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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.
/box
An agent sandbox that pairs OS containment with Dogwood policy.
/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.
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Guided lessons from your first agent to multi-agent systems.
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Docs
/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
AI coding agents
Migrate
Choosing an agent foundation
Migrate from LangGraph
Migrate from OpenAI
Migrate from strands-agents-tools
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
Approve before your agent acts
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
Overview
Search Strategies
Snapshots
Prompts
Hooks
Hook events
Middleware
Context management
Overview
Built-in Modes
Custom Strategies
Strategy Presets
Context Estimation
Retry strategies
Structured output
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
Vended Tools
Overview
Edit
File editor
Read
Write
HTTP request
Notebook
Bash / shell
MCP Router
Sleep
Handoff to User
Stop
Web fetch
A2A Client
Python REPL
Subagent
Swarm
Graph
Memory
Test Store
File 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
Quickstart
BidiAgent
Models
Overview
Bedrock Nova Sonic
Google Gemini Live
OpenAI Realtime
OpenAI Live
Tools
I/O
Input Content
Stream Events
Hooks
Session Management
Interrupts
Observability
Conversation management
Experimental
AgentConfig
Reference
Versioning & Support
API reference
API reference
/box
An agent sandbox that pairs OS containment with Dogwood policy.
Overview
Getting started with Box
Guides
Write a policy and read what it decided
Write policy for shell commands
Egress credential configuration
MCP server configuration
Write policy for an MCP server
Telemetry
Run a Strands Agents SDK agent in a box
Run Claude Code in a box
Run Codex CLI in a box
Run pi in a box
Collect the Strands CLI's traces and metrics
Run Box on Amazon EKS
Run a Box shipment agent on AgentCore Runtime
Security
Security: what Box governs, and what you own
Experimental native filesystem policy
Reference
box.toml configuration reference
Policy
Policy action reference
Strands Shell reference
Python in a box
Cloud deployment profiles
/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
Structured output similarity
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
Writing a custom task
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
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Python
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