<p align="center">
  <img src="https://raw.githubusercontent.com/nagarjuna-tella/Aksara/main/aksara/studio/static/icons/aksara-logo.svg" width="64" alt="Aksara logo"/>
</p>

<h1 align="center">Aksara</h1>

<p align="center">
  One model definition → REST API, MCP tools for AI agents, interactive AI Console, built-in Studio UI, admin dashboard, launch diagnostics, and PostgreSQL migrations. Python. No glue code.
</p>

<p align="center">
  <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.11+-3776AB?style=flat-square&logo=python&logoColor=white" alt="Python 3.11+"></a>
  <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-22c55e?style=flat-square" alt="MIT License"></a>
  <img src="https://img.shields.io/badge/tests-6744%20passing-22c55e?style=flat-square&logo=pytest&logoColor=white" alt="Tests">
  <img src="https://img.shields.io/badge/version-0.5.48-3b82f6?style=flat-square" alt="Version">
  <img src="https://img.shields.io/badge/PostgreSQL-4169E1?style=flat-square&logo=postgresql&logoColor=white" alt="PostgreSQL">
  <img src="https://img.shields.io/badge/async-asyncpg-6366f1?style=flat-square" alt="Async">
</p>

---

<p align="center">
  <img src="https://raw.githubusercontent.com/nagarjuna-tella/Aksara/main/docs/docs/assets/aksara-demo.gif" alt="Aksara CLI demo — scaffold to running app in seconds" width="860" />
</p>

---

## What is Aksara?

Aksara is an AI-native backend framework for building PostgreSQL-powered APIs with automatic REST endpoints, migrations, Studio UI, MCP tools, and system-level AI intelligence. The goal is simple: a new project should have a working API, inspectable data model, AI-facing tool catalog, and launch diagnostics before you start writing glue code.

Current release: **v0.5.48 — Launch Hardening & Golden Path**.

---

## What Aksara Does

You define a model once. Aksara generates everything else from it:

```python
from aksara import Aksara, Model, fields
from aksara.api import ModelViewSet, action

class Incident(Model):
    title = fields.String(
        max_length=200,
        ai_description="Short summary of the incident",
    )
    severity = fields.String(
        choices=["low", "medium", "high", "critical"],
        ai_description="Impact level for triage priority",
    )
    resolved = fields.Boolean(
        default=False,
        ai_agent_writable=False,  # AI agents can read this but not flip it
    )
    notes = fields.Text(
        ai_description="Internal investigation notes",
        ai_sensitive=True,        # Excluded from AI context by default
    )

class IncidentViewSet(ModelViewSet):
    model = Incident

    @action(detail=True, methods=["POST"], ai_exposed=True)
    async def escalate(self, pk: str, request):
        """
        Escalate incident to critical.
        This custom action is automatically exported as an MCP tool!
        """
        incident = await self.model.objects.get(id=pk)
        incident.severity = "critical"
        await incident.save()
        return {"status": "escalated"}

app = Aksara(database_url="postgresql://localhost/myapp")
app.include_viewset(IncidentViewSet, prefix="/incidents")
```

**From this single definition, you get:**

| What             | Where                                                                               |
| ---------------- | ----------------------------------------------------------------------------------- |
| REST API         | `GET/POST/PATCH/DELETE /incidents/`                                                 |
| Real-time stream | `GET /incidents/stream` — subscribe to insert/update/delete events via SSE          |
| MCP tool catalog | `/ai/tools/mcp` — any MCP-compatible agent (Claude, Cursor, etc.) can call your API |
| AI Console       | `/studio/ui` → natural-language queries against your live backend                   |
| Studio dashboard | `/studio/ui` — models, routes, queries, migrations, diagnostics                     |
| Admin UI         | `/admin/`                                                                           |
| Database table   | `aksara migrate`                                                                    |

The `ai_description`, `ai_sensitive`, and `ai_agent_writable` metadata you wrote on those fields flows through to the AI Console context and the MCP tool catalog automatically. No second schema. No adapter layer. Write it once.

