Features
Any model from any provider
Lifecycle hooks (BeforeToolCallEvent, etc.)
Custom tool definitions with Zod (TS) or decorators (Python)
Multi-agent orchestration
Strands Shell sandboxed execution
Strands Evals integration
Context management reduces costs by 50%
Chaos testing for resilience
MLLM-as-a-Judge evaluators (image-to-text)
Structured outputs with type safety
Steering hooks for deterministic guardrails
MCP server for coding agents (Claude, Cursor, etc.)
Observability without config
@ai_fn decorator for LLM-powered Python functions
@ai_classifier decorator for text classification
Structured data extraction via Pydantic models
Agent loops with tool calling and function calling
Built-in streaming (SSE) support
Async-first API for concurrent applications
Rate limiting and automatic retries
LLM response caching for performance
Concurrency control for workload management
CLI tool for monitoring and debugging
SQLite state store for persistence
OpenAI and Anthropic model support
Embeddings generation for semantic search
Local execution with no cloud dependency
Self-hosted deployable as a library
