mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-02-17 16:38:37 +08:00
715 lines
29 KiB
Python
715 lines
29 KiB
Python
"""
|
||
Agent Stream Execution Module - Multi-turn reasoning based on tool-call
|
||
|
||
Provides streaming output, event system, and complete tool-call loop
|
||
"""
|
||
import json
|
||
import time
|
||
from typing import List, Dict, Any, Optional, Callable
|
||
|
||
from agent.protocol.models import LLMRequest, LLMModel
|
||
from agent.tools.base_tool import BaseTool, ToolResult
|
||
from common.log import logger
|
||
|
||
|
||
class AgentStreamExecutor:
|
||
"""
|
||
Agent Stream Executor
|
||
|
||
Handles multi-turn reasoning loop based on tool-call:
|
||
1. LLM generates response (may include tool calls)
|
||
2. Execute tools
|
||
3. Return results to LLM
|
||
4. Repeat until no more tool calls
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
agent, # Agent instance
|
||
model: LLMModel,
|
||
system_prompt: str,
|
||
tools: List[BaseTool],
|
||
max_turns: int = 50,
|
||
on_event: Optional[Callable] = None,
|
||
messages: Optional[List[Dict]] = None
|
||
):
|
||
"""
|
||
Initialize stream executor
|
||
|
||
Args:
|
||
agent: Agent instance (for accessing context)
|
||
model: LLM model
|
||
system_prompt: System prompt
|
||
tools: List of available tools
|
||
max_turns: Maximum number of turns
|
||
on_event: Event callback function
|
||
messages: Optional existing message history (for persistent conversations)
|
||
"""
|
||
self.agent = agent
|
||
self.model = model
|
||
self.system_prompt = system_prompt
|
||
# Convert tools list to dict
|
||
self.tools = {tool.name: tool for tool in tools} if isinstance(tools, list) else tools
|
||
self.max_turns = max_turns
|
||
self.on_event = on_event
|
||
|
||
# Message history - use provided messages or create new list
|
||
self.messages = messages if messages is not None else []
|
||
|
||
# Tool failure tracking for retry protection
|
||
self.tool_failure_history = [] # List of (tool_name, args_hash, success) tuples
|
||
|
||
def _emit_event(self, event_type: str, data: dict = None):
|
||
"""Emit event"""
|
||
if self.on_event:
|
||
try:
|
||
self.on_event({
|
||
"type": event_type,
|
||
"timestamp": time.time(),
|
||
"data": data or {}
|
||
})
|
||
except Exception as e:
|
||
logger.error(f"Event callback error: {e}")
|
||
|
||
def _hash_args(self, args: dict) -> str:
|
||
"""Generate a simple hash for tool arguments"""
|
||
import hashlib
|
||
# Sort keys for consistent hashing
|
||
args_str = json.dumps(args, sort_keys=True, ensure_ascii=False)
|
||
return hashlib.md5(args_str.encode()).hexdigest()[:8]
|
||
|
||
def _check_consecutive_failures(self, tool_name: str, args: dict) -> tuple[bool, str]:
|
||
"""
|
||
Check if tool has failed too many times consecutively
|
||
|
||
Returns:
|
||
(should_stop, reason)
|
||
"""
|
||
args_hash = self._hash_args(args)
|
||
|
||
# Count consecutive failures for same tool + args
|
||
same_args_failures = 0
|
||
for name, ahash, success in reversed(self.tool_failure_history):
|
||
if name == tool_name and ahash == args_hash:
|
||
if not success:
|
||
same_args_failures += 1
|
||
else:
|
||
break # Stop at first success
|
||
else:
|
||
break # Different tool or args, stop counting
|
||
|
||
if same_args_failures >= 3:
|
||
return True, f"Tool '{tool_name}' with same arguments failed {same_args_failures} times consecutively. Stopping to prevent infinite loop."
