Add new compiler backend (#203)

Add new compiler backend

MANIFEST.in

@@ -0,0 +1 @@
+recursive-include controlflow/orchestration/prompt_templates *

examples/teacher_student.py

@@ -1,29 +1,34 @@
 from controlflow import Agent, Task, flow
 from controlflow.instructions import instructions
 
-teacher = Agent(name="teacher")
-student = Agent(name="student")
+teacher = Agent(name="Teacher")
+student = Agent(name="Student")
 
 
 @flow
 def demo():
-    with Task("Teach a class by asking and answering 3 questions") as task:
+    with Task("Teach a class by asking and answering 3 questions", agents=[teacher]):
         for _ in range(3):
             question = Task(
-                "ask the student a question. Wait for the student to answer your question before asking another one.",
-                str,
-                agents=[teacher],
+                "Ask the student a question.", result_type=str, agents=[teacher]
             )
-            with instructions("one sentence max"):
-                Task(
-                    "answer the question",
-                    str,
+
+            with instructions("One sentence max"):
+                answer = Task(
+                    "Answer the question.",
                     agents=[student],
                     context=dict(question=question),
                 )
 
-    task.run()
-    return task
+            grade = Task(
+                "Assess the answer.",
+                result_type=["pass", "fail"],
+                agents=[teacher],
+                context=dict(answer=answer),
+            )
+
+            # run each qa session, one at a time
+            grade.run()
 
 
 t = demo()

pyproject.toml

@@ -6,11 +6,11 @@ authors = [
     { name = "Jeremiah Lowin", email = "153965+jlowin@users.noreply.github.com" },
 ]
 dependencies = [
-    "prefect>=3.0rc4",
+    "prefect>=3.0rc10",
     "jinja2>=3.1.4",
     "langchain_core>=0.2.9",
     "langchain_openai>=0.1.8",
-    "langchain-anthropic>=0.1.15",
+    "langchain-anthropic>=0.1.19",
     "markdownify>=0.12.1",
     "pydantic-settings>=2.2.1",
     "textual>=0.61.1",

src/controlflow/__init__.py

@@ -8,12 +8,12 @@
 
 from .instructions import instructions
 from .decorators import flow, task
-from .llm.tools import tool
+from .tools import tool
 
 # --- Default settings ---
 
 from .llm.models import _get_initial_default_model, get_default_model
-from .flows.history import InMemoryHistory, get_default_history
+from .events.history import InMemoryHistory, get_default_history
 
 # assign to controlflow.default_model to change the default model
 default_model = _get_initial_default_model()

src/controlflow/agents/agent.py

@@ -2,16 +2,18 @@
 import random
 import uuid
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, Any, Callable, Optional
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Callable, Generator, Optional
 
 from langchain_core.language_models import BaseChatModel
 from pydantic import Field, field_serializer
 
 import controlflow
+from controlflow.events.events import Event
 from controlflow.instructions import get_instructions
+from controlflow.llm.messages import AIMessage, BaseMessage
 from controlflow.llm.models import get_default_model
 from controlflow.llm.rules import LLMRules
-from controlflow.tools.talk_to_user import talk_to_user
+from controlflow.tools.tools import handle_tool_call_async
 from controlflow.utilities.context import ctx
 from controlflow.utilities.types import ControlFlowModel
 
@@ -20,6 +22,8 @@
 
 if TYPE_CHECKING:
     from controlflow.tasks.task import Task
+    from controlflow.tools.tools import Tool
+
 logger = logging.getLogger(__name__)
 
 
@@ -109,6 +113,8 @@ def get_llm_rules(self) -> LLMRules:
         return controlflow.llm.rules.rules_for_model(self.get_model())
 
     def get_tools(self) -> list[Callable]:
+        from controlflow.tools.talk_to_user import talk_to_user
+
         tools = self.tools.copy()
         if self.user_access:
             tools.append(talk_to_user)
@@ -141,5 +147,83 @@ def run(self, task: "Task"):
     async def run_async(self, task: "Task"):
         return await task.run_async(agents=[self])
 
