feat: enhance AIAgent's tool usage nudges and content handling
- Introduced a method to strip <think> blocks from content, improving text visibility. - Implemented counters to reset nudge intervals when memory and skill tools are used, enhancing user guidance. - Captured content from turns with tool calls to provide fallback responses, ensuring continuity in conversation. - Updated nudge logic to remind users about saving memories and creating skills based on interaction patterns.
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teknium1
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1 changed files with 71 additions and 19 deletions
90
run_agent.py
90
run_agent.py
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@ -447,6 +447,13 @@ class AIAgent:
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# Check if there's any non-whitespace content remaining
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return bool(cleaned.strip())
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def _strip_think_blocks(self, content: str) -> str:
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"""Remove <think>...</think> blocks from content, returning only visible text."""
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if not content:
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return ""
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return re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL)
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def _extract_reasoning(self, assistant_message) -> Optional[str]:
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"""
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Extract reasoning/thinking content from an assistant message.
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@ -1387,6 +1394,12 @@ class AIAgent:
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for i, tool_call in enumerate(assistant_message.tool_calls, 1):
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function_name = tool_call.function.name
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# Reset nudge counters when the relevant tool is actually used
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if function_name == "memory":
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self._turns_since_memory = 0
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elif function_name == "skill_manage":
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self._iters_since_skill = 0
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try:
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function_args = json.loads(tool_call.function.arguments)
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except json.JSONDecodeError as e:
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@ -1631,6 +1644,9 @@ class AIAgent:
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self._invalid_tool_retries = 0
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self._invalid_json_retries = 0
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self._empty_content_retries = 0
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self._last_content_with_tools = None
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self._turns_since_memory = 0
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self._iters_since_skill = 0
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# Initialize conversation
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messages = conversation_history or []
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@ -1650,16 +1666,29 @@ class AIAgent:
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# Track user turns for memory flush and periodic nudge logic
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self._user_turn_count += 1
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# Periodic memory nudge: remind the model to consider saving memories
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# Periodic memory nudge: remind the model to consider saving memories.
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# Counter resets whenever the memory tool is actually used.
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if (self._memory_nudge_interval > 0
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and self._user_turn_count % self._memory_nudge_interval == 0
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and self._user_turn_count > 0
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and "memory" in self.valid_tool_names
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and self._memory_store):
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self._turns_since_memory += 1
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if self._turns_since_memory >= self._memory_nudge_interval:
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user_message += (
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"\n\n[System: You've had several exchanges in this session. "
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"Consider whether there's anything worth saving to your memories.]"
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)
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self._turns_since_memory = 0
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# Skill creation nudge: fires on the first user message after a long tool loop.
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# The counter increments per API iteration in the tool loop and is checked here.
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if (self._skill_nudge_interval > 0
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and self._iters_since_skill >= self._skill_nudge_interval
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and "skill_manage" in self.valid_tool_names):
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user_message += (
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"\n\n[System: You've had several exchanges in this session. "
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"Consider whether there's anything worth saving to your memories.]"
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"\n\n[System: The previous task involved many steps. "
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"If you discovered a reusable workflow, consider saving it as a skill.]"
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)
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self._iters_since_skill = 0
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# Add user message
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user_msg = {"role": "user", "content": user_message}
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@ -1702,18 +1731,11 @@ class AIAgent:
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api_call_count += 1
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# Periodic skill creation nudge after many tool-calling iterations
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# Track tool-calling iterations for skill nudge.
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# Counter resets whenever skill_manage is actually used.
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if (self._skill_nudge_interval > 0
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and api_call_count > 0
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and api_call_count % self._skill_nudge_interval == 0
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and "skill_manage" in self.valid_tool_names):
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messages.append({
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"role": "user",
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"content": (
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"[System: This task has involved many steps. "
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"If you've discovered a reusable workflow, consider saving it as a skill.]"
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),
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})
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self._iters_since_skill += 1
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# Prepare messages for API call
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# If we have an ephemeral system prompt, prepend it to the messages
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@ -2212,6 +2234,20 @@ class AIAgent:
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assistant_msg = self._build_assistant_message(assistant_message, finish_reason)
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# If this turn has both content AND tool_calls, capture the content
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# as a fallback final response. Common pattern: model delivers its
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# answer and calls memory/skill tools as a side-effect in the same
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# turn. If the follow-up turn after tools is empty, we use this.
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turn_content = assistant_message.content or ""
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if turn_content and self._has_content_after_think_block(turn_content):
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self._last_content_with_tools = turn_content
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# Show intermediate commentary so the user can follow along
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if self.quiet_mode:
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clean = self._strip_think_blocks(turn_content).strip()
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if clean:
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preview = clean[:120] + "..." if len(clean) > 120 else clean
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print(f" \033[2m┊ 💬 {preview}\033[0m")
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messages.append(assistant_msg)
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self._log_msg_to_db(assistant_msg)
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@ -2256,13 +2292,29 @@ class AIAgent:
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print(f"{self.log_prefix}🔄 Retrying API call ({self._empty_content_retries}/3)...")
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continue
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else:
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# Max retries exceeded — keep the message history intact
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# and return what we have so the session is preserved.
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print(f"{self.log_prefix}❌ Max retries (3) for empty content exceeded.")
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self._empty_content_retries = 0
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# Append the empty assistant message so the session
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# log reflects what actually happened
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# If a prior tool_calls turn had real content, salvage it:
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# rewrite that turn's content to a brief tool description,
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# and use the original content as the final response here.
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fallback = getattr(self, '_last_content_with_tools', None)
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if fallback:
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self._last_content_with_tools = None
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# Find the last assistant message with tool_calls and rewrite it
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for i in range(len(messages) - 1, -1, -1):
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msg = messages[i]
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if msg.get("role") == "assistant" and msg.get("tool_calls"):
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tool_names = []
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for tc in msg["tool_calls"]:
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fn = tc.get("function", {})
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tool_names.append(fn.get("name", "unknown"))
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msg["content"] = f"Calling the {', '.join(tool_names)} tool{'s' if len(tool_names) > 1 else ''}..."
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break
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final_response = fallback
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break
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# No fallback -- append the empty message as-is
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empty_msg = {
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"role": "assistant",
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"content": final_response,
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