MiniCrit identifies reasoning flaws in AI outputs before they become costly failures. Enterprise-grade validation with sub-50ms latency.
95% confidence unsupported by evidence. 3-day momentum has minimal predictive value. No consideration of earnings, macro exposure, or sector rotation.
AI systems generate confident outputs with hidden flaws—overconfidence, missing risks, logical errors. Traditional testing catches bugs. It doesn't catch bad reasoning.
Systems express certainty without evidence, making high-conviction calls that ignore uncertainty and edge cases.
"99% confident BTC hits $100k tomorrow"
Pattern matching without causation. Models mistake coincidence for insight, leading to unreliable decisions.
"Stock rises when CEO tweets → buy signal"
Critical considerations absent from analysis. Threats compound silently until catastrophic failure.
"Buy recommendation (ignoring earnings next week)"
MiniCrit acts as a specialized "devil's advocate" that challenges AI reasoning before actions are taken. Four steps. One line of code.
Hook into your AI pipeline via API, MCP, or direct integration
Decompose signal into claims, evidence, and logical structure
Apply adversarial critique across biases and fallacies
Return structured critique with severity and recommendations
Comprehensive coverage of cognitive biases and logical fallacies that lead to flawed AI decisions.
Certainty unsupported by evidence
Overweighting recent data
Ignoring contradicting evidence
Over-relying on initial information
Mistaking coincidence for causation
Gaps in logical chains
Conclusions in premises
Artificial binary choices
MiniCrit integrates with your existing stack via Python SDK, MCP protocol, Docker, or REST API.
from minicrit import MiniCrit
# Initialize the critic
critic = MiniCrit()
# Validate any AI-generated reasoning
result = critic.validate(
"Based on momentum, NVDA will rise 15% this quarter."
)
print(result.severity) # "high"
print(result.critique) # Detailed analysis
print(result.flags) # ["overconfidence", "recency_bias"]# Install from PyPI
pip install minicrit
# Validate from command line
minicrit validate "The market will crash tomorrow based on today's news."
# Or pipe from file
cat signals.txt | minicrit validate --format json// claude_desktop_config.json
{
"mcpServers": {
"minicrit": {
"command": "python",
"args": ["-m", "minicrit.mcp"],
"env": {
"MINICRIT_MODEL": "minicrit-7b"
}
}
}
}# Pull and run with GPU support
docker run --gpus all -p 8000:8000 ghcr.io/antagoninc/minicrit:7b
# Validate via HTTP
curl -X POST http://localhost:8000/validate \
-H "Content-Type: application/json" \
-d '{"text": "AI will replace all jobs by 2025"}'Anywhere autonomous AI makes decisions that matter, MiniCrit provides a critical safety layer.
Validate AI trading signals before execution. Catch overconfident predictions and incomplete analysis.
Add adversarial review to diagnostic recommendations. Flag reasoning gaps before patient decisions.
Critical review layer for autonomous threat assessment and decision support systems.
Validate AI-generated legal analysis for logical consistency and completeness.
Real-time validation of perception and planning decisions in safety-critical scenarios.
Add a reasoning checkpoint before any AI agent takes irreversible actions.
MiniCrit is open source under Apache 2.0. Deploy locally, integrate via MCP, or scale with our enterprise offering.