---
type: entity
subtype: tool
title: "Uvicorn"
description: "The ASGI server used to host the FastAPI app, running on 0.0.0.0:8000."
resource: "https://www.uvicorn.org/"
tags: [asgi-server, api, deployment]
videos: [the-glow-up-every-ai-agent-needs, complete-guide-to-langserve-make-your-ai-agent-actually-useful]
created: 2026-07-21
updated: 2026-07-21
---

# Uvicorn

Uvicorn is the ASGI server Cole uses to actually run his Python agent APIs. When he wraps an agent as a web service, Uvicorn is the process that binds the app to a port and serves requests: "the last line of code here is just using uvicorn to run this app and I'm going to just have it hosted on 00000000 with Port 8000" ([0:05:44](../../sources/complete-guide-to-langserve-make-your-ai-agent-actually-useful.md)). Binding to `0.0.0.0` makes the endpoint reachable on all network interfaces, which is exactly what you want once the service leaves localhost for a container or cloud host. He describes the same role again while upgrading an agent: "we're using this python package called Uvicorn which is going to serve our endpoint on our Local Host" ([0:16:40](../../sources/the-glow-up-every-ai-agent-needs.md)).

Uvicorn is the runtime layer beneath [FastAPI](./fastapi.md): FastAPI defines the routes and the [request/response schema](../../concepts/request-response-schema.md), and Uvicorn is the ASGI server that runs that app and handles the actual connections. This pairing is how Cole turns an agent into an [agent-as-an-API-endpoint](../../concepts/agent-as-api-endpoint.md), the step that makes an agent [frontend-agnostic](../../concepts/frontend-agnostic-agent.md) so any client (a [Streamlit](./streamlit.md) UI, a mobile app, another service) can call it over HTTP. It is a small, invisible piece, but it is the one that makes the endpoint real.

Because it sits at the boundary between code and network, Uvicorn is also part of Cole's [agent deployment](../../concepts/agent-deployment.md) story: the container built with [Docker](./docker.md) typically starts Uvicorn as its entrypoint, serving the FastAPI app on port 8000 so a platform like [Render](./render.md) can route traffic to it.

## Realizes

- [Agent as an API Endpoint](../../concepts/agent-as-api-endpoint.md) - Wrapping an AI agent in a web API (typically FastAPI) turns it from a terminal-bound script into a deployable, scalable service any frontend, SaaS, or client can call remotely.
- [Frontend-Agnostic Agents](../../concepts/frontend-agnostic-agent.md) - Decouple the agent backend from any specific UI by emitting standardized events and subscribing to real-time messages (AG-UI, agentic experiences), so the same agent can power any frontend.

## Works with

- [FastAPI](./fastapi.md) - Python web framework used to serve the swapped-in Python agent endpoint that streams AG-UI events.
- [LangServe](./langserve.md) - 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.
- [Docker](./docker.md) - Containerization prerequisite; a single docker compose command spins up Archon's four containers (agents, MCP server, UI, API server).
- [Render](./render.md) - The cloud deployment platform (used on its free tier) that auto-builds and deploys staging on push and production on commit to main.
- [Streamlit](./streamlit.md) - Used to build the travel-planner's chat UI, where users set preferences and watch the streamed synthesizer output.

## Related

- [Agent Request/Response Schema](../../concepts/request-response-schema.md) - Standardizing how any agent plugs in by fixing a strict input payload (query, user_id, request_id, session_id) and a minimal output (a success boolean), so agents become interchangeable behind a common contract.
- [Agent Deployment](../../concepts/agent-deployment.md) - Getting agents to production, containerizing, networking, reverse-proxying, and choosing a provider-agnostic, cost-predictable host.
- [Memory Systems](../../concepts/memory-systems.md) - Giving agents durable short- and long-term memory so conversations, user context, and facts persist across sessions and users.
- [Context Engineering](../../concepts/context-engineering.md) - The deliberate practice of curating exactly what goes into an LLM's limited context window - system messages, schemas, chat history, and dynamic variables - to avoid overload and keep outputs sharp.

## Sources

- [COMPLETE Guide to LangServe - Make Your AI Agent Actually Useful](../../sources/complete-guide-to-langserve-make-your-ai-agent-actually-useful.md) - "[0:05:44] the last line of code here is just using uvicorn to run this app and I'm going to just have it hosted on 00000000 with Port 8000"
- [The GLOW-UP Every AI Agent Needs](../../sources/the-glow-up-every-ai-agent-needs.md) - "[0:16:40] we're using this python package called Uvicorn which is going to serve our endpoint on our Local Host using Port 801"
