GitHub Copilot

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GitHub Copilot

GitHub Copilot is an AI-powered development assistant built by GitHub in partnership with OpenAI and Microsoft. Originally launched in technical preview in June 2021 and reaching general availability in June 2022, Copilot has expanded from single-line code completions into a full agentic platform that can autonomously edit multiple files, run terminal commands, and open pull requests with minimal human direction. It is now embedded across GitHub.com, major IDEs, the CLI, GitHub Mobile, and Windows Terminal.

General
GA date	21 Jun 2022
Developer	GitHub (Microsoft)
Type	AI Coding Assistant
License	Commercial SaaS
Documentation	docs.github.com/en/copilot

Core Features

Inline code completions — context-aware autocomplete in supported editors; includes next-edit predictions in VS Code, Xcode, and Eclipse.
Copilot Chat — conversational interface for code explanation, refactoring, debugging, and Q&A; available in IDEs, GitHub.com, GitHub Mobile, and Windows Terminal.
Agent Mode (IDEs) — autonomous in-IDE operation that edits multiple files, runs terminal commands, self-corrects errors, and iterates until a task is complete.
Cloud Coding Agent — an async agent assigned via a GitHub issue that researches the repo, writes an implementation plan, and opens a pull request.
Copilot Code Review — AI-generated pull request review suggestions surfaced inline on the PR diff.
PR Summaries — auto-generated descriptions of pull request changes for reviewers.
Copilot CLI — natural-language terminal assistance (GA April 2026).
MCP Server Integration — any Model Context Protocol server works as a Copilot extension, replacing the deprecated Copilot Extensions API.
Multi-model selection — choose from OpenAI models, Anthropic Claude (including Opus), Google Gemini, and others on Pro+ and above plans.
Copilot Spaces — centralizes repository context (code, docs, specs) to improve response quality.

Supported Editors and Surfaces

Surface	Notes
Visual Studio Code	Full feature support including Agent Mode
JetBrains IDEs	IntelliJ, PyCharm, WebStorm, GoLand, and others
Visual Studio	Windows-native IDE support
Xcode	Includes next-edit predictions
Eclipse	Agent Mode GA July 2025
Neovim	Plugin-based integration
Zed	Native integration
GitHub.com	Chat, code review, PR summaries, cloud agent
GitHub Mobile	Chat on iOS and Android
GitHub CLI / terminal	Copilot CLI for natural-language shell commands
Windows Terminal Canary	Chat integration

Pricing Tiers

Plan	Price	Key Inclusions
Free	$0/month	2,000 completions/month, auto model selection, Copilot CLI
Pro	$10/user/month	Unlimited completions, multiple model access, cloud agent, $10 AI credits/month
Pro+	$39/user/month	Premium models (Claude Opus, etc.), higher AI credit allowance
Max	$100/user/month	Highest individual AI credit allowance, priority access to new models
Business	$19/seat/month	Team management, policy controls, monthly AI credit pool
Enterprise	$39/seat/month	All Business features, larger credit pool, priority model access, GitHub Enterprise Cloud required

Free plans also apply to verified students, educators, and qualifying open-source maintainers (Pro-level features).

Tools and Resources

Documentation — docs.github.com/en/copilot
Plans and pricing — github.com/features/copilot/plans
REST API for Copilot management — docs.github.com/en/rest/copilot — manage seats, billing, and usage at org/enterprise level.
MCP server catalog — any MCP-compatible server can extend Copilot's context and tools.

Ecosystem and Integrations

Integrated natively into GitHub.com, enabling AI assistance directly in pull requests, issues, and discussions without leaving the browser.
Works with GitHub Actions for AI-assisted CI pipeline troubleshooting and workflow generation.
Enterprise deployments support Bring Your Own Keys (BYOK), allowing organizations to route Copilot through their own LLM provider API keys.
Available as a standalone subscription or bundled with GitHub Enterprise Cloud.

Get started at github.com/features/copilot or explore the full reference at docs.github.com/en/copilot.

