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Agent Frameworks

AgentCore CLI supports multiple agent frameworks for template-based agent creation, plus a BYO (Bring Your Own) option for existing code.

Available Frameworks

Framework Supported Model Providers
Strands Agents Bedrock, Anthropic, OpenAI, Gemini
LangChain_LangGraph Bedrock, Anthropic, OpenAI, Gemini
GoogleADK Gemini only
OpenAIAgents OpenAI only

Framework Selection Guide

Strands Agents

AWS's native agent framework designed for Amazon Bedrock.

Best for:

  • Projects primarily using Amazon Bedrock models
  • Integration with AWS services
  • Production deployments on AWS infrastructure

Model providers: Bedrock, Anthropic, OpenAI, Gemini

agentcore create --framework Strands --model-provider Bedrock

LangChain / LangGraph

Popular open-source framework with extensive ecosystem.

Best for:

  • Complex multi-step agent workflows
  • Projects requiring LangChain's extensive tool ecosystem
  • Teams already familiar with LangChain

Model providers: Bedrock, Anthropic, OpenAI, Gemini

agentcore create --framework LangChain_LangGraph --model-provider Anthropic

GoogleADK

Google's Agent Development Kit.

Best for:

  • Projects using Google's Gemini models
  • Integration with Google Cloud services

Model providers: Gemini only

agentcore create --framework GoogleADK --model-provider Gemini

OpenAIAgents

OpenAI's native agent framework.

Best for:

  • Projects using OpenAI models exclusively
  • Simple agent workflows with OpenAI's function calling

Model providers: OpenAI only

agentcore create --framework OpenAIAgents --model-provider OpenAI --api-key sk-...

Import from Bedrock Agents

If you have an existing Bedrock Agent, you can import its configuration and translate it into runnable Strands or LangChain/LangGraph code. The imported agent preserves your Bedrock Agent's action groups, knowledge bases, multi-agent collaboration, guardrails, prompts, and memory configuration.

# Interactive (select "Import from Bedrock Agents" in the wizard)
agentcore add agent

# Non-interactive
agentcore add agent \
  --name MyAgent \
  --type import \
  --agent-id AGENT123 \
  --agent-alias-id ALIAS456 \
  --region us-east-1 \
  --framework Strands \
  --memory none

What gets imported

The import process fetches your Bedrock Agent's full configuration and translates it into framework-specific Python code that runs on AgentCore:

  • Action groups (function-schema and built-in) become @tool decorated functions
  • Knowledge bases become retrieval tool integrations
  • Multi-agent collaboration produces separate collaborator files with recursive translation
  • Code interpreter wires to AgentCore's code_interpreter_client
  • Guardrails are configured in the model initialization
  • Prompt overrides are preserved as template variables
  • Memory integrates with AgentCore's memory service when enabled

Import options

Flag Description
--type import Use import mode (required)
--agent-id <id> Bedrock Agent ID
--agent-alias-id <id> Bedrock Agent Alias ID
--region <region> AWS region where the Bedrock Agent exists
--framework <fw> Strands or LangChain_LangGraph
--memory <opt> none, shortTerm, longAndShortTerm

Supported Languages

Language Build Type Dev Server Frameworks
Python CodeZip uvicorn (local) Strands, LangChain/LangGraph, GoogleADK, OpenAI
TypeScript Container tsx watch (local) Strands

TypeScript Agents

TypeScript agents use the Strands Agents SDK with Express and deploy as Container images. AgentCore Runtime's CodeZip mode only supports Python runtimes, so TypeScript agents automatically default to Container build.

Local development runs without Docker. The agentcore dev command runs TypeScript agents directly with tsx watch for fast iteration and hot-reload — no container build needed. Docker/Podman/Finch is only required for agentcore deploy and agentcore package.

# Create a TypeScript agent
agentcore create --name MyProject --language TypeScript --framework Strands --model-provider Bedrock

# Local dev — runs with tsx, no Docker needed
agentcore dev

# Deploy — builds container image via CodeBuild
agentcore deploy

Bring Your Own (BYO) Agent

For existing agent code or frameworks not listed above, use the BYO option:

agentcore add agent \
  --name MyAgent \
  --type byo \
  --code-location ./my-agent \
  --entrypoint main.py \
  --language Python

BYO Requirements

  1. Entrypoint: Your code must expose an HTTP endpoint that accepts agent invocation requests
  2. Code location: Directory containing your agent code
  3. Language: Python or TypeScript

BYO Options

| Flag | Description |\n| ------------------------ | ------------------------------------------ | | --type byo | Use BYO mode (required) | | --code-location <path> | Directory containing your agent code | | --entrypoint <file> | Entry file (e.g., main.py or main.ts) | | --language <lang> | Python or TypeScript |

Framework Comparison

Feature Strands LangChain GoogleADK OpenAIAgents
Multi-provider support Yes Yes No No
AWS Bedrock native Yes No No No
Tool ecosystem Growing Extensive Moderate Moderate
Memory integration Native Via libs Via libs Via libs