FactGuard Python SDK
Real-Time Web Intelligence & Multi-LLM Fact-Checking in 3 lines of Python.
⚡ Installation
pip install factguard-ai
🚀 Quickstart
from factguard import FactGuard
# Initialize FactGuard (connects to live cloud or your local server)
fg = FactGuard()
# 1. Fact-check any statement
result = fg.verify("Bananas are naturally radioactive due to potassium-40.")
print(f"Verdict: {result.verdict}") # 'True'
print(f"Confidence: {result.confidence}") # 0.95
print(f"Reasoning: {result.reasoning}") # Evidence analysis
print(f"Sources: {result.evidence_sources}") # ['Wikipedia', ...]
print(f"Citations: {result.evidence_urls}") # ['https://en.wikipedia.org/...']
🔄 Real-Time SSE Streaming
Stream incremental verification phases live into your terminal, CLI, or UI:
for event in fg.stream("The Eiffel Tower is in Berlin."):
phase = event.get("phase")
msg = event.get("message")
print(f"[{phase}] {msg}")
🔗 Create Shareable Permalinks
Turn any verification into a public permalink with dynamic OpenGraph social cards:
result = fg.verify("Water boils at 100 degrees Celsius.")
share_url = fg.share(result)
print(f"Shareable URL: {share_url}")
# Output: https://factguard-1gge.onrender.com/v/a8f2c3
🤖 LangChain & AI Agent Integration
Use FactGuard as an automated ground-truth tool inside LangChain agents:
from langchain.tools import tool
from factguard import FactGuard
fg = FactGuard()
@tool
def fact_check_statement(statement: str) -> str:
"""Verify factual claims using live DuckDuckGo and Wikipedia retrieval."""
result = fg.verify(statement)
return f"Verdict: {result.verdict} ({result.confidence*100:.0f}% confidence). Reasoning: {result.reasoning}"
⚙️ Configuration Options
# Connect to your local self-hosted FactGuard instance:
fg = FactGuard(api_url="http://localhost:8000")
# Run with local Ollama or custom model:
fg = FactGuard(
api_url="http://localhost:8000",
provider="ollama",
model="gemma4:e2b"
)
📜 License
MIT License • Created with ❤️ by Santosh Kashyap
Metadata
Release files for factguard-ai 2.0.0
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