The AI coding partner that doesn't trust itself.
| Cursor | Copilot | GhostCoder | |
|---|---|---|---|
| UI | Sidebar chat | Inline completion | No UI. Inline hints only |
| Trust | You trust the AI | You trust the AI | AI challenges itself |
| Audit | None | None | Full replay + explain |
| Safety | None | None | Guardrails block dangerous |
| Cost | $20/mo | $10/mo | Free, local, MIT |
| Hardware | Cloud | Cloud | GTX 1650 4GB |
Specialist agents watch your code and whisper fixes. No sidebar. No context switching.
When you run a command that fails, or write code with an obvious bug, GhostCoder classifies the error and routes it to the correct specialist agent (e.g., security engineer, database optimizer, frontend developer) to give you a dismissible inline hint.
Every decision is recorded, explainable, and replayable.
If you want to understand why GhostCoder suggested a particular change or see a step-by-step trace of how an error was handled, you can inspect it or run a full replay.
# Explain what happened in a specific session event
ghostcoder explain --session 20260716-001 --event 0
# Generate a project automation report (weekly/monthly)
ghostcoder report --period week
# Re-apply proposed fixes from a session
ghostcoder replay --session 20260716-001GhostCoder is the first coding assistant that validates its own output.
- Ghost Skeptic: Uses an adversarial classifier model to look for logic flaws and security issues (e.g. plaintext credentials) in every suggested fix. If a flaw is detected, it is either corrected or blocked.
- Safety Guardrail: Automatically screens proposed code blocks for database-destructive operations (
DROP DATABASE) or risky directory deletions (rm -rf) to keep your workspace safe.
GhostCoder is completely model-agnostic and works with any Large Language Model supported by your local Ollama instance (including Llama 3, DeepSeek, Mistral, Qwen, Codellama, etc.).
By default, GhostCoder automatically detects your local GPU resources and dynamically routes requests to the largest model that fits comfortably in your VRAM budget (up to 85% utilization).
| VRAM Capacity | Tier Profile | Classifier Model | Coder Model | Reasoner Model | Skeptic Model |
|---|---|---|---|---|---|
| < 6 GB | entry |
qwen2.5:0.5b |
qwen2.5-coder:1.5b |
qwen2.5-coder:1.5b |
qwen2.5:0.5b |
| 6 - 12 GB | mid |
qwen2.5:0.5b |
qwen2.5-coder:7b |
qwen2.5-coder:7b |
qwen2.5:0.5b |
| 12 - 20 GB | high |
qwen2.5:0.5b |
qwen2.5-coder:14b |
deepseek-coder:6.7b |
qwen2.5:3b |
| 20 - 48 GB | workstation |
qwen2.5:3b |
qwen2.5-coder:32b |
deepseek-coder:33b |
qwen2.5:7b |
| >= 48 GB | datacenter |
qwen2.5:7b |
qwen2.5-coder:72b |
deepseek-coder:33b |
qwen2.5:14b |
You can hot-swap any role to run any custom model pulled locally:
# Configure coder model to use Llama 3
ghostcoder config --model-coder llama3:8b
# Configure skeptic model to use Mistral
ghostcoder config --model-skeptic mistral:7bInstall Ollama and pull the models you want to use. For the default Entry configuration:
ollama pull qwen2.5:0.5b
ollama pull qwen2.5-coder:1.5bInstall the CLI and background daemon:
pip install ghostcoderConfigure your shell and start the daemon:
ghostcoder initThis automatically appends source lines to your shell configuration (.bashrc, .zshrc, or config.fish) and launches the background daemon.
Add the plugin folder to your runtimepath (e.g., using your package manager):
-- lazy.nvim configuration example
{
"anujsingh-cse/GhostCoder",
config = function()
-- Integration works automatically over the Unix Domain Socket
end
}- Open the
vscodefolder in this repository. - Package the extension:
cd vscode npm install npm run compile npx vsce package - Install the generated
.vsixfile in VS Code.
ghostcoder start- Start the background daemon.ghostcoder status- Show active models, VRAM usage, and active integrations.ghostcoder logs- Tail background daemon log output.ghostcoder stop- Stop the daemon gracefully.ghostcoder skeptic --on|--off- Enable or disable the skeptic validation agent.
This project is licensed under the MIT License - see the LICENSE file for details.
