Drop python 3.9 support (#434)

Author: adrianlyjak

.changeset/loud-dingos-repair.md

@@ -0,0 +1,9 @@
+---
+"llama-agents-client": minor
+"llama-agents-dbos": minor
+"llama-agents-server": minor
+"llama-index-utils-workflow": minor
+"llama-index-workflows": minor
+---
+
+Drop python 3.9 support

.github/workflows/test.yml

@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13", "3.14"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
         package:
           [
             llama-index-workflows,
@@ -28,14 +28,7 @@ jobs:
             "llama-agents-dbos"
           ]
         exclude:
-          - package: llama-agents-dbos
-            python-version: "3.9"
-          # Integration tests on 3.14 run in test-docker job with cross-package coverage
-          - package: llama-agents-integration-tests
-            python-version: "3.9"
           # Dev CLI only needs to run on 3.14
-          - package: llama-agents-dev
-            python-version: "3.9"
           - package: llama-agents-dev
             python-version: "3.10"
           - package: llama-agents-dev

docs/api_docs/pyproject.toml

@@ -7,7 +7,7 @@ name = "docs"
 version = "0.1.0"
 description = ""
 authors = [{name = "Your Name", email = "you@example.com"}]
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 readme = "README.md"
 dependencies = [
   "llama-index-workflows[server,client]",

examples/agent.ipynb

@@ -101,7 +101,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Any, List\n",
+    "from typing import Any\n",
     "\n",
     "from llama_index.core.llms.function_calling import FunctionCallingLLM\n",
     "from llama_index.core.memory import ChatMemoryBuffer\n",
@@ -120,7 +120,7 @@
     "    def __init__(\n",
     "        self,\n",
     "        llm: FunctionCallingLLM | None = None,\n",
-    "        tools: List[BaseTool] | None = None,\n",
+    "        tools: list[BaseTool] | None = None,\n",
     "        **workflow_kwargs: Any,\n",
     "    ) -> None:\n",
     "        super().__init__(**workflow_kwargs)\n",

examples/k8s-otel/pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "k8s-otel-example"
 version = "0.0.0"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
   "llama-agents-dbos",
   "opentelemetry-exporter-otlp",

examples/observability/workflows_observability_pt1.ipynb

@@ -182,7 +182,7 @@
     "import os\n",
     "from os import linesep\n",
     "from pathlib import Path\n",
-    "from typing import Callable, Optional, Sequence\n",
+    "from typing import Callable, Sequence\n",
     "\n",
     "from llama_index.observability.otel import LlamaIndexOpenTelemetry\n",
     "from opentelemetry.sdk.trace import ReadableSpan\n",
@@ -200,7 +200,7 @@
     "    def __init__(\n",
     "        self,\n",
     "        service_name: str | None = None,\n",
-    "        file_path: Optional[os.PathLike[str]] = None,\n",
+    "        file_path: os.PathLike[str] | None = None,\n",
     "        formatter: Callable[[ReadableSpan], str] = lambda span: json.dumps(\n",
     "            json.loads(span.to_json())\n",
     "        )\n",

