fix: linting and types for phase 5

examples/orchestrator_demo/run_magentic.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""
+Demo: Magentic-One Orchestrator for DeepCritical.
+
+This script demonstrates Phase 5 functionality:
+- Multi-Agent Coordination (Searcher + Judge + Manager)
+- Magentic-One Workflow
+
+Usage:
+    export OPENAI_API_KEY=...
+    uv run python examples/orchestrator_demo/run_magentic.py "metformin cancer"
+"""
+
+import argparse
+import asyncio
+import os
+import sys
+
+from src.agent_factory.judges import JudgeHandler
+from src.orchestrator_factory import create_orchestrator
+from src.tools.pubmed import PubMedTool
+from src.tools.search_handler import SearchHandler
+from src.tools.websearch import WebTool
+from src.utils.models import OrchestratorConfig
+
+
+async def main() -> None:
+    """Run the magentic agent demo."""
+    parser = argparse.ArgumentParser(description="Run DeepCritical Magentic Agent")
+    parser.add_argument("query", help="Research query (e.g., 'metformin cancer')")
+    parser.add_argument("--iterations", type=int, default=10, help="Max rounds")
+    args = parser.parse_args()
+
+    # Check for keys
+    if not (os.getenv("OPENAI_API_KEY") or os.getenv("ANTHROPIC_API_KEY")):
+        print("Error: No API key found. Magentic requires real LLM.")
+        sys.exit(1)
+
+    print(f"\n{'='*60}")
+    print("DeepCritical Magentic Agent Demo")
+    print(f"Query: {args.query}")
+    print("Mode: MAGENTIC (Multi-Agent)")
+    print(f"{ '='*60}\n")
+
+    # 1. Setup Search Tools
+    search_handler = SearchHandler(tools=[PubMedTool(), WebTool()], timeout=30.0)
+
+    # 2. Setup Judge
+    judge_handler = JudgeHandler()
+
+    # 3. Setup Orchestrator via Factory
+    config = OrchestratorConfig(max_iterations=args.iterations)
+    orchestrator = create_orchestrator(
+        search_handler=search_handler,
+        judge_handler=judge_handler,
+        config=config,
+        mode="magentic",
+    )
+
+    if not orchestrator:
+        print("Failed to create Magentic orchestrator. Is agent-framework installed?")
+        sys.exit(1)
+
+    # 4. Run Loop
+    try:
+        async for event in orchestrator.run(args.query):
+            # Print event with icon
+            # Clean up markdown for CLI
+            msg_obj = event.message
+            msg_text = ""
+            if hasattr(msg_obj, "text"):
+                msg_text = msg_obj.text
+            else:
+                msg_text = str(msg_obj)
+
+            msg = msg_text.replace("\n", " ").replace("**", "")[:150]
+            print(f"[{event.type.upper()}] {msg}...")
+
+            if event.type == "complete":
+                print("\n--- FINAL OUTPUT ---\n")
+                print(msg_text)
+
+    except Exception as e:
+        print(f"\n❌ Error: {e}")
+        import traceback
+
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

pyproject.toml

@@ -46,6 +46,9 @@ dev = [
     "mypy>=1.10",
     "pre-commit>=3.7",
 ]
+magentic = [
+    "agent-framework-core",
+]
 
 [build-system]
 requires = ["hatchling"]
@@ -112,4 +115,7 @@ exclude_lines = [
     "pragma: no cover",
     "if TYPE_CHECKING:",
     "raise NotImplementedError",
-]
+]
+
+[tool.uv.sources]
+agent-framework-core = { path = "reference_repos/agent-framework/python/packages/core", editable = true }

