What Can You Do With LibreChat's Agents, Code Interpreter, and Artifacts?

Updated

LibreChat's Agents, Code Interpreter, and Artifacts each handle a different part of AI-assisted work: Agents let a model take actions and use files, Code Interpreter runs code safely without local setup, and Artifacts turn chat output into runnable React/HTML code or Mermaid diagrams. They're aimed at people who want AI to do more than answer questions — building, automating, and visualizing inside one interface. All three are listed as core features on librechat.ai, so they're available to anyone running the platform.

Agents: AI that uses files, code, and APIs

Agents are the most capable building block. According to the site, they support:

  • File handling — attach and work with documents as part of a task
  • Code interpretation — run code as one of the agent's steps
  • API actions — let the agent call external services to complete work

The practical effect: instead of pasting text in and copying results out, you configure an agent that reads your file, runs a computation, and hits an API — then returns a combined result. This is the feature to reach for when a task has multiple steps or needs to touch something outside the chat.

Code Interpreter: run code with zero setup

Code Interpreter executes code in multiple languages securely, with no local environment to install. The site's phrasing — "zero setup" — is the key benefit: you don't configure runtimes or worry about sandboxing your own machine, because execution happens inside LibreChat.

Use it when you want to:

  • Test a snippet without spinning up a local interpreter
  • Process data as part of a conversation
  • Let an agent run code as one of its steps

The trade-off is the same as any hosted execution: you're relying on the platform's environment rather than your own, so anything requiring specific local libraries or hardware won't fit.

Artifacts: turn chat into working output

Artifacts create React, HTML code, and Mermaid diagrams directly in the chat. Rather than describing a component and leaving you to build it, the model produces the artifact itself.

This is most useful for:

  • Prototyping a UI component or small page
  • Generating a diagram (Mermaid) to explain a flow or architecture
  • Iterating on front-end code without leaving the conversation

Which one fits your task

You want to… Use
Automate a multi-step task with files and external APIs Agents
Run code safely without installing anything Code Interpreter
Generate React/HTML or a Mermaid diagram in chat Artifacts

These features overlap — an Agent can use Code Interpreter as a step — so the choice is usually about the outermost need: automation (Agents), execution (Code Interpreter), or generated output (Artifacts).

What else sits alongside them

The same feature set includes Models (Anthropic, AWS, OpenAI, Azure, and more), Search across messages, files, and code, MCP support for connecting tools and services, Memory for persistent context, Web Search with reranking, and Authentication via OAuth, SAML, LDAP, and two-factor auth. If your work spans several of these, that's the case for keeping everything in one interface rather than separate tools.

To try any of it, the site points to a Quickstart Guide for local or remote install, plus a live demo.