diff --git a/.github/workflows/docs-publish.yml b/.github/workflows/docs-publish.yml
index 77ad0f4a5..dc7494863 100644
--- a/.github/workflows/docs-publish.yml
+++ b/.github/workflows/docs-publish.yml
@@ -105,10 +105,17 @@ jobs:
id: audit_coverage
run: |
set -o pipefail
- uv run python tooling/docs-autogen/audit_coverage.py --docs-dir docs/docs/api --threshold 80 --quality 2>&1 \
+ uv run python tooling/docs-autogen/audit_coverage.py --docs-dir docs/docs/api --threshold 80 2>&1 \
| tee /tmp/audit_coverage.log
continue-on-error: ${{ inputs.strict_validation != true }}
+ - name: Docstring quality gate
+ id: quality_gate
+ run: |
+ set -o pipefail
+ uv run python tooling/docs-autogen/audit_coverage.py --docs-dir docs/docs/api --quality --fail-on-quality --threshold 100 2>&1 \
+ | tee /tmp/quality_gate.log
+
# -- Upload artifact for deploy job --------------------------------------
- name: Upload docs artifact
@@ -141,12 +148,14 @@ jobs:
markdownlint_outcome = "${{ steps.markdownlint.outcome }}"
validate_outcome = "${{ steps.validate_mdx.outcome }}"
coverage_outcome = "${{ steps.audit_coverage.outcome }}"
+ quality_gate_outcome = "${{ steps.quality_gate.outcome }}"
strict = "${{ inputs.strict_validation }}" == "true"
mode = "" if strict else " *(soft-fail)*"
lint_log = read_log("/tmp/markdownlint.log")
validate_log = read_log("/tmp/validate_mdx.log")
coverage_log = read_log("/tmp/audit_coverage.log")
+ quality_gate_log = read_log("/tmp/quality_gate.log")
# Count markdownlint issues (lines matching file:line:col format)
lint_issues = len([l for l in lint_log.splitlines() if re.match(r'.+:\d+:\d+ ', l)])
@@ -186,27 +195,11 @@ jobs:
mdx_detail = parse_validate_detail(validate_log)
- # Docstring quality annotation emitted by audit_coverage.py into the log
+ # Parse docstring quality annotation from quality gate log
# Format: ::notice title=Docstring quality::message
- # or ::warning title=Docstring quality::message
- quality_match = re.search(r"::(notice|warning|error) title=Docstring quality::(.+)", coverage_log)
- if quality_match:
- quality_level, quality_msg = quality_match.group(1), quality_match.group(2)
- quality_icon = "β
" if quality_level == "notice" else "β οΈ"
- quality_status = "pass" if quality_level == "notice" else "warning"
- quality_detail = re.sub(r"\s*β\s*see job summary.*$", "", quality_msg)
- quality_row = f"| Docstring Quality | {quality_icon} {quality_status}{mode} | {quality_detail} |"
- else:
- quality_row = None
-
- # Split coverage log at quality section to avoid duplicate output in collapsibles
- quality_start = coverage_log.find("π¬ Running docstring quality")
- if quality_start != -1:
- quality_log = coverage_log[quality_start:]
- coverage_display_log = coverage_log[:quality_start].strip()
- else:
- quality_log = ""
- coverage_display_log = coverage_log
+ # or ::error title=Docstring quality::message
+ quality_gate_match = re.search(r"::(notice|warning|error) title=Docstring quality::(.+)", quality_gate_log)
+ quality_gate_detail = re.sub(r"\s*β\s*see job summary.*$", "", quality_gate_match.group(2)) if quality_gate_match else ""
lines = [
"## Docs Build β Validation Summary\n",
@@ -215,16 +208,15 @@ jobs:
f"| Markdownlint | {icon(markdownlint_outcome)} {markdownlint_outcome}{mode} | {lint_detail} |",
f"| MDX Validation | {icon(validate_outcome)} {validate_outcome}{mode} | {mdx_detail} |",
f"| API Coverage | {icon(coverage_outcome)} {coverage_outcome}{mode} | {cov_detail} |",
+ f"| Docstring Quality | {icon(quality_gate_outcome)} {quality_gate_outcome} | {quality_gate_detail} |",
]
- if quality_row:
- lines.append(quality_row)
lines.append("")
for title, log, limit in [
("Markdownlint output", lint_log, 5_000),
("MDX validation output", validate_log, 5_000),
- ("API coverage output", coverage_display_log, 5_000),
- ("Docstring quality details", quality_log, 1_000_000),
+ ("API coverage output", coverage_log, 5_000),
+ ("Docstring quality details", quality_gate_log, 1_000_000),
]:
if log:
lines += [
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 302fe676a..513ff68bb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -51,13 +51,12 @@ repos:
language: system
pass_filenames: false
files: (docs/docs/.*\.mdx$|tooling/docs-autogen/)
- # TODO(#616): Move to normal commit flow once docstring quality issues reach 0.
