Strands Agent Chatbot with Amazon Bedrock AgentCore

An end-to-end reference architecture for building agentic workflows using
Strands Agents and Amazon Bedrock AgentCore.

This repository demonstrates how to design and deploy a multi-agent chatbot that combines tool execution, memory, browser automation, and agent-to-agent collaboration. It is intended as a realistic, extensible sample for teams exploring advanced agent architectures on AWS.

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Demo

https://github.com/user-attachments/assets/11b383c2-2e14-4135-833f-b0b2bce62953

Full demo (6 min)

Mobile App

Coding Agent

Financial Analysis

Browser Automation

Web Search

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What this repository demonstrates

• Multi-agent orchestration with Strands Agents
• Integration with Amazon Bedrock AgentCore
• Tool-enabled agents (search, finance, weather, browser, code interpreter)
• Autonomous browser, documentation, and analysis workflows
• Modular architecture adaptable to real customer use cases
• Infrastructure-as-Code (Terraform) for repeatable deployment

If you are building agentic AI applications on AWS and want a concrete, end-to-end example, this repository is designed to be read, run, and extended.

Quick links:
Architecture · Key Features · Quick Start

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Architecture Overview

This sample combines Strands Agent orchestration with Amazon Bedrock AgentCore services:

Component	Role
Strands Agents	Multi-turn reasoning and tool orchestration
AgentCore Runtime	Managed, containerized agent execution
AgentCore Memory	Persistent conversation state and summarization
AgentCore Gateway	MCP-based tool integration with JWT authentication
AgentCore Code Interpreter	Secure Python execution for analysis and document generation
AgentCore Browser	Headless browser automation with live view
AgentCore Identity	End-user authentication and 3LO OAuth delegation
AgentCore Registry	Central catalog for agent skills, MCP servers, and A2A agents
AgentCore Observability	Trace collection and agent execution monitoring
Amazon Nova Act	Visual reasoning model for browser automation

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Key Features

• Strands-based agent orchestration
• Amazon Bedrock AgentCore Runtime, Gateway, and Memory
• MCP Gateway tools (Google, Wikipedia, ArXiv, Yahoo Finance, Tavily, Open-Meteo)
• Agent-to-Agent (A2A) collaboration — including remote Claude Agent SDK (Claude Code) for agentic coding tasks
• Built-in Code Interpreter for charts and documents
• Multimodal input and output (vision, charts, documents, screenshots)
• Real-time voice interaction with Amazon Nova Sonic 2

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Skill System (Progressive Disclosure)

Tools are organized into skills — grouped units with SKILL.md instructions that the agent loads on demand. Instead of injecting all tool documentation into every prompt, the agent activates only the skills it needs:

1. L1 Catalog — Skill names and one-line descriptions (always in system prompt)
2. L2 Instructions — Full SKILL.md loaded via skill_dispatcher when activated
3. L3 Execution — Tool calls via skill_executor

This keeps prompt size small while giving the agent access to detailed instructions when needed.

Design notes:

• https://medium.com/towards-artificial-intelligence/agent-skills-part-2-bridging-skills-with-production-tool-ecosystems-422e4a63fcad

skills/
├── visual-design/          # Charts, posters, infographics (Code Interpreter)
├── code-interpreter/       # General code execution
├── browser-automation/     # Nova Act browser tools
├── word-documents/         # Word document generation
├── excel-spreadsheets/     # Excel spreadsheet generation
├── powerpoint-presentations/  # PowerPoint generation
├── gmail/                  # Gmail read/search/delete (3LO OAuth)
├── google-calendar/        # Calendar events (3LO OAuth)
├── notion/                 # Notion pages and databases (3LO OAuth)
├── github/                 # GitHub repos, issues, PRs, code (3LO OAuth)
└── ...                     # 20+ skills (web search, finance, maps, weather, and more)

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Claude Desktop 3P (Cowork) Integration

Claude Desktop in 3P mode can connect directly to the AgentCore Gateway as an MCP connector, giving Cowork access to the same 23 Gateway tools (web search, arXiv, finance, weather, maps, Wikipedia, etc.) used by the chatbot agents.

Authentication uses Cognito user identity (authorization_code + PKCE flow) with automatic token refresh, so per-user identity propagates to Lambda tools.

cd cowork
./setup.sh    # Cognito login + configure managedMcpServers
# Restart Cowork

See cowork/README.md for setup modes, token lifecycle, and known limitations.

