slimx 1.0.0

A slim, intuitive, lightweight Python library for calling LLMs (high-level + low-level) with multi-provider support.

SlimX

The LLM runtime you can actually read. A tiny, inspectable, vendor-neutral Python library for calling LLMs — one API across OpenAI, Anthropic, Gemini, Ollama, and any OpenAI-compatible server.

from slimx import llm

m = llm("anthropic:claude-haiku-4-5")
print(m("Hello, world").text)

Change the provider by changing the string — the rest of your code stays the same.

Why SlimX

• One API, every model — OpenAI, Anthropic, Gemini, Ollama, and OpenAI-compatible servers (vLLM, llama.cpp, LM Studio, …). No lock-in.
• See exactly what's sent — dry-run the precise request before it leaves, hook every call, and save reproducible call records. Glass box, not black box.
• Tiny & readable — ~3,000 lines of code, one dependency (httpx), fully typed. Read the whole thing in an afternoon.
• Call many models at once — parallel(...) to compare answers, race for the fastest, or let a judge model pick the best.
• Explicit, with batteries — tools, streaming, structured output with auto-repair, a two-layer high/low API, conformance-tested providers, and a slimx CLI.

# See what SlimX would send — exact URL, headers (secrets redacted), body — no network call:
print(llm("openai:gpt-4.1-nano").inspect("Hello").pretty())

# Ask several models and let one judge the best answer:
from slimx import parallel
best = parallel(
    ["openai:gpt-4.1-mini", "google:gemini-3.5-flash"],
    mode="judge", judge="anthropic:claude-haiku-4-5",
)
print(best("Explain SlimX in one line.").text)

Going deeper: ARCHITECTURE.md is a diagram-driven tour of the runtime; DEVELOPMENT.md is the engineering charter and Provider Contract.

---

Install

For users

Create a new project and install SlimX:

uv init my-project
cd my-project
uv add slimx

Run Python through uv so it uses the project virtual environment:

uv run python

Or install with pip:

pip install slimx

For contributors

git clone https://github.com/slimx-ai/slimx.git
cd slimx
uv sync --all-extras
uv run pytest -q

uv sync reads pyproject.toml and uv.lock when present.

uv.lock is committed to help contributors reproduce the development environment.

---

Supported providers

Provider	Prefix	Environment variable	Notes
OpenAI	openai:	OPENAI_API_KEY	Default provider when no prefix is given
OpenAI-compatible	oai:	OpenAI-compatible /v1/chat/completions API	vLLM, LM Studio, llama.cpp server, LocalAI, Ollama /v1, internal gateways
Google Gemini	google:	GOOGLE_API_KEY or GEMINI_API_KEY	Supports chat, streaming, JSON output, and tools
Anthropic	anthropic:	ANTHROPIC_API_KEY	Claude Messages API; supports chat, JSON output, and tools
Ollama	ollama:	optional OLLAMA_BASE_URL	Local models through Ollama

---

Inspect provider capabilities

Check what a provider supports before runtime — no API key or running server required:

from slimx.providers import describe_provider

describe_provider("google")
# {'name': 'google', 'native': True, 'tools': True, 'structured_output': True,
#  'streaming': True, 'async_chat': False, 'async_streaming': False}

from slimx import llm
llm("openai:gpt-4.1-nano").capabilities.tools  # True

Every provider is checked against a shared conformance suite (tests/conformance/), so declared capabilities always match real behavior. See docs: Provider Capabilities and docs: OpenAI-compatible servers.

---

Configure providers

OpenAI

export OPENAI_API_KEY="..."
# optional:
export OPENAI_BASE_URL="https://api.openai.com/v1"

OpenAI-compatible servers

Use oai: for local or self-hosted servers that expose an OpenAI-compatible /v1/chat/completions API.

export SLIMX_OAI_BASE_URL="http://localhost:8000/v1"
export SLIMX_OAI_API_KEY="EMPTY"

SLIMX_OAI_API_KEY can be a real key for authenticated gateways, or EMPTY for local servers that ignore authentication.

Google Gemini

export GOOGLE_API_KEY="..."
# or:
export GEMINI_API_KEY="..."

# optional:
export GOOGLE_BASE_URL="https://generativelanguage.googleapis.com/v1beta"

Anthropic

export ANTHROPIC_API_KEY="..."
# optional:
export ANTHROPIC_BASE_URL="https://api.anthropic.com"
export ANTHROPIC_VERSION="2023-06-01"

Ollama local models

export OLLAMA_BASE_URL="http://localhost:11434"

For Ollama, make sure the server is running and the model is available:

ollama serve

In another terminal:

ollama pull llama3.2:3b
ollama list

---

Quickstart

OpenAI

from slimx import llm

m = llm("openai:gpt-4.1-nano", temperature=0.2)
res = m("Write a haiku about fog and streetlights.")

print(res.text)

OpenAI-compatible local/self-hosted server

from slimx import llm

m = llm(
    "oai:Qwen/Qwen2.5-7B-Instruct",
    provider_kwargs={
        "base_url": "http://localhost:8000/v1",
        "api_key": "EMPTY",
    },
    timeout=120,
)

res = m("Explain why compatibility APIs are useful for local model serving.")

print(res.text)

Google Gemini

from slimx import llm

m = llm("google:gemini-3.5-flash", temperature=0.2)
res = m("Write a haiku about small, inspectable AI software.")

print(res.text)

Ollama local model

from slimx import llm

m = llm("ollama:llama3.2:3b", temperature=0.2, timeout=120)
res = m("Explain why small libraries are easier to inspect.")