examples/observability/workflows_observability_pt2.ipynb

@@ -95,7 +95,7 @@
     "import os\n",
     "from os import linesep\n",
     "from pathlib import Path\n",
-    "from typing import Callable, Optional, Sequence\n",
+    "from typing import Callable, Sequence\n",
     "\n",
     "from llama_index.observability.otel import LlamaIndexOpenTelemetry\n",
     "from opentelemetry.sdk.trace import ReadableSpan\n",
@@ -113,7 +113,7 @@
     "    def __init__(\n",
     "        self,\n",
     "        service_name: str | None = None,\n",
-    "        file_path: Optional[os.PathLike[str]] = None,\n",
+    "        file_path: os.PathLike[str] | None = None,\n",
     "        formatter: Callable[[ReadableSpan], str] = lambda span: json.dumps(\n",
     "            json.loads(span.to_json())\n",
     "        )\n",
@@ -186,7 +186,7 @@
    },
    "outputs": [],
    "source": [
-    "from typing import Dict, List, Literal\n",
+    "from typing import Literal\n",
     "\n",
     "from workflows.events import (\n",
     "    Event,\n",
@@ -208,20 +208,20 @@
     "\n",
     "    sender_details: str\n",
     "    support_category: Literal[\"technical\", \"sales\", \"general_information\"]\n",
-    "    support_questions: List[str]\n",
+    "    support_questions: list[str]\n",
     "    extra_information: str\n",
     "\n",
     "\n",
     "class QuestionEvent(Event):\n",
     "    \"\"\"Event containing one or more questions to retrieve information from the company database.\"\"\"\n",
     "\n",
-    "    questions: List[str]\n",
+    "    questions: list[str]\n",
     "\n",
     "\n",
     "class RetrievalEvent(Event):\n",
     "    \"\"\"Event that contains the retrieved information\"\"\"\n",
     "\n",
-    "    information: Dict[str, str]\n",
+    "    information: dict[str, str]\n",
     "\n",
     "\n",
     "class CandidateEmailEvent(InputRequiredEvent):\n",
@@ -338,16 +338,16 @@
     "class SenderDetails(BaseModel):\n",
     "    name: str = Field(description=\"Name of the sender seeking support\")\n",
     "    email_address: str = Field(description=\"Email address of the sender\")\n",
-    "    company: Optional[str] = Field(\n",
+    "    company: str | None = Field(\n",
     "        descrption=\"Company for which the sender works\", default=None\n",
     "    )\n",
-    "    role: Optional[str] = Field(descrption=\"Job role of the sender\", default=None)\n",
+    "    role: str | None = Field(descrption=\"Job role of the sender\", default=None)\n",
     "\n",
     "\n",
     "class SupportEmailInformation(BaseModel):\n",
     "    sender_details: SenderDetails\n",
     "    support_category: Literal[\"technical\", \"sales\", \"general_information\"]\n",
-    "    support_questions: List[str]\n",
+    "    support_questions: list[str]\n",
     "    extra_information: str"
    ]
   },
@@ -496,7 +496,7 @@
    },
    "outputs": [],
    "source": [
-    "files: List[dict] = []\n",
+    "files: list[dict] = []\n",
     "fls = [os.path.join(\"data\", f) for f in os.listdir(\"data/\")]\n",
     "\n",
     "file_ids = []\n",
@@ -535,7 +535,7 @@
     "\n",
     "\n",
     "class SupportQuestions(BaseModel):\n",
-    "    questions: List[str] = Field(\n",
+    "    questions: list[str] = Field(\n",
     "        description=\"Support questions to ask within the company database to retrieve the most information about the users' requests\"\n",
     "    )\n",
     "\n",
@@ -604,7 +604,7 @@
    "outputs": [],
    "source": [
     "import uuid\n",
-    "from typing import Annotated, Union\n",
+    "from typing import Annotated\n",
     "\n",
     "from llama_cloud_services.extract import SourceText\n",
     "from llama_index.core.llms import ChatMessage\n",
@@ -620,7 +620,7 @@
     "        event: EmailEvent,\n",
     "        ctx: Context[WorkflowState],\n",
