feat: multiturn, streaming, TaskRun tree

.gitignore

@@ -19,6 +19,7 @@ libs/core/build
 libs/server/build
 
 dist/
+test_output/
 
 .mcp.json
 

app/web_ui/src/lib/api_schema.d.ts

@@ -7031,6 +7031,11 @@ export interface components {
          *
          *     Contains the input used, its source, the output produced, and optional
          *     repair information if the output needed correction.
+         *
+         *     Can be nested under another TaskRun; nested runs are stored as child runs
+         *     in a "runs" subfolder (same relationship name as Task's runs).
+         *
+         *     Accepts both Task and TaskRun as parents (polymorphic).
          */
         "TaskRun-Input": {
             /**
@@ -7093,6 +7098,11 @@ export interface components {
          *
          *     Contains the input used, its source, the output produced, and optional
          *     repair information if the output needed correction.
+         *
+         *     Can be nested under another TaskRun; nested runs are stored as child runs
+         *     in a "runs" subfolder (same relationship name as Task's runs).
+         *
+         *     Accepts both Task and TaskRun as parents (polymorphic).
          */
         "TaskRun-Output": {
             /**

libs/core/kiln_ai/adapters/chat/__init__.py

@@ -1,8 +1,10 @@
 from .chat_formatter import (
     BasicChatMessage,
+    ChatCompletionMessageIncludingLiteLLM,
     ChatFormatter,
     ChatMessage,
     ChatStrategy,
+    MultiturnFormatter,
     ToolCallMessage,
     ToolResponseMessage,
     get_chat_formatter,
@@ -11,9 +13,11 @@
 
 __all__ = [
     "BasicChatMessage",
+    "ChatCompletionMessageIncludingLiteLLM",
     "ChatFormatter",
     "ChatMessage",
     "ChatStrategy",
+    "MultiturnFormatter",
     "ToolCallMessage",
     "ToolResponseMessage",
     "build_tool_call_messages",

libs/core/kiln_ai/adapters/chat/chat_formatter.py

@@ -3,15 +3,25 @@
 import json
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Dict, List, Literal, Optional, Sequence, Union
+from typing import Dict, List, Literal, Optional, Sequence, TypeAlias, Union
+
+from litellm.types.utils import Message as LiteLLMMessage
 
 from kiln_ai.datamodel.datamodel_enums import ChatStrategy, InputType
 from kiln_ai.utils.exhaustive_error import raise_exhaustive_enum_error
-from kiln_ai.utils.open_ai_types import ChatCompletionMessageToolCallParam
+from kiln_ai.utils.open_ai_types import (
+    ChatCompletionMessageParam,
+    ChatCompletionMessageToolCallParam,
+)
 
 COT_FINAL_ANSWER_PROMPT = "Considering the above, return a final result."
 
 
+ChatCompletionMessageIncludingLiteLLM: TypeAlias = Union[
+    ChatCompletionMessageParam, LiteLLMMessage
+]
+
+
 @dataclass
 class BasicChatMessage:
     role: Literal["system", "assistant", "user"]
@@ -90,6 +100,10 @@ def intermediate_outputs(self) -> Dict[str, str]:
         """Get the intermediate outputs from the chat formatter."""
         return self._intermediate_outputs
 
+    def initial_messages(self) -> list[ChatCompletionMessageIncludingLiteLLM]:
+        """Messages to seed the conversation. Empty for fresh runs; prior trace for continuation."""
+        return []
+
     @abstractmethod
     def next_turn(self, previous_output: str | None = None) -> Optional[ChatTurn]:
         """Advance the conversation and return the next messages if any."""
@@ -236,6 +250,49 @@ def next_turn(self, previous_output: str | None = None) -> Optional[ChatTurn]:
         return None
 
 
+class MultiturnFormatter(ChatFormatter):
+    """
+    Formatter for continuing a multi-turn conversation with prior trace.
+    Takes prior_trace (existing conversation) and appends the new user message.
+    Produces a single turn: the new user message. Tool calls and multi-turn
+    model responses are handled by _run_model_turn's internal loop.
+    """
+
+    def __init__(
+        self,
+        prior_trace: list[ChatCompletionMessageParam],
+        user_input: InputType,
+    ) -> None:
+        super().__init__(
+            system_message="",
+            user_input=user_input,
+            thinking_instructions=None,
+        )
+        self._prior_trace = prior_trace
+
+    def initial_messages(self) -> list[ChatCompletionMessageIncludingLiteLLM]:
+        """Messages to seed the conversation (prior trace)."""
+        return list(self._prior_trace)
+
+    def next_turn(self, previous_output: str | None = None) -> Optional[ChatTurn]:
+        if self._state == "start":
+            # prior trace is already in the messages list and contains system and so on, we only need
+            # to append the latest new user message
+            user_msg = BasicChatMessage("user", format_user_message(self.user_input))
+            self._state = "awaiting_final"
+            self._messages.append(user_msg)
+            return ChatTurn(messages=[user_msg], final_call=True)
+
+        if self._state == "awaiting_final":
+            if previous_output is None:
+                raise ValueError("previous_output required for final step")
+            self._messages.append(BasicChatMessage("assistant", previous_output))
+            self._state = "done"
+            return None
+
+        return None
+
+
 def get_chat_formatter(
     strategy: ChatStrategy,
     system_message: str,

libs/core/kiln_ai/adapters/chat/test_chat_formatter.py

@@ -1,6 +1,7 @@
 from kiln_ai.adapters.chat import ChatStrategy, get_chat_formatter
 from kiln_ai.adapters.chat.chat_formatter import (
     COT_FINAL_ANSWER_PROMPT,
+    MultiturnFormatter,
     format_user_message,
 )
 
