Local-first · Agent-ready

From task to verified integration.

Your coding agent knows how to write code. AIPI helps it understand what connects—frontend, APIs, contracts and runtime evidence—then check whether the change works.

Explore AIPI See the workflow ▶

Works locally · Designed for IDE side panels · MCP-powered context

IDE / AIPI companion◉ Local

⌂◫⇄⚙

TASK / AUTH-42

Fix login validation

Discover the implementation path behind this task.

Connected integration pathContext ready

LoginPage→authClient→POST /login→LoginSchema

Relevant evidence1 finding

{ }

LoginSchema.tsContract & validation

Linked

↗

POST /api/loginBackend endpoint

Linked

401 UnauthorizedCaptured request evidence

Investigate

↗ Send context to agent✓ Verify

✦ Task-scoped context
Not the entire repository

✓ Evidence-first
Real requests · Contracts · Tests

Designed to complement your existing development workflow

CodexVS CodeCursorGitHubMCPTypeScript

The gap

Writing code is fast. Verifying integrations isn't.

Agentic development is changing how code gets written. But a change can span UI components, request clients, authentication, backend routes and third-party APIs. That context is scattered—and easy to miss.

◇

Find the right implementation path

Connect task intent with relevant frontend routes, API calls, backend handlers, schemas and tests.

⌁

Work from evidence, not guesses

Bring saved API requests, contract mismatches and captured failures into the same task context.

✓

Know what actually passed

Scope checks to the change. Keep executed, skipped and stale verification results distinct.

How it works

One continuous loop. No second IDE.

AIPI supports your coding agent instead of competing with it. The editor makes changes; AIPI supplies integration context and verification evidence.

Connect a local project

Index source relationships, API requests, schemas, and relevant documentation.

Start from a task or issue

Resolve the implementation path and known integration findings.

Hand context to the coding agent

Use MCP to retrieve only the relevant files, relationships, and evidence.

Verify the change

Replay relevant requests and run the configured contract and regression checks.

▣ Illustrative MCP task context

// Task: Fix login validation
get_task_context({
  task: "AUTH-42",
  include: ["routes", "contracts", "evidence"]
})

→ Relevant artifacts
  src/pages/LoginPage.tsx
  src/api/authClient.ts
  api/routes/auth.ts
  api/schema/LoginSchema.ts

→ Observed evidence
  POST /api/login: 401
  Request schema mismatch: candidate

→ Suggested checks
  Replay saved login request
  Validate request contract
  Run auth regression tests

Scoped context. Traceable evidence.

CLOUD UI · LOCAL ENGINE

Keep your project close. Start in one command.

AIPI's hosted dashboard is the UI shell. Your project data, API traffic, credentials, logs and MCP tools stay with the local companion.

Recommended setup

Run AIPI from your project root

Use the existing setup model to configure MCP, start or reuse the loopback companion, register the project and open its authenticated local dashboard.

npx @vmise/aipi-companion@latest init

Run this once per project. The companion keeps local API requests, environment references, captured evidence and task context available to your agents.

Read installation and setup guidance ↗

Initialize locally

Run the command from the repository root and let AIPI create or update the MCP configuration.

Open the local workspace

Connect source files, documentation, environments and API services from the project panel.

Pair only when you need cloud UI

After sign-in, use aipi pair for the hosted dashboard. Local development remains the default.

CLOUD UI for access and pairing · LOCAL ENGINE for project truth and execution

Product experience

A focused companion, not another dashboard.

Explore the core workflow. Select a step to preview what the AIPI IDE panel is designed to surface.

AIPI

● Local

Start with a task

Current project: commerce-app

01 · Task context

A task is the starting point.

Describe the work or link an issue. AIPI can build a compact map of the integration surfaces that matter.

✓ Current project and source revision
✓ Linked issue or pull request
✓ Relevant artifacts with provenance

Explore the approach ↗

Built for integration work

Your existing tools stay. The connections get smarter.

⌘

Local-first by design

Run the companion locally; keep cloud pairing and team features optional.

⇄

API testing & diagnosis

Saved requests, authentication, environments, captured traffic and regression evidence remain part of the workflow.

⤴

IDE-native agent access

MCP brings focused task context into AI editors without replacing their editing workflows.

Why AIPI is different

Not more context. More relevant context.

Approach	Typically centered on	AIPI focuses on
General coding assistant	Code exploration and generation	Task-specific integration evidence
API client	Executing and organizing requests	Linking executions to affected code
Issue tracker	Planning and coordinating work	Technical dependencies and proof
Test runner	Executing predefined checks	Selecting relevant checks for a task

Product model

Start locally. Scale when your team needs it.

The local companion is free. Team is available as a preview while GitHub and billing setup are finalized.

Developer

Free

The local-first foundation for individual development.

API request workspace
Local project and source context
MCP-ready workflows
Local evidence and testing

Get started →

Preview

Team

Team preview

Shared integration verification and coordination.

Shared verification history
Pull-request evidence
Team contract checks
Collaboration and governance

Open Team dashboard →

Enterprise

Let's talk

Deployment and policy options for larger teams.

Private deployment discussions
Retention and audit policies
Cross-repository governance
Enterprise support options

Team preview capabilities depend on account and GitHub App configuration. Pricing will be published before paid launch.

FAQ

Designed to fit the tools you already use.

Is AIPI another coding agent?

No. AIPI is designed to give existing coding agents task-specific context and access to API evidence and checks. Your IDE or coding agent remains in control of source changes.

Does AIPI require a cloud account?

The local companion is designed to run without cloud pairing. Remote collaboration and hosted workflows are optional capabilities.

Does it replace Postman or Jira?

No. AIPI provides API request functionality and connects technical evidence to task context. It is not positioned as a general project-management system.

How does AIPI know which files matter?

The envisioned task intelligence combines source scans, route and schema tracing, contract information and runtime evidence. Inferred relationships must be distinguished from observed ones.

Can AIPI guarantee that a code change works?

No tool can guarantee all behaviors. The goal is to run relevant configured checks and report exactly what passed, failed, was skipped or became stale, with supporting evidence.

Which features are already available?

AIPI's described existing core includes local API requests, source analysis, evidence capture and MCP tools. Task-specific context packs and broader team verification are product direction features whose release status should be checked against the current build.

Build with more confidence

Give your agent the missing integration context.

Explore AIPI's local-first workspace, follow development, and help shape task-aware verification for AI coding workflows.

View GitHub ↗Pilot interest →

Replace the contact email and repository URL if your official public links differ.