     "        extraction_agent: Annotated[ExtractionAgent, Resource(get_extract_agent)],\n",
-    "    ) -> Union[InformationExtractionEvent, SendEmailEvent]:\n",
+    "    ) -> InformationExtractionEvent | SendEmailEvent:\n",
     "        extracted_content = await extraction_agent.aextract(\n",
     "            files=SourceText(\n",
     "                text_content=text, filename=f\"support_email_{str(uuid.uuid4())}.txt\"\n",
@@ -656,7 +656,7 @@
     "        event: InformationExtractionEvent,\n",
     "        ctx: Context[WorkflowState],\n",
     "        llm: Annotated[StructuredLLM, Resource(get_llm)],\n",
-    "    ) -> Union[QuestionEvent, SendEmailEvent]:\n",
+    "    ) -> QuestionEvent | SendEmailEvent:\n",
     "        response = await llm.achat(\n",
     "            messages=[\n",
     "                ChatMessage(\n",
@@ -685,7 +685,7 @@
     "        ctx: Context[WorkflowState],\n",
     "        company_database: Annotated[BaseQueryEngine, Resource(get_company_database)],\n",
     "    ) -> RetrievalEvent:\n",
-    "        q_and_a: Dict[str, str] = {}\n",
+    "        q_and_a: dict[str, str] = {}\n",
     "        for question in event.questions:\n",
     "            response = await company_database.aquery(question)\n",
     "            q_and_a.update({question: response.response or \"\"})\n",
@@ -719,7 +719,7 @@
     "    @step\n",
     "    async def send_email_or_restart(\n",
     "        self, event: HumanFeedbackEvent, ctx: Context[WorkflowState]\n",
-    "    ) -> Union[SendEmailEvent, EmailEvent]:\n",
+    "    ) -> SendEmailEvent | EmailEvent:\n",
     "        if event.approved:\n",
     "            state = await ctx.store.get_state()\n",
     "            return SendEmailEvent(\n",
@@ -1411,7 +1411,7 @@
    "source": [
     "import json\n",
     "import time\n",
-    "from typing import Annotated, Union\n",
+    "from typing import Annotated\n",
     "\n",
     "from workflows import Context, Workflow, step\n",
     "from workflows.resource import Resource\n",
@@ -1425,7 +1425,7 @@
     "        event: EmailEvent,\n",
     "        ctx: Context[WorkflowState],\n",
     "        extraction_agent: Annotated[ExtractionAgent, Resource(get_extract_agent)],\n",
-    "    ) -> Union[InformationExtractionEvent, SendEmailEvent]:\n",
+    "    ) -> InformationExtractionEvent | SendEmailEvent:\n",
     "        async with ctx.store.edit_state() as state:\n",
     "            state.email_text = event.email_content\n",
     "\n",
@@ -1468,7 +1468,7 @@
     "        event: InformationExtractionEvent,\n",
     "        ctx: Context[WorkflowState],\n",
     "        llm: Annotated[StructuredLLM, Resource(get_llm)],\n",
-    "    ) -> Union[QuestionEvent, SendEmailEvent]:\n",
+    "    ) -> QuestionEvent | SendEmailEvent:\n",
     "        response = await llm.achat(\n",
     "            messages=[\n",
     "                ChatMessage(\n",
@@ -1503,7 +1503,7 @@
     "        ctx: Context[WorkflowState],\n",
     "        company_database: Annotated[BaseQueryEngine, Resource(get_company_database)],\n",
     "    ) -> RetrievalEvent:\n",
-    "        q_and_a: Dict[str, str] = {}\n",
+    "        q_and_a: dict[str, str] = {}\n",
     "        st = time.time()\n",
     "        for question in event.questions:\n",
     "            response = await company_database.aquery(question)\n",
@@ -1545,7 +1545,7 @@
     "    @step\n",
     "    async def send_email_or_restart(\n",
     "        self, event: HumanFeedbackEvent, ctx: Context[WorkflowState]\n",
-    "    ) -> Union[SendEmailEvent, EmailEvent]:\n",
+    "    ) -> SendEmailEvent | EmailEvent:\n",
     "        if event.approved:\n",
     "            state = await ctx.store.get_state()\n",
     "            dispatcher.event(event=WorkflowDoneEvent(error=False))\n",

