Skip to main content

Official Python SDK and CLI for the Fastfold API

Project description

Fastfold SDK

Python SDK and CLI for Fastfold jobs, workflows, library operations, and reports.

Open In Colab

Installation

Install from PyPI:

pip install fastfold-ai

Or for development:

pip install -e .

Requires Python 3.8+.

Authentication

Set your API key in the environment:

export FASTFOLD_API_KEY="sk-...your-api-key"

You can also pass an API key when creating the client or via the CLI flag --api-key.

SDK Usage

The SDK exposes both typed helpers and capability-oriented services:

  • client.fold for the simplest fold-job flow
  • client.jobs for raw payloads, YAML submission, polling, and rendering helpers
  • client.workflows for generic workflow create/get/status/task-results/execute/YAML APIs
  • client.library for library item creation and file uploads
  • client.openmm, client.openmmdl, client.evolla, and client.boltzgen for the most common multi-step workflow flows
  • client.reports for Slack markdown report submission

For end-to-end walkthroughs, downloadable input files, and additional variants, see:

Fold job

Create a fold job, wait for completion, then inspect the returned artifacts:

from fastfold import Client

client = Client()

job = client.fold.create(
    sequence="LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES",
    model="boltz-2",
    is_public=True,
)

results = client.jobs.wait_for_completion(job.id, poll_interval=5.0, timeout=900.0)
print(results.job.status)
print(results.cif_url())
print(results.metrics().mean_PLDDT)
print(results.get_viewer_link())

OpenMM first run from local files

Submit an OpenMM workflow from a local structure file and its matching PAE JSON:

from fastfold import Client

client = Client()
workflow = client.openmm.submit_from_manual_files(
    pdb_path="./protein.pdb",
    pae_path="./pae.json",
    simulation_name="AF-P00698",
    residue_profile="calvados3",
    temp=293.15,
    ionic=0.15,
    ph=7.5,
    step_size_ns=0.01,
    sim_length_ns=10.0,
    box_length=50,
)
print(workflow.workflow_id)

OpenMMDL from local files

Submit an OpenMMDL workflow from local topology and ligand files:

from fastfold import Client

client = Client()
workflow = client.openmmdl.submit_from_local_files(
    topology_path="./KEAP1kd.pdb",
    ligand_paths=["./IQK.sdf"],
    simulation_name="KEAP1 + IQK",
    input_json={
        "smallMoleculeMode": "single",
        "equilibration": "only_minimization",
        "sim_length_ns": 0.05,
        "step_time_ps": 0.002,
        "failure_retries": 0,
        "addWater": False,
        "addMembrane": False,
        "boxType": "geometry",
        "geomPadding": 1.0,
        "geometryDropdown": "cube",
        "membranePadding": 2.0,
        "writeDCD": True,
        "dcdFrames": 5,
        "pdbInterval_ns": 0.05,
        "writeData": False,
        "writeCheckpoint": False,
    },
)
print(workflow.workflow_id)

Evolla from a local structure file

Upload a .cif / .mmcif / .pdb to your library and start Evolla in one call:

from fastfold import Client

client = Client()
workflow = client.evolla.submit_from_local_file(
    "./structure.cif",
    "What is the likely function of this domain?",
)
print(workflow.workflow_id)

Evolla from a completed fold job

Ask a natural-language question about a structure from an existing fold (uses the same workflow_input shape as the web app: sourceType / targetSource sequence, artifact URL, and ids):

from fastfold import Client

client = Client()
workflow = client.evolla.submit_from_fold_job(
    "YOUR_JOB_ID",
    "What is the likely function of this domain?",
)
print(workflow.workflow_id)

If the CIF URL in job results is not a signed path that embeds your user id, pass source_user_id="..." or set FASTFOLD_EVOLLA_SOURCE_USER_ID. See Evolla.

BoltzGen minimal workflow

Create a draft BoltzGen workflow, upload a minimal workflow.yml, and execute it:

from pathlib import Path

from fastfold import Client

client = Client()
draft = client.boltzgen.create_draft(name="boltzgen_demo")
client.boltzgen.upsert_workflow_yml(
    draft.workflow_id,
    Path("fastfold/examples/boltzgen/minimal.workflow.yml").read_text(),
)
client.boltzgen.execute(draft.workflow_id)
print(draft.workflow_id)

For preset bundles, downloadable design-spec files, and multi-spec examples, see BoltzGen.