- # Griffe loads the full package (~10s), so this is manual-only for now to avoid
- # slowing down every Python commit. Re-enable (remove stages: [manual]) and add
- # --fail-on-quality once quality issues are resolved.
+ # Docstring quality gate β manual only (CI is the hard gate via docs-publish.yml).
+ # Run locally with: pre-commit run docs-docstring-quality --hook-stage manual
+ # Requires generated API docs (run `uv run python tooling/docs-autogen/build.py` first).
- id: docs-docstring-quality
- name: Audit docstring quality (informational)
- entry: bash -c 'test -d docs/docs/api && uv run --no-sync python tooling/docs-autogen/audit_coverage.py --quality --docs-dir docs/docs/api || true'
+ name: Audit docstring quality
+ entry: uv run --no-sync python tooling/docs-autogen/audit_coverage.py --quality --fail-on-quality --threshold 0 --docs-dir docs/docs/api
language: system
pass_filenames: false
files: (mellea/.*\.py$|cli/.*\.py$)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index fc2b12b09..0e0cb9918 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -174,6 +174,25 @@ differs in type or behaviour from the constructor input β for example, when a
argument is wrapped into a `CBlock`, or when a class-level constant is relevant to
callers. Pure-echo entries that repeat `Args:` verbatim should be omitted.
+**`TypedDict` classes are a special case.** Their fields *are* the entire public
+contract, so when an `Attributes:` section is present it must exactly match the
+declared fields. The audit will flag:
+
+- `typeddict_phantom` β `Attributes:` documents a field that is not declared in the `TypedDict`
+- `typeddict_undocumented` β a declared field is absent from the `Attributes:` section
+
+```python
+class ConstraintResult(TypedDict):
+ """Result of a constraint check.
+
+ Attributes:
+ passed: Whether the constraint was satisfied.
+ reason: Human-readable explanation.
+ """
+ passed: bool
+ reason: str
+```
+
#### Validating docstrings
Run the coverage and quality audit to check your changes before committing:
@@ -194,6 +213,8 @@ Key checks the audit enforces:
| `no_args` | Standalone function has params but no `Args:` section |
| `no_returns` | Function has a non-trivial return annotation but no `Returns:` section |
| `param_mismatch` | `Args:` documents names not present in the actual signature |
+| `typeddict_phantom` | `TypedDict` `Attributes:` documents a field not declared in the class |
+| `typeddict_undocumented` | `TypedDict` has a declared field absent from its `Attributes:` section |
**IDE hover verification** β open any of these existing classes in VS Code and hover
over the class name or a constructor call to confirm the hover card shows `Args:` once
diff --git a/README.md b/README.md
index ff5a7682e..6c8cfea5f 100644
--- a/README.md
+++ b/README.md
@@ -1,206 +1,86 @@
-
+
-# Mellea
-
-Mellea is a library for writing generative programs.
-Generative programming replaces flaky agents and brittle prompts
-with structured, maintainable, robust, and efficient AI workflows.
+# Mellea β build predictable AI without guesswork
+Inside every AI-powered pipeline, the unreliable part is the same: the LLM call itself.
+Silent failures, untestable outputs, no guarantees.
+Mellea is a Python library for writing *generative programs* β replacing brittle prompts and flaky agents
+with structured, testable AI workflows built around type-annotated outputs, verifiable requirements, and automatic retries.