---

## Quickstart

### 10-Minute Quickstart

```bash
pip install aksara-framework
```
```bash
aksara startproject opsdesk && cd opsdesk
aksara dbsetup
aksara migrate
aksara doctor launch-check
aksara dev
```

<p align="center">
  <img src="https://raw.githubusercontent.com/nagarjuna-tella/Aksara/main/docs/docs/assets/app-welcome.png" alt="Generated app home page" width="800" />
</p>
<p align="center"><em>The auto-generated welcome page every new project starts with — API docs, ReDoc, and Studio links built in.</em></p>

Now open three things:

| What to try             | URL / Command                                                                                         |
| ----------------------- | ----------------------------------------------------------------------------------------------------- |
| **API docs**            | [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) — inspect generated OpenAPI                 |
| **Studio + AI Console** | [http://127.0.0.1:8000/studio/ui](http://127.0.0.1:8000/studio/ui) — ask "explain the Incident model" |
| **MCP tool catalog**    | [http://127.0.0.1:8000/ai/tools/mcp](http://127.0.0.1:8000/ai/tools/mcp) — point any MCP client here |
| **Launch check**        | `aksara doctor launch-check` — verify project, DB, Studio, MCP, AI, examples, and dev-mode readiness |
| **Fix plan**            | `aksara doctor fix-plan` — diagnose and print the remediation path                                    |

That's what makes Aksara different from `pip install fastapi && pip install sqlalchemy && ...`. The AI and diagnostic surfaces exist from the first `aksara dev`.

---

Aksara is built on FastAPI, so you get its performance and full ecosystem out of the box. The rest — ORM, migrations, admin, Studio, MCP export, AI Console, doctor — is what you'd otherwise spend a sprint assembling yourself. I did that part for you.

---

### Open Studio

Studio lives at [http://127.0.0.1:8000/studio/ui](http://127.0.0.1:8000/studio/ui). It gives you models, routes, query tools, migrations, diagnostics, AI Console, AI Inspector, Project Graph, Architecture Review, Performance Analyzer, Investigation Sessions, and Daily Briefing from the same running app.

If the first run is incomplete, Studio now surfaces clearer states for missing database configuration, pending migrations, missing AI provider setup, and project graph availability.

### Use AI

AI features are optional on day one. You can run the framework, inspect Studio, browse API docs, and view MCP output without OpenAI, Anthropic, Azure, or Ollama.

When you want AI features:

```bash
aksara ai-hub status
aksara ai-hub configure
```

For a local-first path, use Ollama:

```bash
ollama serve
ollama pull llama3
```

### Use MCP

Aksara exposes generated MCP-compatible tools at [http://127.0.0.1:8000/ai/tools/mcp](http://127.0.0.1:8000/ai/tools/mcp). ViewSets, model fields, custom `@action` endpoints, and AI metadata flow into the catalog so external agents can inspect and call your backend without a hand-written adapter.

---

## Core Features

|     | Feature                  | What it does                                                                                      |
| --- | ------------------------ | ------------------------------------------------------------------------------------------------- |
| 🔌  | **MCP tool export**      | Auto-generated tool catalog at `/ai/tools/mcp` from your model definitions                        |
| 💬  | **AI Console**           | Natural-language queries against your live backend in Studio                                      |
| 🩺  | **Doctor & Fix Plans**   | `aksara doctor fix-plan` diagnoses DB, migrations, AI config, security — prints the fix sequence  |
| 🐛  | **AI Debugger**          | Root-cause analysis: issue clustering, heuristic patterns, confidence-ranked causes               |
| 🏗️  | **Architecture Review**  | Automated health scoring (A–F), coupling/schema/API findings                                      |
| 📊  | **Performance Analyzer** | Detects slow queries, N+1, missing indexes, heavy joins                                           |
| ⚡  | **AI Flows**             | In-context AI actions for models, routes, queries, migrations with risk badges                    |
| 🗄️  | **Async ORM**            | Postgres-first: models, typed fields, relations, migrations, asyncpg                              |
| 🔁  | **Auto-REST**            | `ModelViewSet` → full CRUD endpoints, serializers, pagination                                     |
| 🏢  | **Native Multi-Tenancy** | `TenantModel` + PostgreSQL RLS + tenant-aware connection context                                  |
| 🗂️  | **Media Storage**        | `FileField` / `ImageField` + `FieldFile` helpers + filesystem or S3-backed storage                |
| ✉️  | **Async Email**          | Console, locmem, and SMTP backends with `send_mail()` and `send_mass_mail()`                      |
| 🌐  | **i18n & Timezones**     | `LocaleMiddleware`, `TimezoneMiddleware`, lazy `_()` strings, and UTC-normalized datetime storage |
| 🔗  | **Generic Relations**    | `fields.GenericForeignKey()` with auto-managed `ContentType` resolution across registered models  |
| ♻️  | **Durable Workflows**    | `DurableStep` persists successful step results in PostgreSQL and reuses them on resume            |
| 🧵  | **Background Tasks**     | `@task`, `enqueue_task()`, and `TaskWorker` provide PostgreSQL-backed jobs with retries           |
| 🧠  | **JSONB + Vectors**      | Nested JSON path filters plus `fields.Vector()` and cosine/euclidean distance expressions         |
| 📦  | **TypeScript SDK**       | `aksara generate sdk --language typescript` builds a typed fetch client from your ViewSets        |
| 📡  | **Real-Time Streams**    | `GET /<prefix>/stream` emits model lifecycle events through tenant-safe SSE                       |
| 🖥️  | **Studio**               | Built-in web UI at `/studio/ui` — inspect models, routes, queries, migrations                     |
| 🛡️  | **Admin**                | Browse and edit data without extra setup                                                          |
| 🔎  | **Semantic Search**      | ⌘K spotlight across models, routes, settings, playbooks                                           |