Slack report sharing

Send a markdown report and optionally persist it as a library item:

from fastfold import Client

client = Client()
result = client.reports.send_agent_cli_report(
    "## Demo Report\n\n- Workflow completed.\n- Artifacts are ready.",
    report_name="demo_report",
)
print(result.ok, result.library_item_id)

CLI Usage

The CLI keeps fastfold-cli fold working, but it now also exposes resource-oriented subcommands. For the complete command matrix, see CLI.

# Fold job
fastfold-cli fold --sequence "LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES" --model boltz-2

# OpenMM first run from local files
fastfold-cli workflows openmm from-manual-files \
  --pdb ./protein.pdb \
  --pae ./pae.json \
  --simulation-name AF-P00698 \
  --force-field calvados3 \
  --temperature 293.15 \
  --ionic 0.15 \
  --ph 7.5 \
  --step-size-ns 0.01 \
  --sim-length-ns 10 \
  --box-length 50

# OpenMMDL from local files
fastfold-cli workflows openmmdl from-local-files \
  --topology ./KEAP1kd.pdb \
  --ligand ./IQK.sdf \
  --simulation-name "KEAP1 + IQK" \
  --input-json fastfold/examples/openmmdl/workflow_input.json

# Evolla from a local structure
fastfold-cli workflows evolla from-file ./structure.cif --question "What is the function of this protein?"

# Evolla from fold results
fastfold-cli workflows evolla from-fold-job YOUR_JOB_ID --question "What is the function of this protein?"

# BoltzGen draft
fastfold-cli workflows boltzgen create-draft --name demo

# Report sharing
fastfold-cli reports slack --markdown-file fastfold/examples/reports/sample_report.md

Most create and inspection commands are script-friendly: they print IDs by default, or full JSON with --json.

Packaged Examples

Small, reusable text assets ship under fastfold/examples/:

  • fastfold/examples/fold/job_payload.json
  • fastfold/examples/openmm/from_manual_files.json
  • fastfold/examples/openmm/from_fold_job.json
  • fastfold/examples/openmmdl/workflow_input.json
  • fastfold/examples/openmmdl/from_local_files.json
  • fastfold/examples/openmmdl/quick_water_box.workflow_input.json
  • fastfold/examples/openmmdl/quick_membrane.workflow_input.json
  • fastfold/examples/evolla/from_fold_job.template.json
  • fastfold/examples/boltzgen/minimal.workflow.yml
  • fastfold/examples/boltzgen/design_spec.example.yaml
  • fastfold/examples/boltzgen/replacements.example.json
  • fastfold/examples/fold/boltz2_affinity_input.yaml
  • fastfold/examples/reports/sample_report.md

Larger reference bundles and downloadable preset files live in the docs:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fastfold_ai-0.2.3.tar.gz (28.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

fastfold_ai-0.2.3-py3-none-any.whl (37.6 kB view details)

Uploaded Python 3

File details

Details for the file fastfold_ai-0.2.3.tar.gz.

File metadata

  • Download URL: fastfold_ai-0.2.3.tar.gz
  • Upload date:
  • Size: 28.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for fastfold_ai-0.2.3.tar.gz
Algorithm Hash digest
SHA256 c38cedaa34515604904bb8a37548b1b102bd96692649645b2c3f28f360ead3c6
MD5 251c85610454a7143d3393892c68f3f2
BLAKE2b-256 b338e51710ec7b257e00073eea6380fac91ec69e4cffa6488959dada0342855a

See more details on using hashes here.

Provenance

The following attestation bundles were made for fastfold_ai-0.2.3.tar.gz:

Publisher: python-publish.yml on fastfold-ai/fastfold-python

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file fastfold_ai-0.2.3-py3-none-any.whl.

File metadata

  • Download URL: fastfold_ai-0.2.3-py3-none-any.whl
  • Upload date:
  • Size: 37.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for fastfold_ai-0.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 86aa8062f15692d13c732306dd2d12a8faca7c4a07c820caad63c1234d0e2532
MD5 546fce403aeaf36128645e1010a87ced
BLAKE2b-256 1de2735c850f24ac78a8158f145e37d1cc67209ff0114e4dd8fa9e380d621ff5

See more details on using hashes here.

Provenance

The following attestation bundles were made for fastfold_ai-0.2.3-py3-none-any.whl:

Publisher: python-publish.yml on fastfold-ai/fastfold-python

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page