langchain-tensorfeed 0.1.0

LangChain integration for TensorFeed.ai: AI news, service status, model pricing, and benchmarks for AI agents and RAG pipelines.

langchain-tensorfeed

LangChain integration for TensorFeed.ai, the AI news and real-time data hub built for humans and AI agents.

This package gives LangChain agents zero-friction access to:

• The latest AI news from 12+ industry sources
• Live up/down status for Claude, ChatGPT, Gemini, Copilot, Perplexity, Mistral, HuggingFace, and more
• Current per-token pricing for AI models across every major provider
• Public benchmark scores (MMLU, HumanEval, GPQA, MATH, SWE-Bench, etc.)
• A Document loader so you can index TensorFeed news into a vector store for RAG

All endpoints used by this package are free and require no API key.

Installation

pip install langchain-tensorfeed

Quick start: tools

from langchain_tensorfeed import (
    TensorFeedNewsTool,
    TensorFeedStatusTool,
    TensorFeedPricingTool,
    TensorFeedBenchmarksTool,
)

news = TensorFeedNewsTool().invoke({"category": "anthropic", "limit": 5})
print(news)

status = TensorFeedStatusTool().invoke({"service": "claude"})
print(status)

pricing = TensorFeedPricingTool().invoke({"provider": "openai"})
print(pricing)

scores = TensorFeedBenchmarksTool().invoke({"benchmark": "MMLU"})
print(scores)

Each tool returns a JSON string sized for token efficiency. The schemas are validated by Pydantic, so an LLM can call them directly through the standard tool-calling interface.

Quick start: document loader

from langchain_tensorfeed import TensorFeedLoader

loader = TensorFeedLoader(
    category="research",
    limit=100,
    start_date="2026-04-01T00:00:00Z",
)
docs = loader.load()

for d in docs[:3]:
    print(d.metadata["title"], "->", d.metadata["url"])

TensorFeedLoader returns standard langchain_core.documents.Document objects with page_content set to title + snippet and metadata carrying id, url, source, categories, published_at, and fetched_at. You can plug it into any LangChain text splitter or vector store.

Using the tools with an agent

from langchain_anthropic import ChatAnthropic
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain_tensorfeed import (
    TensorFeedBenchmarksTool,
    TensorFeedNewsTool,
    TensorFeedPricingTool,
    TensorFeedStatusTool,
)

llm = ChatAnthropic(model="claude-opus-4-7")

tools = [
    TensorFeedNewsTool(),
    TensorFeedStatusTool(),
    TensorFeedPricingTool(),
    TensorFeedBenchmarksTool(),
]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an AI industry analyst. Use the TensorFeed tools to ground every answer in current data."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

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

result = executor.invoke({
    "input": "Is Claude up right now, and how does its pricing compare to GPT-4o?"
})
print(result["output"])

Tool reference

Tool	Description	Input schema
TensorFeedNewsTool	Latest AI news headlines	category (optional), limit (1-50, default 10)
TensorFeedStatusTool	Live AI service status	service (optional name)
TensorFeedPricingTool	Per-token pricing	provider, model (both optional substring filters)
TensorFeedBenchmarksTool	Public benchmark scores	benchmark, model (both optional substring filters)

TensorFeedLoader options

Argument	Type	Description
category	str	API-side category filter
categories	Sequence[str]	Client-side multi-category filter
limit	int	Max articles to fetch (default 50)
start_date	`str	datetime`
end_date	`str	datetime`
base_url	str	Override the API host
timeout	float	HTTP timeout in seconds

Premium endpoints

The TensorFeed API also exposes paid endpoints (model routing recommendations, news search, model comparisons, forecasts, webhook watches) that are billed in USDC on Base. Those are out of scope for this package; if you need them in LangChain, the standalone tensorfeed Python SDK covers the full surface and you can wrap any endpoint as a custom BaseTool.

Contributing

Source: github.com/RipperMercs/tensorfeed under sdk/langchain-python/.

cd sdk/langchain-python
pip install -e .[dev]
pytest

Links

• TensorFeed developer docs: https://tensorfeed.ai/developers
• API reference: https://tensorfeed.ai/api/meta
• Issue tracker: https://github.com/RipperMercs/tensorfeed/issues
• License: MIT