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/harness
Strands harness is a state-of-the-art, fully assembled agent harness.
/harness-sdk
The Strands Harness SDK for building an agent harness from the ground up.
/shell
A virtual shell designed for AI agents to use safely.
/evals
Validate your agent before you ship.
/labs
Experimental projects pushing the boundaries of what agents can do.
Learn
Courses
Guided lessons from your first agent to multi-agent systems.
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Runnable samples you can drop into your own project.
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Projects
/harness
Strands harness is a state-of-the-art, fully assembled agent harness.
Get started
Overview
Quickstart
Compose with the Strands Harness SDK
Configure the agent
Choose a model and reasoning
Add tools and instructions
Delegate to subagents
Load agent skills
Persist sessions
Give long-term memory
Checkpoints vs memory
Manage context and caching
Gate tool calls with interventions
Connect MCP servers
Run work in the background
Built-in tools
Shell and file tools
Web access
Programmatic tool calling
Task tracking and environment
Take Strands harness to production
Reference
Configuration reference
Versioning & Support
/harness-sdk
The Strands Harness SDK for building an agent harness from the ground up.
Get started
Overview
Quickstart
Quickstart
Migrate
Choosing an agent foundation
Migrate from OpenAI
Build guides
Tools
Overview
Attach and invoke tools
Use MCP tools
Create custom tools
Sessions
Persist state across sessions
Memory
Overview
Control what the agent remembers
Responses
Return structured output
Stream responses
Manage the context window
Pause for input and control
Coordinate multiple agents
Run guides
Deploy to production
Operating Agents in Production
Amazon Bedrock AgentCore
Overview
Python
TypeScript
AWS Lambda
AWS Fargate
AWS App Runner
Amazon EKS
Amazon EC2
Docker
Overview
Python
TypeScript
Kubernetes
Terraform
Nx Plugin for AWS
Observe your agent
Observability
Metrics
Traces
Logs
Secure for production
Responsible AI
Guardrails
Prompt Engineering
Trusted Message History
PII Redaction
Components
Agent loop
Production lifecycle controls
State
Storage
Snapshots
Prompts
Hooks
Hook events
Conversation management
Context management
Overview
Built-in Modes
Custom Strategies
Strategy Presets
Context Estimation
Retry strategies
Interrupts
Overview
Interrupts in Multi-Agent Systems
Models
Overview
Model Routing
Amazon Bedrock
Bedrock Prompt Caching
Amazon Nova
Anthropic
Google
LiteLLM
llama.cpp
LlamaAPI
MistralAI
Ollama
OpenAI
OpenAI Responses API
SageMaker
Vercel
Writer
Custom Providers
Tools
Tool result format
MCP Transports
Executors
Community Tools Package
Vended Tools
Memory
Test Store
Bedrock Knowledge Base
Plugins
Overview
Skills
Context Offloader
Context Injector
GoalLoop
Build a custom plugin
Interventions
Overview
Human in the loop
Cedar Authorization
Steering
Sandbox
Overview
Available Sandboxes
Building a Custom Sandbox
Multi-agent
Agent2Agent (A2A)
A2A server configuration
Agents as Tools
Swarm
Graph
Graph components
Workflow
Streaming
Async Iterators
Callback Handlers
Event types
Voice
Overview
BidiAgent
Session Management
Interrupts
Bedrock Nova Sonic
Google Gemini Live
OpenAI Realtime
IO
Events
Barge-in
Hooks
Observability
Experimental
AgentConfig
Reference
API reference
API reference
/shell
A virtual shell designed for AI agents to use safely.
Get started
Overview
Quickstart
How it works
Guides
Configure the sandbox
Run the MCP server
Inspect the configuration
Components
Commands
Shell language and builtins
Lua scripting
Security
Security model
Reference
Reference
/evals
Validate your agent before you ship.