[//]: # ([](https://arxiv.org/abs/2408.09869))
-[](https://docs.mellea.ai/)
+[](https://mellea.ai/)
+[](https://docs.mellea.ai/)
[](https://pypi.org/project/mellea/)
[](https://pypi.org/project/mellea/)
[](https://github.com/astral-sh/uv)
[](https://github.com/astral-sh/ruff)
[](https://github.com/pre-commit/pre-commit)
-[](https://img.shields.io/github/license/generative-computing/mellea)
+[](https://github.com/generative-computing/mellea/blob/main/LICENSE)
[](CODE_OF_CONDUCT.md)
-[](https://ibm.biz/mellea-discord)
-
-
-## Features
-
- * A standard library of opinionated prompting patterns.
- * Sampling strategies for inference-time scaling.
- * Clean integration between verifiers and samplers.
- - Batteries-included library of verifiers.
- - Support for efficient checking of specialized requirements using
- activated LoRAs.
- - Train your own verifiers on proprietary classifier data.
- * Compatible with many inference services and model families. Control cost
- and quality by easily lifting and shifting workloads between:
- - inference providers
- - model families
- - model sizes
- * Easily integrate the power of LLMs into legacy code-bases (mify).
- * Sketch applications by writing specifications and letting `mellea` fill in
- the details (generative slots).
- * Get started by decomposing your large unwieldy prompts into structured and maintainable mellea problems.
-
-
-
-## Getting Started
-
-You can get started with a local install, or by using Colab notebooks.
-
-### Getting Started with Local Inference
-
-
-Install with [uv](https://docs.astral.sh/uv/getting-started/installation/):
+## Install
```bash
uv pip install mellea
```
-Install with pip:
-
-```bash
-pip install mellea
-```
-
-> [!NOTE]
-> `mellea` comes with some additional packages as defined in our `pyproject.toml`. If you would like to install all the extra optional dependencies, please run the following commands:
->
-> ```bash
-> uv pip install "mellea[hf]" # for Huggingface extras and Alora capabilities
-> uv pip install "mellea[watsonx]" # for watsonx backend
-> uv pip install "mellea[docling]" # for docling
-> uv pip install "mellea[smolagents]" # for HuggingFace smolagents tools
-> uv pip install "mellea[all]" # for all the optional dependencies
-> ```
->
-> You can also install all the optional dependencies with `uv sync --all-extras`
-
-> [!NOTE]
-> If running on an Intel mac, you may get errors related to torch/torchvision versions. Conda maintains updated versions of these packages. You will need to create a conda environment and run `conda install 'torchvision>=0.22.0'` (this should also install pytorch and torchvision-extra). Then, you should be able to run `uv pip install mellea`. To run the examples, you will need to use `python ` inside the conda environment instead of `uv run --with mellea `.
-
-> [!NOTE]
-> If you are using python >= 3.13, you may encounter an issue where outlines cannot be installed due to rust compiler issues (`error: can't find Rust compiler`). You can either downgrade to python 3.12 or install the [rust compiler](https://www.rust-lang.org/tools/install) to build the wheel for outlines locally.
-
-For running a simple LLM request locally (using Ollama with Granite model), this is the starting code:
-```python
-# file: https://github.com/generative-computing/mellea/blob/main/docs/examples/tutorial/example.py
-import mellea
-
-m = mellea.start_session()
-print(m.chat("What is the etymology of mellea?").content)
-```
-
-
-Then run it:
-> [!NOTE]
-> Before we get started, you will need to download and install [ollama](https://ollama.com/). Mellea can work with many different types of backends, but everything in this tutorial will "just work" on a Macbook running IBM's Granite 4 Micro 3B model.
-```shell
-uv run --with mellea docs/examples/tutorial/example.py
-```
+See [installation docs](https://docs.mellea.ai/getting-started/installation) for extras (`[hf]`, `[watsonx]`, `[docling]`, `[all]`, β¦) and source installation.
-### Get Started with Colab
-
-| Notebook | Try in Colab | Goal |
-|----------|--------------|------|
-| Hello, World | 
| Quickβstart demo |
-| Simple Email | | Using the `m.instruct` primitive |
-| Instruct-Validate-Repair | | Introduces our first generative programming design pattern |
-| Model Options | | Demonstrates how to pass model options through to backends |
-| Sentiment Classifier | | Introduces the `@generative` decorator |
-| Managing Context | | Shows how to construct and manage context in a `MelleaSession` |
-| Generative OOP | | Demonstrates object-oriented generative programming in Mellea |
-| Rich Documents | | A generative program that uses Docling to work with rich-text documents |
-| Composing Generative Functions | | Demonstrates contract-oriented programming in Mellea |
-| `m serve` | | Serve a generative program as an openai-compatible model endpoint |
-| MCP | | Mellea + MCP |
-
-
-### Installing from Source
-
-If you want to contribute to Mellea or need the latest development version, see the
-[Getting Started](CONTRIBUTING.md#getting-started) section in our Contributing Guide for
-detailed installation instructions.
