Alibi

Open Source

Explainability and Fairness Updated Feb 15, 2026

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Compared with 13 other Explainability and Fairness agents Open source Self-hostable Updated Feb 2026

💰 Open Source 🔄 Updated Feb 2026 🖥️ Self-hostable

💰 Pricing

Open Source

Pricing not publicly listed.

🎯 Use Cases

Explaining predictions of black-box models Detecting biases in ML models Auditing model fairness Debugging model behavior Enhancing regulatory compliance for AI systems

⚖️ Pros & Cons

✅ Pros

Specialized in black-box model interpretability
Open-source and community-supported
Integrates with popular ML frameworks

❌ Cons

Requires Python expertise to use effectively
Limited to specific explanation methods
May lack some enterprise-grade features

Overview

Alibi is an open-source Python library designed for interpreting and explaining machine learning models, with a focus on black-box model explanations. It provides tools for model inspection, fairness evaluation, and instance-based explanations to enhance transparency.

Problem It Solves

It helps users understand how machine learning models make predictions and assess potential biases, enabling better trust and accountability in AI systems.

Target Audience: Developers and teams working with explainability and fairness automation.

Inputs

• User configuration
• API credentials (if required)
• Task parameters

Outputs

• Automated task results
• Status reports
• Generated content or actions

Example Workflow

1 User configures the agent with required parameters
2 Agent receives input data or trigger
3 Agent processes the request using its core logic
4 Agent interacts with external services if needed
5 Results are returned to the user

Sample System Prompt


              You are Alibi, an AI assistant. Help the user accomplish their task efficiently.

Tools & Technologies

LLM APIs Python

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FAQs

Is this agent open-source?: Yes
Can this agent be self-hosted?: Yes
What skill level is required?: Intermediate

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