ckg-ai-platforms 0.2.1

AI developer platforms as traversable knowledge graphs — LangChain, LangGraph, OpenAI, Anthropic, HuggingFace, AWS Bedrock, Vertex AI — MCP-native

ckg-ai-platforms

AI developer platforms as traversable knowledge graphs — MCP-native.

7 domains · 314 nodes · 445 edges · 11× fewer tokens than RAG

Domain	Nodes	Edges	Coverage
langchain	48	71	Chains, agents, LCEL, memory, retrievers, LangSmith
langgraph	42	58	Graph state, nodes, edges, cycles, persistence
openai-api	43	59	Chat completions, assistants, embeddings, DALL-E
anthropic-sdk	44	62	Messages API, tool use, prompt caching, computer use
huggingface	46	65	Hub, Transformers, Spaces, Datasets, Inference API
aws-bedrock	46	67	Converse API, Guardrails, Knowledge Bases, agents
vertex-ai	45	63	Model Garden, Gemini API, evaluation, fine-tuning

Install

pip install ckg-ai-platforms
uvx ckg-ai-platforms   # MCP server

Use as MCP server

Add to your Claude Desktop or any MCP client:

{
  "mcpServers": {
    "ai-platforms": {
      "command": "uvx",
      "args": ["ckg-ai-platforms"]
    }
  }
}

Use as Python library

from ckg_ai_platforms import ask_platform, list_domains

# Ask a question — auto-detects platform
result = ask_platform("How does LCEL RunnableSequence work?")
print(result)

# Explicit domain
result = ask_platform("explain tool use", domain="anthropic-sdk")

Benchmark

Part of the CKG benchmark:

• F1: 0.471 vs RAG 0.123 (3.8×)
• Tokens/query: 269 vs 2,982 (11×)
• Cost/correct answer: $7.81 vs $76.23

License

MIT · graphifymd.com