chumak 0.2.0

Thin inference substrate: user-authored profiles, LangChain as a handler, optional provenance.

chumak

A thin inference substrate for Python projects: user-authored profiles, LangChain as a handler, optional provenance/meta on every response.

Chumaks (Чумаки) were wandering Ukrainian salt-traders who traversed the steppe between distant places. They named the Milky Way after themselves — Чумацький Шлях, the Chumaks' Way — because they navigated by it.

What it is

A small library that abstracts away which LLM you're calling and how.

1. Author profiles (TOML files) under the app's XDG config dir.
2. Load a profile via ProfileLoader (with inheritance + env-var overrides)
3. Call infer(prompt=..., output_schema=..., profile=...) and get back a validated pydantic payload, normalised citations, and (optionally) a provenance Meta stamp. output_schema is optional: omit it for an untyped call whose payload is the model's plain response text (langchain handler only — see below).

Two built-in handlers:

• langchain — uses langchain.chat_models.init_chat_model(profile.model) so a single identifier (anthropic:claude-opus-4-7, openai:gpt-5, …) routes to the right provider. Structured output, citations, and token usage all handled. With an output_schema the call returns a validated instance; without one it returns the model's plain response text (a liveness/smoke probe or one-shot free-text question).
• subprocess — shells out to a CLI (claude --print, codex exec, etc.). Useful for prompt iteration via an existing, authorised tool. Schema is injected into the prompt as JSON Schema; stdout is parsed and validated. Requires an output_schema — untyped generation is a langchain-handler capability.

Profiles

Profiles are user-authored. chumak ships at most one generic example (anthropic-claude-opus-4-7 via the LangChain handler). Everything else is yours.

Profiles live in consumer app directory, e.g. ~/.config/<your-app>/chumak/profiles/. chumak does not impose a config dir; the app passes search_paths to ProfileLoader.

File shape

# ~/.config/galops-vision/chumak/profiles/claude.toml
handler = "langchain"
model = "anthropic:claude-opus-4-7"
temperature = 0.0

[model_kwargs]
max_tokens = 4096

Inheritance

# claude-account-b.toml
extends = "claude"

[model_kwargs]
# api_key sourced from env — see below

Env-var overlay

Every field on a profile is overridable from the environment. chumak does not provision special fields; the convention is uniform:

{APP_PREFIX}_PROFILE_{PROFILE_NAME}_{FIELD_PATH}

with __ as the nested-field delimiter (single _ stays inside field names):

# top-level field
export MYAPP_VISION_PROFILE_CLAUDE_MODEL=anthropic:claude-haiku-4-5

# nested into model_kwargs
export MYAPP_VISION_PROFILE_CLAUDE_ACCOUNT_B_MODEL_KWARGS__API_KEY=sk-ant-...

This means a profile file can be effectively empty on disk (just declaring the profile's existence and maybe an extends), with all values supplied by the environment. You decide which fields are sensitive and never touch disk.

Usage

from pathlib import Path
from chumak import ProfileLoader, infer

loader = ProfileLoader(
    search_paths=[Path.home() / ".config/my-app/chumak/profiles"],
    env_prefix="MYAPP",
)
loader.names()                # -> ["claude", "claude-creative", ...]
profile = loader.load("claude")

from pydantic import BaseModel

class AnneSchema(BaseModel):
    title: str
    value: int

result = infer(
    prompt="Extract title and value from this text: ...",
    output_schema=AnneSchema,
    profile=profile,
)
result.payload          # -> MissionTitle(title=..., bounty=...)
result.citations        # -> [Citation, ...] (if the model supplied any)
result.meta             # -> Meta with cost, generated_at, model identity

With provenance

from chumak import Provenance

result = infer(
    prompt="...",
    output_schema=AnneSchema,
    profile=profile,
    provenance=Provenance(
        artefact_type="model@v1",
        artefact_id="artifact-type:2026-05-20T12:34:56Z",
    ),
)
result.meta.artefact_type    # "mission_title@v1"
result.meta.derived_from     # [...]

Design notes

• No domain knowledge: chumak carries no built-in prompts, no role concepts (tactical/narrator etc. — that's an app concern; just name your profile).
• LangChain is a handler, not the spine: subprocess CLIs are first-class.
• Provenance is opt-in: omit provenance= and meta.artefact_type is None.
• The lib never reads env directly for its own settings. The env overlay for profiles is a deliberate, scoped exception, gated on the prefix the consumer passes in.

Tooling

uv, Python 3.12+, ruff, ty, pytest. See CONTRIBUTING.md for dev setup, the integration test, and quality-check commands.