Platform

Open-source building blocks

LLM Works provides a modular open-source platform that organizes the infrastructure layer between LLMs and agents. The libraries are hosted on GitHub and published on PyPI.

appinfra

Reliable infrastructure: config, logging, scheduling, lifecycle.

llm-saia

Clean LLM communication: structured requests, typed responses, guards.

llm-infer

Inference across backends: same code across providers.

llm-kelt

Feedback collection, context management, fine-tuning.

llm-gent

Trait-based agents: explicit capabilities and configuration.

To GitHub →

Open-source grading

Scores below are produced by the open-source grade-python-project grader, which runs against any Python project and has inspectable criteria. appinfra scoreboard:

Category	Score			Verified by
Architecture & Design	10.0	/	10	22 packages, `py.typed`
Code Quality	10.0	/	10	mypy strict, ~200 files
Security	10.0	/	10	172 dedicated security tests
Testing	10.0	/	10	95% coverage, ~4800 tests
Documentation	9.5	/	10	15 guides, 18 API refs
Production Readiness	10.0	/	10	PyPI, semver, deprecations
Dependencies	10.0	/	10	clean `pip-audit`

Graded on June 24, 2026 against 81ef184 with Opus 4.7. These scores can be independently verified by running the grader against this commit with any model. Any LLM can review the criteria at the grader repo.

Example

An appinfra CLI creates a SAIA object, connects to an Anthropic backend via llm-infer, and sends one request.

import sys, asyncio
from appinfra.app import AppBuilder
from llm_infer.client import Factory, SAIAAdapter
from llm_saia import SAIA

# build the CLI app via appinfra (provides self.lg, self.args, app.main)
app = AppBuilder("hello-llm").build()

# bind run() as the "run" subcommand with a positional "prompt" argument
@app.tool(name="run")
@app.argument("prompt")
def run(self):
    asyncio.run(_send(self.lg, self.args.prompt))

async def _send(lg, prompt):
    # open an async Anthropic client via llm-infer
    async with Factory(lg).anthropic() as client:
        # build a SAIA session using the llm-infer client as its backend
        saia = SAIA.builder().backend(SAIAAdapter(client)).build()
        # invoke the instruct verb to get a typed response
        print((await saia.instruct(prompt)).value)

if __name__ == "__main__":
    sys.exit(app.main())

Usage:

$ pip install appinfra llm-saia "llm-infer[anthropic,saia]"
$ export ANTHROPIC_API_KEY=sk-ant-...
$ python app.py run "What is systems engineering?"