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Multi-Protocol Tool Architecture

Tool Category	Protocol	Examples	Authentication
Local Tools	Direct Python	Web Search, URL Fetcher, Visualization	None
Built-in Tools	AWS SDK / WebSocket	Code Interpreter, Browser (Nova Act)	IAM
Gateway Tools	MCP	Google Search, Maps, Wikipedia, ArXiv, Finance	SigV4
Private API Tools	MCP (3LO OAuth)	Gmail, Google Calendar, Notion, GitHub	OAuth 2.0
A2A Tools	A2A	Research Agent, Browser-Use Agent	JWT

Total: 100+ tools across 20 tool groups See docs/guides/TOOLS.md for full details.

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Voice Mode

Real-time voice interaction using Amazon Nova Sonic 2, Strands BidiAgent, and AgentCore Runtime WebSocket.

• Seamless switching between voice and text within a single session
• Shared conversation history across both modes
• Full tool execution support during voice conversations

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Memory Architecture and Long-Context Management

The system uses AgentCore Memory with:

• Short-term session memory
• Long-term summarized memory

Long conversations are compacted using a context summarization strategy to retain key information while controlling token growth.

Design notes:

• https://medium.com/@revoir07/long-context-compaction-for-ai-agents-part-1-design-principles-2bf4a5748154

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Token Optimization via Prompt Caching

Prompt caching reduces input token usage by reusing system prompts, stable instruction blocks, and repeated conversation context across agent loop iterations.

This project originally implemented caching via custom Strands hooks. The approach has since been upstreamed into the Strands SDK as a built-in feature (strands-agents/sdk-python#1438):

from strands.models import BedrockModel, CacheConfig

model = BedrockModel(
    model_id="us.anthropic.claude-sonnet-4-6-v1",
    cache_config=CacheConfig(strategy="auto")
)

Design notes:

• https://medium.com/@revoir07/agent-loop-caching-the-missing-optimization-for-agent-workflows-230cc530eb72

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Multi-Agent Architecture

Agent-to-Agent communication is handled via the A2A protocol, allowing the supervisor agent to delegate tasks to specialized worker agents such as a deep research agent. Multiple agents collaborating in sequence — each handling its own role — are coordinated using the Swarm pattern.

This architecture also includes a Claude Agent SDK (Claude Code) instance deployed as a remote A2A agent on AgentCore Runtime. The supervisor delegates agentic coding tasks — multi-file implementation, refactoring, test suites — to this agent over A2A, with an S3-backed workspace that persists files across sessions.

Design notes:

• https://medium.com/@revoir07/extend-your-chatbot-with-deep-research-using-a2a-ba4de3ed23e9

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Use Cases

• Financial research agents
• Technical research assistants using multi-agent patterns
• Autonomous web automation agents
• Memory-backed conversational assistants
• Hybrid research workflows using MCP, A2A, and AWS SDK tools

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Quick Start

Prerequisites

• AWS account with Bedrock access
• AWS CLI configured
• Docker installed
• Node.js 18+ and Python 3.13+

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Local Development

git clone https://github.com/aws-samples/sample-strands-agent-with-agentcore.git
cd sample-strands-agent-with-agentcore

cd chatbot-app
./setup.sh

cd ../agent-blueprint
cp .env.example .env
# configure credentials

cd ../chatbot-app
./start.sh

Frontend will be available at http://localhost:3000.

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Cloud Deployment

# Configure
cp infra/environments/dev/terraform.tfvars.example infra/environments/dev/terraform.tfvars
# Edit terraform.tfvars with your settings

# Deploy all
./infra/scripts/deploy.sh apply

# Deploy specific modules
./infra/scripts/deploy.sh apply -target=module.chat
./infra/scripts/deploy.sh apply -target=module.runtime_orchestrator

See DEPLOYMENT.md for full instructions.

Project Structure

sample-strands-agent-chatbot/
├── chatbot-app/
│   ├── frontend/          # Next.js UI + BFF
│   └── agentcore/         # Python backend (Strands Agent)
├── cowork/                # Claude Desktop 3P ↔ AgentCore Gateway connector
├── infra/                 # Terraform infrastructure
│   ├── modules/           # Reusable modules (auth, runtime, gateway, chat, ...)
│   ├── environments/dev/  # Environment configuration
│   └── scripts/           # Deploy orchestrator
└── agent-blueprint/       # Legacy CDK (deprecated)

Documentation

• DEPLOYMENT.md
• docs/guides/TROUBLESHOOTING.md

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License

MIT License. See LICENSE for details.