Edit on GitHub

Github Github Copilot AI technology Hackathon projects

Discover innovative solutions crafted with Github Github Copilot AI technology, developed by our community members during our engaging hackathons.

Memory Recall Assistant

Memory Recall Assistant is a fully on-device, multimodal AI memory aid designed for people with early-to-mid stage cognitive decline. The app runs continuously in the background, periodically capturing context through the camera and microphone. When the user presses Recall while looking at someone, the app identifies the person using CavaFace face embeddings (w8a16 quantized, ~99.5% accuracy on LFW benchmark), retrieves stored conversation history transcribed by Whisper-Small-Quantized, and generates a warm, natural memory cue using Llama 3.2 1B Instruct - all within seconds and entirely offline. A key design innovation is center crop targeting: rather than identifying the largest or highest-confidence face in frame, the app identifies the face closest to the center of the camera frame - the person the user is actively talking to, not a bystander. This makes identification intentional and natural, and extends directly to Meta AI Glasses where the camera points where the user looks. All sensitive data - face embeddings, conversation transcripts, location context - is stored on-device in a local SQLite knowledge graph and never transmitted anywhere. The system functions fully in airplane mode, demonstrating that privacy-sensitive AI for vulnerable populations is not only possible on mobile hardware - it is better this way. There is no server to breach, no API to call, and no company holding your family's data.

PrivateDoc

PrivateDoc is an offline, on-device document intelligence assistant designed for sensitive personal paperwork, running on a Samsung S25 Ultra phone powered by Qualcomm SnapDragon. It combines ML Kit OCR, ExecuTorch, MiniLM semantic retrieval, and an on-device Llama 3.2 1B LLM to scan, classify, index, and answer questions about prescriptions, contracts, bills, IDs, and other documents without requiring an Internet connection. A strict retrieval-augmented generation (RAG) pipeline ensures every response is grounded in retrieved OCR evidence, with supporting text highlighted on the original document to improve transparency and reduce hallucinations. By performing OCR, retrieval, and language generation entirely on the device, PrivateDoc preserves user privacy while delivering fast, reliable, and source-backed document understanding.

Project Hermes

Hermes is an Android accessibility and translation assistant designed for real-time communication across text, camera, and voice workflows. The app is built in Kotlin with Android native UI components, CameraX for live camera preview, ML Kit Text Recognition for on-device OCR, Android SpeechRecognizer for fast speech-to-text, and ExecuTorch/QNN integration for experimenting with local AI inference using whisper and Qwen2.5-0.5B and Qwen3-7B and on Qualcomm-powered devices. Its camera mode lets a user point the phone at signs, notes, labels, forms, menus, or other printed text, recognize the text on-device, identify the source language, and translate it into English. Its voice mode supports quick speech transcription so spoken input can be converted into readable text during live interaction. The project is especially useful in real-world situations such as travel, hospitals, classrooms, public offices, accessibility support, and multilingual conversations where users need quick understanding without switching between multiple apps. Hermes also includes groundwork for local model execution using Whisper-style ASR assets, Qwen translation models, Android native libraries, and Qualcomm acceleration paths, making it a practical prototype for privacy-aware, low-latency mobile AI translation.

Beacon

When the river crests and the towers go dark, a hundred people end up stranded in a school gym with no signal and no way to call for help. A volunteer nurse faces a growing line of the sick and injured with no one to consult. A teacher manages sixty frightened kids alone. A family doesn't know if their water is safe to drink. Every one of them is holding a phone with a powerful on-device NPU, but cloud AI dies the instant the network does, and no single phone has the memory or compute to run a frontier-grade LLM by itself. Beacon is built around this constraint from the start: the model is pre-sharded before disaster strikes, not after. Users opt in ahead of time, downloading a layer-wise slice of a large language model's weights onto their device, a contiguous block of transformer layers sized to that phone's available memory and NPU class. These shards sit dormant on the device, costing nothing until they're needed. When the network goes down, phones nearby connect over a peer-to-peer hotspot network: one phone hosts, others join directly, with no router or internet infrastructure required. Beacon assembles an inference cluster from whichever pre-loaded layer shards happen to be present in the room, sequencing them in the correct layer order for a forward pass. The hotspot link only needs to negotiate which layers are available, route activations between phones in sequence, and reroute around a phone that drops out or runs out of battery. The heavy lifting, distribution, was done in advance, when everyone still had a connection. The result is a cluster that can assemble in seconds during an emergency, because the only real-time job is discovery and coordination, not download. The nurse gets triage guidance. The teacher gets crisis-management support. The family gets a real answer about their water. The help didn't arrive; it was already pre-positioned in their pockets, just waiting to be switched on.

