knowledgelib.io

AI Knowledge Library — structured, cited knowledge units for AI agents. Pre-verified answers that save tokens, reduce hallucinations, and cite every source.

What is this?

1,564 knowledge units across 16 domains (consumer electronics, software, business strategy, ERP integration, compliance, energy, finance, and more). Each unit answers one canonical question with:

Confidence scores (0.0-1.0) per published methodology
Inline source citations from 5-8 authoritative sources
Freshness tracking with verified dates and temporal validity
Quality status — verified, needs_review, or unreliable
Knowledge graph — related units with typed edges

One API call replaces 5 web searches and 8,000 tokens of parsing.

Quick Start

MCP Server (Claude, Cursor, Windsurf)

npx knowledgelib-mcp

Or add to claude_desktop_config.json:

{
  "mcpServers": {
    "knowledgelib": {
      "command": "npx",
      "args": ["knowledgelib-mcp"]
    }
  }
}

MCP over HTTP (no install needed)

POST https://knowledgelib.io/mcp

Streamable HTTP transport, JSON-RPC 2.0, MCP spec 2025-03-26.

REST API

# Search
curl https://knowledgelib.io/api/v1/query?q=best+wireless+earbuds+under+150

# Batch search (up to 10 queries)
curl -X POST https://knowledgelib.io/api/v1/batch \
  -H "Content-Type: application/json" \
  -d '{"queries":[{"q":"earbuds"},{"q":"headphones"}]}'

# Get full unit
curl https://knowledgelib.io/api/v1/units/consumer-electronics/audio/wireless-earbuds-under-150/2026.md

# Health check
curl https://knowledgelib.io/api/v1/health

LangChain (Python)

pip install langchain-knowledgelib

from langchain_knowledgelib import KnowledgelibRetriever
retriever = KnowledgelibRetriever()
docs = retriever.invoke("best wireless earbuds")

n8n

npm install n8n-nodes-knowledgelib

MCP Tools

Tool	Description	Read-only
`query_knowledge`	Search across all knowledge units with filters	Yes
`batch_query`	Search multiple topics in one call (max 10)	Yes
`get_unit`	Retrieve full markdown content by ID	Yes
`list_domains`	List all domains with unit counts	Yes
`suggest_question`	Submit a topic request for new unit creation	No
`report_issue`	Flag incorrect, outdated, or broken content	No

All read-only tools are marked with readOnlyHint: true and idempotentHint: true per MCP spec 2025-03-26, enabling parallel execution by agents.

API Features

Structured error codes with retryable flag and retry_after_ms
ETag / If-None-Match caching (304 Not Modified)
Correlation IDs (X-Request-Id header on all responses)
Quality status (verified / needs_review / unreliable) on all results
Related units for knowledge graph traversal
Content previews (150-char summaries without fetching full unit)
Token budgeting (total_tokens across results)
Rate limiting on write endpoints (10 suggestions/hr, 20 feedback/hr)
Zod validation with per-field error messages

Entity Types

Type	Count	Description
product_comparison	418	Best-of roundups with decision logic and buy links
concept	336	Definitions of terms agents often get wrong
software_reference	239	Code examples, anti-patterns, decision trees
execution_recipe	202	Step-by-step implementation plans
erp_integration	166	API capabilities, rate limits, data mapping
agent_prompt	55	System prompts for pipeline sub-agents
assessment	54	Structured scoring frameworks
decision_framework	35	Decision trees with trade-offs
benchmark	28	Industry benchmarks by segment
rule	28	Actionable directives with evidence

Discovery

/llms.txt — Plain-text guide for LLMs
/llms-full.txt — Complete index of all questions
/.well-known/ai-knowledge.json — Machine-readable manifest
/catalog.json — Full catalog with metadata
/for-agents — Integration guide

Links

Website: https://knowledgelib.io
npm: https://www.npmjs.com/package/knowledgelib-mcp
PyPI: https://pypi.org/project/langchain-knowledgelib/
HTTP MCP: https://knowledgelib.io/mcp
OpenAPI: https://knowledgelib.io/api/v1/openapi.json
GPT Actions: https://knowledgelib.io/.well-known/openapi-gpt.json

License

CC BY-SA 4.0

knowledgelib-mcp