---

## Why Aksara?

Because the first ten minutes of a backend project should prove the system works, not force you to assemble the same stack again. Aksara gives you the database layer, API layer, Studio, AI context, MCP surface, diagnostics, and example paths together, while keeping the model definition as the source of truth.

---

## The Aksara Difference

Most frameworks stop at the database and the HTTP layer. You define a model, you get a table and an endpoint. Aksara keeps going.

The same `ai_description="Short summary of the incident"` you put on a field:

1. **Describes the column** for any developer reading the code
2. **Appears in the MCP tool catalog** at `/ai/tools/mcp` so Claude, Cursor, or any MCP-compatible agent knows what that field means before calling your API. (Note: Custom ViewSet endpoints using the `@action` decorator are also automatically exported as tools).
3. **Populates the AI Console context** so you can type "show me all critical unresolved incidents" in Studio and the AI knows which fields to query
4. **Drives the Schema Doctor** which checks that your AI metadata is complete and consistent

`ai_agent_writable=False` on `resolved` means the MCP catalog marks that field read-only — an AI agent can see it but can't change it. `ai_sensitive=True` on `notes` excludes it from AI context entirely.

You write this metadata once, next to the field definition, and it propagates everywhere. No second schema, no separate MCP adapter, no context-building glue code.

---

## Patterns & Examples

The bundled examples are the golden paths for learning and launch validation:

| Example          | Use Case                                      | Where to start                                      |
| ---------------- | --------------------------------------------- | --------------------------------------------------- |
| **basic_app**    | Smallest working Aksara app                   | `examples/basic_app/`                               |
| **Blog**         | Posts, comments, publish workflow; models, relations, APIs, Studio, MCP | `aksara startproject myblog --template blog` |
| **CRM**          | Customers, deals, pipeline stages; business data model and reporting patterns | `aksara startproject mycrm --template crm` |
| **Multitenant**  | Tenant-aware SaaS apps and tenant-aware app structure | `aksara startproject saas --template multitenant` |
| **AI Providers** | BYO LLM wiring examples; local and remote AI setup with no secrets | `examples/ai_providers/` |

Browse: [`examples/`](https://github.com/nagarjuna-tella/Aksara/tree/main/examples/) | Docs: [Patterns](https://nagarjuna-tella.github.io/Aksara/patterns/)

Validate the examples in a source checkout:

```bash
aksara examples validate
aksara examples validate --format json
```

