adaptyv

name	adaptyv
author	K-Dense, Inc.
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`.

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 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.

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):

ADAPTYV_API_KEY=your_api_key
ADAPTYV_API_URL=https://foundry-api-public.adaptyvbio.com/api/v1

Decorator Pattern

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

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)

# 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)

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 creating an experiment. Adaptyv POSTs to that URL on every status transition with the experiment ID, previous status, and new status.

Sequences

• Simple format: {"seq1": "EVQLVESGGGLVQPGGSLRLSCAAS"}
• Rich format: {"seq1": {"aa_string": "EVQLVESGGGLVQ...", "control": false, "metadata": {"type": "scfv"}}}
• Multi-chain: use colon separator — "MVLS:EVQL"
• Valid amino acids: A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y (case-insensitive, stored uppercase)
• Sequences can only be added to experiments in Draft status

Filtering, Sorting, and Pagination

All list endpoints support pagination (limit 1-100, default 50; offset), search (free-text on name fields), and sorting.

Filtering uses s-expression syntax via the filter query parameter:

• Comparison: eq(field,value), neq, gt, gte, lt, lte, contains(field,substring)
• Range/set: between(field,lo,hi), in(field,v1,v2,...)
• Logic: and(expr1,expr2,...), or(...), not(expr)
• Null: is_null(field), is_not_null(field)
• JSONB: at(field,key) — e.g., eq(at(metadata,score),42)
• Cast: float(), int(), text(), timestamp(), date()

Sorting uses asc(field) or desc(field), comma-separated (max 8):

sort=desc(created_at),asc(name)

Example: filter=and(gte(created_at,2026-01-01),eq(status,done))

Error Handling

All errors return:

{
  "error": "Human-readable description",
  "request_id": "req_019462a4-b1c2-7def-8901-23456789abcd"
}

The request_id is also in the x-request-id response header — include it when contacting support.

Token Management

Tokens use Biscuit-based cryptographic attenuation. You can create restricted tokens scoped by organization, resource type, actions (read/create/update), and expiry via POST /tokens/attenuate. Revoking a token (POST /tokens/revoke) revokes it and all its descendants.

Detailed API Reference

For the full list of all 32 endpoints with request/response schemas, read references/api-endpoints.md.