Get started
Overview
Quickstart
How evaluation works
Evaluate with AI
Build guides
Generate test cases
Plan topic coverage
Run guides
Task decorator
Result caching
Experiment management
Serialization
Evaluating remote traces
AgentCore evaluations
Components
Evaluators
Overview
Quality
Output
Trajectory
Interactions
Helpfulness
Faithfulness
Correctness
Coherence
Conciseness
Response relevance
Safety
Harmfulness
Refusal
Stereotyping
Multimodal
Multimodal output
Multimodal overall quality
Multimodal correctness
Multimodal faithfulness
Multimodal instruction following
Agentic
Instruction following
Goal success rate
Failure communication
Partial completion
Recovery strategy
Tool selection accuracy
Tool parameter accuracy
Skill
Skill selection accuracy
Skill instruction following
Deterministic
Custom
Detectors
Overview
Failure detection
Root cause analysis
Session diagnosis
Simulators
Overview
User simulation
Customizing user simulation
Tool simulation
Red teaming
Overview
Quickstart
Attack strategies
Writing custom cases
Scoring attacks
Reading the report
Chaos testing
CLI
Overview
run
generate
diagnose & fetch
report & validate
/labs
Experimental projects pushing the boundaries of what agents can do.
Learn
Courses
Guided lessons from your first agent to multi-agent systems.
Examples
Runnable samples you can drop into your own project.
Integrations
Contribute
Blog
TS
TypeScript
Python
Search
Theme
GitHub
Discord
Search
Ctrl
K
Cancel
/harness-sdk
Get started
Overview
Quickstart
Quickstart
Migrate
Choosing an agent foundation
Migrate from OpenAI
Build guides
Tools
Overview
Attach and invoke tools
Use MCP tools
Create custom tools
Sessions
Persist state across sessions
Memory
Overview
Control what the agent remembers
Responses
Return structured output
Stream responses
Manage the context window
Pause for input and control
Coordinate multiple agents
Run guides
Deploy to production
Operating Agents in Production
Amazon Bedrock AgentCore
Overview
Python
TypeScript
AWS Lambda
AWS Fargate
AWS App Runner
Amazon EKS
Amazon EC2
Docker
Overview
Python
TypeScript
Kubernetes
Terraform
Nx Plugin for AWS
Observe your agent
Observability
Metrics
Traces
Logs
Secure for production
Responsible AI
Guardrails
Prompt Engineering
Trusted Message History
PII Redaction
Components
Agent loop
Production lifecycle controls
State
Storage
Snapshots
Prompts
Hooks
Hook events
Conversation management
Context management
Overview
Built-in Modes
New
Custom Strategies
New
Strategy Presets
New
Context Estimation
New
Retry strategies
Interrupts
Overview
Interrupts in Multi-Agent Systems
Models
Overview
Model Routing
Amazon Bedrock
Bedrock Prompt Caching
Amazon Nova
Anthropic
Google
LiteLLM
llama.cpp
LlamaAPI
MistralAI
Ollama
OpenAI
OpenAI Responses API
SageMaker
Vercel
Writer
Custom Providers
Tools
Tool result format
MCP Transports
Executors
Community Tools Package
Vended Tools
Memory
Test Store
Bedrock Knowledge Base
Plugins
Overview
Skills
Context Offloader
Context Injector
GoalLoop
Build a custom plugin
Interventions
Overview
Human in the loop
Cedar Authorization
Steering
Sandbox
Overview
Available Sandboxes
Building a Custom Sandbox
Multi-agent
Agent2Agent (A2A)
A2A server configuration
Agents as Tools
Swarm
Graph
Graph components
Workflow
Streaming
Async Iterators
Callback Handlers
Event types
Voice
Overview
BidiAgent
Session Management
Interrupts
Bedrock Nova Sonic
Google Gemini Live
OpenAI Realtime
IO
Events
Barge-in
Hooks
Observability
Experimental
AgentConfig
Experimental
Reference
API reference
API reference
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