File size: 21,747 Bytes
a2ec7b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 | """ReAct agent implementation for the MCPMark pipeline."""
from __future__ import annotations
import asyncio
import json
import time
from typing import Any, Dict, List, Optional, Callable
import litellm
from src.logger import get_logger
from .base_agent import BaseMCPAgent
logger = get_logger(__name__)
class ReActAgent(BaseMCPAgent):
"""ReAct-style agent that reuses MCPMark infrastructure."""
DEFAULT_SYSTEM_PROMPT = (
"You are a careful ReAct (reasoning and acting) agent. "
"At each step you must decide whether to call a tool or provide a final response. "
"Only use the tools that are listed for you. When you finish, respond with either the final answer "
"or the phrase \"Task completed.\" if no further detail is required. "
"Every reply must be valid JSON without code fences."
)
COMPACTION_PROMPT = (
"You are performing a CONTEXT CHECKPOINT COMPACTION.\n"
"Summarize the conversation so far for another model to continue.\n\n"
"Include:\n"
"- Current progress and key decisions made\n"
"- Important context, constraints, or user preferences\n"
"- What remains to be done (clear next steps)\n"
"- Any critical data, examples, or references needed to continue\n\n"
"Be concise and structured. Do NOT call tools."
)
def __init__(
self,
litellm_input_model_name: str,
api_key: str,
base_url: str,
mcp_service: str,
timeout: int = BaseMCPAgent.DEFAULT_TIMEOUT,
service_config: Optional[Dict[str, Any]] = None,
service_config_provider: Optional[Callable[[], Dict[str, Any]]] = None,
reasoning_effort: Optional[str] = "default",
max_iterations: int = 100,
system_prompt: Optional[str] = None,
compaction_token: int = BaseMCPAgent.COMPACTION_DISABLED_TOKEN,
):
super().__init__(
litellm_input_model_name=litellm_input_model_name,
api_key=api_key,
base_url=base_url,
mcp_service=mcp_service,
timeout=timeout,
service_config=service_config,
service_config_provider=service_config_provider,
reasoning_effort=reasoning_effort,
compaction_token=compaction_token,
)
self.max_iterations = max_iterations
self.react_system_prompt = system_prompt or self.DEFAULT_SYSTEM_PROMPT
async def execute(
self,
instruction: str,
tool_call_log_file: Optional[str] = None,
) -> Dict[str, Any]:
start_time = time.time()
try:
self._reset_progress()
self._refresh_service_config()
async def _run_react():
return await self._execute_react_loop(instruction, tool_call_log_file)
result = await asyncio.wait_for(_run_react(), timeout=self.timeout)
execution_time = time.time() - start_time
self.usage_tracker.update(
success=result.get("success", False),
token_usage=result.get("token_usage", {}),
turn_count=result.get("turn_count", 0),
execution_time=execution_time,
)
result["execution_time"] = execution_time
return result
except Exception as exc: # noqa: BLE001
execution_time = time.time() - start_time
if isinstance(exc, asyncio.TimeoutError):
error_msg = f"Execution timed out after {self.timeout} seconds"
logger.error(error_msg)
else:
error_msg = f"ReAct agent execution failed: {exc}"
logger.error(error_msg, exc_info=True)
self.usage_tracker.update(
success=False,
token_usage=self._partial_token_usage or {},
turn_count=self._partial_turn_count or 0,
execution_time=execution_time,
)
if self._partial_messages:
final_msg = self._convert_to_sdk_format(self._partial_messages)
else:
final_msg = []
return {
"success": False,
"output": final_msg,
"token_usage": self._partial_token_usage or {},
"turn_count": self._partial_turn_count or 0,
"execution_time": execution_time,
"error": error_msg,
"litellm_run_model_name": self.litellm_run_model_name,
}
async def _execute_react_loop(
self,
instruction: str,
tool_call_log_file: Optional[str],
) -> Dict[str, Any]:
system_message = {"role": "system", "content": self.react_system_prompt}
total_tokens = {
"input_tokens": 0,
"output_tokens": 0,
"total_tokens": 0,
"reasoning_tokens": 0,
}
turn_count = 0
success = False
final_error: Optional[str] = None
mcp_server = await self._create_mcp_server()
async with mcp_server:
tools = await mcp_server.list_tools()
tool_map = {tool.get("name"): tool for tool in tools}
tools_description = self._render_tools_description(tools)
task_message = {
"role": "user",
"content": self._build_task_prompt(
instruction=instruction,
tools_description=tools_description,
),
}
messages: List[Dict[str, Any]] = [system_message, task_message]
self._update_progress(messages, total_tokens, turn_count)