|
||
|
||
# Count consecutive failures for same tool (any args)
|
||
same_tool_failures = 0
|
||
for name, ahash, success in reversed(self.tool_failure_history):
|
||
if name == tool_name:
|
||
if not success:
|
||
same_tool_failures += 1
|
||
else:
|
||
break # Stop at first success
|
||
else:
|
||
break # Different tool, stop counting
|
||
|
||
if same_tool_failures >= 6:
|
||
return True, f"Tool '{tool_name}' failed {same_tool_failures} times consecutively (with any arguments). Stopping to prevent infinite loop."
|
||
|
||
return False, ""
|
||
|
||
def _record_tool_result(self, tool_name: str, args: dict, success: bool):
|
||
"""Record tool execution result for failure tracking"""
|
||
args_hash = self._hash_args(args)
|
||
self.tool_failure_history.append((tool_name, args_hash, success))
|
||
# Keep only last 50 records to avoid memory bloat
|
||
if len(self.tool_failure_history) > 50:
|
||
self.tool_failure_history = self.tool_failure_history[-50:]
|
||
|
||
def run_stream(self, user_message: str) -> str:
|
||
"""
|
||
Execute streaming reasoning loop
|
||
|
||
Args:
|
||
user_message: User message
|
||
|
||
Returns:
|
||
Final response text
|
||
"""
|
||
# Log user message with model info
|
||
logger.info(f"{'='*50}")
|
||
logger.info(f"🤖 Model: {self.model.model}")
|
||
logger.info(f"👤 用户: {user_message}")
|
||
logger.info(f"{'='*50}")
|
||
|
||
# Add user message (Claude format - use content blocks for consistency)
|
||
self.messages.append({
|
||
"role": "user",
|
||
"content": [
|
||
{
|
||
"type": "text",
|
||
"text": user_message
|
||
}
|
||
]
|
||
})
|
||
|
||
self._emit_event("agent_start")
|
||
|
||
final_response = ""
|
||
turn = 0
|
||
|
||
try:
|
||
while turn < self.max_turns:
|
||
turn += 1
|
||
logger.info(f"第 {turn} 轮")
|
||
self._emit_event("turn_start", {"turn": turn})
|
||
|
||
# Check if memory flush is needed (before calling LLM)
|
||
# 使用独立的 flush 阈值（50K tokens 或 20 轮）
|
||
if self.agent.memory_manager and hasattr(self.agent, 'last_usage'):
|
||
usage = self.agent.last_usage
|
||
if usage and 'input_tokens' in usage:
|
||
current_tokens = usage.get('input_tokens', 0)
|
||
|
||
if self.agent.memory_manager.should_flush_memory(
|
||
current_tokens=current_tokens
|
||
):
|
||
self._emit_event("memory_flush_start", {
|
||
"current_tokens": current_tokens,
|
||
"turn_count": self.agent.memory_manager.flush_manager.turn_count
|
||
})
|
||
|
||
# TODO: Execute memory flush in background
|
||
# This would require async support
|
||
logger.info(
|
||
f"Memory flush recommended: tokens={current_tokens}, turns={self.agent.memory_manager.flush_manager.turn_count}")
|
||
|
||
# Call LLM
|
||
assistant_msg, tool_calls = self._call_llm_stream()
|
||
final_response = assistant_msg
|
||
|
||
# No tool calls, end loop
|
||
if not tool_calls:
|
||
# 检查是否返回了空响应
|
||
if not assistant_msg:
|
||
logger.warning(f"[Agent] LLM returned empty response (no content and no tool calls)")
|
||
|
||
# 生成通用的友好提示
|
||
final_response = (
|
||
"抱歉，我暂时无法生成回复。请尝试换一种方式描述你的需求，或稍后再试。"
|
||
)
|
||
logger.info(f"Generated fallback response for empty LLM output")
|
||
else:
|
||
logger.info(f"💭 {assistant_msg[:150]}{'...' if len(assistant_msg) > 150 else ''}")
|
||
|
||
logger.info(f"✅ 完成 (无工具调用)")
|
||
