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README.md

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### 📊 Evaluation & Testing
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#### [**Braintrust Integration**](./examples/integrations/braintrust/)
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#### [**Braintrust Integration**](./examples/integrations/braintrust/README.md)
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Integrate Browserbase with Braintrust for evaluation and testing of AI agent performance in web environments. Monitor, measure, and improve your browser automation workflows.
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## 🏗️ Monorepo Structure
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# AgentKit
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## Using AgentKit with Browserbase
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Develop AI Agents that can browse the web autonomously using Browserbase and AgentKit (by inngest).
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## Overview
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By the end of this guide, you'll have an AI agent built with AgentKit that can browse the web using Browserbase.
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### You'll learn how to:
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- Create AgentKit tools that leverage Browserbase's managed headless browsers
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- Build autonomous web browsing agents that can search, extract data, and interact with websites
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- Use Stagehand, Browserbase's AI-powered browser automation library, to create resilient web agents
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### This integration is useful for:
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- Building AI agents that can fetch real-time information from the web
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- Creating autonomous browsing capabilities within your agents
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- Developing resilient web scraping and interaction systems
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- Enabling your agents to perform complex web-based tasks
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### Integration Approaches
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There are two approaches to using Browserbase with AgentKit:
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1. **Create custom Browserbase tools**: Ideal for simple actions on webpages with manual browser control
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2. **Use Stagehand library as tools**: Better for autonomous browsing and resilient web scraping
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### Prerequisites
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Before you start, make sure you have:
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- AgentKit installed
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- Browserbase Project ID & API key
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- (Optional) LLM API key of your choice to use with Stagehand
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Next, let's dive into building web browsing agents with AgentKit and Browserbase.
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# Braintrust Integration
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Utilize Browserbase with Braintrust for advanced browser automation.
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## Overview
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Braintrust is a platform for building AI applications, making it more efficient for Large Language Models (LLMs) to become more robust, reliable, and interactive.
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### Key Features
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- Prototyping with different prompts and LLMs in a sandboxed environment
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- Real-time monitoring and performance insights
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- Data management through intuitive UI
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## Tool Calling Support
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Most Large Language Models support tool calling, which allows you to define tools with well-defined input and output types. Common use cases include:
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1. Enabling models to "call" tools that perform external tasks and use those results to produce a final response
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2. Coercing models into producing structured outputs that match a given JSON schema
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Braintrust supports both use cases, allowing prompts to be executed directly using TypeScript or Python.
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### Custom Functions
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Custom functions enhance LLM capabilities by enabling:
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- Web-browsing capabilities (using tools like Browserbase)
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- Complex computations
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- External API access

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