Quickstart

Install the package:

pip install biolmai

Basic usage (one-off calls with biolm()). These calls are synchronous (blocking) but use an async backend internally, so you get concurrent batch performance without writing async code.

from biolmai import biolm

# Encode a single sequence
result = biolm(entity="esm2-8m", action="encode", type="sequence", items="MSILVTRPSPAGEEL")

# Predict a batch of sequences
result = biolm(entity="esmfold", action="predict", type="sequence", items=["SEQ1", "SEQ2"])

# Write results to disk
biolm(entity="esmfold", action="predict", type="sequence", items=["SEQ1", "SEQ2"], output='disk', file_path="results.jsonl")

# Check for errors when not raising (raise_httpx=False)
result = biolm(entity="esmfold", action="predict", type="sequence", items=["SEQ1", "BADSEQ"], raise_httpx=False)
for r in result:
    if isinstance(r, dict) and "error" in r:
        print("Error:", r["error"])
    else:
        print("OK")

Or use the class-based Model when you’re working with one model and want to call .encode(), .predict(), or .generate() on it:

from biolmai import Model

model = Model("esm2-8m")
result = model.encode(type="sequence", items="MSILVTRPSPAGEEL")

model = Model("esmfold")
result = model.predict(type="sequence", items=["SEQ1", "SEQ2"])

For core concepts (sync/async, batching, error handling, etc.), see Concepts. For SDK usage and examples, see Models.