@@ -119,6 +120,76 @@ def test_chat_formatter_r1_style():
     assert formatter.intermediate_outputs() == {}
 
 
+def test_multiturn_formatter_initial_messages():
+    prior_trace = [
+        {"role": "user", "content": "hi"},
+        {"role": "assistant", "content": "hello"},
+    ]
+    formatter = MultiturnFormatter(prior_trace=prior_trace, user_input="new input")
+    assert formatter.initial_messages() == prior_trace
+
+
+def test_multiturn_formatter_next_turn():
+    prior_trace = [
+        {"role": "user", "content": "hi"},
+        {"role": "assistant", "content": "hello"},
+    ]
+    formatter = MultiturnFormatter(prior_trace=prior_trace, user_input="follow-up")
+
+    first = formatter.next_turn()
+    assert first is not None
+    assert len(first.messages) == 1
+    assert first.messages[0].role == "user"
+    assert first.messages[0].content == "follow-up"
+    assert first.final_call
+
+    assert formatter.next_turn("assistant response") is None
+
+
+def test_multiturn_formatter_preserves_tool_call_messages():
+    prior_trace = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "4"},
+        {
+            "role": "assistant",
+            "content": "",
+            "reasoning_content": "Let me multiply 4 by 7.\n",
+            "tool_calls": [
+                {
+                    "id": "call_abc123",
+                    "function": {"arguments": '{"a": 4, "b": 7}', "name": "multiply"},
+                    "type": "function",
+                }
+            ],
+        },
+        {
+            "content": "28",
+            "role": "tool",
+            "tool_call_id": "call_abc123",
+            "kiln_task_tool_data": None,
+        },
+        {
+            "role": "assistant",
+            "content": "4 multiplied by 7 is 28.",
+            "reasoning_content": "Done.\n",
+        },
+    ]
+    formatter = MultiturnFormatter(prior_trace=prior_trace, user_input="now double it")
+    initial = formatter.initial_messages()
+    assert initial == prior_trace
+    assert initial[2]["tool_calls"][0]["id"] == "call_abc123"
+    assert initial[2]["tool_calls"][0]["function"]["name"] == "multiply"
+    assert initial[3]["role"] == "tool"
+    assert initial[3]["tool_call_id"] == "call_abc123"
+
+    first = formatter.next_turn()
+    assert first is not None
+    assert len(first.messages) == 1
+    assert first.messages[0].role == "user"
+    assert first.messages[0].content == "now double it"
+    assert first.final_call
+
+
 def test_format_user_message():
     # String
     assert format_user_message("test input") == "test input"

libs/core/kiln_ai/adapters/litellm_utils/litellm_streaming.py

@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from typing import Any, AsyncIterator, Optional, Union
+
+import litellm
+from litellm.types.utils import (
+    ModelResponse,
+    ModelResponseStream,
+    TextCompletionResponse,
+)
+
+
+class StreamingCompletion:
+    """
+    Async iterable wrapper around ``litellm.acompletion`` with streaming.
+
+    Yields ``ModelResponseStream`` chunks as they arrive.  After iteration
+    completes, the assembled ``ModelResponse`` is available via the
+    ``.response`` property.
+
+    Usage::
+
+        stream = StreamingCompletion(model=..., messages=...)
+        async for chunk in stream:
+            # handle chunk however you like (print, log, send over WS, …)
+            pass
+        final = stream.response   # fully assembled ModelResponse
+    """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        kwargs = dict(kwargs)
+        kwargs.pop("stream", None)
+        self._args = args
+        self._kwargs = kwargs
+        self._response: Optional[Union[ModelResponse, TextCompletionResponse]] = None
+        self._iterated: bool = False
+
+    @property
+    def response(self) -> Optional[Union[ModelResponse, TextCompletionResponse]]:
+        """The final assembled response. Only available after iteration."""
+        if not self._iterated:
+            raise RuntimeError(
+                "StreamingCompletion has not been iterated yet. "
+                "Use 'async for chunk in stream:' before accessing .response"
+            )
+        return self._response
+
+    async def __aiter__(self) -> AsyncIterator[ModelResponseStream]:
+        self._response = None
+        self._iterated = False
+
+        chunks: list[ModelResponseStream] = []
+        stream = await litellm.acompletion(*self._args, stream=True, **self._kwargs)
+
+        async for chunk in stream:
+            chunks.append(chunk)
+            yield chunk
+
+        self._response = litellm.stream_chunk_builder(chunks)
+        self._iterated = True