bedrock-agent provides a modular Python-based framework to build, configure, and deploy AI agents leveraging AWS Bedrock’s LLM models. It supports tool registration, conversational memory, dynamic tool selection, and chain-of-thought reasoning. With a built-in CLI and customizable workflows, developers can integrate external APIs, define task-specific tools, and deploy interactive agents locally or in cloud environments. It simplifies agent orchestration, ensuring scalable and extensible AI-driven workflows.
bedrock-agent provides a modular Python-based framework to build, configure, and deploy AI agents leveraging AWS Bedrock’s LLM models. It supports tool registration, conversational memory, dynamic tool selection, and chain-of-thought reasoning. With a built-in CLI and customizable workflows, developers can integrate external APIs, define task-specific tools, and deploy interactive agents locally or in cloud environments. It simplifies agent orchestration, ensuring scalable and extensible AI-driven workflows.
bedrock-agent is a versatile AI agent framework that integrates with AWS Bedrock’s suite of large language models to orchestrate complex, task-driven workflows. It offers a plugin architecture for registering custom tools, memory modules for context persistence, and a chain-of-thought mechanism for improved reasoning. Through a simple Python API and command-line interface, it enables developers to define agents that can call external services, process documents, generate code, or interact with users via chat. Agents can be configured to automatically select relevant tools based on user prompts and maintain conversational state across sessions. This framework is open-source, extensible, and optimized for rapid prototyping and deployment of AI-powered assistants on local or AWS cloud environments.
Who will use bedrock-agent?
Python developers
Machine learning engineers
AI researchers
DevOps teams
Enterprises building conversational interfaces
How to use the bedrock-agent?
Step1: pip install bedrock-agent
Step2: Configure AWS credentials via AWS CLI or environment variables
Step3: Import BedrockAgentClient and initialize with Bedrock model parameters
Step4: Register custom tools using tool decorators or classes
Step5: Define agent workflows and chain-of-thought settings
Step6: Launch the agent via CLI or Python script
Step7: Interact with the agent through chat or API endpoints