for step in range(1, self.max_iterations + 1):
current_prompt_tokens = 0
if self._compaction_enabled():
current_prompt_tokens = self._count_prompt_tokens_litellm(messages)
if self._compaction_enabled() and current_prompt_tokens >= self.compaction_token:
logger.info(
f"| [compaction] Triggered at prompt tokens: {current_prompt_tokens:,}"
)
if tool_call_log_file:
try:
with open(tool_call_log_file, "a", encoding="utf-8") as log_file:
log_file.write(
f"| [compaction] Triggered at prompt tokens: {current_prompt_tokens:,}\n"
)
except Exception: # noqa: BLE001
pass
compact_messages = [
{"role": "system", "content": self.COMPACTION_PROMPT},
{"role": "user", "content": json.dumps(messages, ensure_ascii=False)},
]
compact_kwargs = {
"model": self.litellm_input_model_name,
"messages": compact_messages,
"api_key": self.api_key,
}
if self.base_url:
compact_kwargs["base_url"] = self.base_url
compact_response = await litellm.acompletion(**compact_kwargs)
usage = getattr(compact_response, "usage", None)
if usage:
prompt_tokens = (
getattr(usage, "prompt_tokens", None)
or getattr(usage, "input_tokens", None)
or 0
)
completion_tokens = (
getattr(usage, "completion_tokens", None)
or getattr(usage, "output_tokens", None)
or 0
)
total_tokens_count = getattr(usage, "total_tokens", None)
if total_tokens_count is None:
total_tokens_count = prompt_tokens + completion_tokens
total_tokens["input_tokens"] += int(prompt_tokens or 0)
total_tokens["output_tokens"] += int(completion_tokens or 0)
total_tokens["total_tokens"] += int(total_tokens_count or 0)
summary = ""
try:
summary = compact_response.choices[0].message.content or ""
except Exception: # noqa: BLE001
summary = ""
summary = summary.strip() or "(no summary)"
messages = [
system_message,
task_message,
{
"role": "user",
"content": (
"Context summary (auto-compacted due to token limit):\n"
f"{summary}"
),
},
]
self._update_progress(messages, total_tokens, turn_count)
completion_kwargs = {
"model": self.litellm_input_model_name,
"messages": messages,
"api_key": self.api_key,
}
if self.base_url:
completion_kwargs["base_url"] = self.base_url
if self.reasoning_effort != "default":
completion_kwargs["reasoning_effort"] = self.reasoning_effort
try:
response = await asyncio.wait_for(
litellm.acompletion(**completion_kwargs),
timeout=self.timeout / 2,
)
except asyncio.TimeoutError:
final_error = f"LLM call timed out on step {step}"
logger.error(final_error)
break
except Exception as exc: # noqa: BLE001
final_error = f"LLM call failed on step {step}: {exc}"
logger.error(final_error)
if "ContextWindowExceededError" in str(exc):
continue
break
if turn_count == 0 and getattr(response, "model", None):
self.litellm_run_model_name = response.model.split("/")[-1]
usage = getattr(response, "usage", None)
if usage:
prompt_tokens = (
getattr(usage, "prompt_tokens", None)
or getattr(usage, "input_tokens", None)
or 0
)
completion_tokens = (
getattr(usage, "completion_tokens", None)
or getattr(usage, "output_tokens", None)
or 0
)
total_tokens_count = getattr(usage, "total_tokens", None)
if total_tokens_count is None:
total_tokens_count = prompt_tokens + completion_tokens
total_tokens["input_tokens"] += prompt_tokens
total_tokens["output_tokens"] += completion_tokens
total_tokens["total_tokens"] += total_tokens_count
# Extract reasoning tokens if available
if hasattr(response.usage, 'completion_tokens_details'):
details = response.usage.completion_tokens_details
if hasattr(details, 'reasoning_tokens'):
total_tokens["reasoning_tokens"] += details.reasoning_tokens or 0
choice = response.choices[0]
message_obj = getattr(choice, "message", None)
if message_obj is None and isinstance(choice, dict):
message_obj = choice.get("message")
if message_obj is None:
content_raw = getattr(choice, "text", "")
else:
content_raw = message_obj.get("content", "")
assistant_text = self._normalize_content(content_raw)
assistant_message = {"role": "assistant", "content": assistant_text}
messages.append(assistant_message)
turn_count += 1
self._update_progress(messages, total_tokens, turn_count)
parsed = self._parse_react_response(assistant_text)
if not parsed or "thought" not in parsed:
warning = (
"The previous response was not valid JSON following the required schema. "
"Please respond again using the JSON formats provided."
)
messages.append({"role": "user", "content": warning})
self._update_progress(messages, total_tokens, turn_count)
final_error = "Model produced an invalid response format."
