AI CONTEXT EXPORT

AI Context Export: Give Any AI Tool Your Complete Architecture

Copilot sees one file. ChatGPT guesses dependencies. Export your codebase structure and give every AI tool complete architectural awareness.

The Blind Spot Every AI Coding Tool Shares

You ask an AI to refactor a function. It rewrites it beautifully. But it had no idea that function is called by 23 other modules. Now you have 23 broken imports and a deployment that fails in production.

AI coding tools are remarkably capable at the file level. But they operate in isolation. They cannot see the web of relationships that defines your actual architecture. They hallucinate dependencies, miss callers, and suggest changes that ripple through your system in ways they cannot predict.

Raw code dumps do not solve this problem. Pasting thousands of lines into a context window wastes tokens and buries the signal in noise. What AI tools need is not more code. They need structure.

LOOM exports your codebase as structured context that AI tools can actually use.

[IMAGE: Split view. Left: AI suggesting a "simple" rename, unaware of consequences. Right: Dependency graph showing 23 callers that would break. The AI never saw this.]

Three Export Formats, One Complete Picture

LOOM exports your codebase structure in formats optimized for different workflows. Choose the right format for your use case.

JSON: Full Structured Graph

The complete representation of your codebase as a graph data structure. Nodes represent code elements: functions, classes, files, modules. Edges represent relationships: calls, imports, dependencies, inheritance.

{
  "nodes": [
    {
      "id": "processPayment",
      "type": "function",
      "file": "services/payment.ts",
      "params": ["amount", "cardToken"],
      "callerCount": 12,
      "complexity": 8
    }
  ],
  "edges": [...]
}

Machine-readable for programmatic consumption
Includes all metadata: complexity, caller counts, line spans
Perfect for custom AI pipelines and automation
Can be filtered by scope, module, or depth

Markdown: Human-Readable Summaries

Architecture documentation generated from your actual code structure. Module overviews, function profiles with callers and callees, dependency summaries. Formatted for direct use in chat interfaces.

## processPayment()
File: services/payment.ts:45-89

Callers (12):
- CheckoutController.submit()
- SubscriptionService.renew()
- RefundHandler.process()
...

Calls:
- validateCard()
- chargeStripe()
- logTransaction()

Paste directly into ChatGPT, Claude, or any chat AI
Includes architectural context, not just code
Generates module-level or function-level profiles
Documents what your code actually does

CSV: Tabular Data

Your codebase as structured tables. Function lists with metrics, dependency matrices, module relationships. Import into spreadsheets, databases, or data analysis tools.

function,file,callers,complexity,lines
processPayment,payment.ts,12,8,44
validateCard,payment.ts,3,4,22
chargeStripe,stripe.ts,2,6,38
logTransaction,logging.ts,15,2,12

Analyze codebase metrics in Excel or Google Sheets
Generate reports and visualizations
Feed into BI tools for codebase intelligence
Track metrics over time with version control

Works With Every AI Tool You Use

LOOM exports integrate into your existing AI-assisted development workflow. Here is how to use them with popular tools.

GitHub Copilot

Add architecture context to your prompt prefix. When you ask Copilot to modify code, include the relevant dependency profile in a comment block above your cursor. Copilot will consider callers and callees when generating suggestions.


                        // Context: processPayment has 12 callers

                        // including CheckoutController, SubscriptionService

                        // Modifying signature will break these imports

                        function processPayment(...

Result: Copilot suggests backward-compatible changes that preserve existing caller signatures.

ChatGPT / Claude

Paste the Markdown dependency profile before asking about code. Chat-based AI tools excel when given structured context upfront. Instead of dumping raw code, give them the architectural map first.


                        "Here is the dependency profile for processPayment:

                        [paste Markdown export]


                        Given these 12 callers, how should I safely

                        add a new required parameter?"

Result: The AI suggests a migration strategy that accounts for all existing callers.

Cursor

Feed project structure for context-aware completions. Cursor can consume structured context files. Export your module graph and include it in your project to give Cursor persistent architectural awareness across your entire session.


                        .cursor/

                          context/

                            architecture.json

                            dependencies.md

Result: Cursor completions consider cross-file relationships automatically.

Custom Integrations

Use the JSON export for automated AI pipelines. Build CI/CD workflows that feed architectural context to AI review tools. Create custom agents that understand your codebase structure. Integrate with any tool that accepts structured input.


                        loom export --format json --scope src/

                        | ai-review-agent --context-file -

                        | slack-notify #code-review

Result: Automated code review with full architectural awareness.

Structure Beats Size: The Token Efficiency Advantage

AI context windows are limited. Every token matters. LOOM exports deliver maximum signal with minimum noise.

Raw Code Dump

2.5M tokens

A 500-file codebase pasted as raw text. Exceeds most context windows. Buries important relationships in syntax noise. AI cannot distinguish entry points from utilities.

LOOM Structured Export

50K tokens

The same codebase as a structured graph. Fits easily in any modern context window. Relationships are explicit. AI understands architecture immediately.

What You Get in 50K Tokens

Every function and class
All caller/callee relationships
Import dependency chains
Complexity metrics
Module boundaries
Entry points identified
Dead code flagged
Circular deps marked

The point is not just fitting in the window. It is giving AI the right information to reason about your code correctly.

Before and After: The Same Question, Different Answers

See how architectural context transforms AI code assistance from dangerous guessing to informed recommendations.

Without LOOM Context

"Refactor processPayment to use async/await instead of callbacks"

AI Response:

"Here is the refactored function with async/await..."

[Changes function signature. Breaks 12 callers. Does not mention this.]

AI sees only the function body
No awareness of existing callers
Breaking changes suggested blindly
You discover the problem in production

With LOOM Context

"Refactor processPayment to use async/await. Context: 12 callers (see dependency profile below)"

AI Response:

"Given 12 callers, I recommend a migration strategy: 1) Create async version processPaymentAsync, 2) Deprecate original with wrapper, 3) Update callers incrementally..."

[Suggests safe migration. Lists affected callers. Preserves backward compatibility.]

AI sees complete dependency context
Knows exactly who calls this function
Suggests migration path, not breaking change
You deploy with confidence

The AI is not smarter. It just has the information it needs to give you a useful answer.

Start Exporting Context in Minutes

LOOM makes it simple to generate AI-ready context for any part of your codebase.

Scan Your Codebase

Point LOOM at your repository. The scanner extracts structure and relationships automatically.

Navigate to Target

Find the function, module, or scope you need context for. Use the registry search or 3D navigator.

Click Export

Choose your format: JSON for APIs, Markdown for chat, CSV for analysis. One click generates the output.

Feed Your AI

Paste into ChatGPT, add to Copilot context, or pipe to your custom pipeline. Watch the AI give better answers.

Export Scoping Options

You control exactly what context gets exported:

Single Function

Just this function and its immediate relationships

Module Scope

Everything in a file or directory

Full Graph

Complete codebase structure

Start Exporting Context Today

Give your AI tools the architectural awareness they have been missing. Better context means better code suggestions, fewer hallucinations, and deployments that actually work.

[IMAGE: LOOM export interface showing one-click generation of JSON, Markdown, and CSV outputs. Preview panes showing each format. Copy-to-clipboard buttons ready.]

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