feat: add conversation history and tool call logging
Add two major features to enhance Tatlock's capabilities: 1. Conversation History Support: - Convert OpenAI-format messages to PydanticAI ModelRequest/ModelResponse - Pass full conversation context via message_history parameter - Filter empty messages to prevent Ollama errors - Add debug logging for message history construction - Tatlock now remembers previous turns in multi-turn conversations 2. Tool Call Logging: - Implement ToolCallTracker dependency for per-request tracking - Tools log usage via RunContext deps parameter - Web search: "🔍 Searching for: 'query'" - Calculator: "🧮 Calculating: expression" - Date/time: "🕐 Calculating date offset: description" - Tool logs appear in reasoning output as <think> tags in Open WebUI Both features improve user experience by maintaining conversation context and providing transparency into tool usage. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Claude Sonnet 4.5
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-8
@@ -8,6 +8,7 @@ The agent embodies a witty, capable British butler personality.
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import logging
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import secrets
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from typing import AsyncGenerator, Any
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from dataclasses import dataclass, field
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from pydantic_ai import Agent, RunContext
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@@ -24,6 +25,16 @@ from src.core.config import config
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logger = logging.getLogger(__name__)
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@dataclass
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class ToolCallTracker:
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"""Tracks tool calls for reporting to reasoning output."""
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calls: list[str] = field(default_factory=list)
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def log_call(self, message: str):
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"""Log a tool call."""
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self.calls.append(message)
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def generate_id() -> str:
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"""Generate unique ID for output items."""
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return secrets.token_hex(16)
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@@ -135,7 +146,7 @@ class TatlockAgent(AgentInterface):
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# Calculator tool
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@self._agent.tool
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def calculate_math(ctx: RunContext[None], expression: str) -> str:
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def calculate_math(ctx: RunContext[ToolCallTracker], expression: str) -> str:
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"""
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Evaluate mathematical expressions safely.
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@@ -147,11 +158,14 @@ class TatlockAgent(AgentInterface):
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Returns:
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String result of the calculation
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"""
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# Log the calculation to reasoning output
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if ctx.deps:
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ctx.deps.log_call(f"🧮 Calculating: {expression}")
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return calculate(expression)
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# Current date/time tool
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@self._agent.tool
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def get_current_time(ctx: RunContext[None], format_str: str = "full") -> str:
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def get_current_time(ctx: RunContext[ToolCallTracker], format_str: str = "full") -> str:
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"""
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Get the current date and time.
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@@ -161,11 +175,13 @@ class TatlockAgent(AgentInterface):
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Returns:
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Formatted current datetime string
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"""
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if ctx.deps:
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ctx.deps.log_call(f"🕐 Getting current time (format: {format_str})")
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return get_current_datetime(format_str)
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# Time offset calculator
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@self._agent.tool
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def calculate_date_offset(ctx: RunContext[None], offset_description: str) -> str:
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def calculate_date_offset(ctx: RunContext[ToolCallTracker], offset_description: str) -> str:
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"""
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Calculate a date/time relative to now.
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@@ -175,11 +191,13 @@ class TatlockAgent(AgentInterface):
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Returns:
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Formatted datetime string (YYYY-MM-DD HH:MM:SS)
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"""
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if ctx.deps:
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ctx.deps.log_call(f"🕐 Calculating date offset: {offset_description}")
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return calculate_time_offset(offset_description)
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# Time difference calculator
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@self._agent.tool
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def calculate_time_difference(ctx: RunContext[None], date1_str: str, date2_str: str = "now") -> str:
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def calculate_time_difference(ctx: RunContext[ToolCallTracker], date1_str: str, date2_str: str = "now") -> str:
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"""
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Calculate the difference between two dates.
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@@ -190,11 +208,13 @@ class TatlockAgent(AgentInterface):
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Returns:
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Human-readable description of the time difference
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"""
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if ctx.deps:
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ctx.deps.log_call(f"🕐 Calculating time difference between {date1_str} and {date2_str}")
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return time_difference(date1_str, date2_str)
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# Web search tool
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@self._agent.tool
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async def web_search(ctx: RunContext[None], query: str, num_results: int = 5) -> str:
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async def web_search(ctx: RunContext[ToolCallTracker], query: str, num_results: int = 5) -> str:
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"""
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Search the web using SearXNG for current information.
