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| Yes | | suggest_question | Submit a topic request for new unit creation | No | | report_issue | Flag incorrect, outdated, or broken content | No |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

License

CC BY-SA 4.0

knowledgelib-io