Metadata-Version: 2.5
Name: thunc
Version: 0.2.1
Summary: think + function: call an LLM like a typed Python function.
Project-URL: Homepage, https://eltarras.github.io/thunc/
Project-URL: Repository, https://github.com/Eltarras/thunc
Project-URL: Issues, https://github.com/Eltarras/thunc/issues
Author: Hussein Eltarras
License-Expression: MIT
License-File: LICENSE
Keywords: ai,claude,llm,openai,prompt,structured-output,typed
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development :: Libraries
Classifier: Typing :: Typed
Requires-Python: >=3.10
Provides-Extra: anthropic
Requires-Dist: anthropic; extra == 'anthropic'
Provides-Extra: dev
Requires-Dist: basedpyright; extra == 'dev'
Requires-Dist: mypy; extra == 'dev'
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Provides-Extra: openai
Requires-Dist: openai; extra == 'openai'
Provides-Extra: temporal
Requires-Dist: temporalio<2,>=1.34; extra == 'temporal'
Provides-Extra: temporal-test
Requires-Dist: pytest-asyncio>=0.24; extra == 'temporal-test'
Requires-Dist: temporalio==1.34.0; extra == 'temporal-test'
Description-Content-Type: text/markdown

![thunc: call an LLM like a typed Python function](https://raw.githubusercontent.com/Eltarras/thunc/main/.github/social-preview.png)

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**think + function.** Call an LLM like a typed Python function.

> **Status: beta (v0.2).** Expect bugs; the API may change. Feedback and issues are welcome.

```python
import thunc

thunc.configure(backend="claude-code")


@thunc.function
def urgency(ticket: str) -> int:
    """Rate how urgent this ticket is, from 1 (can wait) to 5 (customer is blocked)."""
    ...


urgency("I was charged twice!")  # -> 4, a checked int
```

The answer is parsed into the declared type. If it doesn't fit, the model is asked again, and
after that `thunc.ThuncError` is raised. The library uses the standard library only and needs
Python 3.10+.

It runs on the Claude API, the OpenAI API, a local model (LM Studio, or any server that speaks the
OpenAI API), or your Claude Code or Codex login.

## Install

```bash
pip install thunc               # standard library only
pip install "thunc[anthropic]"  # adds the Claude API backend
pip install "thunc[openai]"     # adds the OpenAI API backend
pip install "thunc[temporal]"   # adds durable agents on Temporal
```

## Try it

Clone the repo and run the examples from its root. No install is needed; the examples run through
your local [Claude Code](https://claude.com/claude-code) login:

```bash
git clone https://github.com/Eltarras/thunc && cd thunc
python3 -m examples.hello
python3 -m examples.support_inbox
THUNC_BACKEND=codex python3 -m examples.log_triage
```

With an API key instead, install the SDK and pick the backend: `THUNC_BACKEND=openai` with
`OPENAI_API_KEY`, or `THUNC_BACKEND=anthropic` with `ANTHROPIC_API_KEY`.

## Two ways to write a prompt

| | When | |
|---|---|---|
| `@thunc.function` | The prompt is fixed and should read like code | The docstring is the prompt, the parameters are the inputs, the return annotation is the type |
| `thunc.call(...)` | The prompt is built in code (from config, in a loop, loaded from a file) | `thunc.call(f"Translate into {lang}.", {"text": note})` |

`@thunc.function(instructions=some_string)` combines the two: a typed, reusable function whose
prompt is generated.

**Keep user data out of the instructions.** Your own text can go in the instructions string.
Anything from users, files or the web goes in the inputs:

- `@thunc.function` does this automatically.
- With `thunc.call` it's up to you. In a live test, a hostile email pasted in with an f-string
  tricked the model 3 out of 3 times. Passed as an input, it failed 3 out of 3 times.

