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Let AI use tools and context wherever they are
just as you use the internet.

AI needs the right tools and context to get work done. Yours are spread across devices and platforms. EasyNet indexes the resources you connect, so agents can discover them and use them with permission.

Choose a resource to connectSee the scenarios

Setup guides available · Guided function integration through Technical Alpha

Why EasyNet

Your AI should know what is already there.

A useful script on your laptop. A model on the lab GPU. Project context in another session. Each is valuable, but a new agent only sees the small part connected to its current environment.

Recorded local function result

Use the pricing rule. Don’t ask AI to recreate it.

When an assistant prepares a quote, it needs your pricing rules—not a guess. Publish the existing function so an authorized caller can send the plan and seat count, then use the returned amount.

See the call and source

Recorded request

Seats
48
Plan
team
Billing period
annual

Returned quote

Total
1,909,440 cents
Discount
336,960 cents

Sample pricing · recorded local CLI call, 4 Sep 2026. Not an AI-generated quote or a cross-device run. The record does not specify a currency.

People can find and use resources across devices and platforms.

AI should be able to do the same.

EasyNet organizes connected capabilities around a common address, owner, and access policy. The resource keeps its own execution environment.

Select a resource, then switch agents to see how access changes.

Your existing resources

EasyNet Index

model.embed_text

A function beside a loaded model. Inputs and released results cross the call boundary.

Owner
research-team
Access
Functions · Builder: Allowed for this agent
Read the connection guide

Your agents

Interactive illustration. Entries and access decisions are examples, not live network status.

Start with a real task

What could your next agent do?

A connected tool should return something useful. Explore source examples you can adapt to your own task.

Prepare text for a retrieval workflow

A research agent needs vectors for document retrieval. Connect your existing embedding function; document indexing and search remain separate steps.

Input
A passage of text
Result
A vector

Public source exampleDeterministic 8-value stub. No model or GPU is used.

See example and setup

Get selected fields, not the whole record

An agent needs a summary for a known record. Send its ID to the provider’s function and return only the fields that interface allows.

Input
A known record ID
Result
Short summary and allowed labels

Public source exampleTwo synthetic records, exact-name replacement and a 160-character limit. Not general anonymization.

See example and setup

Request a few images, not an endless stream

An agent needs a bounded visual sample. Request a frame count through one operation, which finishes after returning those frames.

Input
A frame count
Result
A finite sequence of images

Public source exampleGenerates 1–10 grayscale frames. No camera driver or device pairing.

See example and setup

What is your AI missing?

Start with one tool, one source of context, or one device. Find its connection path and see what you can set up today.

Choose what to connectTalk through your scenario

Runtime, MCP, and Context guides are available. Guided function integration with a second caller is part of Technical Alpha.