src/agents/judge_agent.py

@@ -0,0 +1,96 @@
+"""Judge agent wrapper for Magentic integration."""
+
+from collections.abc import AsyncIterable
+from typing import Any
+
+from agent_framework import (
+    AgentRunResponse,
+    AgentRunResponseUpdate,
+    AgentThread,
+    BaseAgent,
+    ChatMessage,
+    Role,
+)
+
+from src.orchestrator import JudgeHandlerProtocol
+from src.utils.models import Evidence, JudgeAssessment
+
+
+class JudgeAgent(BaseAgent):  # type: ignore[misc]
+    """Wraps JudgeHandler as an AgentProtocol for Magentic."""
+
+    def __init__(
+        self,
+        judge_handler: JudgeHandlerProtocol,
+        evidence_store: dict[str, list[Evidence]],
+    ) -> None:
+        super().__init__(
+            name="JudgeAgent",
+            description="Evaluates evidence quality and determines if sufficient for synthesis",
+        )
+        self._handler = judge_handler
+        self._evidence_store = evidence_store  # Shared state for evidence
+
+    async def run(
+        self,
+        messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        **kwargs: Any,
+    ) -> AgentRunResponse:
+        """Assess evidence quality."""
+        # Extract original question from messages
+        question = ""
+        if isinstance(messages, list):
+            for msg in reversed(messages):
+                if isinstance(msg, ChatMessage) and msg.role == Role.USER and msg.text:
+                    question = msg.text
+                    break
+                elif isinstance(msg, str):
+                    question = msg
+                    break
+        elif isinstance(messages, str):
+            question = messages
+
+        # Get evidence from shared store
+        evidence = self._evidence_store.get("current", [])
+
+        # Assess
+        assessment: JudgeAssessment = await self._handler.assess(question, evidence)
+
+        # Format response
+        response_text = f"""## Assessment
+
+**Sufficient**: {assessment.sufficient}
+**Confidence**: {assessment.confidence:.0%}
+**Recommendation**: {assessment.recommendation}
+
+### Scores
+- Mechanism: {assessment.details.mechanism_score}/10
+- Clinical: {assessment.details.clinical_evidence_score}/10
+
+### Reasoning
+{assessment.reasoning}
+"""
+
+        if assessment.next_search_queries:
+            response_text += "\n### Next Queries\n" + "\n".join(
+                f"- {q}" for q in assessment.next_search_queries
+            )
+
+        return AgentRunResponse(
+            messages=[ChatMessage(role=Role.ASSISTANT, text=response_text)],
+            response_id=f"judge-{assessment.recommendation}",
+            additional_properties={"assessment": assessment.model_dump()},
+        )
+
+    async def run_stream(
+        self,
+        messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentRunResponseUpdate]:
+        """Streaming wrapper for judge."""
+        result = await self.run(messages, thread=thread, **kwargs)
+        yield AgentRunResponseUpdate(messages=result.messages, response_id=result.response_id)

src/agents/search_agent.py

@@ -0,0 +1,99 @@
+from collections.abc import AsyncIterable
+from typing import Any
+
+from agent_framework import (
+    AgentRunResponse,
+    AgentRunResponseUpdate,
+    AgentThread,
+    BaseAgent,
+    ChatMessage,
+    Role,
+)
+
+from src.orchestrator import SearchHandlerProtocol
+from src.utils.models import Evidence, SearchResult
+
+
+class SearchAgent(BaseAgent):  # type: ignore[misc]
+    """Wraps SearchHandler as an AgentProtocol for Magentic."""
+
+    def __init__(
+        self,
+        search_handler: SearchHandlerProtocol,
+        evidence_store: dict[str, list[Evidence]],
+    ) -> None:
+        super().__init__(
+            name="SearchAgent",
+            description="Searches PubMed and web for drug repurposing evidence",
+        )
+        self._handler = search_handler
+        self._evidence_store = evidence_store
+
+    async def run(
+        self,
+        messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        **kwargs: Any,
+    ) -> AgentRunResponse:
+        """Execute search based on the last user message."""
+        # Extract query from messages
+        query = ""
+        if isinstance(messages, list):
+            for msg in reversed(messages):
+                if isinstance(msg, ChatMessage) and msg.role == Role.USER and msg.text:
+                    query = msg.text
+                    break
+                elif isinstance(msg, str):
+                    query = msg
+                    break
+        elif isinstance(messages, str):
+            query = messages
+
+        if not query:
+            return AgentRunResponse(
+                messages=[ChatMessage(role=Role.ASSISTANT, text="No query provided")],
+                response_id="search-no-query",
+            )
+
+        # Execute search
+        result: SearchResult = await self._handler.execute(query, max_results_per_tool=10)
+
+        # Update shared evidence store
+        # We append new evidence, deduplicating by URL is handled in Orchestrator usually,
+        # but here we should probably add to the list.
+        # For simplicity in this MVP phase, we just extend the list.
+        # Ideally, we should dedupe.
+        existing_urls = {e.citation.url for e in self._evidence_store["current"]}
+        new_unique = [e for e in result.evidence if e.citation.url not in existing_urls]
+        self._evidence_store["current"].extend(new_unique)
+
+        # Format response
+        evidence_text = "\n".join(
+            [
+                f"- [{e.citation.title}]({e.citation.url}): {e.content[:200]}..."
+                for e in result.evidence[:5]
+            ]
+        )
+
+        response_text = (
+            f"Found {result.total_found} sources ({len(new_unique)} new):\n\n{evidence_text}"
+        )
+
+        return AgentRunResponse(
+            messages=[ChatMessage(role=Role.ASSISTANT, text=response_text)],
+            response_id=f"search-{result.total_found}",
+            additional_properties={"evidence": [e.model_dump() for e in result.evidence]},
+        )
+
+    async def run_stream(
+        self,
+        messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentRunResponseUpdate]:
+        """Streaming wrapper for search (search itself isn't streaming)."""
+        result = await self.run(messages, thread=thread, **kwargs)
+        # Yield single update with full result
+        yield AgentRunResponseUpdate(messages=result.messages, response_id=result.response_id)