self._emit_event("turn_end", {
|
||
"turn": turn,
|
||
"has_tool_calls": False
|
||
})
|
||
break
|
||
|
||
# Log tool calls with arguments
|
||
tool_calls_str = []
|
||
for tc in tool_calls:
|
||
args_str = ', '.join([f"{k}={v}" for k, v in tc['arguments'].items()])
|
||
if args_str:
|
||
tool_calls_str.append(f"{tc['name']}({args_str})")
|
||
else:
|
||
tool_calls_str.append(tc['name'])
|
||
logger.info(f"🔧 {', '.join(tool_calls_str)}")
|
||
|
||
# Execute tools
|
||
tool_results = []
|
||
tool_result_blocks = []
|
||
|
||
try:
|
||
for tool_call in tool_calls:
|
||
result = self._execute_tool(tool_call)
|
||
tool_results.append(result)
|
||
|
||
# Log tool result in compact format
|
||
status_emoji = "✅" if result.get("status") == "success" else "❌"
|
||
result_data = result.get('result', '')
|
||
# Format result string with proper Chinese character support
|
||
if isinstance(result_data, (dict, list)):
|
||
result_str = json.dumps(result_data, ensure_ascii=False)
|
||
else:
|
||
result_str = str(result_data)
|
||
logger.info(f" {status_emoji} {tool_call['name']} ({result.get('execution_time', 0):.2f}s): {result_str[:200]}{'...' if len(result_str) > 200 else ''}")
|
||
|
||
# Build tool result block (Claude format)
|
||
# Format content in a way that's easy for LLM to understand
|
||
is_error = result.get("status") == "error"
|
||
|
||
if is_error:
|
||
# For errors, provide clear error message
|
||
result_content = f"Error: {result.get('result', 'Unknown error')}"
|
||
elif isinstance(result.get('result'), dict):
|
||
# For dict results, use JSON format
|
||
result_content = json.dumps(result.get('result'), ensure_ascii=False)
|
||
elif isinstance(result.get('result'), str):
|
||
# For string results, use directly
|
||
result_content = result.get('result')
|
||
else:
|
||
# Fallback to full JSON
|
||
result_content = json.dumps(result, ensure_ascii=False)
|
||
|
||
tool_result_block = {
|
||
"type": "tool_result",
|
||
"tool_use_id": tool_call["id"],
|
||
"content": result_content
|
||
}
|
||
|
||
# Add is_error field for Claude API (helps model understand failures)
|
||
if is_error:
|
||
tool_result_block["is_error"] = True
|
||
|
||
tool_result_blocks.append(tool_result_block)
|
||
|
||
finally:
|
||
# CRITICAL: Always add tool_result to maintain message history integrity
|
||
# Even if tool execution fails, we must add error results to match tool_use
|
||
if tool_result_blocks:
|
||
# Add tool results to message history as user message (Claude format)
|
||
self.messages.append({
|
||
"role": "user",
|
||
"content": tool_result_blocks
|
||
})
|
||
elif tool_calls:
|
||
# If we have tool_calls but no tool_result_blocks (unexpected error),
|
||
# create error results for all tool calls to maintain message integrity
|
||
logger.warning("⚠️ Tool execution interrupted, adding error results to maintain message history")
|
||
emergency_blocks = []
|
||
for tool_call in tool_calls:
|
||
emergency_blocks.append({
|
||
"type": "tool_result",
|
||
"tool_use_id": tool_call["id"],
|
||
"content": "Error: Tool execution was interrupted",
|
||
"is_error": True
|
||
})
|
||
self.messages.append({
|
||
"role": "user",
|
||
"content": emergency_blocks
|
||
})
|
||
|
||
self._emit_event("turn_end", {
|
||