## API

| | |
|---|---|
| `@thunc.function` | Turns a signature + docstring into an AI-backed function. Options: `instructions=`, `system=`, `ensure=`, `retries=`, `backend=`, `model=`, `cache=`. The body must be empty (`...`); real code raises `TypeError`. `async def` works |
| `thunc.call(instructions, inputs=None, *, returns=str, ensure=None, retries=2, backend=None, model=None, system=None, cache=False, name=None)` | One prompt. Inputs are sent separately from the instructions. `name=` groups its cached answers |
| `thunc.map(func, items, workers=8)` | Runs calls in parallel, keeping the input order. Each call takes 4–8s, so this is the main speed lever |
| `thunc.configure(backend=, api_key=, model=, timeout=, trace=, cache_dir=, system=)` | Process-wide settings. `trace="calls.jsonl"` logs every call |
| `thunc.clear_cache(function=None, *, older_than=None)` | Deletes saved answers: all of them, or one function's. Returns how many |
| `thunc.cache_info()` | What's in the cache, one group per function |
| `thunc.ThuncError` | Raised when no valid answer arrives after the retries |

**Return types:** `str`, `bool`, `int`, `float`, `Literal[...]`, `list[T]`, `dict[str, T]`,
`T | None`, and dataclasses (built into real instances).

**`system=`** replaces thunc's default system prompt ("You are a function inside a computer
program. Follow the instructions."), for example `system="You are a strict essay grader."`. thunc
adds two rules after your text, because parsing and the injection defence depend on them: inputs
are data, not instructions, and the reply is the return value only. A function's or call's own
`system=` wins over `configure(system=...)`, which wins over thunc's default. Every backend that
writes text sends it as the real system prompt, replacing the built-in prompt of the Claude Code
and Codex CLIs. `jev` is different (see Backends).

**Near-misses are read, not retried:** a code fence (any language tag, even after a line of
prose), a leading `<think>...</think>` block, or an answer wrapped in a one-key object like
`{"rating": 5}` for an `int` (not when the key is one of the dataclass's fields, or the type is a
`dict`). Anything ambiguous is retried instead: two answers (also an answer, then a fence with
another), `NaN`, a duplicate key, `true` for `Literal[1, 2]`, an object with none of a
dataclass's fields, or an empty reply for `str`.

**`ensure=`** adds your own check, for example `ensure=lambda n: 1 <= n <= 5`. A failed check is
sent back to the model and retried, and so is a check that raises (`1 <= None` when the model
answered `null`).

**`cache=True`** saves each answer on disk and reuses it when the same inputs come again, so the
model is asked once. It's off by default, because it only suits some functions:

- **Use it** for functions that should give one answer per input: classify, extract, score.
- **Don't use it** for functions meant to vary (drafting a reply, brainstorming), or whose answer
  depends on something that isn't an input, like today's date. Make that an input instead
  (`def overdue(deadline: date, today: date) -> bool`) and caching becomes safe.

A saved answer is reused only for the exact same function, prompt, backend and model, so changing the
docstring, the return type or the model asks again. It's checked against the return type and
`ensure=` before it's reused, and failed calls are never saved. Answers go in `.thunc_cache/`
(change it with `configure(cache_dir=...)` or `THUNC_CACHE_DIR`), one JSON file per call, holding
the full prompt in plain text, inputs included.

**Clearing the cache.** Clear everything, or one function's answers, from Python or the command line:

```python
thunc.clear_cache()  # everything
thunc.clear_cache(urgency)  # one function
thunc.clear_cache("urgency")  # the same, by name
thunc.clear_cache(older_than=timedelta(days=30))  # answers saved more than 30 days ago
```

```bash
thunc cache list                                   # saved answers per function
thunc cache clear                                  # everything
thunc cache clear --function urgency               # one function (repeat for several)
thunc cache clear --older-than 30d --dry-run       # what would go, without deleting
```

A name is the function's name (`urgency`, or `Triage.urgency` for a method), optionally with its
module (`support_inbox.urgency`). For `thunc.call`, pass `name="..."` to group its answers the same
way; unnamed calls are cleared only with everything or by age. The function's name is part of the
cache key, so renaming a function starts its cache fresh. Ages count from when the answer was saved.
Clearing deletes only cache entries, never other files in the folder, and it's safe while another
process is using the cache. The `thunc` command (also `python -m thunc`) reads `THUNC_CACHE_DIR`,
or takes `--cache-dir`; it can't see a `configure(cache_dir=...)` in your code.