continue
thought = parsed.get("thought", "")
action = parsed.get("action")
answer = parsed.get("answer")
result = parsed.get("result")
logger.info(f"|\n| \033[1;3mThought\033[0m: {str(thought)}")
if tool_call_log_file:
try:
with open(tool_call_log_file, "a", encoding="utf-8") as log_file:
log_file.write(f"| {str(thought)}\n")
except Exception: # noqa: BLE001
pass
if action is not None:
func_name = action.get("tool")
arguments = action.get("arguments", {}) or {}
args_str = json.dumps(arguments, separators=(",", ": "))
display_arguments = args_str[:140] + "..." if len(args_str) > 140 else args_str
logger.info(f"| \033[1;3mAction\033[0m: \033[1m{func_name}\033[0m \033[2;37m{display_arguments}\033[0m")
if answer is not None:
success = True
break
if action is not None and isinstance(action, dict):
tool_name = action.get("tool")
arguments = action.get("arguments", {}) or {}
if tool_name not in tool_map:
observation = (
f"Invalid tool '{tool_name}'. Available tools: "
f"{', '.join(tool_map)}"
)
else:
try:
tool_response = await asyncio.wait_for(
mcp_server.call_tool(tool_name, arguments),
timeout=60,
)
observation = self._tool_result_to_text(tool_response)
except asyncio.TimeoutError:
observation = f"Tool '{tool_name}' timed out"
except Exception as tool_exc: # noqa: BLE001
observation = f"Tool '{tool_name}' failed: {tool_exc}"
if tool_call_log_file:
try:
with open(tool_call_log_file, "a", encoding="utf-8") as log_file:
log_file.write(f"| {tool_name} {json.dumps(arguments, ensure_ascii=False)}\n")
except Exception: # noqa: BLE001
pass
observation_message = {
"role": "user",
"content": (
f"Observation:\n{observation}\n"
"Please continue reasoning and reply using the required JSON format."
),
}
messages.append(observation_message)
self._update_progress(messages, total_tokens, turn_count)
continue
if result is not None:
observation_message = {
"role": "user",
"content": (
f"Observation:\n{result}\n"
"Please continue reasoning and reply using the required JSON format."
),
}
messages.append(observation_message)
self._update_progress(messages, total_tokens, turn_count)
continue
# Unexpected structure: ask model to restate properly
messages.append(
{
"role": "user",
"content": (
"The previous reply did not include an action, result, or answer. "
"Please respond again using the JSON formats provided."
),
}
)
self._update_progress(messages, total_tokens, turn_count)
if not success and final_error is None:
final_error = (
f"Max iterations ({self.max_iterations}) reached without a final answer."
)
if total_tokens["total_tokens"] > 0:
log_msg = (
f"|\n|\n| Token usage: Total: {total_tokens['total_tokens']:,} | "
f"Input: {total_tokens['input_tokens']:,} | "
f"Output: {total_tokens['output_tokens']:,}"
)
if total_tokens.get("reasoning_tokens", 0) > 0:
log_msg += f" | Reasoning: {total_tokens['reasoning_tokens']:,}"
logger.info(log_msg)
logger.info(f"| Turns: {turn_count}")
sdk_messages = self._convert_to_sdk_format(messages)
return {
"success": success,
"output": sdk_messages,
"token_usage": total_tokens,
"turn_count": turn_count,
"error": None if success else final_error,
"litellm_run_model_name": self.litellm_run_model_name,
}
def _build_task_prompt(
self,
instruction: str,
tools_description: str,
) -> str:
return (
f"Task:\n{instruction}\n\n"
f"Available MCP tools:\n{tools_description}\n\n"
"Respond using the JSON formats below.\n\n"
"If you need to use a tool:\n"
"{\n"
' "thought": "Reasoning for the next action",\n'
' "action": {\n'
' "tool": "tool-name",\n'
' "arguments": {\n'
' "parameter": value\n'
" }\n"
" }\n"
"}\n\n"
"If you can provide the final answer:\n"
"{\n"
' "thought": "Reasoning that justifies the answer",\n'
' "answer": "Either the final solution or \'Task completed.\' when no more detail is required"\n'
"}\n\n"
"Remember: omitting the action object ends the task, so only do this when finished."
)
def _render_tools_description(self, tools: List[Dict[str, Any]]) -> str:
descriptions = []
for tool in tools:
name = tool.get("name", "unknown")
description = tool.get("description", "No description provided.")
input_schema = tool.get("inputSchema", {}) or {}
properties = input_schema.get("properties", {}) or {}
required = set(input_schema.get("required", []) or [])
arg_lines = []
for prop_name, prop_details in properties.items():
details = json.dumps(prop_details, ensure_ascii=False, indent=2)
suffix = " (required)" if prop_name in required else ""
arg_lines.append(f"- {prop_name}{suffix}: {details}")
if arg_lines:
arguments_text = "\n".join(arg_lines)
else:
arguments_text = "(no arguments)"
descriptions.append(
f"Tool: {name}\nDescription: {description}\nArguments:\n{arguments_text}"
)
return "\n\n".join(descriptions) if descriptions else "(no tools available)"
def _normalize_content(self, content: Any) -> str:
if isinstance(content, str):
return content
if isinstance(content, list):
parts = []
for block in content:
if isinstance(block, dict):
if block.get("type") == "text":
parts.append(block.get("text", ""))
elif "text" in block:
parts.append(str(block.get("text")))
else:
parts.append(str(block))
return "\n".join(part for part in parts if part)
return json.dumps(content, ensure_ascii=False)
def _parse_react_response(self, payload: str) -> Dict[str, Any]:
candidate = payload.strip().strip("`").strip()
if candidate.lower().startswith("json"):
candidate = candidate[4:].lstrip()
try:
return json.loads(candidate)
except json.JSONDecodeError:
return {}
def _tool_result_to_text(self, result: Any) -> str:
if result is None:
return ""
if isinstance(result, str):
return result
try:
return json.dumps(result, ensure_ascii=False)
except TypeError:
return str(result)
|