"turn": turn,
|
||
"has_tool_calls": True,
|
||
"tool_count": len(tool_calls)
|
||
})
|
||
|
||
if turn >= self.max_turns:
|
||
logger.warning(f"⚠️ 已达到最大轮数限制: {self.max_turns}")
|
||
if not final_response:
|
||
final_response = (
|
||
"抱歉，我在处理你的请求时遇到了一些困难，尝试了多次仍未能完成。"
|
||
"请尝试简化你的问题，或换一种方式描述。"
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Agent执行错误: {e}")
|
||
self._emit_event("error", {"error": str(e)})
|
||
raise
|
||
|
||
finally:
|
||
logger.info(f"🏁 完成({turn}轮)")
|
||
self._emit_event("agent_end", {"final_response": final_response})
|
||
|
||
# 每轮对话结束后增加计数（用户消息+AI回复=1轮）
|
||
if self.agent.memory_manager:
|
||
self.agent.memory_manager.increment_turn()
|
||
|
||
return final_response
|
||
|
||
def _call_llm_stream(self, retry_on_empty=True, retry_count=0, max_retries=3) -> tuple[str, List[Dict]]:
|
||
"""
|
||
Call LLM with streaming and automatic retry on errors
|
||
|
||
Args:
|
||
retry_on_empty: Whether to retry once if empty response is received
|
||
retry_count: Current retry attempt (internal use)
|
||
max_retries: Maximum number of retries for API errors
|
||
|
||
Returns:
|
||
(response_text, tool_calls)
|
||
"""
|
||
# Validate and fix message history first
|
||
self._validate_and_fix_messages()
|
||
|
||
# Trim messages if needed (using agent's context management)
|
||
self._trim_messages()
|
||
|
||
# Prepare messages
|
||
messages = self._prepare_messages()
|
||
|
||
# Debug: log message structure
|
||
logger.debug(f"Sending {len(messages)} messages to LLM")
|
||
for i, msg in enumerate(messages):
|
||
role = msg.get("role", "unknown")
|
||
content = msg.get("content", "")
|
||
if isinstance(content, list):
|
||
content_types = [c.get("type") for c in content if isinstance(c, dict)]
|
||
logger.debug(f" Message {i}: role={role}, content_blocks={content_types}")
|
||
else:
|
||
logger.debug(f" Message {i}: role={role}, content_length={len(str(content))}")
|
||
|
||
# Prepare tool definitions (OpenAI/Claude format)
|
||
tools_schema = None
|
||
if self.tools:
|
||
tools_schema = []
|
||
for tool in self.tools.values():
|
||
tools_schema.append({
|
||
"name": tool.name,
|
||
"description": tool.description,
|
||
"input_schema": tool.params # Claude uses input_schema
|
||
})
|
||
|
||
# Create request
|
||
request = LLMRequest(
|
||
messages=messages,
|
||
temperature=0,
|
||
stream=True,
|
||
tools=tools_schema,
|
||
system=self.system_prompt # Pass system prompt separately for Claude API
|
||
)
|
||
|
||
self._emit_event("message_start", {"role": "assistant"})
|
||
|
||
# Streaming response
|
||
full_content = ""
|
||
tool_calls_buffer = {} # {index: {id, name, arguments}}
|
||
|
||
try:
|
||
stream = self.model.call_stream(request)
|
||
|
||
for chunk in stream:
|
||
# Check for errors
|
||
if isinstance(chunk, dict) and chunk.get("error"):
|
||
# Extract error message from nested structure
|
||
error_data = chunk.get("error", {})
|
||
if isinstance(error_data, dict):
|
||
error_msg = error_data.get("message", chunk.get("message", "Unknown error"))
|
||
error_code = error_data.get("code", "")
|
||
else:
|
||
error_msg = chunk.get("message", str(error_data))
|
||
error_code = ""
|
||
|
||
status_code = chunk.get("status_code", "N/A")
|
||
