Spaces:
Running
Running
File size: 58,506 Bytes
2eb41d7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import json
import os
import re
import tempfile
import textwrap
import time
from collections import deque
from logging import getLogger
from pathlib import Path
from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, TypedDict, Union
import jinja2
import yaml
from huggingface_hub import create_repo, metadata_update, snapshot_download, upload_folder
from jinja2 import StrictUndefined, Template
from rich.console import Group
from rich.panel import Panel
from rich.rule import Rule
from rich.text import Text
from .agent_types import AgentAudio, AgentImage, AgentType, handle_agent_output_types
from .default_tools import TOOL_MAPPING, FinalAnswerTool
from .local_python_executor import BASE_BUILTIN_MODULES, LocalPythonExecutor, PythonExecutor, fix_final_answer_code
from .memory import ActionStep, AgentMemory, PlanningStep, SystemPromptStep, TaskStep, ToolCall
from .models import (
ChatMessage,
MessageRole,
Model,
)
from .monitoring import (
YELLOW_HEX,
AgentLogger,
LogLevel,
Monitor,
)
from .remote_executors import DockerExecutor, E2BExecutor
from .tools import Tool
from .utils import (
AgentError,
AgentExecutionError,
AgentGenerationError,
AgentMaxStepsError,
AgentParsingError,
make_init_file,
parse_code_blobs,
parse_json_tool_call,
truncate_content,
)
logger = getLogger(__name__)
def get_variable_names(self, template: str) -> Set[str]:
pattern = re.compile(r"\{\{([^{}]+)\}\}")
return {match.group(1).strip() for match in pattern.finditer(template)}
def populate_template(template: str, variables: Dict[str, Any]) -> str:
compiled_template = Template(template, undefined=StrictUndefined)
try:
return compiled_template.render(**variables)
except Exception as e:
raise Exception(f"Error during jinja template rendering: {type(e).__name__}: {e}")
class PlanningPromptTemplate(TypedDict):
"""
Prompt templates for the planning step.
Args:
initial_facts (`str`): Initial facts prompt.
initial_plan (`str`): Initial plan prompt.
update_facts_pre_messages (`str`): Update facts pre-messages prompt.
update_facts_post_messages (`str`): Update facts post-messages prompt.
update_plan_pre_messages (`str`): Update plan pre-messages prompt.
update_plan_post_messages (`str`): Update plan post-messages prompt.
"""
initial_facts: str
initial_plan: str
update_facts_pre_messages: str
update_facts_post_messages: str
update_plan_pre_messages: str
update_plan_post_messages: str
class ManagedAgentPromptTemplate(TypedDict):
"""
Prompt templates for the managed agent.
Args:
task (`str`): Task prompt.
report (`str`): Report prompt.
"""
task: str
report: str
class FinalAnswerPromptTemplate(TypedDict):
"""
Prompt templates for the final answer.
Args:
pre_messages (`str`): Pre-messages prompt.
post_messages (`str`): Post-messages prompt.
"""
pre_messages: str
post_messages: str
class PromptTemplates(TypedDict):
"""
Prompt templates for the agent.
Args:
system_prompt (`str`): System prompt.
planning ([`~agents.PlanningPromptTemplate`]): Planning prompt templates.
managed_agent ([`~agents.ManagedAgentPromptTemplate`]): Managed agent prompt templates.
final_answer ([`~agents.FinalAnswerPromptTemplate`]): Final answer prompt templates.
"""
system_prompt: str
planning: PlanningPromptTemplate
managed_agent: ManagedAgentPromptTemplate
final_answer: FinalAnswerPromptTemplate
EMPTY_PROMPT_TEMPLATES = PromptTemplates(
system_prompt="",
planning=PlanningPromptTemplate(
initial_facts="",
initial_plan="",
update_facts_pre_messages="",
update_facts_post_messages="",
update_plan_pre_messages="",
update_plan_post_messages="",
),
managed_agent=ManagedAgentPromptTemplate(task="", report=""),
final_answer=FinalAnswerPromptTemplate(pre_messages="", post_messages=""),
)
class MultiStepAgent:
"""
Agent class that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of action (given by the LLM) and observation (obtained from the environment).
Args:
tools (`list[Tool]`): [`Tool`]s that the agent can use.
model (`Callable[[list[dict[str, str]]], ChatMessage]`): Model that will generate the agent's actions.
prompt_templates ([`~agents.PromptTemplates`], *optional*): Prompt templates.
max_steps (`int`, default `20`): Maximum number of steps the agent can take to solve the task.
tool_parser (`Callable`, *optional*): Function used to parse the tool calls from the LLM output.
add_base_tools (`bool`, default `False`): Whether to add the base tools to the agent's tools.
verbosity_level (`LogLevel`, default `LogLevel.INFO`): Level of verbosity of the agent's logs.
grammar (`dict[str, str]`, *optional*): Grammar used to parse the LLM output.
managed_agents (`list`, *optional*): Managed agents that the agent can call.
step_callbacks (`list[Callable]`, *optional*): Callbacks that will be called at each step.
planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.
name (`str`, *optional*): Necessary for a managed agent only - the name by which this agent can be called.
description (`str`, *optional*): Necessary for a managed agent only - the description of this agent.
provide_run_summary (`bool`, *optional*): Whether to provide a run summary when called as a managed agent.
final_answer_checks (`list`, *optional*): List of Callables to run before returning a final answer for checking validity.