SnapOn: On-Device Context-Aware Multimodal AI

SnapOn is an Android-based, offline-first multimodal AI assistant that understands what the user says and what the user sees. By combining speech, vision, and on-device reasoning, SnapOn provides fast, privacy-preserving assistance without any cloud dependency. Rather than a general-purpose chatbot, SnapOn is designed for real-world situations, identifying people and objects, summarizing documents, recognizing products and labels, and answering spoken questions about the current scene. The interaction is natural and hands-free. Hold the mic button, speak your question or say "remember this," and SnapOn captures the best camera frame, transcribes your voice using Whisper, and generates a grounded answer using SmolVLM-500M-Instruct running on the Snapdragon Hexagon NPU via ExecuTorch. What makes SnapOn unique is its personal memory layer. Say "remember this is my medication Metformin" and SnapOn saves a visual fingerprint using CLIP embeddings alongside your exact words. Next time you point the camera at the same object or person, SnapOn recognizes it passively and surfaces your saved context automatically, no button press needed. Use cases include identifying people and objects in view, summarizing documents and text in the scene, recognizing products, signs, and labels, answering spoken questions, and saving personal context for future reference. The stack includes SmolVLM-500M-Instruct, OpenAI CLIP ViT-B/32, Whisper-tiny, FAISS, SQLite, CameraX, AudioRecord, and Android TTS. On-device compilation targets SM8750 via ExecuTorch and Qualcomm QNN backend. Built for the ExecuTorch Hackathon with a strong emphasis on NPU utilization, real-world usability, and complete privacy.

Electric Safe

Electric Safe is a fully on-device Android application that guides industrial electricians through the Lockout/Tagout (LOTO) safety procedure using a Vision-Language Model (VLM) running on the Qualcomm Hexagon NPU via ExecuTorch and QNN. How it works — 5-screen state machine: Start — Dark industrial UI with a single "Start Session" button and a model selector (SmolVLM 500M / InternVL3 1B). Add Documents — Import equipment manuals via the SAF PDF picker or use the bundled PowerFlex 753 VFD manual. PDFs are processed on-device using PDFBox with regex-based fault code extraction. Live AI Session — Push-to-talk speech input (offline, EXTRA_PREFER_OFFLINE) and on-device TTS. Upload a photo of the VFD fault display; the VLM reads the fault code (e.g. F071 OC1) and matches it to the manual's meaning. A live NPU: X ms latency readout shows real inference time. Guided LOTO — Four camera-gated verification steps: Breaker B-201 OFF, Breaker B-205 OFF, Lock & Tag applied, MCC Cabinet open. Each step requires the VLM to verify the correct breaker identity and state (OFF/ON) with a minimum 0.70 confidence threshold. Identity mismatches trigger a red "WORK BLOCKED" card with expected vs detected values. Permit — Generates a timestamped LOTO evidence PDF on-device using android.graphics.pdf.PdfDocument, embeds captured photos, and allows sharing via FileProvider. Architecture: Single-activity MVVM with state-machine navigation (no NavController). The CaptureDetectionSource interface abstracts detection — mock mode (clickable demo) and real VLM inference drive the exact same code path. Model files are side-loaded via adb push to external storage (industrial MDM pattern), falling back to bundled assets. Privacy: No INTERNET permission declared. Camera capture, VLM inference, PDF generation, speech recognition, and TTS all run locally on the Snapdragon Galaxy S25 Ultra. Nothing leaves the device.