-
-## Getting started with validation
-
-Mellea supports validation of generation results through a **instruct-validate-repair** pattern.
-Below, the request for *"Write an email.."* is constrained by the requirements of *"be formal"* and *"Use 'Dear interns' as greeting."*.
-Using a simple rejection sampling strategy, the request is sent up to three (loop_budget) times to the model and
-the output is checked against the constraints using (in this case) LLM-as-a-judge.
+## Example
+The `@generative` decorator turns a typed Python function into a structured LLM call.
+Docstrings become prompts, type hints become schemas β no templates, no parsers:
```python
-# file: https://github.com/generative-computing/mellea/blob/main/docs/examples/instruct_validate_repair/101_email_with_validate.py
-from mellea import MelleaSession
-from mellea.backends import ModelOption
-from mellea.backends.ollama import OllamaModelBackend
-from mellea.backends import model_ids
-from mellea.stdlib.sampling import RejectionSamplingStrategy
-
-# create a session with Mistral running on Ollama
-m = MelleaSession(
- backend=OllamaModelBackend(
- model_id=model_ids.MISTRALAI_MISTRAL_0_3_7B,
- model_options={ModelOption.MAX_NEW_TOKENS: 300},
- )
-)
-
-# run an instruction with requirements
-email_v1 = m.instruct(
- "Write an email to invite all interns to the office party.",
- requirements=["be formal", "Use 'Dear interns' as greeting."],
- strategy=RejectionSamplingStrategy(loop_budget=3),
-)
-
-# print result
-print(f"***** email ****\n{str(email_v1)}\n*******")
-```
-
-
-## Getting Started with Generative Slots
-
-Generative slots allow you to define functions without implementing them.
-The `@generative` decorator marks a function as one that should be interpreted by querying an LLM.
-The example below demonstrates how an LLM's sentiment classification
-capability can be wrapped up as a function using Mellea's generative slots and
-a local LLM.
-
-
-```python
-# file: https://github.com/generative-computing/mellea/blob/main/docs/examples/tutorial/sentiment_classifier.py#L1-L13
-from typing import Literal
+from pydantic import BaseModel
from mellea import generative, start_session
+class UserProfile(BaseModel):
+ name: str
+ age: int
@generative
-def classify_sentiment(text: str) -> Literal["positive", "negative"]:
- """Classify the sentiment of the input text as 'positive' or 'negative'."""
+def extract_user(text: str) -> UserProfile:
+ """Extract the user's name and age from the text."""
-
-if __name__ == "__main__":
- m = start_session()
- sentiment = classify_sentiment(m, text="I love this!")
- print("Output sentiment is:", sentiment)
+m = start_session()
+user = extract_user(m, text="User log 42: Alice is 31 years old.")
+print(user.name) # Alice
+print(user.age) # 31 β always an int, guaranteed by the schema
```
+## What Mellea Does
-## Contributing
+- **Structured output** β `@generative` turns typed functions into LLM calls; Pydantic schemas are enforced at generation time
+- **Requirements & repair** β attach natural-language requirements to any call; Mellea validates and retries automatically
+- **Sampling strategies** β run a generation multiple times and pick the best result; swap between rejection sampling, majority voting, and more with one parameter change
+- **Multiple backends** β Ollama, OpenAI, vLLM, HuggingFace, WatsonX, LiteLLM, Bedrock
+- **Legacy integration** β easily drop Mellea into existing codebases with `mify`
+- **MCP compatible** β expose any generative program as an MCP tool
-We welcome contributions to Mellea! There are several ways to contribute:
+## Learn More
-1. **Contributing to this repository** - Core features, bug fixes, standard library components
-2. **Applications & Libraries** - Build tools using Mellea (host in your own repo with `mellea-` prefix)
-3. **Community Components** - Contribute to [mellea-contribs](https://github.com/generative-computing/mellea-contribs)
+| Resource | Description |
+| --- | --- |
+| [mellea.ai](https://mellea.ai) | Vision and features |
+| [docs.mellea.ai](https://docs.mellea.ai) | Full docs β tutorials, API reference, how-to guides |
+| [Colab notebooks](docs/examples/notebooks/) | Interactive examples you can run immediately |
+| [Code examples](docs/examples/) | Runnable examples: RAG, agents, Instruct-Validate-Repair (IVR), MObjects, and more |
-Please see our **[Contributing Guide](CONTRIBUTING.md)** for detailed information on:
-- Getting started with development
-- Coding standards and workflow
-- Testing guidelines
-- How to contribute specific types of components
+## Contributing
-Questions? Join our [Discord](https://ibm.biz/mellea-discord)!
