Agent tools and infrastructure

Agents need more than a model and a prompt. They need a safe place to run code, a way to log in to other apps on a user's behalf, standard connections to tools and data, and somewhere to keep what they learned last time. The tools in this category supply those pieces so a team building agents does not have to.

Sandboxes give an agent its own short-lived computer, usually a small virtual machine or container, where it can run generated code, install packages and work with files. The point is isolation: if the agent runs something wrong or harmful, the damage stays inside the sandbox. Check how quickly sandboxes start, how long they can live, what network access they get, and whether you can run them in your own cloud account.

Tool and auth layers handle the work of connecting an agent to email, calendars, CRMs and code hosts for many users at once. They store each user's login, refresh tokens and expose actions as tools the agent can call. Ask how they scope permissions per user and per action, and how a user can see and revoke what an agent can do for them.

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8 AI agent tools in agent tools and infrastructure

E2B

Cloud sandboxes where agents can run generated code and use a file system without touching your servers.

  • Open source
  • Developer API

Mem0

Memory layer that stores facts from past conversations so agents can recall user preferences later.

  • Open source
  • Developer API

Model Context Protocol

by Model Context Protocol project

Open protocol for connecting AI apps to tools and data through small servers that any compatible client can use.

  • Open source
  • Works with MCP

Daytona

Secure sandboxes for running AI-generated code, with SDKs to start, control and snapshot each environment.

  • Open source
  • Developer API

Composio

Library of ready tool integrations for agents, with managed login for each user's apps and MCP support.

  • Developer API
  • Works with MCP

Arcade

Tool-calling platform that lets agents act in apps such as email and calendars on behalf of a signed-in user.

  • Developer API

Smithery

Registry for finding, installing and hosting MCP servers that give agents access to outside tools and data.

  • Works with MCP

Zep

Memory and context service that builds a knowledge graph from chats and business data for agents to query.

  • Developer API

The Model Context Protocol has become a common way to package tools so many different agents can use them. Registries and hosts for MCP servers make them easier to find and run. A server runs with the access you give it, so review its source or publisher and give it the narrowest credentials that work.

Memory services store facts from past conversations and look them up later, so an agent can remember a user's preferences or the history of a project. Decide what should be remembered, how users can see or delete it, and how long it is kept, because stored memory is personal data.

These pieces are often the first place agent costs and risks show up, so measure them early. Sandbox minutes, tool calls and memory lookups each add to the cost of a run. Logs from the tool layer show which actions agents actually take on users' behalf, and they are the record you will want if something goes wrong. Choose services that let you export your data and configuration, because infrastructure for agents is changing quickly and you may want to swap one piece without rebuilding the rest. Where you can, prefer standards such as OAuth and MCP over custom integrations, since they make those swaps easier.

Questions about agent tools and infrastructure

Why does an agent need a sandbox?

When an agent writes and runs code, a sandbox keeps that code away from your own servers and data. If the code is wrong or the agent is misled, the effects stay inside a short-lived, isolated environment.

What is an MCP server?

It is a small program that exposes tools, data or prompts through the Model Context Protocol, so any compatible AI app or agent can use them. One server can serve many different clients.

How do agents log in to other apps for a user?

Usually through OAuth, the same sign-in flow used when you connect one app to another. Tool and auth platforms store and refresh those tokens and let the agent act within the permissions the user granted.

Is agent memory a privacy concern?

Yes. Stored memories can include personal details and business information. Set clear rules on what is kept, let users view and delete their memories, and check where the memory service stores data.

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