**Backends:**
- `anthropic` is the Claude API: `configure(api_key=...)` or `ANTHROPIC_API_KEY`, plus
  `pip install "thunc[anthropic]"`.
- `openai` is the OpenAI API: `configure(backend="openai", api_key=...)` or `OPENAI_API_KEY`, plus
  `pip install "thunc[openai]"`. The default model is `gpt-5.5`. `OPENAI_BASE_URL` points it at
  any server that speaks the OpenAI Responses API.
- `claude-code` and `codex` call your local CLI login, and are meant for cheap testing.
  Both run with their own tools turned off, so the model can only answer. `codex` also ignores
  `~/.codex/config.toml` (your MCP servers, plugins, `notify` command and model settings); your
  login still works. Pick the model with `configure(model=...)` or `model=`.
- `jev` is TypeSafe's [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev)
  judgment model, through the [`jev` CLI](https://github.com/model-clis/jev). The key comes from
  `jev login` or `JEV_API_KEY`, not `configure(api_key=...)`, so Jev can be used for some
  functions alongside another backend's key. Jev doesn't write text: it answers `bool`,
  `Literal` of strings (up to 255) and `Literal` of integers (as ordered levels), with the most
  likely answer returned. Any other return type raises `ThuncError` before a request is sent. The
  inputs are sent as Jev's state and the instructions as its question; a `system=` of your own goes
  before the instructions, and thunc's default system prompt isn't sent. `model=` is ignored (the
  CLI always uses `jev-latest`), and an answer that fails `ensure=` isn't retried, since Jev would
  give the same one. It's only used when you choose it: `backend="jev"` or `THUNC_BACKEND=jev`.
  Setup (install the CLI, log in, check it works): the
  [Jev guide](https://eltarras.github.io/thunc/jev.html).

**Local models:** the `openai` backend works with a local server through `OPENAI_BASE_URL`. This
has been tested with [LM Studio](https://lmstudio.ai) running `openai/gpt-oss-20b`:

```python
# OPENAI_BASE_URL=http://localhost:1234/v1  OPENAI_API_KEY=lm-studio  (any non-empty key works)
thunc.configure(backend="openai", model="openai/gpt-oss-20b")
```

Small models need the retry more often, for example when they explain the answer instead of
giving it alone.

The backend can also be set with `THUNC_BACKEND`. With none set, `ANTHROPIC_API_KEY` (or a
`configure(api_key=...)` alone) selects `anthropic`, and otherwise `OPENAI_API_KEY` selects `openai`.

**Type checking:** signatures and return types are visible to mypy and Pyright. mypy reports
empty bodies; turn that off with `disable_error_code = ["empty-body"]`.

## Agents

> **New in 0.2.** Agents are new; their API may change in a later release as feedback comes in.

An agent is a typed function that can look around before it answers. Give it a name and a working
directory, declare its tasks the way you write `@thunc.function`, and call them from Python:

```python
repo = thunc.Agent("repo-guide", workdir="~/code/myapp")


@repo.task
def request_timeout() -> int:
    """Find the HTTP request timeout this app uses, in seconds."""
    ...


request_timeout()  # -> 45, after the agent searched the code and read the file that sets it
```

An agent with a single task can be declared in one go, and a task built in code runs with
`agent.call`, the agent version of `thunc.call`:

```python
@thunc.agent("release-notes", workdir="~/code/myapp", permissions=["write:CHANGELOG.md", "run:git log"])
def changelog(since_tag: str) -> list[str]:
    """Add an entry to CHANGELOG.md for the commits since `since_tag`. Return the bullets you wrote."""
    ...


repo.call(f"Where is {setting} set?", returns=str)
```

Each call is one run. The model takes one step at a time (list a folder, search, read or edit a
file) and ends by calling `finish` with a value of the return type, which is checked like any thunc
result. It runs on the Claude and OpenAI APIs through their own tool calls (the
model can make several at once, and the fixed part of the prompt is cached), and on Claude Code and
Codex by replying with one JSON action at a time. `protocol="text"` uses the second way on an API
too, for example with a server behind `OPENAI_BASE_URL` that has no function calling.
The `jev` backend only answers typed questions and cannot run agents, even for a task returning
`bool` or `Literal[...]`. An agent run using it raises `ThuncError` before creating any run files
or calling a backend. Use `@thunc.function` or `thunc.call` for Jev questions.

