adaptyv

# Adaptyv Bio Foundry API

Adaptyv Bio is a cloud lab that turns protein sequences into experimental data. Users submit amino acid sequences via API or UI; Adaptyv's automated lab runs assays (binding, thermostability, expression, fluorescence) and delivers results in ~21 days.

**Official docs:** [docs.adaptyvbio.com/api-reference](https://docs.adaptyvbio.com/api-reference) · [llms.txt index](https://docs.adaptyvbio.com/llms.txt) · [OpenAPI spec](https://foundry-api-public.adaptyvbio.com/api/v1/openapi.json)

## Quick Start

**Base URL:** `https://foundry-api-public.adaptyvbio.com/api/v1`

**Authentication:** Bearer token in the `Authorization` header. Tokens are obtained from [foundry.adaptyvbio.com](https://foundry.adaptyvbio.com/) sidebar.

When writing code, always read the API key from the environment variable `ADAPTYV_API_KEY` or from a `.env` file — never hardcode tokens. Check for a `.env` file in the project root first; if one exists, use a library like `python-dotenv` to load it.

The [official API docs](https://docs.adaptyvbio.com/api-reference/api-introduction) use `FOUNDRY_API_TOKEN` in curl examples; that is the same bearer token — prefer `ADAPTYV_API_KEY` in Python and new shell scripts for consistency with the SDK.

```bash
export ADAPTYV_API_KEY="abs0_..."
curl https://foundry-api-public.adaptyvbio.com/api/v1/targets?limit=3 \
  -H "Authorization: Bearer $ADAPTYV_API_KEY"
```

Every request except `GET /openapi.json` requires authentication. Store tokens in environment variables or `.env` files — never commit them to source control.

## Python SDK

**Version note:** `adaptyv-sdk` **0.1.0** (beta) is not yet on PyPI — install from GitHub:

```bash
uv pip install "git+https://github.com/adaptyvbio/adaptyv-sdk.git"
```

In a project with `pyproject.toml`:

```bash
uv add "adaptyv-sdk @ git+https://github.com/adaptyvbio/adaptyv-sdk.git"
```

**Environment variables** (set in shell or `.env` file):

```bash
ADAPTYV_API_KEY=your_api_key
ADAPTYV_API_URL=https://foundry-api-public.adaptyvbio.com/api/v1
ADAPTYV_ORGANIZATION_ID=your_org_id  # optional
```

The `@lab.experiment` decorator and `FoundryClient` both read `ADAPTYV_API_KEY` and `ADAPTYV_API_URL` from the environment when not passed explicitly.

### Decorator Pattern

```python
from adaptyv import lab

@lab.experiment(target="PD-L1", experiment_type="screening", method="bli")
def design_binders():
    return {"design_a": "MVKVGVNG...", "design_b": "MKVLVAG..."}

result = design_binders()
print(f"Experiment: {result.experiment_url}")
```

### Client Pattern

```python
import os
from adaptyv import FoundryClient

client = FoundryClient(
    api_key=os.environ["ADAPTYV_API_KEY"],
    base_url=os.environ.get(
        "ADAPTYV_API_URL",
        "https://foundry-api-public.adaptyvbio.com/api/v1",
    ),
)

# Browse targets
targets = client.targets.list(search="EGFR", selfservice_only=True)

# Estimate cost
estimate = client.experiments.cost_estimate({
    "experiment_spec": {
        "experiment_type": "screening",
        "method": "bli",
        "target_id": "target-uuid",
        "sequences": {"seq1": "EVQLVESGGGLVQ..."},
        "n_replicates": 3
    }
})

# Create and submit
exp = client.experiments.create({...})
client.experiments.submit(exp.experiment_id)

# Later: retrieve results
results = client.experiments.get_results(exp.experiment_id)
```

## Experiment Types

| Type | Method | Measures | Requires Target |
|---|---|---|---|
| `affinity` | `bli` or `spr` | KD, kon, koff kinetics | Yes |
| `screening` | `bli` or `spr` | Yes/no binding | Yes |
| `thermostability` | — | Melting temperature (Tm) | No |
| `expression` | — | Expression yield | No |
| `fluorescence` | — | Fluorescence intensity | No |

## Experiment Lifecycle

```
Draft → WaitingForConfirmation → QuoteSent → WaitingForMaterials → InQueue → InProduction → DataAnalysis → InReview → Done
```

| Status | Who Acts | Description |
|---|---|---|
| `Draft` | You | Editable, no cost commitment |
| `WaitingForConfirmation` | Adaptyv | Under review, quote being prepared |
| `QuoteSent` | You | Review and confirm the quote |
| `WaitingForMaterials` | Adaptyv | Gene fragments and target ordered |
| `InQueue` | Adaptyv | Materials arrived, queued for lab |
| `InProduction` | Adaptyv | Assay running |
| `DataAnalysis` | Adaptyv | Raw data processing and QC |
| `InReview` | Adaptyv | Final validation |
| `Done` | You | Results available |
| `Canceled` | Either | Experiment canceled |

The `results_status` field on an experiment tracks: `none`, `partial`, or `all`.

## Common Workflows

### 1. Submit a Binding Screen (Step by Step)

```python
# 1. Find a target
targets = client.targets.list(search="EGFR", selfservice_only=True)
target_id = targets.items[0].id

# 2. Preview cost
estimate = client.experiments.cost_estimate({
    "experiment_spec": {
        "experiment_type": "screening",
        "method": "bli",
        "target_id": target_id,
        "sequences": {"seq1": "EVQLVESGGGLVQ...", "seq2": "MKVLVAG..."},
        "n_replicates": 3
    }
})

# 3. Create experiment (starts as Draft)
exp = client.experiments.create({
    "name": "EGFR binder screen batch 1",
    "experiment_spec": {
        "experiment_type": "screening",
        "method": "bli",
        "target_id": target_id,
        "sequences": {"seq1": "EVQLVESGGGLVQ...", "seq2": "MKVLVAG..."},
        "n_replicates": 3
    }
})

# 4. Submit for review
client.experiments.submit(exp.experiment_id)

# 5. Poll or use webhooks until Done
# 6. Retrieve results
results = client.experiments.get_results(exp.experiment_id)
```

### 2. Automated Pipeline (Skip Draft + Auto-Accept Quote)

```python
exp = client.experiments.create({
    "name": "Auto pipeline run",
    "experiment_spec": {...},
    "skip_draft": True,
    "auto_accept_quote": True,
    "webhook_url": "https://my-server.com/webhook"
})
# Webhook fires on each status transition; poll or wait for Done
```

### 3. Using Webhooks

Pass `webhook_url` when crea