chore!: bump minimum Python version to 3.11 (#81)

* chore!: bump minimum Python version to 3.11

BREAKING CHANGE: Drop support for Python 3.9 and 3.10.

This change:
- Updates requires-python from >=3.9 to >=3.11
- Removes Python 3.9 and 3.10 from CI matrix and classifiers
- Removes eval-type-backport dependency (only needed for Python <3.10)
- Removes python_version conditions from mcp and crewai dependencies
- Updates ruff target-version from py39 to py311
- Updates README requirements section

Python 3.11 is now the minimum supported version, allowing the use of
modern typing features and simplifying dependency management.

* chore(deps): update lockfile for Python 3.11+ changes

Regenerate uv.lock after removing eval-type-backport and updating
python_version conditions in optional dependencies.

* fix(lint): add strict=True to zip() calls for Python 3.11+

Ruff B905 rule requires explicit strict= parameter for zip() when
targeting Python 3.11+. Using strict=True ensures both iterables
have the same length, which is the expected behaviour in these cases.

* refactor(models): use Python 3.11+ stdlib features

- Import TypeAlias from typing instead of typing_extensions
- Use datetime.UTC instead of datetime.timezone.utc
- Remove obsolete TODO comments about Python 3.9 support

Author: ryoppippi

.github/workflows/ci.yaml

@@ -34,11 +34,9 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.13"]
+        python-version: ["3.11", "3.13"]
         include:
-          - python-version: "3.9"
-            sync-extras: "--all-extras --no-extra mcp"
-          - python-version: "3.10"
+          - python-version: "3.11"
             sync-extras: "--all-extras"
           - python-version: "3.13"
             sync-extras: "--all-extras"

README.md

@@ -25,7 +25,7 @@ StackOne AI provides a unified interface for accessing various SaaS tools throug
 
 ## Requirements
 
-- Python 3.10+ (MCP extra required for `fetch_tools()`)
+- Python 3.11+
 
 ## Installation
 
@@ -265,12 +265,10 @@ _ = app.invoke({"messages": [("user", "Get employee with id emp123") ]})
 </details>
 
 <details>
-<summary>CrewAI Integration (Python 3.10+)</summary>
+<summary>CrewAI Integration</summary>
 
 CrewAI uses LangChain tools natively, making integration seamless:
 
-> **Note**: CrewAI requires Python 3.10+. Install with `pip install 'stackone-ai[mcp,examples]'`
-
 ```python
 from crewai import Agent, Crew, Task
 from stackone_ai import StackOneToolSet

pyproject.toml

@@ -3,7 +3,7 @@ name = "stackone-ai"
 version = "0.3.4"
 description = "agents performing actions on your SaaS"
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.11"
 authors = [
     { name = "StackOne", email = "[email protected]" }
 ]
@@ -12,8 +12,6 @@ classifiers = [
     "Intended Audience :: Developers",
     "License :: OSI Approved :: MIT License",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.9",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
@@ -26,7 +24,6 @@ dependencies = [
     "bm25s>=0.2.2",
     "numpy>=1.24.0",
     "typing-extensions>=4.0.0",
-    "eval-type-backport; python_version<'3.10'",  # TODO: Remove when Python 3.9 support is dropped
 ]
 
 [build-system]
@@ -43,12 +40,11 @@ packages = ["stackone_ai"]
 "py.typed" = "py.typed"
 
 [project.optional-dependencies]
-# TODO: Remove python_version conditions when Python 3.9 support is dropped
 mcp = [
-    "mcp>=1.3.0; python_version>='3.10'",
+    "mcp>=1.3.0",
 ]
 examples = [
-    "crewai>=0.102.0; python_version>='3.10'",
+    "crewai>=0.102.0",
     "langchain-openai>=0.3.6",
     "langgraph>=0.2.0",
     "openai>=1.63.2",
@@ -82,7 +78,7 @@ markers = [
 
 [tool.ruff]
 line-length = 110
-target-version = "py39"
+target-version = "py311"
 
 [tool.ruff.lint]
 select = [

stackone_ai/meta_tools.py

@@ -122,7 +122,7 @@ def search(self, query: str, limit: int = 5, min_score: float = 0.0) -> list[Met
         score_map: dict[str, dict[str, float]] = {}
 
         # Add BM25 scores
-        for idx, score in zip(bm25_results[0], bm25_scores[0]):
+        for idx, score in zip(bm25_results[0], bm25_scores[0], strict=True):
             tool_name = self.tool_names[idx]
             # Normalize BM25 score to 0-1 range
             normalized_score = float(1 / (1 + np.exp(-score / 10)))

stackone_ai/models.py

@@ -1,22 +1,18 @@
-# TODO: Remove when Python 3.9 support is dropped
 from __future__ import annotations
 
 import base64
 import json
 import logging
 from collections.abc import Sequence
-from datetime import datetime, timezone
+from datetime import UTC, datetime
 from enum import Enum
-from typing import Annotated, Any, ClassVar, cast
+from typing import Annotated, Any, ClassVar, TypeAlias, cast
 from urllib.parse import quote
 
 import httpx
 from langchain_core.tools import BaseTool
 from pydantic import BaseModel, BeforeValidator, Field, PrivateAttr
 
-# TODO: Remove when Python 3.9 support is dropped
-from typing_extensions import TypeAlias
-
 # Type aliases for common types
 JsonDict: TypeAlias = dict[str, Any]
 Headers: TypeAlias = dict[str, str]
@@ -200,7 +196,7 @@ def execute(
             StackOneAPIError: If the API request fails
             ValueError: If the arguments are invalid
         """
-        datetime.now(timezone.utc)
+        datetime.now(UTC)
         feedback_options: JsonDict = {}
         result_payload: JsonDict | None = None
         response_status: int | None = None
@@ -270,7 +266,7 @@ def execute(
             status = "error"
             raise StackOneError(f"Request failed: {exc}") from exc
         finally:
-            datetime.now(timezone.utc)
+            datetime.now(UTC)
             metadata: JsonDict = {
                 "http_method": self._execute_config.method,
                 "url": url_used,

stackone_ai/utils/tfidf_index.py

@@ -138,7 +138,7 @@ def build(self, corpus: list[TfidfDocument]) -> None:
 
         # Build document vectors
         self.docs = []
-        for doc, tokens in zip(corpus, docs_tokens):
+        for doc, tokens in zip(corpus, docs_tokens, strict=True):
             # Compute term frequency (TF)
             tf: dict[int, int] = {}
             for token in tokens: