strata-forge

Typed, async-first toolkit for AI/LLM work — inference, evaluation, fine-tuning, retrieval, and remote compute orchestration across the major foundation-model providers.

Built on LiteLLM for provider breadth and Pydantic v2 for strict typing. The public API is async, with sync wrappers in strata_forge.sync for CLI and notebook ergonomics. Heavy optional dependencies (training, serving, remote compute) live behind extras, so the base install stays light.

Install

pip install strata-forge

Requires Python 3.14+.

Optional extras pull in the heavy dependencies only when you need them:

pip install "strata-forge[rag]"        # embeddings, vector store, reranking
pip install "strata-forge[compute]"    # SkyPilot + SSH remote execution
pip install "strata-forge[serving]"    # vLLM
pip install "strata-forge[finetuning]" # torch, transformers, trl, peft
pip install "strata-forge[all]"        # everything

Full set: redis, multimodal, inspect, langfuse, hf, evals, rag, compute, serving, bedrock, storage, finetuning, all.

Quickstart

import asyncio

from strata_forge.llm import LLMClient, Message


async def main() -> None:
    client = LLMClient("gpt-5.5", provider="openai")
    response = await client.complete([Message.user("Say hello in one short sentence.")])
    print(response.text)


asyncio.run(main())

Provider credentials are read from the environment (for example OPENAI_API_KEY). A CLI is installed as strata-forge.

What's in it

Module Purpose
strata_forge.llm Provider abstraction, structured output, tool calling, two-axis fallback
strata_forge.core Cross-cutting errors, retry, logging, budget ceilings, reproducibility
strata_forge.config Pydantic Settings root with YAML overlays and .env support
strata_forge.prompts Jinja2 and Langfuse-backed prompt registry
strata_forge.tracing Langfuse observability
strata_forge.datasets Dataset CRUD with a Hugging Face bridge
strata_forge.evals Experiment runner, graders, metrics
strata_forge.agents Agent runtime, memory, multi-agent composition
strata_forge.rag Embed, chunk, store, retrieve, rerank
strata_forge.storage fsspec gateway and Hugging Face Hub
strata_forge.compute SkyPilot and SSH backends, serving, batch inference
strata_forge.training SFT / DPO / ORPO / KTO / GRPO with PEFT
strata_forge.pipelines Runnable entrypoints executed on a compute target

Documentation

Reference docs live in the repository:

Development

uv sync        # materialize the venv + lockfile
make check     # lint (ruff) + type-check (pyright strict)
make test      # unit tests

License

Apache License 2.0.

Project links

Data verified by PyPI on Aug 6, 2026
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Key dates

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  • Released:
Newer release available (0.1.0)

1 maintainer

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Avatar for Gemovic from gravatar.com Gemovic

Credits

Author: Nemanja Gemovic

GitHub Statistics

Data verified by PyPI on Aug 6, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.14

Provides Extra

all bedrock compute evals finetuning hf inspect langfuse multimodal rag redis serving storage

Tags

agents ai async evaluation fine-tuning llm rag

Classifiers

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