**Introducing Open Bias: A Reliable AI Agent Reliability Layer**

In today's world of artificial intelligence (AI) and machine learning (ML), ensuring the reliability and trustworthiness of AI agents is crucial. With the rise of Large Language Models (LLMs) and their increasing use in various applications, it has become imperative to monitor and regulate their behavior to prevent potential data breaches or harm to users. Enter Open Bias, a transparent proxy that monitors LLM API calls and enforces policies on AI agent behavior.

**What is Open Bias?**

Open Bias is an open-source reliability layer for AI agents that defines rules, monitors responses, and intervenes automatically when necessary. It acts as a bridge between your application (App) and the LLM provider, ensuring that every response from the LLM is evaluated before it reaches the user.

**How Does Open Bias Work?**

Open Bias uses four policy engines: Judge, FSM, LLM, and NeMo Guardrails. Each engine has its unique mechanism for enforcing policies and detecting potential issues:

* **Judge Engine**: This is the default engine that scores each response against built-in or custom rubrics (tone, safety, instruction following) using a sidecar LLM. * **FSM Engine**: A state machine with LTL-lite temporal constraints for more complex rules and scenarios. * **LLM Engine**: Uses an LLM to classify states and detect drift in behavior. * **NeMo Guardrails Engine**: Wraps NVIDIA NeMo Guardrails for content safety, dialog rails, and topical control.

**Benefits of Using Open Bias**

The benefits of using Open Bias include:

* Ensures the reliability and trustworthiness of AI agents * Prevents potential data breaches or harm to users * Easy to use and configure with YAML-first configuration * Supports multiple policy engines for different scenarios * Integrates well with existing LLM providers

**Getting Started with Open Bias**

To get started with Open Bias, you can follow these steps:

1. **Install Open Bias**: Install the openbias package using pip. 2. **Configure Open Bias**: Define your policies in YAML and configure Open Bias to use a specific policy engine. 3. **Integrate with Your App**: Point your LLM client at the Open Bias proxy and define rules for each response.

**Future Development**

The development of Open Bias is ongoing, with several features planned or in progress:

* Persistent session storage * Dashboard UI * Pre-built policy library * Rate limiting

By using Open Bias, you can ensure that your AI agents are reliable, trustworthy, and compliant with your policies.