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 · llms.txt index · OpenAPI spec

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.

The official API docs 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.

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:

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

In a project with pyproject.toml:

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

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
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

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

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)

# 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.

Composing with the rest of the bundle

Adaptyv is where computational designs stop being hypotheses. Everything upstream produces candidates; this produces measurements.

• protein-binder-design → before: BindCraft or RFdiffusion designs, already filtered on interface metrics. Submitting an unfiltered design set wastes the assay.
• esm → before: embeddings, likelihoods and generated sequences. Model likelihood is not binding — that is precisely what a BLI/SPR run settles.
• antibody-engineering / immunogenicity / glycoengineering → before: sequence liabilities, T-cell epitopes and sequons are cheaper to fix in silico than to discover in an expression run.
• boltz / tamarind → before: a predicted complex is a reason to order the experiment, not a substitute for it.
• uniprot-rcsb → before: the target construct and its boundaries.

Report measured values as measured and predicted values as predicted, and keep the design provenance with the result — an assay is only worth its traceability back to which design it tested.