Metadata-Version: 2.4
Name: ai-research
Version: 0.1.33
Summary: Python SDK for the AIR Backend API
Author-email: Boris Bolliet <denario.astropilot.ai@gmail.com>, Pablo Villanueva-Domingo <denario.astropilot.ai@gmail.com>, Francisco Villaesusa-Navarro <denario.astropilot.ai@gmail.com>, Íñigo Zubeldia Lafuente <denario.astropilot.ai@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/air-ai/air-sdk
Project-URL: Repository, https://github.com/air-ai/air-sdk.git
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: httpx>=0.27
Requires-Dist: python-dotenv
Requires-Dist: websockets>=12.0
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == "dev"
Provides-Extra: docs
Requires-Dist: mkdocs>=1.2; extra == "docs"
Requires-Dist: mkdocstrings[python]; extra == "docs"
Requires-Dist: mkdocs-material>=9.6.12; extra == "docs"
Requires-Dist: mkdocs-jupyter>=0.25.1; extra == "docs"
Requires-Dist: mkdocs-git-committers-plugin-2>=2.5.0; extra == "docs"
Dynamic: license-file

# AIR SDK

The AIR SDK is a Python client for the AIR platform — a suite of AI assistant tools for scientific research. It gives you programmatic access to AI-assisted pipeline steps (idea generation, literature search, methods, data analysis, paper writing, peer review) as well as a set of standalone tools you can use independently.

Key capabilities:

- **AI-assisted research pipeline** — use AI assistants for idea generation, literature search, methods development, data analysis, paper writing, and review as a connected workflow or individual steps
- **Local code execution** — the `results`, `one_shot`, and `deep_research` methods coordinate an AI agent on the backend while all generated code runs locally on your machine in an isolated virtual environment
- **File sync** — push local data files to a project and pull generated outputs (plots, LaTeX, reports) back to disk after each step
- **Standalone AI tools** — extract keywords, download and summarise arXiv papers, enhance text with paper context, run OCR, or get an AI review of a paper PDF independently of any project

## Installation

Install the SDK from PyPI:

```bash
pip install air-research
```

## Quick Start

```python
import air

client = air.AIR(api_key="air_k1_...", base_url="http://localhost:8000", local_dir="~/my-research")

# Standalone tools
keywords = client.keywords("dark matter and lensing", n=5, kw_type="aas")
enhanced = client.enhance("My research with https://arxiv.org/abs/2301.12345")

# Full research workflow
project = client.create_project("my-research", data_description="We study...")
idea = project.idea()
literature = project.literature()
methods = project.methods()
results = project.results()
paper = project.paper(journal="AAS")
review = project.review()

# File access
print(project.get_file("Iteration0/input_files/idea.md"))
print(project.list_files())

# Sync files locally
project.pull_files()                       # download all project files
project.push_files("local/data.csv")       # upload a local file

# One-shot and deep-research tasks
result = client.one_shot("Analyse the attached dataset and produce a summary plot")
result = client.deep_research("Investigate the impact of dark energy on large-scale structure")
```

## Configuration

Set environment variables, or pass them directly to the constructor:

| Variable | Description |
|----------|-------------|
| `AIR_API_KEY` | API key (`air_k1_...`) |
| `AIR_BASE_URL` | Backend URL (default: `http://localhost:8000`) |
| `AIR_LOCAL_DIR` | Local root for `pull_files` / auto-pull (default: `~/ai-scientist`) |
| `AIR_WORK_DIR` | Local root for `results`, `one_shot`, `deep_research` code execution (default: `~/ai-scientist`) |

```python
client = air.AIR(
    api_key="air_k1_...",
    base_url="https://api.example.com",
    local_dir="~/my-research",   # where pull_files writes files
)
```

## API Reference

### `AIR` class

| Method | Description |
|--------|-------------|
| `keywords(text, n=5, kw_type="unesco")` | Extract keywords |
| `arxiv(text)` | Download arXiv papers from URLs in text |
| `enhance(text, max_workers=2, max_depth=10)` | Enhance text with arXiv context |
| `ocr(file_path)` | Process PDF with OCR (server path) |
| `review(file_path, *, thoroughness, figures_review, verify_statements, review_maths, review_numerics, emails, timeout)` | Standalone paper review with Skepthical |
| `create_project(name, data_description, iteration, local_dir, auto_pull=True)` | Create a project |
| `get_project(name, auto_pull=True)` | Get existing project |
| `list_projects()` | List all projects |
| `delete_project(name)` | Delete a project |
| `one_shot(task, *, model, max_rounds, agent, work_dir, timeout, on_output, python_path, venv_path)` | Run a one-shot task with local code execution |
| `deep_research(task, *, engineer_model, researcher_model, planner_model, max_plan_steps, work_dir, timeout, on_output, ...)` | Run a multi-step deep-research task with local code execution |

### `Project` class

| Method | Description |
|--------|-------------|
| `idea(mode="fast", iteration=0, timeout=600)` | Generate research idea |
| `literature(iteration=0, timeout=600)` | Run literature search |
| `methods(mode="fast", iteration=0, timeout=600)` | Develop methods |
| `results(iteration=0, timeout=86400, max_attempts=10, max_steps=6, work_dir, on_output, python_path, venv_path)` | Run analysis/experiments with local code execution; plots are auto-uploaded |
| `paper(journal="NONE", iteration=0, timeout=900)` | Write paper |
| `review(iteration=0, timeout=600, **kwargs)` | Run review (kwargs: `review_engine`, `review_thoroughness`, `pdf_version`, `emails`, etc.) |
| `get_file(path)` | Read a project file |
| `list_files()` | List all project files |
| `write_file(path, content, encoding="utf-8")` | Write a file |
| `pull_files(local_dir, iteration=0, key_only=False)` | Download project files to a local directory |
| `push_files(local_path, remote_path=None)` | Upload a local file or directory to the project |
| `delete()` | Delete the project |

#### `auto_pull`

When `auto_pull=True` (default), key project files are automatically downloaded to `local_dir` after each pipeline step (`idea`, `methods`, `results`, `paper`).  Set `auto_pull=False` to disable this behaviour.

#### Standalone `review()` on `AIR`

```python
result = client.review(
    "/server/path/to/paper.pdf",
    thoroughness="High",       # "Standard" (default) or "High"
    figures_review=True,
    verify_statements=True,
    review_maths=False,
    review_numerics=False,
    emails=["you@example.com"],
)
```

#### `one_shot` and `deep_research`

Both methods connect via WebSocket and execute generated code **locally** on your machine:

```python
result = client.one_shot(
    "Plot a histogram of the attached CSV",
    model="gpt-4.1-2025-04-14",
    work_dir="~/my-tasks",
)
print(result.output)   # streamed text
print(result.files)    # list of created local files

result = client.deep_research(
    "Reproduce Figure 3 from arxiv:2301.12345",
    max_plan_steps=5,
    work_dir="~/my-tasks",
)
```

## Docs

Install the documentation with:

```sh
pip install air-research[docs]
```

and build it with

```sh
mkdocs serve --livereload
```

## Tests

Test with `pytest`:

```sh
pytest tests
```

You can also skip slow tests (e.g. idea generation) with

```sh
pytest tests/ -m "not slow".
```