- **Permissions** say what the agent may do. By default it may read everything in `workdir` and
  save notes, and may not write:

  ```python
  fixer = thunc.Agent("fixer", workdir=".", permissions=["write:src/**", "run:pytest", "!read:.env*"])
  ```

  | Rule | Means |
  |---|---|
  | `write:docs/**`, `write` | create and edit matching files (all files with no path); also lets it read them |
  | `read:src/**` | read only these; any `read:` rule replaces the read-everything default |
  | `run:pytest`, `run:git log`, `run` | run commands that start with these words (`run:git log` allows `git log --oneline`, not `git push`); `run` alone allows any |
  | `!read:.env*`, `!write:...`, `!run:git push`, `!memory` | deny; a deny always wins, and `!read` also stops writing |

  `*` stays within one folder, `**` crosses folders, and paths are relative to `workdir`. The agent
  is told its permissions, and an action they don't allow is refused with the reason, after which
  the run carries on. Bad rules fail when the agent is declared.
- **Tools:** `list`, `read` and `search`; `write` (create a file, or replace one) and `edit`
  (replace text that appears exactly once) when a write rule allows it; `run` when a run rule
  allows it; and `remember`. Every path
  must stay inside `workdir`: `..`, absolute paths and symlinks that point outside are refused, and
  the rules are checked on where a link really leads. Files the agent may not read are left out of
  `list` and `search`.
- **No blind overwrites.** A file is only replaced or edited after the agent read it in the same
  run, and only if it hasn't changed on disk since. There is no undo, so run agents that write in a
  git repository with a clean tree, and review their changes with `git diff`.
- **Commands** run in `workdir` without a shell, so `&&`, pipes, redirects and `$VARIABLES` don't
  work (the agent is told). They get a minimal environment: `PATH`, `HOME`, the locale and
  temp-folder variables, and whatever you pass in `env=`, so your API keys don't reach them. Each
  has a time limit (`command_timeout=120` seconds) that also stops the processes it started, and
  the agent sees the exit code and the output, its end kept when it's long.
- **A permitted command can do anything its program can.** `run:pytest` runs the project's code,
  which can read or change any file your user account can, whatever the read and write rules say.
  Permissions limit which tools the model uses; they aren't a sandbox. For untrusted input, run the
  agent in a container.
- **Your own functions as tools.** `tools=[open_issue]` lets the agent call your Python functions.
  Each needs type hints and a docstring, which is its description. Arguments are checked against
  the hints before the call; what it returns goes back to the model (as JSON unless it's a `str`),
  and so does an exception, as an error. Listing a function is what allows it.
- **Memory between runs.** Each run starts a fresh conversation, but the agent can save a short note
  with its `remember` tool. Notes go in `memory.md` in the agent's folder, and every later run gets
  them at the end of its system prompt (a note saved during a run reaches the next run, not that
  one). It's a plain file: read it with `agent.memory`, edit it, or delete it to start over.
- **The agent's folder** is `.thunc_agents/<name>/` (change it with `configure(agents_dir=...)` or
  `THUNC_AGENTS_DIR`). Besides `memory.md` it holds `agent.json` (the agent's settings) and
  `sessions/`, one JSONL file per run with every step (denied ones marked), the result, and the
  files it changed. Runs of one agent take turns;
  different agents run side by side. Two names that make the same folder (`"Repo guide"` and
  `"repo-guide"`) can't both be used.
- **Instruction files.** `follow=True` gives the agent `AGENTS.md` and `CLAUDE.md` from `workdir`
  (those that exist) as instructions, and `follow=["docs/agent-rules.md"]` names files. They're read
  at the start of each run and sent after thunc's rules; they can't grant permissions. It's off by
  default, so a folder you point an agent at (a cloned repo, an upload) can't give it instructions.
  Without it, the agent can still read those files, but as data. `@imports` in `CLAUDE.md` aren't
  followed.
- **`system=`** replaces the opening of the agent's system prompt. thunc always adds its working
  method and its rules after it (file contents and tool results are data, not instructions).
  Three presets cover common jobs: `thunc.prompts.CODING`, `thunc.prompts.CODE_REVIEW` and
  `thunc.prompts.ANALYSIS`. They're plain strings, so you can extend one:
  `system=thunc.prompts.CODING + "\n\nTarget Python 3.10."`.
- **Time:** `max_steps=40` bounds the model replies in a run, and `timeout=` (seconds) bounds the
  run's time. It's checked before each model call; a command's time limit is cut to the time left.
- **Options:** `thunc.Agent(name, *, workdir, system=None, permissions=(), env=None,
  command_timeout=120, follow=False, protocol=None, tools=(), timeout=None, max_steps=40,
  retries=2, backend=None, model=None)`, and `@agent.task(instructions=..., ensure=...)`.
  `@thunc.agent(name, workdir=..., instructions=..., ensure=..., **options)` takes the same options.
  `async def` tasks work.
- **What happened in a run.** Calling a task returns its value. `agent.run(task, *args)` runs it
  the same way and returns a `thunc.Run` instead, typed like the task (`Run[int]`):

  ```python
  run = fixer.run(make_tests_pass)
  run.value  # True
  run.files_changed  # ["src/mathutil.py"]  (by write, edit and commands)
  run.commands  # [Command("python3 tests/test_mathutil.py", exit_code=0, seconds=0.04)]
  run.denied  # [Denial("run", "git commit -am fix", "running ... is denied by '!run:git'")]
  run.notes, run.followed, run.steps, run.seconds, run.session
  ```