print(res.text)

---

Response structure

Calling a SlimX model returns a Result object.

from slimx import llm

m = llm("ollama:llama3.2:3b", timeout=120)
res = m("Explain why small libraries are easier to inspect.")

print(res.text)

A Result contains:

Result(
    text="...",          # Normalized assistant text
    raw={...},           # Raw provider response
    usage=Usage(...),    # Token usage when available
    tool_calls=[],       # Tool/function calls requested by the model
    data=None,           # Parsed structured output, used by .json(...)
    trace={...},         # Runtime metadata: provider, model, latency, retries, tools
)

Most applications should use:

print(res.text)

Use res.raw when you need provider-specific details, and res.trace when you want runtime diagnostics such as provider name, model name, elapsed time, retries, and tool-call count.

Streaming

from slimx import llm

m = llm("google:gemini-3.5-flash", temperature=0.2)

for ev in m.stream("Tell a short story in 5 lines."):
    if ev.type == "text_delta":
        print(ev.text, end="", flush=True)

print()

---

Tools

SlimX tools are provider-neutral. The same @tool interface can be used across providers that support tool/function calling.

from slimx import llm, tool


@tool
def add(a: int, b: int) -> int:
    "Add two integers."
    return a + b


m = llm("google:gemini-3.5-flash", tools=[add], tool_runtime="auto")
res = m("What is 12 + 30?")

print(res.text)

---

Parallel execution

Fan one prompt out to several models at once with parallel(...). Use mode="all" to compare every answer, or mode="race" for the first successful response.

from slimx import parallel

ensemble = parallel(["google:gemini-3.5-flash", "openai:gpt-4.1-nano"])
res = ensemble("Explain SlimX in one paragraph.")

for item in res.results:
    print(item.model, item.result.text if item.ok else item.error)

Failures are surfaced in res.errors (never swallowed) and each result keeps its raw provider response. See docs: Parallel execution.

---

Structured output

SlimX can parse structured JSON output into a dataclass.

from dataclasses import dataclass

from slimx import llm


@dataclass
class City:
    name: str
    country: str


m = llm("google:gemini-3.5-flash")
res = m.json("Paris is in France.", schema=City)

print(res.data)

---

Inspectability

See exactly what SlimX does — dry-run a request, observe calls with hooks, and save reproducible call records. No hosted platform, no extra dependency.

from slimx import llm, CallRecord

m = llm("openai:gpt-4.1-nano")

# 1) Dry-run: the exact request, secrets redacted, without sending it
print(m.inspect("Hello").pretty())

# 2) Hooks: observe every call (log it, push metrics, anything)
traced = llm("openai:gpt-4.1-nano", hooks={"after_call": print})

# 3) Reproducible records: save the whole call to JSON and reload it
res = m("Capital of France?")
res.to_record().save("run.json")
CallRecord.load("run.json")

See docs: Inspectability.

---

CLI & model discovery

Installing SlimX adds a slimx command (no extra dependencies):

slimx doctor              # which keys/servers are configured and reachable
slimx models ollama       # list models a provider exposes (no guessing model strings)
slimx providers           # registered providers + capabilities

slimx doctor is the fastest way to answer "why isn't my model working?" — usually a missing key or wrong base URL. The same discovery is available in code via list_models(...). See docs: CLI & discovery.

---

Low-level API

Use the low-level API when you want explicit control over messages, requests, clients, and providers.

from slimx import Message
from slimx.low import ChatRequest, Client
from slimx.providers import get_provider

provider = get_provider("google")
client = Client(provider, timeout=30, retries=2)

req = ChatRequest(
    model="gemini-3.5-flash",
    messages=[Message.user("Explain provider-neutral LLM clients in one paragraph.")],
    temperature=0.2,
)

res = client.chat(req)

print(res.text)
print(res.trace)

---

Provider plugins

SlimX supports third-party provider plugins through the slimx.providers entry point group.

Built-in providers are registered lazily, so importing slimx does not load provider modules or require API keys.

---

Stability

As of 1.0, SlimX commits to semantic versioning. The public API is stable:

• the top-level surface (llm, allm, Model, AsyncModel, tool, Message, Result, StreamEvent, ToolCall, Usage, InspectedRequest, CallRecord, parallel, list_models, describe_provider, and slimx.low's Client / ChatRequest),
• the Provider Contract that every provider implements (see DEVELOPMENT.md), which is enforced by the conformance suite in tests/conformance/.

Breaking changes to these will only land in a new major version. The package ships type information (PEP 561), so type checkers see SlimX's types out of the box.

Troubleshooting

ModuleNotFoundError: No module named 'slimx'

If you installed with uv add slimx, run Python through uv:

uv run python

Or activate the virtual environment first:

source .venv/bin/activate
python

Ollama model not found

Check which models are installed:

ollama list

Pull a model before using it:

ollama pull llama3.2:3b

Then use the exact model name:

m = llm("ollama:llama3.2:3b", timeout=120)

Ollama server not running

Start Ollama:

ollama serve

Then retry your SlimX script.

---

Development

Run the full validation suite before opening a pull request or tagging a release:

uv sync --all-extras
uv run ruff check .
uv run pyright
uv run pytest -q
uv run python -m build

---

Repo automation

This repository includes GitHub Actions for:

• CI (.github/workflows/ci.yml)
• Docs deployment to GitHub Pages (docs.yml)

See docs/ for more detailed documentation.