LangChain vs Strands: Agent Frameworks Comparison

LangChain is the most widely adopted framework for building LLM-powered applications and AI agents, founded in 2022 by Harrison Chase. The company provides an open-source Python and TypeScript framework with abstractions for chains, agents, tools, memory, and retrieval that make it easy to compose complex AI systems.

LangGraph, its agent orchestration layer, enables building stateful, multi-actor workflows with human-in-the-loop capabilities. LangSmith provides tracing, evaluation, and monitoring for LLM applications in production. The LangChain ecosystem is the largest in the AI application development space, with the company reaching $16M in revenue and 1,000 customers by 2025.

Backed by $260M in total funding at a $1.25B valuation, LangChain has grown to 199 employees and is headquartered in San Francisco. The company serves as the de facto orchestration layer for teams building production AI applications.

Strands Agents is an open-source AI agent SDK developed by AWS that takes a model-driven approach to building and running AI agents in just a few lines of code. Launched as a preview in May 2025, Strands reached version 1.0 in July 2025, bringing production-ready multi-agent orchestration capabilities. The framework uses the reasoning abilities of modern LLMs to handle planning and tool usage autonomously, eliminating the need for hardcoding complex task flows.

Strands is actively used in production by multiple AWS teams, including Kiro, Amazon Q Developer, and AWS Glue. The SDK supports multiple AI providers including Amazon Bedrock, Anthropic, Gemini, LiteLLM, Llama, Ollama, OpenAI, and Writer, making it truly provider-agnostic. Strands 1.0 introduced new primitives for multi-agent architectures, support for the Agent-to-Agent (A2A) protocol, a session manager for retrieving agent state from remote datastores, and improved async support throughout the SDK.

The framework offers comprehensive features including multi-modal support (text, speech, and image processing), rich AWS service integrations, extensibility for custom tools, and robust observability capabilities. With natural language workflow definitions through Agent SOPs and integration with Model Context Protocol (MCP), Strands provides a modern, scalable approach to building production-grade AI agents.