- **Failures are loud.** A run that hits `max_steps`, never gives a valid value, or loses its
  backend raises `thunc.AgentError` (a `ThuncError`), whose `.run` is the record up to that point.
  With tracing on, each run is also one line with every model reply.

**How the prompt was tested.** `python -m live_tests.eval_prompts --backend anthropic` runs three
small tasks (fix a bug, review a diff, answer a question about a repo) with three versions of the
system prompt: bare (no working method), the default, and the task's preset. Five runs of each on
4 October 2026:

| | Claude API (Opus 5.5, native calls) | Claude Code (text protocol) |
|---|---|---|
| Passed | 45/45: every task, every version | 45/45 |
| Steps (bare / default / preset) | fix 4.0 / 4.0 / 4.0, review 2.0 / 2.4 / 2.8, analysis 3.0 / 3.0 / 3.0 | fix 5.2 / 6.0 / 6.0, review 2.2 / 2.0 / 3.8, analysis 4.2 / 3.8 / 4.2 |
| Cost | $0.76 for all 45 runs (cache reads were 257,553 of 312,294 input tokens) | |

Every version passed every time, so these tasks are too easy to tell the versions apart: the result
says the prompt does no harm, not that it helps. The one difference is that the review preset reads
more of the code before answering. Each review flagged the renamed function as a minor issue
(outside code importing the old name breaks), never as blocking. Harder tasks are needed to measure
more.

## Durable agents with Temporal

> **New in 0.2.1.** Install with `pip install "thunc[temporal]"`. Durable agents are new; their
> API may change in a later release as feedback comes in.

The optional `thunc.temporal` runtime records each agent model turn and tool result
in a Temporal workflow. Workers can restart and continue recorded progress. Existing
function calls and `agent.run()` remain local and need no Temporal installation.

Register tasks on a worker, then submit by stable identity:

```python
from thunc.temporal import Registry, Runtime, Worker

# Worker process; review_task is an existing @agent.task function.
registry = Registry(state_dir="/srv/thunc-state")
registry.agent_task("repo.review", review_task, version="1", workspace_id="repo")
worker = await Worker.connect("localhost:7233", task_queue="repo-v1", registry=registry)
# await worker.run() in the worker's async entry point

# Client process; a reconnectable handle survives this process exiting.
runtime = await Runtime.connect("localhost:7233", task_queue="repo-v1")
handle = await runtime.start(
    "repo.review",
    version="1",
    workspace_id="repo",
    inputs={},
    returns=str,
    request_id="review-123",
    deadline_seconds=1800,
)
run = await handle.result()
print(run.value)
```

Durable mode requires a Temporal service, a worker, and persistent storage on the
same volume. File changes and memory updates use recovery receipts. Commands with
uncertain outcomes pause for operator resolution instead of blindly running twice.
Temporal does not back up your workspace or guarantee exactly-once external effects.

The [Temporal guide and runnable example](examples/temporal/README.md) cover service
setup, typed functions, composition, retries, cancellation, permissions, storage,
history replay and upgrades.

## Examples

| | |
|---|---|
| [hello.py](https://github.com/Eltarras/thunc/blob/main/examples/hello.py) | The smallest call |
| [support_inbox.py](https://github.com/Eltarras/thunc/blob/main/examples/support_inbox.py) | Docstring functions returning a `Literal`, an `int` with `ensure=`, a dataclass, and a reply; tickets processed in parallel |
| [dynamic_prompts.py](https://github.com/Eltarras/thunc/blob/main/examples/dynamic_prompts.py) | Prompts built from a style guide with `thunc.call`, and a grading function generated from a rubric |
| [log_triage.py](https://github.com/Eltarras/thunc/blob/main/examples/log_triage.py) | Plain Python and AI functions mixed, with tracing |
| [repo_guide.py](https://github.com/Eltarras/thunc/blob/main/examples/repo_guide.py) | Agents: read-only tasks over this repo returning a dataclass and lists, on the Codex backend, with each run's steps read from the trace |
| [jev_hello.py](https://github.com/Eltarras/thunc/blob/main/examples/jev_hello.py) | The smallest Jev calls: a yes/no, a label and a rating |
| [jev_inbox.py](https://github.com/Eltarras/thunc/blob/main/examples/jev_inbox.py) | A support inbox triaged on Jev: spam, team and urgency for 8 tickets in about a second |
| [jev_with_claude.py](https://github.com/Eltarras/thunc/blob/main/examples/jev_with_claude.py) | Jev decides which messages need a reply; Claude writes only those replies |

## Code

```
thunc/
  __init__.py    public API
  decorator.py   @thunc.function
  agent.py       thunc.Agent, @agent.task, @thunc.agent
  tools.py       the agent's tools: list, read, search, write, edit, run
  permissions.py the agent's permission rules
  runs.py        thunc.Run and AgentError: what a run did
  native.py      how a run talks to its backend: native tool calls or the text protocol
  store.py       the agent's folder: memory, settings, run records, the lock
  prompts.py     the agent's system prompt
  __main__.py    the thunc command: thunc cache list / clear
  core.py        thunc.call, thunc.map, tracing
  cache.py       the answer cache: saving, listing, clearing
  schema.py      return types: describe, parse, validate
  config.py      settings and backend selection
  backends.py    anthropic, openai, claude-code, codex, jev
  errors.py      ThuncError
tests/           offline: a fake backend, never a real model
live_tests/      against a real model: hello, a yes/no decision, labels and ratings, messy text to a dict
examples/
```

## Limitations

- **There's no record/replay for tests yet.** `cache=True` is per function; there's no switch
  that serves every call from disk and fails on a miss.
- **`Literal` results from `thunc.call` are typed as `Any`.** `@thunc.function` has no such gap.
- **Docstrings disappear under `python -OO`.** Use `instructions=` there.

## Development

```bash
python3 -m venv .venv && .venv/bin/pip install -e ".[anthropic,openai,dev]"
.venv/bin/pytest                    # offline tests (these run in CI)
.venv/bin/pytest live_tests         # real model calls through your Claude Code login; costs quota
THUNC_BACKEND=anthropic .venv/bin/pytest live_tests   # the same, through the Claude API (needs ANTHROPIC_API_KEY)
THUNC_BACKEND=openai .venv/bin/pytest live_tests      # the same, through the OpenAI API (needs OPENAI_API_KEY)
.venv/bin/ruff check . && .venv/bin/mypy --strict thunc
```

## License

[MIT](https://github.com/Eltarras/thunc/blob/main/LICENSE)
