HugeContext

The Problem

AI coding assistants are only as good as the context they receive. But existing context engines have critical flaws:

• Bloated infrastructure: Solutions like Qdrant require running separate services
• Poor retrieval quality: Generic embedding models miss code-specific semantics
• No intent awareness: They don’t understand why you’re asking
• Cloud dependencies: Sending your code to external services isn’t always an option

My Solution

HugeContext is a local context engine that runs entirely within your development environment:

Intent-Aware Retrieval

Instead of just matching keywords or embeddings, HugeContext understands the type of query:

• LOCATE queries: “Find the auth handler” → Precise symbol location
• EXPLORE queries: “How does authentication work?” → Comprehensive coverage with related files

Intelligent Snippet Generation

Actual relevant code snippets with complete context:

• Preserves function boundaries
• Includes necessary imports and dependencies
• Provides caller/callee relationships

MCP-Native

Built for the Model Context Protocol from the ground up:

• Works with Claude, Gemini, and other MCP-compatible assistants
• Seamless tool calling integration
• Real-time index updates as you code

Architecture

┌─────────────────────────────────────────────────────────────┐
│                     HugeContext Engine                       │
├──────────────┬──────────────┬──────────────┬───────────────┤
│  Intent      │   Code       │   Semantic   │   Git         │
│  Classifier  │   Graph      │   Index      │   History     │
├──────────────┼──────────────┼──────────────┼───────────────┤
│ Query type   │ Symbol       │ Embeddings   │ Recent        │
│ detection    │ resolution   │ & ranking    │ changes       │
└──────────────┴──────────────┴──────────────┴───────────────┘
               │               │              │
               ▼               ▼              ▼
┌─────────────────────────────────────────────────────────────┐
│            MCP Interface / VS Code Extension                 │
└─────────────────────────────────────────────────────────────┘

Performance

Benchmarked against Kilo Code + Qdrant on real-world codebases:

Metric	HugeContext	Kilo Code + Qdrant
Index Size	1x	17x
Retrieval Quality	95-97%	90-92%
Query Latency	Under 100ms	200-500ms
Setup Required	Zero	Docker + Config

Why I Built This

As someone who builds AI applications professionally, I was frustrated with the state of context engines. The commercial options were expensive and cloud-dependent. The open-source options required complex infrastructure.

I wanted something that:

• Just works out of the box
• Runs locally for security and speed
• Actually delivers better results than the alternatives

So I built it.

Current Status

Live — Actively used in my own development workflow and being adopted by early users. Continuous improvements based on real-world usage.

Open Source

HugeContext is open source. Check out the code, open issues, or contribute:

• GitHub Repository
• VS Code Marketplace (coming soon)

Get Started

# Install the VS Code extension
code --install-extension hugecontext

# Or use via MCP
# Configure in your MCP settings