def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }
def calculate_loss(predictions, targets): return torch.mean((predictions - targets) ** 2)
async function fetchUserData(id) { const res = await api.get(`/users/${id}`); return res.data; }
class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attn = MultiHeadAttention(d_model, n_heads)
const handleScroll = useCallback(() => { setScrolled(window.scrollY > 50); }, []);
@app.route('/api/generate', methods=['POST']) def generate(): prompt = request.json.get('prompt') return jsonify(model.predict(prompt))
interface User { id: string; name: string; role: 'admin' | 'user'; }
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
export const AktaCodeAgent = ({ model = 'gpt-5', temperature = 0.7 }) => { // Initialize agent }

Stop burning tokens. Start building with momentum.

AI coding should make you feel faster, sharper, and more in control — not stuck watching costs climb with every small change. OPENAKTA gives solo founders and small teams a cleaner way to build: less waste, less setup, more flow.

Join My Beta SpotView on GitHub
Threads
AktaCode
AktaCode CLI
Marketing Website
Create AktaCode launch CTA
openakta/aktacode
Create a compelling launch hero for the new AktaCode app on openakta.com/aktacode

I’ll update the hero copy to clearly communicate what AktaCode does, add outcome-focused bullets, and ensure the CTAs align with launch goals.

Thought 7s
Explored 2 files
Editedhero.tsx
Read build.py
Editedbuild.py

Updated the launch hero to emphasize real developer outcomes (repo understanding, safe execution, PR delivery), and aligned the CTAs with launch intent.

Ola
To try AktaCode, download here
2 files changed
+9-6
src/hero.tsx
+8-5
export const hero = {
- eyebrow: "New",
- title: "AktaCode",
- subtitle: "AI for developers",
+ eyebrow: "Introducing",
+ title: "AktaCode launch",
+ subtitle: "Your AI pair programmer",
+ primaryCta: "Get started",
+ secondaryCta: "Download the CLI",
};
export const heroBullets = [
- "Write code faster",
- "Understand any repo",
+ "Understands your repo in seconds",
+ "Executes commands safely in a sandbox",
+ "Turns issues into reviewed, production-ready PRs",
];
tools/build.py
+1-1
def build():
- print("building")
+ print("building launch hero…")
if __name__ == "__main__":
build()

Stay on the frontier

Use the best model for every task

Choose between cutting-edge models from OpenAI, Anthropic, Google, xAI, and more—right inside OpenAkta.

Explore models ↗

Ask OpenAkta to plan or build anything

Complete codebase understanding

OpenAkta learns how your codebase works—no matter the scale or complexity.

Learn about codebase indexing ↗
Where are these menu label colors defined?
  • Grepped Choose a model
  • Searched Where is the model picker UI implemented?
  • Searched How are model labels colored in the UI?
  • Read ContextMenu.tsx

Develop enduring software

Built for teams that need speed, security, and clarity as they scale—from startups to the enterprise.

Explore enterprise →
export function ContextMenu() {
const theme = useTheme();
return <Menu tone={theme.labelColor} />;
}

Most AI coding tools waste tokens on work you did not ask for.

Change one class name. Fix one typo. Update one function.

Too many tools still send huge blocks of code back and forth. That burns tokens, hits rate limits, and eats budget fast.

OPENAKTA takes the opposite approach.

It is built to keep edits small, setup light, and costs under control.

Typical Tool
Request2,847 tokens
Response4,192 tokens

Full file rewrite for one class change

OPENAKTA
Request124 tokens
Response89 tokens

Precise patch for the same change

Built for builders who watch their runway.

Pay for the model. Not the markup.

Bring your own OpenAI or Anthropic keys and pay providers directly.

Start fast.

Pre-configured MCPs, routing, and essential skills are ready from the first command.

Edit precisely.

OPENAKTA generates patch-style diffs, so you are not paying to rewrite whole files for tiny changes.

Ready on day one. No setup spiral.

You should not need a week of repo surgery just to get useful AI coding help.

OPENAKTA comes with the parts most teams end up wiring together by hand.

Pre-configured MCPs and skills so tools, memory, and routing work from the start

Fast model switching so you can use the right model without changing your whole flow

Built-in context compression so the model sees what matters, not a bloated mess

Your keys. Your limits. Your budget.

Connect your own provider keys and keep control of spend.

No hidden markup. No forced pricing layer between you and the model provider. No guessing where the money went.

You choose the models. You pay the provider directly. You stay in control.

Ideal for solo founders and small teams that need strong models without SaaS overhead.

