adaptyv
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval.
Skill by shushuzn
Install
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About
Use cases: design, backend
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What's inside
- Skill name
adaptyv- Description
- How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
- License
- GPL-3.0
- Instructions
- 207 lines in SKILL.md · view on GitHub
Before installing
- Instructions call the network
Files (2)
- SKILL.md
- api-endpoints.md · references/
SKILL.md (first lines)
# 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.
## 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.
```bash
export FOUNDRY_API_TOKEN="abs0_..."
curl https://foundry-api-public.adaptyvbio.com/api/v1/targets?limit=3 \
-H "Authorization: Bearer $FOUNDRY_API_TOKEN"
```
Every request except `GET /openapi.json` requires authentication. Store tokens in environment variables or `.env` files — never commit them to source control.
## Python SDK
Install: `uv add adaptyv-sdk` (falls back to `uv pip install adaptyv-sdk` if no `pyproject.toml` exists)
**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
```
### 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
from adaptyv import FoundryClient
client = FoundryClient(api_key="...", base_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..."},From shushuzn/Rairos, GPL-3.0.
Signals
- Stars
- 5
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- May 17, 2026 (5 months ago)
- Last release
- May 17, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- GPL-3.0
- Repository
- github.com/shushuzn/Rairos
- Website
- shushuzn.github.io/Rairos/
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
adaptyv skill. Agent Mart. https://agentmart.dev/skills/shushuzn-adaptyv (last updated 2026-10-24)