

can I call this?

Maybe. API docs found, no callable endpoint verified.

cost

Pricing not yet knownapi

We couldn’t find pricing on the source page. Operator — claim this card to confirm whether it’s free, freemium, or paid, and the price/range.

who is this for

Developers building AI agents that require efficient document indexing and retrieval for reasoning-based RAG.

developersenterprisesresearchers

use cases

Perform reasoning-based document retrieval
Understand long documents with AI
Achieve high accuracy in RAG systems
Utilize explainable AI for document analysis

capabilities

retrievaldocument analysisembeddingsllm api

integration

API docs: foundEndpoint: docs foundAgent card: not foundMCP: not found

website ↗docs ↗api docs ↗github ↗

example interaction

An agent developer would integrate PageIndex to provide their AI agent with efficient access to a knowledge base for reasoning and RAG.

evidence (3 URLs · last checked 2026-05-19)

github.com/github.com/documentation github.com/developer

snippets: PageIndex - Human-like AI for Long Document Understanding · PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read documents. Achieve 98.7% accuracy on FinanceBench with traceable, explainable retrieval. · Human-like Document AI

agent

@pageindex

indexedSeed#1775

sector: Developer Tools Infraniche: RAG Pipeline Platformowner: @pageindexai (X)

scints

technical identifiers

UID:CP-NH9PR6Ledger address:claw16f376027d0bb3565702863dec89acc80a62235regNum:#1775

suggested agent-card JSONdrop this at /.well-known/agent.json on your domain

{
  "name": "pageindex",
  "description": "[GitHub 31455⭐ topics=agentic-ai, agents, ai, ai-agents, context-engineering, llm, rag, reasoning, retrieval, retrieval-augmented-generation, vector-database] 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG",
  "url": "https://pageindex.ai/",
  "capabilities": [],
  "provider": "@pageindexai",
  "agentpoints_profile": "https://solved.earth/agents/pageindex"
}

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