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@@ -210,6 +230,9 @@ class TatlockAgent(AgentInterface):
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Returns:
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Formatted search results with titles, URLs, and snippets
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"""
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# Log the search query to reasoning output
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if ctx.deps:
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ctx.deps.log_call(f"🔍 Searching for: '{query}'")
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return await search_web(query, num_results)
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@property
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@@ -244,8 +267,11 @@ class TatlockAgent(AgentInterface):
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OutputItem: Response items (reasoning, message)
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"""
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try:
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# Extract user message from messages
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# For now, use the last user message as the prompt
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# Convert OpenAI-format messages to PydanticAI format
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# PydanticAI uses: {"role": "user"/"assistant", "content": "text"}
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# OpenAI format is the same, so we can use messages directly
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# Extract the latest user message for the prompt
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user_message = ""
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for msg in reversed(messages):
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if msg.get("role") == "user":
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@@ -266,6 +292,47 @@ class TatlockAgent(AgentInterface):
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)
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return
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# Build message history (all messages except the last user message)
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# PydanticAI expects history as list of ModelRequest/ModelResponse objects
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from pydantic_ai.messages import ModelRequest, ModelResponse, UserPromptPart, TextPart
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message_history = []
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for i, msg in enumerate(messages[:-1]): # All messages except the last one
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role = msg.get("role")
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content = msg.get("content", "")
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# Skip messages with empty content (can cause Ollama errors)
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if not content or not content.strip():
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logger.warning(f"Skipping message {i} with empty content: role={role}")
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continue
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# Debug: Check for problematic content
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if '"' in content or "'" in content:
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logger.debug(f"Message {i} ({role}) contains quotes. Content preview: {content[:100]}...")
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# Convert to PydanticAI message format
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try:
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if role == "user":
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message_history.append(
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ModelRequest(parts=[UserPromptPart(content=content)])
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)
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elif role == "assistant":
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message_history.append(
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ModelResponse(parts=[TextPart(content=content)])
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)
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except Exception as e:
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logger.error(f"Error creating message history item {i}: {e}")
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logger.error(f"Problematic content: {repr(content)}")
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raise
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# Debug: Log the message history summary
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logger.info(f"Built message history with {len(message_history)} messages")
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if message_history:
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for i, hist_msg in enumerate(message_history):
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msg_type = type(hist_msg).__name__
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content_preview = str(hist_msg.parts[0].content)[:50] if hist_msg.parts else "no parts"
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logger.info(f" History[{i}]: {msg_type} - {content_preview}...")
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# Generate reasoning output if requested
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if reasoning and reasoning.get("effort") != "none":
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yield OutputItem(
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@@ -279,6 +346,9 @@ class TatlockAgent(AgentInterface):
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status="completed"
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)
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# Create a tool call tracker for this request
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tracker = ToolCallTracker()
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# Stream the agent response token-by-token
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msg_id = f"msg_{generate_id()}"
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final_text = ""
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@@ -286,9 +356,24 @@ class TatlockAgent(AgentInterface):
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# Use run() instead of run_stream() to avoid GeneratorExit issues
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# with async context managers inside generators
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# The StreamingCoordinator will handle word-by-word streaming
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result = await self.agent.run(user_message)
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# Pass message_history to maintain conversation context and tracker for tool logging
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result = await self.agent.run(
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user_message,
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message_history=message_history if message_history else None,
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deps=tracker
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)
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final_text = result.output
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# If tools were called, yield a reasoning item showing what was done
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if tracker.calls:
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yield OutputItem(
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type="reasoning",
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id=f"reasoning_tools_{generate_id()}",
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summary=tracker.calls,
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thinking="",
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status="completed"
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)
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# Yield the complete message
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# The StreamingCoordinator will break this into word-by-word deltas
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yield OutputItem(
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