LangChain v1

LangChain is an open-source framework and agent engineering platform for building agents and applications powered by large language models. It chains interoperable components and third-party integrations, providing standard interfaces for models, embeddings, vector stores, retrievers, tools, and other data sources. It supports sequential agentic workflows, context and tool-call orchestration, model substitution, and application development primarily through Python; the project also provides a separate JavaScript/TypeScript library. LangChain can be used standalone or with related tools for agent orchestration, evaluation, observability, debugging, and deployment.

LangChain4j is a Java framework for building applications that connect language models with tools, memory, retrievers, query transformers, routers, context injectors, and content aggregators.

LangFlow is an open-source, low-code visual builder for creating and running language-model pipelines, agent workflows, and retrieval-augmented generation (RAG) applications. The project is developed and maintained by the open-source community led by the GitHub user logaretm and integrates with language-model providers and tools such as LangChain.

LangSmith is an AI-agent observability and evaluation platform from LangChain. It captures agent traces, including tool calls, sub-agent calls, and retrieved context, so teams can inspect agent runs, debug behavior, and use traces as evidence in feedback and memory workflows. The LangChain site describes step-by-step tracing, trace-based dataset creation, evaluation of agent steps, complex-trace querying, online evaluations, dashboards, and alerts for production agent runs.

Strands Agents is an open-source, model-agnostic AI agent SDK and toolkit maintained by AWS for Python and TypeScript. It runs the agent loop in the user's process without a hosted control plane and uses a model-driven architecture based on system prompts, tools, and a selected model. Its Strands harness provides a preconfigured agent with tools, a one-line agent loop, context management, sessions, and memory, while the Harness SDK lets developers assemble agents from their own models and tools. It supports providers including Amazon Bedrock, Anthropic, OpenAI, Gemini, and others; MCP connections; structured output; lifecycle controls; multi-agent patterns including delegation, swarms, graphs, handoffs, and workflows; conversation managers for sliding-window, summarizing, and combined histories; short-term, long-term, and graph memory; memory pointers that keep large tool outputs outside the active context; agent state for sharing pointers across multi-agent swarms; tool-call limits; asynchronous handles for slow MCP tools; streaming; guardrails; tracing; evaluations; hooks; telemetry; context management; and deployment support. Extensions include a command-line interface, sandboxed and virtual shells, evaluation utilities, research labs, samples, reusable agent instructions, lower-level SDKs for custom agent loops, and Strands Box, an open-source sandbox.