The autocomplete that gets better as you code.
Cursor locks you to their model. Copilot sends your code to GitHub. Vishwa connects to Claude, GPT-5, or any local Ollama model — and runs a reinforcement learning engine locally to adapt to how you actually write code.
Your keys never leave the OS keychain. Your code never leaves your machine except for the one API call you control.
from dataclasses import dataclass@dataclassclass Order:id: stramount: floatstatus: str = "pending"def fulfill(order: Order) -> bool:if order.status != "pending":return Falsecharge = process_payment(order.amount)if charge.success:order.status = "fulfilled"notify_customer(order.id)return True
Why not Cursor or Copilot?
Both are good tools. But they make one decision for you — they own the model, they own the prompting strategy, and your code flows through their infrastructure. Vishwa inverts that.
Vishwa | Cursor | GitHub Copilot | |
|---|---|---|---|
| Monthly price | From $5.99 | $20 | $10 |
| Your direct LLM costs | BYO key or included | Included | Included |
| Model choice | Claude, GPT-5, Ollama, any | Cursor's selection | Copilot model only |
| Local model support | |||
| Code sent to vendor servers | |||
| API key storage | OS Keychain | Their servers | GitHub / MS servers |
| Adapts to your patterns | |||
| Context strategies | 5 adaptive (RL-selected) | Fixed | Fixed |
| Learns per code context | |||
| IDE | VS Code extension | Custom VS Code fork | VS Code, JetBrains |
Cursor and Copilot pricing as of early 2026. LLM costs vary by model and usage.
A learning system that gets better as you use it.
Vishwa runs a reinforcement learning layer locally. It observes which completions you accept, tracks what works per code context, and shifts its strategy over time. The policy lives on your machine and is never shared.
Five context strategies
The engine selects among these automatically. Scores below are illustrative.
Tight window around the cursor. Fast, low-noise.
A bit more context. Still no imports or signatures.
Includes imports and referenced function signatures.
Wider context window with more imports and signatures.
Sends from the enclosing function definition to the cursor.
01 · model
Your model. Your API key. Your bill.
Vishwa connects to Claude Haiku, GPT-5, or anything running locally on Ollama. Switch between providers with a single config change. Your API keys are stored in the OS keychain — not a dotfile, not an env file, not someone else's server.
Anthropic
OpenAI
Ollama (local)
ANTHROPIC_API_KEY=sk-ant-api03-••••••••keychain
02 · privacy
Nothing leaves your machine except the model call.
The completion engine runs as a local Python subprocess. No telemetry, no code logging, no third-party relay. The only network request is the one you make directly to your LLM provider — exactly as if you had called their API yourself from the terminal.
$ security find-generic-password \
-s "vishwa-autocomplete" -w
▶ sk-ant-api03-xxxx··················
Running in three minutes.
No config files to manage. No Docker. No separate terminal to keep open. Vishwa handles the plumbing so you stay in the editor.
Install
Search "Vishwa Autocomplete" in the VS Code marketplace. The Python backend sets itself up in an isolated virtual environment the first time you open a file.
›ext install vishwa-autocompleteConnect your model
Run the setup command from the command palette. Your API key is stored in the OS keychain — nothing written to disk.
›> Vishwa: Configure API KeyStart coding
Ghost-text suggestions appear as you type. Accept with Tab, dismiss with Escape. The RL engine observes your choices and adjusts its strategy silently in the background.
›# Suggestions appear inlinePricing
Starts at $5.99/mo. Bring your own model or use ours.
Use your own API keys for $5.99/mo, or get managed cloud inference for $10.99/mo. Both include the full RL engine. 14-day free trial, no card required.
We're in closed testing.
Leave your email and we'll reach out when a spot opens. Python developers are first in line.