logger.error(f"API Error: {error_msg} (Status: {status_code}, Code: {error_code})")
|
||
logger.error(f"Full error chunk: {chunk}")
|
||
|
||
# Raise exception with full error message for retry logic
|
||
raise Exception(f"{error_msg} (Status: {status_code})")
|
||
|
||
# Parse chunk
|
||
if isinstance(chunk, dict) and "choices" in chunk:
|
||
choice = chunk["choices"][0]
|
||
delta = choice.get("delta", {})
|
||
|
||
# Handle text content
|
||
if "content" in delta and delta["content"]:
|
||
content_delta = delta["content"]
|
||
full_content += content_delta
|
||
self._emit_event("message_update", {"delta": content_delta})
|
||
|
||
# Handle tool calls
|
||
if "tool_calls" in delta:
|
||
for tc_delta in delta["tool_calls"]:
|
||
index = tc_delta.get("index", 0)
|
||
|
||
if index not in tool_calls_buffer:
|
||
tool_calls_buffer[index] = {
|
||
"id": "",
|
||
"name": "",
|
||
"arguments": ""
|
||
}
|
||
|
||
if "id" in tc_delta:
|
||
tool_calls_buffer[index]["id"] = tc_delta["id"]
|
||
|
||
if "function" in tc_delta:
|
||
func = tc_delta["function"]
|
||
if "name" in func:
|
||
tool_calls_buffer[index]["name"] = func["name"]
|
||
if "arguments" in func:
|
||
tool_calls_buffer[index]["arguments"] += func["arguments"]
|
||
|
||
except Exception as e:
|
||
error_str = str(e).lower()
|
||
# Check if error is retryable (timeout, connection, rate limit, server busy, etc.)
|
||
is_retryable = any(keyword in error_str for keyword in [
|
||
'timeout', 'timed out', 'connection', 'network',
|
||
'rate limit', 'overloaded', 'unavailable', 'busy', 'retry',
|
||
'429', '500', '502', '503', '504', '512'
|
||
])
|
||
|
||
if is_retryable and retry_count < max_retries:
|
||
wait_time = (retry_count + 1) * 2 # Exponential backoff: 2s, 4s, 6s
|
||
logger.warning(f"⚠️ LLM API error (attempt {retry_count + 1}/{max_retries}): {e}")
|
||
logger.info(f"Retrying in {wait_time}s...")
|
||
time.sleep(wait_time)
|
||
return self._call_llm_stream(
|
||
retry_on_empty=retry_on_empty,
|
||
retry_count=retry_count + 1,
|
||
max_retries=max_retries
|
||
)
|
||
else:
|
||
if retry_count >= max_retries:
|
||
logger.error(f"❌ LLM API error after {max_retries} retries: {e}")
|
||
else:
|
||
logger.error(f"❌ LLM call error (non-retryable): {e}")
|
||
raise
|
||
|
||
# Parse tool calls
|
||
tool_calls = []
|
||
for idx in sorted(tool_calls_buffer.keys()):
|
||
tc = tool_calls_buffer[idx]
|
||
try:
|
||
arguments = json.loads(tc["arguments"]) if tc["arguments"] else {}
|
||
except json.JSONDecodeError as e:
|
||
logger.error(f"Failed to parse tool arguments: {tc['arguments']}")
|
||
logger.error(f"JSON decode error: {e}")
|
||
# Return a clear error message to the LLM instead of empty dict
|
||
# This helps the LLM understand what went wrong
|
||
tool_calls.append({
|
||
"id": tc["id"],
|
||
"name": tc["name"],
|
||
"arguments": {},
|
||
"_parse_error": f"Invalid JSON in tool arguments: {tc['arguments'][:100]}... Error: {str(e)}"
|
||
})
|
||
continue
|
||
|
||
tool_calls.append({
|
||
"id": tc["id"],
|
||
"name": tc["name"],
|
||
"arguments": arguments
|
||
})
|
||
|
||
# Check for empty response and retry once if enabled
|
||
if retry_on_empty and not full_content and not tool_calls:
|
||
logger.warning(f"⚠️ LLM returned empty response, retrying once...")
