---
type: entity
subtype: tool
title: "LangServe"
description: "LangChain's library that wraps any chain or LangGraph runnable in a FastAPI web server via a single add_routes call, plus a RemoteRunnable client."
resource: "https://python.langchain.com/docs/langserve/"
tags: [deployment, api, fastapi, langchain, serving]
videos: [complete-guide-to-langserve-make-your-ai-agent-actually-useful, the-best-tool-for-ai-agent-workflows-langgraph-full-guide, the-only-ai-developer-roadmap-you-need-in-2024]
created: 2026-07-21
updated: 2026-07-21
---

# LangServe

LangServe is the LangChain library that turns an agent from a local script into something the world can call: "deploy it to the cloud as an API endpoint using a tool called LangServe the tool that does all the magic of creating an API wrapper around my AI agent" [0:00:17]. It closes the last gap in the LangChain stack, the deployment step. In Cole's roadmap framing the division of labor is clean: LangChain lays the foundation, LangGraph builds the agentic workflow, "then you have LangServe which helps you deploy these applications" [0:10:59]. Its whole appeal is that it removes serving boilerplate, "LangServe makes it so easy to set up these endpoints" [0:04:31].

The mechanism is that any LangChain chain or compiled LangGraph runnable can be handed to LangServe, which wraps it in a FastAPI web server through a single `add_routes` call and exposes invoke, batch, and stream endpoints (plus a `RemoteRunnable` client for calling it back). Because it operates on the runnable abstraction, it is framework-generic within the ecosystem: "what I'm about to show you is going to work no matter what agent you have built with LangChain" [0:00:33], so "how you can deploy any AI agent built with LangChain super easily using LangServe" [0:16:41]. In the LangGraph guide Cole points at the same handoff from the other side, noting "you can even use LangServe which is another tool from LangChain to turn this runnable into an API endpoint" [0:18:08].

This makes LangServe the concrete realization of turning an agent into an [API endpoint](../../concepts/agent-deployment.md), the serving half of the graph-based workflow story: build the [graph](../../concepts/langgraph-nodes-edges-state.md), then serve it. It sits squarely inside the LangChain and LangGraph ecosystem rather than standing alone.

## Realizes

- [Agent Deployment](../../concepts/agent-deployment.md) - Getting agents to production, containerizing, networking, reverse-proxying, and choosing a provider-agnostic, cost-predictable host.
- [Agentic Workflow Engineering](../../concepts/agentic-workflow-engineering.md) - Designing repeatable AI workflows as engineered systems with defined steps, orchestration, and background execution rather than ad-hoc prompting.

## Works with

- [LangChain](./langchain.md) - The framework used in prior masterclass videos to build the Asana agent; its message objects and invoke/stream methods underpin the chatbot logic.
- [LangGraph](./langgraph.md) - The orchestration framework wiring agents into a stateful graph of nodes and edges, used here to run agents in parallel and add human-in-the-loop.
- [Graph-Based Agent Workflows](../../concepts/langgraph-nodes-edges-state.md) - Model an agent as a directed graph of nodes, edges, and shared state (LangGraph) with routing between steps, giving explicit control over branching, loops, and serving of complex agent workflows.
- [Agent Graphs](../../concepts/agent-graphs.md) - Modeling agents as stateful graphs of nodes and conditional edges with persisted checkpoints, as in LangGraph.

## Related

- [Tool Calling](../../concepts/tool-use.md) - Letting an LLM invoke external functions and APIs so it can take actions and fetch data beyond its parametric knowledge.
- [Model Selection](../../concepts/model-selection.md) - Choosing the right LLM per task by trading off quality, speed, and cost, often routing easy requests to cheap models and hard ones to expensive models.

## Sources

- [COMPLETE Guide to LangServe - Make Your AI Agent Actually Useful](../../sources/complete-guide-to-langserve-make-your-ai-agent-actually-useful.md) - "[0:00:17] deploy it to the cloud as an API endpoint using a tool called LangServe the tool that does all the magic of creating an API wrapper around my AI agent"
- [COMPLETE Guide to LangServe - Make Your AI Agent Actually Useful](../../sources/complete-guide-to-langserve-make-your-ai-agent-actually-useful.md) - "[0:00:33] what I'm about to show you is going to work no matter what agent you have built with LangChain"
- [THE BEST Tool for AI Agent Workflows - LangGraph FULL Guide](../../sources/the-best-tool-for-ai-agent-workflows-langgraph-full-guide.md) - "[0:18:08] you can even use LangServe which is another tool from LangChain to turn this runnable into an API endpoint which is something I'm going to be making content on later"
- [The ONLY AI Developer Roadmap You Need in 2024](../../sources/the-only-ai-developer-roadmap-you-need-in-2024.md) - "[0:10:59] then you have LangServe which helps you deploy these applications"