examples/state_management_with_vector_databases.ipynb

@@ -65,7 +65,7 @@
    "outputs": [],
    "source": [
     "import json\n",
-    "from typing import Any, Dict, List\n",
+    "from typing import Any\n",
     "\n",
     "from qdrant_client import AsyncQdrantClient, models\n",
     "from sentence_transformers import SentenceTransformer\n",
@@ -93,7 +93,7 @@
     "        )\n",
     "\n",
     "    async def upload(\n",
-    "        self, texts: List[str], metadatas: List[Dict[str, Any]], ids: List[str]\n",
+    "        self, texts: list[str], metadatas: list[dict[str, Any]], ids: list[str]\n",
     "    ) -> None:\n",
     "        self._has_vectors = True\n",
     "        embeddings = self.model.encode(texts).tolist()\n",
@@ -192,7 +192,6 @@
    "outputs": [],
    "source": [
     "from dataclasses import dataclass\n",
-    "from typing import Optional\n",
     "\n",
     "from openai import AsyncOpenAI\n",
     "from pydantic import BaseModel, Field\n",
@@ -209,11 +208,11 @@
     "\n",
     "class DeepWikiOutput(BaseModel):\n",
     "    summary: str = Field(description=\"Summary of the research output\")\n",
-    "    focal_points: List[str] = Field(description=\"Focal points of the research output\")\n",
-    "    references: List[str] = Field(\n",
+    "    focal_points: list[str] = Field(description=\"Focal points of the research output\")\n",
+    "    references: list[str] = Field(\n",
     "        description=\"References contained in the reseaerch output\", default_factory=list\n",
     "    )\n",
-    "    similar_topics: List[str] = Field(\n",
+    "    similar_topics: list[str] = Field(\n",
     "        description=\"Topics similar to the one of the research output\"\n",
     "    )\n",
     "\n",
@@ -222,7 +221,7 @@
     "class OpenAILlm:\n",
     "    llm: AsyncOpenAI\n",
     "\n",
-    "    async def deep_wiki(self, message: str, context: Optional[str] = None) -> str:\n",
+    "    async def deep_wiki(self, message: str, context: str | None = None) -> str:\n",
     "        if not context:\n",
     "            response = await self.llm.responses.create(\n",
     "                model=\"gpt-4.1\",\n",
@@ -368,7 +367,7 @@
     "\n",
     "\n",
     "class ContextRelevanceEvent(Event, ContextRelevance):\n",
-    "    context: Optional[str]"
+    "    context: str | None"
    ]
   },
   {
@@ -380,7 +379,7 @@
    "outputs": [],
    "source": [
     "import uuid\n",
-    "from typing import Annotated, Union\n",
+    "from typing import Annotated\n",
     "\n",
     "from workflows import Context, Workflow, step\n",
     "from workflows.resource import Resource\n",
@@ -391,13 +390,13 @@
     "    summary: str = Field(\n",
     "        description=\"Summary of the research output\", default_factory=str\n",
     "    )\n",
-    "    focal_points: List[str] = Field(\n",
+    "    focal_points: list[str] = Field(\n",
     "        description=\"Focal points of the research output\", default_factory=list\n",
     "    )\n",
-    "    references: List[str] = Field(\n",
+    "    references: list[str] = Field(\n",
     "        description=\"References contained in the reseaerch output\", default_factory=list\n",
     "    )\n",
-    "    similar_topics: List[str] = Field(\n",
+    "    similar_topics: list[str] = Field(\n",
     "        description=\"Topics similar to the one of the research output\",\n",
     "        default_factory=list,\n",
     "    )\n",
@@ -410,7 +409,7 @@
     "        ev: ResearchQuestionEvent,\n",
     "        ctx: Context[WorkflowState],\n",
     "        vdb: Annotated[QdrantVectorDatabase, Resource(get_vdb)],\n",
-    "    ) -> Union[ContextRelevanceEvent, RetrieveContextEvent]:\n",
+    "    ) -> ContextRelevanceEvent | RetrieveContextEvent:\n",
     "        ctx.write_event_to_stream(ev)\n",
     "        async with ctx.store.edit_state() as state:\n",
     "            state.question = ev.question\n",

packages/llama-agents-client/pyproject.toml

@@ -17,7 +17,7 @@ name = "llama-agents-client"
 version = "0.2.3"
 description = "HTTP client for connecting to and interacting with LlamaIndex workflow servers"
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
   "httpx>=0.28.1,<1",
   "llama-index-workflows>=2.15.0,<3.0.0"

packages/llama-agents-client/src/llama_agents/client/client.py

@@ -11,7 +11,6 @@
     AsyncGenerator,
     AsyncIterator,
     Literal,
-    Union,
     overload,
 )
 
@@ -71,7 +70,7 @@ class _QueuedDone:
     pass
 
 
-_QueueItem = Union[_QueuedEvent, _QueuedError, _QueuedDone]
+_QueueItem = _QueuedEvent | _QueuedError | _QueuedDone
 
 
 class EventStream: