LangChain
The most widely-adopted framework for building LLM-powered applications and agents
LangChain is an open-source orchestration framework that provides composable primitives for connecting LLMs with tools, memory, and external data sources. It abstracts the plumbing of agent loops, prompt management, and tool calling behind a consistent interface, available in both Python and TypeScript.
Teams building production agents that need tool use, RAG pipelines, or multi-step reasoning chains with broad ecosystem support and many pre-built integrations.
Engineers comfortable with Python or TypeScript. Works for solo founders prototyping fast or larger teams building production systems. Free to use; costs scale with your LLM provider.
Agent Architecture Fit
LangChain sits at the orchestration layer of your agent blueprint. It handles the agent loop — deciding which tool to call, parsing responses, managing conversation state, and retrying failures. Think of it as the conductor: it doesn't play the instruments (models, tools, memory stores) but coordinates when and how they fire. Most blueprints that involve multi-step reasoning or tool use will have LangChain (or an equivalent) at their centre.
when your agent's primary job is querying and reasoning over structured documents or large knowledge bases
when you need explicit graph-based control flow for stateful, multi-actor agent workflows
when you're building multi-agent systems with defined role hierarchies and inter-agent delegation
Next step
Your agent starts with a blueprint.
A blueprint tells you which tools to use, where they fit, and how they connect — before you write a line of code.
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