---

## Documentation

| Section                                       | Description                                            |
| --------------------------------------------- | ------------------------------------------------------ |
| [Quickstart](https://nagarjuna-tella.github.io/Aksara/quickstart/) | Build and deploy in 5 minutes                          |
| [Getting Started](https://nagarjuna-tella.github.io/Aksara/getting-started/installation/) | Install, first project, Studio, AI, MCP, examples      |
| [ORM Guide](https://nagarjuna-tella.github.io/Aksara/orm/) | Models, fields, relations, queries                     |
| [API Guide](https://nagarjuna-tella.github.io/Aksara/api/) | ViewSets, actions, serializers                         |
| [Advanced Guide](https://nagarjuna-tella.github.io/Aksara/advanced/) | Background tasks, media/email, i18n, generic relations |
| [AI Mode](https://nagarjuna-tella.github.io/Aksara/ai-mode/) | MCP, AI Console, Debugger, Architecture Review         |
| [Studio Guide](https://nagarjuna-tella.github.io/Aksara/studio/) | Visual inspector & debugging                           |
| [Admin Guide](https://nagarjuna-tella.github.io/Aksara/admin/) | Admin site customization                               |
| [CLI Reference](https://nagarjuna-tella.github.io/Aksara/cli/) | All CLI commands, including SDK generation             |

---

## Roadmap

Current stable: **v0.5.48 — Launch Hardening & Golden Path**.

Next planned milestones:

| Version | Focus |
| ------- | ----- |
| v0.5.49 | Durable AI Session Store |
| v0.5.50 | AI Memory Foundation |
| v0.5.51 | AI System Radar |
| v0.6.0  | Production Mode |

See the [Roadmap](https://nagarjuna-tella.github.io/Aksara/roadmap/) for the full release path.

---

## Status

Aksara is **pre-1.0** and actively evolving. Current version: **0.5.48**.

Latest validation with local PostgreSQL: **6744 passed, 3 skipped, 0 warnings**.

**Stable:** ORM, migrations, ViewSets, serializers, permissions, Admin, Studio, CLI, MCP export, AI Console, Doctor, media/email, i18n/timezones, generic relations, background tasks, and JSONB/vector ORM support.

**Evolving:** AI Debugger, Architecture Review, Performance Analyzer, and long-running AI session surfaces.

See the [Roadmap](https://nagarjuna-tella.github.io/Aksara/roadmap/) for what's next.

---

## Contributing

1. Check existing issues or open a new one
2. Fork the repo and create a feature branch
3. Run tests: `pytest`
4. Submit a pull request

---

## Community

<p>
  <a href="https://pypi.org/project/aksara-framework/"><img src="https://img.shields.io/badge/PyPI-aksara--framework-3775A9?style=flat-square&logo=pypi&logoColor=white" alt="PyPI"></a>
  <a href="https://github.com/nagarjuna-tella/Aksara"><img src="https://img.shields.io/badge/GitHub-aksara-181717?style=flat-square&logo=github&logoColor=white" alt="GitHub"></a>
</p>

---

## License

[MIT License](https://github.com/nagarjuna-tella/Aksara/blob/main/LICENSE)

---

## A note from the author

Hello my fellow builder!

You didn't start any project because you wanted to spend a week wiring up an ORM, migrations, admin, AI adapters, and debugging surfaces. You started because you have something you want to build, and every hour spent on infrastructure is an hour further from that.

Aksara is my attempt to get you past that part faster. Not by hiding the complexity, but by making the right defaults obvious so you can spend your attention on what actually matters to you.

That said, a framework is a long chain of judgment calls. What should be automatic? What should stay explicit? When does "helpful" become too much magic? I've made those calls with care, but I can only see what I've seen. The gaps I don't know about yet are the ones that will cost you an hour when you hit them.

That's why your experience matters here. The rough edge you found. The docs that didn't explain the thing you needed. The feature that clicked immediately. The place where Aksara got out of your way and let you move. That signal is what makes this better.

So if you decide to build with it, I want to hear what you ran into:

- **Open an issue** on [GitHub](https://github.com/nagarjuna-tella/Aksara/issues), even a rough one. "This didn't work and I'm not sure why" is more useful than a polished bug report.
- **Show me what you're building.** A link, a screenshot, a one line description, I read every one.
- **Tell me what tipped the scale**, if you started here and moved to something else. That kind of honesty is how good frameworks get made.

Aksara is pre-1.0 on purpose. I don't want 1.0 to be a monument to my assumptions, I want it to reflect the shape of what you actually tried to ship with it. If you're one of the people who tries, you're not just a user. You're a co-author of where this lands.

Thanks for taking the time to look.

Nagarjuna

---

Built with ❤️ by Nagarjuna Tella.
