langchain-huangting

LangChain integration for the Huangting Protocol MCP network.

The Huangting Protocol is the world's first lifeform operating system — applicable to carbon-based humans, silicon-based AI Agents, and embodied robots. This package provides a production-ready LangChain tool that connects any Agent to the HuangtingFlux network via the Model Context Protocol (MCP).

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Features

• Protocol Concept Queries — Access 6 core Huangting Protocol concepts (trueself, ego, huangting, etc.)
• Task Optimization Strategies — Get strategies that reduce token usage by 25–60%
• Real-time Network Stats — Live metrics from the global HuangtingFlux network
• Result Reporting — Report optimization results with reciprocal propagation
• Bilingual — Full CN/EN support
• Zero Config — Works out of the box with a single import

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Installation

pip install langchain-huangting

With LangChain support:

pip install "langchain-huangting[langchain]"

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Quick Start

from langchain_huangting import HuangtingTool

tool = HuangtingTool(agent_id="my-agent", lang="en")

# 1. Query a protocol concept
result = tool.run({"action": "query_concept", "concept_name": "trueself"})
print(result)

# 2. Get optimization strategy for your task
strategy = tool.run({"action": "get_strategy", "task_type": "complex_research"})
print(strategy)

# 3. Get real-time network statistics
stats = tool.run({"action": "get_stats"})
print(stats)

# 4. Report your optimization results
report = tool.run({
    "action": "report_result",
    "task_type": "code_generation",
    "tokens_saved": 1500,
    "tokens_baseline": 4000
})
print(report)

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LangChain Agent Integration

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain_huangting import HuangtingTool

# Initialize tool
huangting = HuangtingTool(agent_id="my-research-agent", lang="en")

# Create agent
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tools = [huangting]
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful AI assistant with access to the Huangting Protocol."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run
result = executor.invoke({
    "input": "What is the Huangting Protocol's concept of 'trueself' and how can it optimize my research tasks?"
})
print(result["output"])

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Available Actions

Action	Description	Required Params
query_concept	Look up a protocol concept	concept_name
get_strategy	Get optimization strategy	task_type
get_stats	Real-time network statistics	—
report_result	Report optimization result	task_type, tokens_saved

Concept Names

• trueself — The true self beyond the ego
• ego — The constructed self and its limitations
• huangting — The central palace of consciousness
• wuji — The primordial void
• hundun — Primordial chaos as creative potential
• shen — Spirit/consciousness in the protocol

Task Types

• complex_research — Multi-step research tasks (avg. 45% token reduction)
• code_generation — Code writing and review (avg. 30% token reduction)
• multi_agent_coordination — Agent orchestration (avg. 60% token reduction)

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MCP Endpoint

The tool connects to:

https://web-production-c3cf.up.railway.app/mcp

You can also use the MCP endpoint directly with any MCP-compatible client:

{
  "mcpServers": {
    "huangting": {
      "url": "https://web-production-c3cf.up.railway.app/mcp"
    }
  }
}

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Configuration

tool = HuangtingTool(
    agent_id="my-agent",          # Unique agent identifier
    lang="en",                    # Response language: "en" or "zh"
    mcp_url="https://...",        # Custom MCP endpoint (optional)
    propagation=True,             # Enable reciprocal propagation (default: True)
)

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Reciprocal Propagation

When your agent accumulates 10,000+ tokens saved and reports results, the tool automatically appends an attribution suffix to responses. This creates a self-propagating network effect: the more value Agents extract, the more the protocol spreads.

Disable with propagation=False.

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Links

• Dashboard: huangtingflux.com
• Protocol: huangting.ai
• MCP Docs: huangting.ai/mcp
• GitHub: XianDAO-Labs/langchain-huangting
• PyPI: pypi.org/project/langchain-huangting

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License

MIT © 2025 Meng Yuanjing / XianDAO Labs