What are LangChain agents?

LangChain agents are AI agent implementations built using the LangChain framework — combining LangChain's tool-calling abstractions, memory management, and chain composition to create agents that can reason, invoke tools, and complete multi-step tasks using the framework's standardized interfaces.

How LangChain implements agent behavior

In LangChain, an agent combines a language model, a set of tool definitions, a prompt template that includes tool descriptions and the reasoning format, and an executor loop that processes model outputs and runs tool calls. The framework provides standard tool interfaces so that tools built for one agent can be reused in others, and standardized output parsers that extract tool call instructions from model completions. LangChain supports several reasoning patterns including ReAct (reason and act), which interleaves reasoning steps and tool calls in a single context, and OpenAI function-calling format, which uses the model's native tool-use capability.

Built-in tools and toolkits

LangChain ships with a library of pre-built tools and toolkits covering web search, database queries, code execution, file system access, and integrations with common external services. Toolkits group related tools — for example, a SQL database toolkit includes tools for listing tables, describing schemas, and executing queries. Pre-built tools reduce the development work for common agent capabilities, though they may not match all production requirements and often need to be replaced with custom implementations for specific system integrations.

When to use LangChain agents vs. alternatives

LangChain agents are well-suited to prototyping and to agents that can be built from the framework's existing tool library without significant customization. The framework's abstractions add overhead that becomes a constraint for agents requiring fine-grained control over the execution loop, custom retry logic, or tight latency requirements. For complex multi-agent coordination with state management across long-running workflows, LangGraph — which builds on LangChain with a graph-based execution model — provides better abstractions. Simple agents that require only a few tool calls and no complex state management often do not need a framework at all.

What are LangChain agents? — FAQ

What is the difference between a LangChain chain and a LangChain agent?

A chain in LangChain is a predefined sequence of steps — each step feeds into the next in a fixed order. An agent uses the language model to decide at each step what action to take, meaning the execution path is not determined at design time. Chains are appropriate for workflows with a known structure; agents are appropriate when the sequence of steps depends on the model's reasoning and the results of intermediate actions.

Are LangChain agents suitable for production deployment?

LangChain agents are used in production, but with caveats. The framework's abstractions can make debugging difficult because failures occur inside framework code rather than your own. Production use typically requires adding observability tooling to inspect what the agent is doing, careful exception handling to catch framework-level errors, and sometimes replacing framework components with custom implementations where the defaults do not meet production requirements.