+    def _run_model(
+        self,
+        messages: list[BaseMessage],
+        additional_tools: list["Tool"] = None,
+        stream: bool = True,
+    ) -> Generator[Event, None, None]:
+        from controlflow.events.agent_events import (
+            AgentMessageDeltaEvent,
+            AgentMessageEvent,
+        )
+        from controlflow.events.tool_events import ToolCallEvent, ToolResultEvent
+        from controlflow.tools.tools import as_tools, handle_tool_call
+
+        model = self.get_model()
+
+        tools = as_tools(self.get_tools() + (additional_tools or []))
+        if tools:
+            model = model.bind_tools([t.to_lc_tool() for t in tools])
+
+        if stream:
+            response = None
+            for delta in model.stream(messages):
+                if response is None:
+                    response = delta
+                else:
+                    response += delta
+                yield AgentMessageDeltaEvent(agent=self, delta=delta, snapshot=response)
+
+        else:
+            response: AIMessage = model.invoke(messages)
+
+        yield AgentMessageEvent(agent=self, message=response)
+
+        for tool_call in response.tool_calls + response.invalid_tool_calls:
+            yield ToolCallEvent(agent=self, tool_call=tool_call, message=response)
+
+            result = handle_tool_call(tool_call, tools=tools)
+            yield ToolResultEvent(agent=self, tool_call=tool_call, tool_result=result)
+
+    async def _run_model_async(
+        self,
+        messages: list[BaseMessage],
+        additional_tools: list["Tool"] = None,
+        stream: bool = True,
+    ) -> AsyncGenerator[Event, None]:
+        from controlflow.events.agent_events import (
+            AgentMessageDeltaEvent,
+            AgentMessageEvent,
+        )
+        from controlflow.events.tool_events import ToolCallEvent, ToolResultEvent
+        from controlflow.tools.tools import as_tools
+
+        model = self.get_model()
+
+        tools = as_tools(self.get_tools() + (additional_tools or []))
+        if tools:
+            model = model.bind_tools([t.to_lc_tool() for t in tools])
+
+        if stream:
+            response = None
+            async for delta in model.astream(messages):
+                if response is None:
+                    response = delta
+                else:
+                    response += delta
+                yield AgentMessageDeltaEvent(agent=self, delta=delta, snapshot=response)
+
+        else:
+            response: AIMessage = model.invoke(messages)
+
+        yield AgentMessageEvent(agent=self, message=response)
+
+        for tool_call in response.tool_calls + response.invalid_tool_calls:
+            yield ToolCallEvent(agent=self, tool_call=tool_call, message=response)
+
+            result = await handle_tool_call_async(tool_call, tools=tools)
+            yield ToolResultEvent(agent=self, tool_call=tool_call, tool_result=result)
+
 
 DEFAULT_AGENT = Agent(name="Marvin")

src/controlflow/agents/memory.py

@@ -1,13 +1,15 @@
 import abc
 import uuid
-from typing import ClassVar, Optional, cast
+from typing import TYPE_CHECKING, ClassVar, Optional, cast
 
 from pydantic import Field
 
-from controlflow.tools import Tool
 from controlflow.utilities.context import ctx
 from controlflow.utilities.types import ControlFlowModel
 
+if TYPE_CHECKING:
+    from controlflow.tools import Tool
+
 
 class Memory(ControlFlowModel, abc.ABC):
     id: str = Field(default_factory=lambda: uuid.uuid4().hex)
@@ -27,7 +29,9 @@ def update(self, value: str, index: int = None):
     def delete(self, index: int):
         raise NotImplementedError()
 
-    def get_tools(self) -> list[Tool]:
+    def get_tools(self) -> list["Tool"]:
+        from controlflow.tools import Tool
+
         update_tool = Tool.from_function(
             self.update,
             name="update_memory",