|
||
self._emit_event("message_end", {
|
||
"content": "",
|
||
"tool_calls": [],
|
||
"empty_retry": True
|
||
})
|
||
# Retry without retry flag to avoid infinite loop
|
||
return self._call_llm_stream(
|
||
retry_on_empty=False,
|
||
retry_count=retry_count,
|
||
max_retries=max_retries
|
||
)
|
||
|
||
# Add assistant message to history (Claude format uses content blocks)
|
||
assistant_msg = {"role": "assistant", "content": []}
|
||
|
||
# Add text content block if present
|
||
if full_content:
|
||
assistant_msg["content"].append({
|
||
"type": "text",
|
||
"text": full_content
|
||
})
|
||
|
||
# Add tool_use blocks if present
|
||
if tool_calls:
|
||
for tc in tool_calls:
|
||
assistant_msg["content"].append({
|
||
"type": "tool_use",
|
||
"id": tc["id"],
|
||
"name": tc["name"],
|
||
"input": tc["arguments"]
|
||
})
|
||
|
||
# Only append if content is not empty
|
||
if assistant_msg["content"]:
|
||
self.messages.append(assistant_msg)
|
||
|
||
self._emit_event("message_end", {
|
||
"content": full_content,
|
||
"tool_calls": tool_calls
|
||
})
|
||
|
||
return full_content, tool_calls
|
||
|
||
def _execute_tool(self, tool_call: Dict) -> Dict[str, Any]:
|
||
"""
|
||
Execute tool
|
||
|
||
Args:
|
||
tool_call: {"id": str, "name": str, "arguments": dict}
|
||
|
||
Returns:
|
||
Tool execution result
|
||
"""
|
||
tool_name = tool_call["name"]
|
||
tool_id = tool_call["id"]
|
||
arguments = tool_call["arguments"]
|
||
|
||
# Check if there was a JSON parse error
|
||
if "_parse_error" in tool_call:
|
||
parse_error = tool_call["_parse_error"]
|
||
logger.error(f"Skipping tool execution due to parse error: {parse_error}")
|
||
result = {
|
||
"status": "error",
|
||
"result": f"Failed to parse tool arguments. {parse_error}. Please ensure your tool call uses valid JSON format with all required parameters.",
|
||
"execution_time": 0
|
||
}
|
||
self._record_tool_result(tool_name, arguments, False)
|
||
return result
|
||
|
||
# Check for consecutive failures (retry protection)
|
||
should_stop, stop_reason = self._check_consecutive_failures(tool_name, arguments)
|
||
if should_stop:
|
||
logger.error(f"🛑 {stop_reason}")
|
||
self._record_tool_result(tool_name, arguments, False)
|
||
# 返回错误给 LLM,让它尝试其他方法
|
||
result = {
|
||
"status": "error",
|
||
"result": f"{stop_reason}\n\nThis approach is not working. Please try a completely different method or ask the user for more information/clarification.",
|
||
"execution_time": 0
|
||
}
|
||
return result
|
||
|
||
self._emit_event("tool_execution_start", {
|
||
"tool_call_id": tool_id,
|
||
"tool_name": tool_name,
|
||
"arguments": arguments
|
||
})
|
||
|
||
try:
|
||
tool = self.tools.get(tool_name)
|
||
if not tool:
|
||
raise ValueError(f"Tool '{tool_name}' not found")
|
||
|
||
# Set tool context
|
||
tool.model = self.model
|
||
tool.context = self.agent
|
||
|
||
# Execute tool
|
||
start_time = time.time()
|
||
result: ToolResult = tool.execute_tool(arguments)
|
||
execution_time = time.time() - start_time
|
||
|
||
result_dict = {
|
||
"status": result.status,
|
||
"result": result.result,
|
||
"execution_time": execution_time
|
||
}
|
||
|
||
# Record tool result for failure tracking
|
||
success = result.status == "success"
|
||
self._record_tool_result(tool_name, arguments, success)
|
||
|
||
# Auto-refresh skills after skill creation
|
||
if tool_name == "bash" and result.status == "success":
|
||
command = arguments.get("command", "")
|
||
if "init_skill.py" in command and self.agent.skill_manager:
|
||
logger.info("Detected skill creation, refreshing skills...")