src/app.py

@@ -7,19 +7,20 @@ from typing import Any
 import gradio as gr
 
 from src.agent_factory.judges import JudgeHandler, MockJudgeHandler
-from src.orchestrator import Orchestrator
+from src.orchestrator_factory import create_orchestrator
 from src.tools.pubmed import PubMedTool
 from src.tools.search_handler import SearchHandler
 from src.tools.websearch import WebTool
 from src.utils.models import OrchestratorConfig
 
 
-def create_orchestrator(use_mock: bool = False) -> Orchestrator:
+def configure_orchestrator(use_mock: bool = False, mode: str = "simple") -> Any:
     """
     Create an orchestrator instance.
 
     Args:
         use_mock: If True, use MockJudgeHandler (no API key needed)
+        mode: Orchestrator mode ("simple" or "magentic")
 
     Returns:
         Configured Orchestrator instance
@@ -43,16 +44,18 @@ def create_orchestrator(use_mock: bool = False) -> Orchestrator:
     else:
         judge_handler = JudgeHandler()
 
-    return Orchestrator(
+    return create_orchestrator(
         search_handler=search_handler,
         judge_handler=judge_handler,
         config=config,
+        mode=mode,  # type: ignore
     )
 
 
 async def research_agent(
     message: str,
     history: list[dict[str, Any]],
+    mode: str = "simple",
 ) -> AsyncGenerator[str, None]:
     """
     Gradio chat function that runs the research agent.
@@ -60,6 +63,7 @@ async def research_agent(
     Args:
         message: User's research question
         history: Chat history (Gradio format)
+        mode: Orchestrator mode ("simple" or "magentic")
 
     Yields:
         Markdown-formatted responses for streaming
@@ -70,7 +74,16 @@ async def research_agent(
 
     # Create orchestrator (use mock if no API key)
     use_mock = not (os.getenv("OPENAI_API_KEY") or os.getenv("ANTHROPIC_API_KEY"))
-    orchestrator = create_orchestrator(use_mock=use_mock)
+
+    # If magentic mode requested but no keys, fallback/warn
+    if mode == "magentic" and use_mock:
+        yield (
+            "⚠️ **Warning**: Magentic mode requires valid API keys. "
+            "Falling back to Mock Simple mode."
+        )
+        mode = "simple"
+
+    orchestrator = configure_orchestrator(use_mock=use_mock, mode=mode)
 
     # Run the agent and stream events
     response_parts = []
@@ -126,6 +139,14 @@ def create_demo() -> Any:
                 "What medications show promise for Long COVID treatment?",
                 "Can statins be repurposed for neurological conditions?",
             ],
+            additional_inputs=[
+                gr.Radio(
+                    choices=["simple", "magentic"],
+                    value="simple",
+                    label="Orchestrator Mode",
+                    info="Simple: Linear loop | Magentic: Multi-Agent (Requires OpenAI Key)",
+                )
+            ],
         )
 
         gr.Markdown("""

src/orchestrator_factory.py

@@ -0,0 +1,52 @@
+"""Factory for creating orchestrators."""
+
+from typing import Any, Literal
+
+from src.orchestrator import JudgeHandlerProtocol, Orchestrator, SearchHandlerProtocol
+from src.utils.models import OrchestratorConfig
+
+# Define protocols again or import if they were in a shared place.
+
+# Since they are in src/orchestrator.py, we can import them?
+
+# But SearchHandler and JudgeHandler in arguments are concrete classes in the type hint,
+
+# which satisfy the protocol.
+
+
+def create_orchestrator(
+    search_handler: SearchHandlerProtocol,
+    judge_handler: JudgeHandlerProtocol,
+    config: OrchestratorConfig | None = None,
+    mode: Literal["simple", "magentic"] = "simple",
+) -> Any:
+    """
+    Create an orchestrator instance.
+
+    Args:
+        search_handler: The search handler
+        judge_handler: The judge handler
+        config: Optional configuration
+        mode: "simple" for Phase 4 loop, "magentic" for Phase 5 multi-agent
+
+    Returns:
+        Orchestrator instance (same interface regardless of mode)
+    """
+    if mode == "magentic":
+        try:
+            from src.orchestrator_magentic import MagenticOrchestrator
+
+            return MagenticOrchestrator(
+                search_handler=search_handler,
+                judge_handler=judge_handler,
+                max_rounds=config.max_iterations if config else 10,
+            )
+        except ImportError:
+            # Fallback to simple if agent-framework not installed
+            pass
+
+    return Orchestrator(
+        search_handler=search_handler,
+        judge_handler=judge_handler,
+        config=config,
+    )

src/orchestrator_magentic.py

@@ -0,0 +1,185 @@
+"""Magentic-based orchestrator for DeepCritical."""
+
+from collections.abc import AsyncGenerator
+
+import structlog
+from agent_framework import (
+    MagenticAgentDeltaEvent,
+    MagenticAgentMessageEvent,
+    MagenticBuilder,
+    MagenticFinalResultEvent,
+    MagenticOrchestratorMessageEvent,
+    WorkflowOutputEvent,
+)
+from agent_framework.openai import OpenAIChatClient
+
+from src.agents.judge_agent import JudgeAgent
+from src.agents.search_agent import SearchAgent
+from src.orchestrator import JudgeHandlerProtocol, SearchHandlerProtocol
+from src.utils.config import settings
+from src.utils.models import AgentEvent, Evidence
+
+logger = structlog.get_logger()
+
+
+class MagenticOrchestrator:
+    """
+    Magentic-based orchestrator - same API as Orchestrator.
+
+    Uses Microsoft Agent Framework's MagenticBuilder for multi-agent coordination.
+    """
+
+    def __init__(
+        self,
+        search_handler: SearchHandlerProtocol,
+        judge_handler: JudgeHandlerProtocol,
+        max_rounds: int = 10,
+    ) -> None:
+        self._search_handler = search_handler
+        self._judge_handler = judge_handler
+        self._max_rounds = max_rounds
+        self._evidence_store: dict[str, list[Evidence]] = {"current": []}
+
+    async def run(self, query: str) -> AsyncGenerator[AgentEvent, None]:  # noqa: PLR0912
+        """
+        Run the Magentic workflow - same API as simple Orchestrator.
+
+        Yields AgentEvent objects for real-time UI updates.
+        """
+        logger.info("Starting Magentic orchestrator", query=query)
+
+        yield AgentEvent(
+            type="started",
+            message=f"Starting research (Magentic mode): {query}",
+            iteration=0,
+        )
+
+        # Create agent wrappers
+        search_agent = SearchAgent(self._search_handler, self._evidence_store)
+        judge_agent = JudgeAgent(self._judge_handler, self._evidence_store)
+
+        # Build Magentic workflow
+        # Note: MagenticBuilder.participants takes named arguments for agent instances
+        workflow = (
+            MagenticBuilder()
+            .participants(
+                searcher=search_agent,
+                judge=judge_agent,
+            )
+            .with_standard_manager(
+                chat_client=OpenAIChatClient(
+                    model_id=settings.openai_model, api_key=settings.openai_api_key
+                ),
+                max_round_count=self._max_rounds,
+                max_stall_count=3,
+                max_reset_count=2,
+            )
+            .build()
+        )
+
+        # Task instruction for the manager
+        task = f"""Research drug repurposing opportunities for: {query}
+
+Instructions:
+1. Use SearcherAgent to find evidence. SEND ONLY A SIMPLE KEYWORD QUERY (e.g. "metformin aging")
+   as the instruction. Complex queries fail.
+2. Use JudgeAgent to evaluate if evidence is sufficient.
+3. If JudgeAgent says "continue", search with refined queries.
+4. If JudgeAgent says "synthesize", provide final synthesis
+5. Stop when synthesis is ready or max rounds reached
+
+Focus on finding:
+- Mechanism of action evidence
+- Clinical/preclinical studies
+- Specific drug candidates
+"""
+
+        iteration = 0
+        try:
+            # workflow.run_stream returns an async generator of workflow events
+            # We use 'await' in the for loop for async generator
+            async for event in workflow.run_stream(task):
+                if isinstance(event, MagenticOrchestratorMessageEvent):
+                    # Manager events (planning, instruction, ledger)
+                    # The 'message' attribute might be None if it's just a state change,
+                    # check message presence
+                    message_text = (
+                        event.message.text
+                        if event.message and hasattr(event.message, "text")
+                        else ""
+                    )
+                    # kind might be 'plan', 'instruction', etc.
+                    kind = getattr(event, "kind", "manager")
+
+                    if message_text:
+                        yield AgentEvent(
+                            type="judging",
+                            message=f"Manager ({kind}): {message_text[:100]}...",
+                            iteration=iteration,
+                        )
+
+                elif isinstance(event, MagenticAgentMessageEvent):
+                    # Complete agent response
+                    iteration += 1
+                    agent_name = event.agent_id or "unknown"
+                    msg_text = (
+                        event.message.text
+                        if event.message and hasattr(event.message, "text")
+                        else ""
+                    )
+
+                    if "search" in agent_name.lower():
+                        # Check if we found evidence (based on SearchAgent logic)
+                        yield AgentEvent(
+                            type="search_complete",
+                            message=f"Search agent: {msg_text[:100]}...",
+                            iteration=iteration,
+                        )
+                    elif "judge" in agent_name.lower():
+                        yield AgentEvent(
+                            type="judge_complete",
+                            message=f"Judge agent: {msg_text[:100]}...",
+                            iteration=iteration,
+                        )
+
+                elif isinstance(event, MagenticFinalResultEvent):
+                    # Final workflow result
+                    final_text = (
+                        event.message.text
+                        if event.message and hasattr(event.message, "text")
+                        else "No result"
+                    )
+                    yield AgentEvent(
+                        type="complete",
+                        message=final_text,
+                        data={"iterations": iteration},
+                        iteration=iteration,
+                    )
+
+                elif isinstance(event, MagenticAgentDeltaEvent):
+                    # Streaming token chunks from agents (optional "typing" effect)
+                    # Only emit if we have actual text content
+                    if event.text:
+                        yield AgentEvent(
+                            type="streaming",
+                            message=event.text,
+                            data={"agent_id": event.agent_id},
+                            iteration=iteration,
+                        )
+
+                elif isinstance(event, WorkflowOutputEvent):
+                    # Alternative final output event
+                    if event.data:
+                        yield AgentEvent(
+                            type="complete",
+                            message=str(event.data),
+                            iteration=iteration,
+                        )
+
+        except Exception as e:
+            logger.error("Magentic workflow failed", error=str(e))
+            yield AgentEvent(
+                type="error",
+                message=f"Workflow error: {e!s}",
+                iteration=iteration,
+            )

src/utils/models.py

@@ -106,6 +106,7 @@ class AgentEvent(BaseModel):
         "synthesizing",
         "complete",
         "error",
+        "streaming",
     ]
     message: str
     data: Any = None

uv.lock

@@ -19,6 +19,54 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8f/78/eb55fabaab41abc53f52c0918a9a8c0f747807e5306273f51120fd695957/ag_ui_protocol-0.1.10-py3-none-any.whl", hash = "sha256:c81e6981f30aabdf97a7ee312bfd4df0cd38e718d9fc10019c7d438128b93ab5", size = 7889 },
 ]
 
+[[package]]
+name = "agent-framework-core"
+version = "1.0.0b251120"
+source = { editable = "reference_repos/agent-framework/python/packages/core" }
+dependencies = [
+    { name = "azure-identity" },
+    { name = "mcp", extra = ["ws"] },
+    { name = "openai" },
+    { name = "opentelemetry-api" },
+    { name = "opentelemetry-exporter-otlp-proto-grpc" },
+    { name = "opentelemetry-sdk" },
+    { name = "opentelemetry-semantic-conventions-ai" },
+    { name = "packaging" },
+    { name = "pydantic" },
+    { name = "pydantic-settings" },
+    { name = "typing-extensions" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "agent-framework-a2a", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/a2a" },
+    { name = "agent-framework-ag-ui", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/ag-ui" },
+    { name = "agent-framework-anthropic", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/anthropic" },
+    { name = "agent-framework-azure-ai", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/azure-ai" },
+    { name = "agent-framework-azure-ai-search", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/azure-ai-search" },
+    { name = "agent-framework-azurefunctions", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/azurefunctions" },
+    { name = "agent-framework-chatkit", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/chatkit" },
+    { name = "agent-framework-copilotstudio", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/copilotstudio" },
+    { name = "agent-framework-declarative", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/declarative" },
+    { name = "agent-framework-devui", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/devui" },
+    { name = "agent-framework-lab", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/lab" },
+    { name = "agent-framework-mem0", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/mem0" },
+    { name = "agent-framework-purview", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/purview" },
+    { name = "agent-framework-redis", marker = "extra == 'all'", editable = "reference_repos/agent-framework/python/packages/redis" },
+    { name = "azure-identity", specifier = ">=1,<2" },
+    { name = "mcp", extras = ["ws"], specifier = ">=1.13" },
+    { name = "openai", specifier = ">=1.99.0" },
+    { name = "opentelemetry-api", specifier = ">=1.24" },
+    { name = "opentelemetry-exporter-otlp-proto-grpc", specifier = ">=1.36.0" },
+    { name = "opentelemetry-sdk", specifier = ">=1.24" },
+    { name = "opentelemetry-semantic-conventions-ai", specifier = ">=0.4.13" },
+    { name = "packaging", specifier = ">=24.1" },
+    { name = "pydantic", specifier = ">=2,<3" },
+    { name = "pydantic-settings", specifier = ">=2,<3" },
+    { name = "typing-extensions" },
+]
+provides-extras = ["all"]
+
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 version = "24.1.0"
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+[[package]]
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+version = "1.36.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "requests" },
+    { name = "typing-extensions" },
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+version = "1.25.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "azure-core" },
+    { name = "cryptography" },
+    { name = "msal" },
+    { name = "msal-extensions" },
+    { name = "typing-extensions" },
+]
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+wheels = [
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+
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 version = "1.2.0"
@@ -684,9 +761,13 @@ dev = [
     { name = "respx" },
     { name = "ruff" },
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+magentic = [
+    { name = "agent-framework-core" },
+]
 
 [package.metadata]
 requires-dist = [
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     { name = "duckduckgo-search", specifier = ">=6.0" },
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     { name = "xmltodict", specifier = ">=0.13" },
 ]
-provides-extras = ["dev"]
+provides-extras = ["dev", "magentic"]
 
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+[package.optional-dependencies]
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