+We welcome contributions of all kinds β bug fixes, new backends, standard library components, examples, and docs.
-### IBM β€οΈ Open Source AI
+- **[Contributing Guide](CONTRIBUTING.md)** β development setup, workflow, and coding standards
+- **[Building Extensions](https://docs.mellea.ai/community/building-extensions)** β create reusable components in your own repo
+- **[mellea-contribs](https://github.com/generative-computing/mellea-contribs)** β community library for shared components
-Mellea has been started by IBM Research in Cambridge, MA.
+Questions? See [GitHub Discussions](https://github.com/generative-computing/mellea/discussions).
+
+### IBM β€οΈ Open Source AI
+Mellea was started by IBM Research in Cambridge, MA.
+---
+Licensed under the [Apache-2.0 License](LICENSE). Copyright Β© 2026 Mellea.
diff --git a/docs/docs/guide/CONTRIBUTING.md b/docs/docs/guide/CONTRIBUTING.md
index fd0a58434..e92e4a5e4 100644
--- a/docs/docs/guide/CONTRIBUTING.md
+++ b/docs/docs/guide/CONTRIBUTING.md
@@ -353,6 +353,11 @@ Add `Attributes:` only when a stored value differs in type or behaviour from the
input (e.g. a `str` wrapped into a `CBlock`, or a class-level constant).
Pure-echo entries that repeat `Args:` verbatim should be omitted.
+**`TypedDict` classes** are a special case β their fields are the entire public contract,
+so when an `Attributes:` section is present it must exactly match the declared fields.
+The CI audit will fail on phantom fields (documented but not declared) and undocumented
+fields (declared but missing from `Attributes:`).
+
See [CONTRIBUTING.md](../../CONTRIBUTING.md) for the full validation workflow.
---
diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py
index 424d5b2f3..e6236e5c6 100644
--- a/mellea/backends/huggingface.py
+++ b/mellea/backends/huggingface.py
@@ -1133,18 +1133,11 @@ class used during generation, if any.
)
span = mot._meta.get("_telemetry_span")
- from ..telemetry.metrics import is_metrics_enabled
- metrics_enabled = is_metrics_enabled()
-
- # Extract token counts only if needed
+ # Derive token counts from the output sequences (HF models have no usage object).
hf_output = mot._meta.get("hf_output")
n_prompt, n_completion = None, None
- if (span is not None or metrics_enabled) and isinstance(
- hf_output, GenerateDecoderOnlyOutput
- ):
- # HuggingFace local models don't provide usage objects, but we can
- # calculate token counts from sequences
+ if isinstance(hf_output, GenerateDecoderOnlyOutput):
try:
if input_ids is not None and hf_output.sequences is not None:
n_prompt = input_ids.shape[1]
@@ -1152,7 +1145,6 @@ class used during generation, if any.
except Exception:
pass
- # Populate standardized usage field (convert to OpenAI format)
if n_prompt is not None and n_completion is not None:
mot.usage = {
"prompt_tokens": n_prompt,
diff --git a/tooling/docs-autogen/audit_coverage.py b/tooling/docs-autogen/audit_coverage.py
index eb9506c9f..353ae3288 100755
--- a/tooling/docs-autogen/audit_coverage.py
+++ b/tooling/docs-autogen/audit_coverage.py
@@ -102,6 +102,7 @@ def walk_module(module, module_path: str):
# ---------------------------------------------------------------------------
_ARGS_RE = re.compile(r"^\s*(Args|Arguments|Parameters)\s*:", re.MULTILINE)
+_TYPEDDICT_BASES = re.compile(r"\bTypedDict\b")
_RETURNS_RE = re.compile(r"^\s*Returns\s*:", re.MULTILINE)
_YIELDS_RE = re.compile(r"^\s*Yields\s*:", re.MULTILINE)
_RAISES_RE = re.compile(r"^\s*Raises\s*:", re.MULTILINE)
@@ -274,6 +275,45 @@ def _check_member(member, full_path: str, short_threshold: int) -> list[dict]:
}
)
+ # TypedDict field mismatch check.
+ # Unlike regular classes (where Attributes: is optional under Option C),
+ # TypedDict fields *are* the entire public contract. When an Attributes:
+ # section exists, every entry must match an actual declared field and every
+ # declared field must appear β stale or missing entries are always a bug.
+ is_typeddict = any(
+ _TYPEDDICT_BASES.search(str(base))
+ for base in getattr(member, "bases", [])
+ )
+ if is_typeddict and _ATTRIBUTES_RE.search(doc_text):
+ attrs_block = re.search(
+ r"Attributes\s*:(.*?)(?:\n\s*\n|\Z)", doc_text, re.DOTALL
+ )
+ if attrs_block:
+ doc_field_names = set(_ARGS_ENTRY_RE.findall(attrs_block.group(1)))
+ actual_fields = {
+ name
+ for name, m in member.members.items()
+ if not name.startswith("_") and getattr(m, "is_attribute", False)
+ }
+ phantom = doc_field_names - actual_fields
+ if phantom:
+ issues.append(
+ {
+ "path": full_path,
+ "kind": "typeddict_phantom",
+ "detail": f"Attributes: documents {sorted(phantom)} not declared in TypedDict",
+ }
+ )
+ undocumented = actual_fields - doc_field_names
+ if undocumented:
+ issues.append(
+ {
+ "path": full_path,
+ "kind": "typeddict_undocumented",
+ "detail": f"TypedDict fields {sorted(undocumented)} missing from Attributes: section",
+ }
+ )
+
return issues
@@ -296,11 +336,15 @@ def audit_docstring_quality(
- no_class_args: class whose __init__ has typed params but no Args section on the class
- duplicate_init_args: Args: present in both class docstring and __init__ (Option C violation)
- param_mismatch: Args section documents names absent from the real signature
+ - typeddict_phantom: TypedDict Attributes: section documents fields not declared in the class
+ - typeddict_undocumented: TypedDict has declared fields absent from its Attributes: section
- Note: Attributes: sections are intentionally not enforced. Under the Option C
- convention, Attributes: is only used when stored values differ in type or
- behaviour from the constructor inputs (e.g. type transforms, computed values,
- class constants). Pure-echo entries that repeat Args: verbatim are omitted.
+ Note: Attributes: sections are intentionally not enforced for regular classes. Under
+ the Option C convention, Attributes: is only used when stored values differ in type or
+ behaviour from the constructor inputs (e.g. type transforms, computed values, class
+ constants). Pure-echo entries that repeat Args: verbatim are omitted. TypedDicts are
+ a carve-out: their fields are the entire public contract, so when an Attributes:
+ section is present it must exactly match the declared fields.
Only symbols (and methods whose parent class) present in `documented` are
checked when that set is provided β ensuring the audit is scoped to what is
@@ -401,6 +445,8 @@ def _print_quality_report(issues: list[dict]) -> None:
"no_class_args": "Missing class Args section",
"duplicate_init_args": "Duplicate Args: in class + __init__ (Option C violation)",
"param_mismatch": "Param name mismatches (documented but not in signature)",
+ "typeddict_phantom": "TypedDict phantom fields (documented but not declared)",
+ "typeddict_undocumented": "TypedDict undocumented fields (declared but missing from Attributes:)",
}
total = len(issues)
@@ -419,6 +465,8 @@ def _print_quality_report(issues: list[dict]) -> None:
"no_class_args",
"duplicate_init_args",
"param_mismatch",
+ "typeddict_phantom",
+ "typeddict_undocumented",
):
items = by_kind.get(kind, [])
if not items: