AIBackends

Run AI tasks and workflows locally.

Build extraction, classification, embeddings, redaction, and analysis pipelines in plain Python with llamacpp and transformers.

• First-class llamacpp and transformers runtimes
• Typed outputs for extraction and analysis tasks
• Reusable tasks and workflows for scripts, apps, and batch jobs
• Practical local examples for text, image OCR, documents, audio, and video

Install

pip install aibackends

# Local runtimes
pip install aibackends[llamacpp]
pip install aibackends[llamacpp-cuda]
pip install aibackends[llamacpp-metal]
pip install aibackends[transformers]

# Capability extras
pip install aibackends[pdf]
pip install aibackends[audio]
pip install aibackends[video]
pip install aibackends[pii]

For GPU clouds (RunPod, Modal, ...), a CUDA-enabled Dockerfile is included; see docs/docker.md.

Quickstart

Extract an invoice locally

from aibackends.models import GEMMA4_E2B
from aibackends.runtimes import LLAMACPP
from aibackends.tasks import ExtractInvoiceTask, create_task

task = create_task(
    ExtractInvoiceTask,
    runtime=LLAMACPP,
    model=GEMMA4_E2B,
)

result = task.run("invoice.pdf")
print(result.total)

Examples

Single tasks

Classify text locally and redact PII

from aibackends.models import GEMMA4_E2B
from aibackends.runtimes import LLAMACPP
from aibackends.tasks import ClassifyTask, RedactPIITask, create_task

classifier = create_task(
    ClassifyTask,
    runtime=LLAMACPP,
    model=GEMMA4_E2B,
    labels=["invoice", "contract", "receipt"],
)
redactor = create_task(
    RedactPIITask,
    backend="gliner",
    labels=["email", "phone_number"],
)

classification = classifier.run("invoice text")
redacted = redactor.run("john@example.com called from +1 555 0100")

RedactPIITask uses a dedicated backend such as gliner or openai-privacy (the local privacy-filter model) rather than the general LLM runtime interface.

Generate local embeddings

from aibackends.models import MINILM_L6
from aibackends.runtimes import TRANSFORMERS
from aibackends.tasks import EmbedTask, create_task

embedder = create_task(
    EmbedTask,
    runtime=TRANSFORMERS,
    model=MINILM_L6,
)

vector = embedder.run("Payments failed after checkout deploy.")
print(len(vector))
print(vector[:5])

Workflows

Batch-process sales calls analysis locally

from pathlib import Path

from aibackends.models import GEMMA4_E2B
from aibackends.runtimes import LLAMACPP
from aibackends.workflows import SalesCallAnalyser, create_workflow

workflow = create_workflow(
    SalesCallAnalyser,
    runtime=LLAMACPP,
    model=GEMMA4_E2B,
)

results = workflow.run_batch(
    inputs=Path("./calls").glob("*.m4a"),
    max_concurrency=4,
    on_error="collect",
)

Run OCR locally

from pydantic import BaseModel, Field

from aibackends.models import QWEN3_VL_4B
from aibackends.runtimes import LLAMACPP
from aibackends.schemas.common import LineItem
from aibackends.steps.enrich import VisionExtractor
from aibackends.steps.ingest import ImageIngestor
from aibackends.workflows import Pipeline


class Receipt(BaseModel):
    merchant: str | None = None
    total: float | None = None
    line_items: list[LineItem] = Field(default_factory=list)


class ReceiptOCR(Pipeline):
    steps = [
        ImageIngestor(),
        VisionExtractor(
            schema=Receipt,
            prompt="Extract merchant, total, and line_items from this receipt.",
        ),
    ]


result = ReceiptOCR(runtime=LLAMACPP, model=QWEN3_VL_4B).run("receipt.jpeg")
print(result.model_dump_json(indent=2))

Swap QWEN3_VL_4B for LFM25_VL_3B (LiquidAI LFM2.5-VL-3B) for a smaller vision model that runs on CPU with the default Q4_K_M GGUF; add device="cpu" to force CPU inference. CPU latency numbers are committed in benchmarks/reports/.

Tool calling

Run a local agent loop with LiquidAI LFM2.5-2.6B

from aibackends import get_runtime
from aibackends.models import LFM25_2_6B
from aibackends.runtimes import LLAMACPP

runtime = get_runtime(
    {
        "runtime": LLAMACPP,
        "model": LFM25_2_6B,
        "device": "cpu",          # "cpu" | "gpu" | None for auto-detect
        "quantization": "Q4_K_M",  # default; use Q8_0 etc. for higher capacity
    }
)
response = runtime.complete(
    [{"role": "user", "content": "What is the weather in Paris right now?"}]
)

See examples/tasks/tool_calling_lfm.py for the full tool-calling loop with LFM2.5's native Pythonic tool-call format.

Included

• Local runtimes: llamacpp, transformers
• Tasks: summarize, extract, classify, embed, extract_invoice, redact_pii, analyse_sales_call, analyse_video_ad
• Workflows: InvoiceProcessor, PIIRedactor, SalesCallAnalyser, VideoAdIntelligence
• Outputs: InvoiceOutput, SalesCallReport, VideoAdReport, RedactedText, Classification

Tool and agent integrations can be added later without changing the core task and workflow layer.

CLI

# Install the runtime or backend extra first
pip install 'aibackends[llamacpp]'
pip install 'aibackends[pii]'

aibackends task extract-invoice --input invoice.pdf --runtime llamacpp --model gemma4-e2b
aibackends task classify --input doc.txt --labels invoice,contract,receipt --runtime llamacpp --model gemma4-e2b
aibackends task redact-pii --input transcript.txt --backend gliner --labels email,phone_number
aibackends pull gemma4-e2b --runtime llamacpp
aibackends check llamacpp --model gemma4-e2b

Full command reference: docs/cli.md.

Docs and Examples

• docs/usage.md for install, local runtimes, tasks, and workflows
• docs/concepts.md for task, runtime, backend, model, and workflow terms
• docs/extending.md for custom runtimes, backends, tasks, and workflows
• docs/api-reference/index.md for the public API
• examples/README.md for runnable examples, including local image OCR
• benchmarks/README.md for latency benchmarks, evals/README.md for accuracy evals (e.g. tool-call accuracy)

Development

python3 -m pip install -e ".[dev]"
python3 -m pytest tests
python3 -m mypy src tests
ruff check .

See CONTRIBUTING.md for contribution guidelines.