ProviderYour API Key
AI
OpenAI
Connected
An
Anthropic
Connected
Ge
Google
Optional
BillingDirect to provider
file: Button.tsx
-className="px-4 py-2 bg-blue-500"
+className="px-4 py-2 bg-brand-500"
Precise patch+12 -12

Exact change only. No full-file rewrite.

Small edits should cost small money.

OPENAKTA uses a deterministic, patch-first approach.

Instead of rewriting a whole file, it creates git-style diffs for the exact change needed.

That means lower token use, cleaner reviews, and less noise in your workflow.

Save up to 70% in token costs with precise patches instead of full-file replacements.

Open source by default. Transparent by design.

OPENAKTA is MIT licensed and built in the open.

Audit the code. Extend it. Adapt it to your workflow. Keep control of how your AI coding stack works.

For developers, trust starts with visibility. This gives you that.

MIT licensed
Open source on GitHub
Bring Your Own Keys supported
No vendor lock-in

The best way to build with agents

Built to accelerate real engineering work

From routine pull requests to your hardest problems, AktaCode completes end-to-end tasks reliably—developing features, complex refactors, migrations, and more—with OpenAkta’s frontier coding models.

I'll trace the existing theme entry points, add a proper light/dark theme model, persist the user preference, and apply the dark class early so we avoid a flash on load.

Thought 8s

Explored 3 files

Edited theme.ts
Edited main.tsx
Edited index.css

Dark mode is now wired end-to-end. I updated the theme model to support both light and dark, resolve the initial theme from localStorage with a prefers-color-scheme fallback, and added an applyTheme helper that toggles the document-level dark class immediately at startup.

Adapts to how your team builds

With Skills, AktaCode goes beyond writing code and directly contributes to the work that turns pull requests into products—understanding code, prototyping, and documenting—all aligned with your team’s standards.

Thought 3s

Using skill $imagegen

Thought 5s

Generated cloud hero image:

Cloud dog

Done — I generated a cloud-themed hero banner and it's ready to drop into your homepage. It's sized for a typical hero slot, should crop cleanly on smaller screens, and keeps the airy, sky-forward tone. If you want, I can generate two more variations (stormy, sunrise) so you can A/B them in the header.

Your new coding partner

Switch seamlessly between real-time collaboration and async delegation with AktaCode across every tool you use.

Work with AktaCode in your terminal or IDE

Start from a prompt or spec: AktaCode navigates the repo to edit files, run commands, and execute tests. Ship features, fix bugs, explore solutions, and tackle any task with the AktaCode CLI and IDE extension for VS Code, Cursor, and Windsurf.

_ AktaCode (v0.34.0)
model:gpt-5 /model to changedirectory:~/eb/code/acme
To get started, describe a task or try one
/init - create an AGENTS.md file with instru
/status - show current session configuration
/approvals - choose what AktaCode can do withou
/model - choose what model and reasoning ef
>_Hey AktaCode, implement dark mode
? for help
import { useState } from 'react'; export function Slider() { const [val, setVal] = useState(0); return <input type="range" /> }
CX

I implemented the slider including ARIA labels, focus rings, step snapping, and haptic feedback on mobile.

Do you want me to add tests?

2 files edited+123-42
slider.tsx+83-0
page.tsx+40-42
97%

Delegate to AktaCode in the cloud

AktaCode runs in the background. You keep your flow and move faster. Each task runs in an isolated sandbox with your repo and environment, producing code you can review, merge, or download locally to keep going.

Changelog

Mar 19, 2026

AktaCode Composer 2

Mar 12, 2026

New Skills in the marketplace

Mar 5, 2026

Cloud task automations

Feb 28, 2026

AktaCode for JetBrains IDEs

See what's new in AktaCode

Simple pricing while we’re in beta

Start building today. No hidden fees, no complex tiers.

Early access
Free

Full product access. We may adjust limits as we learn what stable usage looks like.

Teams / Orgs
Custom

Need governance, invoicing, or a pilot? Contact us—we’ll shape something that fits.

Managed Option

Want the same approach without managing keys?

OPENAKTA Managed is for teams that want the simpler path.

We handle model access, routing, and availability for you. You get the same low-friction runtime with a more hands-off setup.

OPENAKTA Managed

We handle the infra

Model access managed
Routing optimized
Availability guaranteed
Same low-friction runtime

Frequently asked questions

Stop paying for bloated AI edits.

Join the beta for the open-source runtime that keeps setup light, edits precise, and costs under control.

Early users get first access, updates, and launch pricing news.