Bedrock AgentCore SDK
Deploy your local AI agent to Bedrock AgentCore with zero infrastructure
Documentation ◆ Samples ◆ Discord ◆ Boto3 Python SDK ◆ Runtime Python SDK ◆ Starter Toolkit
Overview
Amazon Bedrock AgentCore enables you to deploy and operate highly effective agents securely, at scale using any framework and model. With Amazon Bedrock AgentCore, developers can accelerate AI agents into production with the scale, reliability, and security, critical to real-world deployment. AgentCore provides tools and capabilities to make agents more effective and capable, purpose-built infrastructure to securely scale agents, and controls to operate trustworthy agents. Amazon Bedrock AgentCore services are composable and work with popular open-source frameworks and any model, so you don’t have to choose between open-source flexibility and enterprise-grade security and reliability.
🚀 From Local Development to Bedrock AgentCore
# Your existing agent (any framework)
from strands import Agent
# or LangGraph, CrewAI, Autogen, custom logic - doesn't matter
def my_local_agent(query):
# Your carefully crafted agent logic
return agent.process(query)
# Deploy to Bedrock AgentCore
from bedrock_agentcore import BedrockAgentCoreApp
app = BedrockAgentCoreApp()
@app.entrypoint
def production_agent(request):
return my_local_agent(request.get("prompt")) # Same logic, enterprise platform
app.run() # Ready to run on Bedrock AgentCore
What you get with Bedrock AgentCore:
- ✅ Keep your agent logic - Works with Strands, LangGraph, CrewAI, Autogen, custom frameworks
- ✅ Zero infrastructure management - No servers, containers, or scaling concerns
- ✅ Enterprise-grade platform - Built-in auth, memory, observability, security
- ✅ Production-ready deployment - Reliable, scalable, compliant hosting
Amazon Bedrock AgentCore services
- 🚀 Runtime - Secure and session isolated compute: Runtime Quick Start
- 🧠 Memory - Persistent knowledge across sessions: Memory Quick Start
- 🔗 Gateway - Transform APIs into MCP tools: Gateway Quick Start
- 💻 Code Interpreter - Secure sandboxed execution: Code Interpreter Quick Start
- 🌐 Browser - Cloud-based web automation: Browser Quick Start
- 📊 Observability - OpenTelemetry tracing: Observability Quick Start
- 🔐 Identity - AWS & third-party auth: Identity Quick Start
🏗️ Deployment
Quick Start: Use the Bedrock AgentCore Starter Toolkit for rapid prototyping.
Production: AWS CDK.
📝 License & Contributing
- License: Apache 2.0 - see LICENSE.txt
- Contributing: See CONTRIBUTING.md
- Security: Report vulnerabilities via SECURITY.md
2 maintainers
Metadata
2 maintainers
Release files for bedrock-agentcore 1.4.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| bedrock_agentcore-1.4.2.tar.gz | 435.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bedrock_agentcore-1.4.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 561.5 kB
Release files / bedrock_agentcore-1.4.2.tar.gz
| Download URL | bedrock_agentcore-1.4.2.tar.gz |
|---|---|
| Size | 435.9 kB |
| Tags | Source |
|
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| Upload date | |
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No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Release files / bedrock_agentcore-1.4.2-py3-none-any.whl
| Download URL | bedrock_agentcore-1.4.2-py3-none-any.whl |
|---|---|
| Size | 125.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