|
||
self.agent.refresh_skills()
|
||
logger.info(f"Skills refreshed! Now have {len(self.agent.skill_manager.skills)} skills")
|
||
|
||
self._emit_event("tool_execution_end", {
|
||
"tool_call_id": tool_id,
|
||
"tool_name": tool_name,
|
||
**result_dict
|
||
})
|
||
|
||
return result_dict
|
||
|
||
except Exception as e:
|
||
logger.error(f"Tool execution error: {e}")
|
||
error_result = {
|
||
"status": "error",
|
||
"result": str(e),
|
||
"execution_time": 0
|
||
}
|
||
# Record failure
|
||
self._record_tool_result(tool_name, arguments, False)
|
||
|
||
self._emit_event("tool_execution_end", {
|
||
"tool_call_id": tool_id,
|
||
"tool_name": tool_name,
|
||
**error_result
|
||
})
|
||
return error_result
|
||
|
||
def _validate_and_fix_messages(self):
|
||
"""
|
||
Validate message history and fix incomplete tool_use/tool_result pairs.
|
||
Claude API requires each tool_use to have a corresponding tool_result immediately after.
|
||
"""
|
||
if not self.messages:
|
||
return
|
||
|
||
# Check last message for incomplete tool_use
|
||
if len(self.messages) > 0:
|
||
last_msg = self.messages[-1]
|
||
if last_msg.get("role") == "assistant":
|
||
# Check if assistant message has tool_use blocks
|
||
content = last_msg.get("content", [])
|
||
if isinstance(content, list):
|
||
has_tool_use = any(block.get("type") == "tool_use" for block in content)
|
||
if has_tool_use:
|
||
# This is incomplete - remove it
|
||
logger.warning(f"⚠️ Removing incomplete tool_use message from history")
|
||
self.messages.pop()
|
||
|
||
def _trim_messages(self):
|
||
"""
|
||
Trim message history to stay within context limits.
|
||
Uses agent's context management configuration.
|
||
"""
|
||
if not self.messages or not self.agent:
|
||
return
|
||
|
||
# Get context window from agent (based on model)
|
||
context_window = self.agent._get_model_context_window()
|
||
|
||
# Reserve 10% for response generation
|
||
reserve_tokens = int(context_window * 0.1)
|
||
max_tokens = context_window - reserve_tokens
|
||
|
||
# Estimate current tokens
|
||
current_tokens = sum(self.agent._estimate_message_tokens(msg) for msg in self.messages)
|
||
|
||
# Add system prompt tokens
|
||
system_tokens = self.agent._estimate_message_tokens({"role": "system", "content": self.system_prompt})
|
||
current_tokens += system_tokens
|
||
|
||
# If under limit, no need to trim
|
||
if current_tokens <= max_tokens:
|
||
return
|
||
|
||
# Keep messages from newest, accumulating tokens
|
||
available_tokens = max_tokens - system_tokens
|
||
kept_messages = []
|
||
accumulated_tokens = 0
|
||
|
||
for msg in reversed(self.messages):
|
||
msg_tokens = self.agent._estimate_message_tokens(msg)
|
||
if accumulated_tokens + msg_tokens <= available_tokens:
|
||
kept_messages.insert(0, msg)
|
||
accumulated_tokens += msg_tokens
|
||
else:
|
||
break
|
||
|
||
old_count = len(self.messages)
|
||
self.messages = kept_messages
|
||
new_count = len(self.messages)
|
||
|
||
if old_count > new_count:
|
||
logger.info(
|
||
f"Context trimmed: {old_count} -> {new_count} messages "
|
||
f"(~{current_tokens} -> ~{system_tokens + accumulated_tokens} tokens, "
|
||
f"limit: {max_tokens})"
|
||
)
|
||
|
||
def _prepare_messages(self) -> List[Dict[str, Any]]:
|
||
"""
|
||
Prepare messages to send to LLM
|
||
|
||
Note: For Claude API, system prompt should be passed separately via system parameter,
|
||
not as a message. The AgentLLMModel will handle this.
|
||
"""
|
||
# Don't add system message here - it will be handled separately by the LLM adapter
|
||
return self.messages |