"""
def __init__(
self,
tools: List[Tool],
model: Callable[[List[Dict[str, str]]], ChatMessage],
prompt_templates: Optional[PromptTemplates] = None,
max_steps: int = 20,
tool_parser: Optional[Callable] = None,
add_base_tools: bool = False,
verbosity_level: LogLevel = LogLevel.INFO,
grammar: Optional[Dict[str, str]] = None,
managed_agents: Optional[List] = None,
step_callbacks: Optional[List[Callable]] = None,
planning_interval: Optional[int] = None,
name: Optional[str] = None,
description: Optional[str] = None,
provide_run_summary: bool = False,
final_answer_checks: Optional[List[Callable]] = None,
):
self.agent_name = self.__class__.__name__
self.model = model
self.prompt_templates = prompt_templates or EMPTY_PROMPT_TEMPLATES
self.max_steps = max_steps
self.step_number = 0
self.tool_parser = tool_parser or parse_json_tool_call
self.grammar = grammar
self.planning_interval = planning_interval
self.state = {}
self.name = name
self.description = description
self.provide_run_summary = provide_run_summary
self.final_answer_checks = final_answer_checks
self._setup_managed_agents(managed_agents)
self._setup_tools(tools, add_base_tools)
self._validate_tools_and_managed_agents(tools, managed_agents)
self.system_prompt = self.initialize_system_prompt()
self.input_messages = None
self.task = None
self.memory = AgentMemory(self.system_prompt)
self.logger = AgentLogger(level=verbosity_level)
self.monitor = Monitor(self.model, self.logger)
self.step_callbacks = step_callbacks if step_callbacks is not None else []
self.step_callbacks.append(self.monitor.update_metrics)
def _setup_managed_agents(self, managed_agents):
self.managed_agents = {}
if managed_agents:
assert all(agent.name and agent.description for agent in managed_agents), (
"All managed agents need both a name and a description!"
)
self.managed_agents = {agent.name: agent for agent in managed_agents}
def _setup_tools(self, tools, add_base_tools):
assert all(isinstance(tool, Tool) for tool in tools), "All elements must be instance of Tool (or a subclass)"
self.tools = {tool.name: tool for tool in tools}
if add_base_tools:
self.tools.update(
{
name: cls()
for name, cls in TOOL_MAPPING.items()
if name != "python_interpreter" or self.__class__.__name__ == "ToolCallingAgent"
}
)
self.tools.setdefault("final_answer", FinalAnswerTool())
def _validate_tools_and_managed_agents(self, tools, managed_agents):
tool_and_managed_agent_names = [tool.name for tool in tools]
if managed_agents is not None:
tool_and_managed_agent_names += [agent.name for agent in managed_agents]
if self.name:
tool_and_managed_agent_names.append(self.name)
if len(tool_and_managed_agent_names) != len(set(tool_and_managed_agent_names)):
raise ValueError(
"Each tool or managed_agent should have a unique name! You passed these duplicate names: "
f"{[name for name in tool_and_managed_agent_names if tool_and_managed_agent_names.count(name) > 1]}"
)
def run(
self,
task: str,
stream: bool = False,
reset: bool = True,
images: Optional[List[str]] = None,
additional_args: Optional[Dict] = None,
max_steps: Optional[int] = None,
):
"""
Run the agent for the given task.
Args:
task (`str`): Task to perform.
stream (`bool`): Whether to run in a streaming way.
reset (`bool`): Whether to reset the conversation or keep it going from previous run.
images (`list[str]`, *optional*): Paths to image(s).
additional_args (`dict`, *optional*): Any other variables that you want to pass to the agent run, for instance images or dataframes. Give them clear names!
max_steps (`int`, *optional*): Maximum number of steps the agent can take to solve the task. if not provided, will use the agent's default value.
Example:
```py
from smolagents import CodeAgent
agent = CodeAgent(tools=[])
agent.run("What is the result of 2 power 3.7384?")
```
"""
max_steps = max_steps or self.max_steps
self.task = task
if additional_args is not None:
self.state.update(additional_args)
self.task += f"""
You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
{str(additional_args)}."""
self.system_prompt = self.initialize_system_prompt()
self.memory.system_prompt = SystemPromptStep(system_prompt=self.system_prompt)
if reset:
self.memory.reset()
self.monitor.reset()
self.logger.log_task(
content=self.task.strip(),
subtitle=f"{type(self.model).__name__} - {(self.model.model_id if hasattr(self.model, 'model_id') else '')}",
level=LogLevel.INFO,
title=self.name if hasattr(self, "name") else None,
)
self.memory.steps.append(TaskStep(task=self.task, task_images=images))
if getattr(self, "python_executor", None):
self.python_executor.send_variables(variables=self.state)
self.python_executor.send_tools({**self.tools, **self.managed_agents})
if stream:
# The steps are returned as they are executed through a generator to iterate on.
return self._run(task=self.task, max_steps=max_steps, images=images)
# Outputs are returned only at the end. We only look at the last step.
return deque(self._run(task=self.task, max_steps=max_steps, images=images), maxlen=1)[0]
def _run(
self, task: str, max_steps: int, images: List[str] | None = None
) -> Generator[ActionStep | AgentType, None, None]:
final_answer = None
self.step_number = 1
while final_answer is None and self.step_number <= max_steps:
step_start_time = time.time()
memory_step = self._create_memory_step(step_start_time, images)
try:
final_answer = self._execute_step(task, memory_step)
except AgentError as e:
memory_step.error = e
finally:
self._finalize_step(memory_step, step_start_time)
yield memory_step
self.step_number += 1
if final_answer is None and self.step_number == max_steps + 1:
final_answer = self._handle_max_steps_reached(task, images, step_start_time)
yield memory_step
yield handle_agent_output_types(final_answer)
def _create_memory_step(self, step_start_time: float, images: List[str] | None) -> ActionStep:
return ActionStep(step_number=self.step_number, start_time=step_start_time, observations_images=images)
def _execute_step(self, task: str, memory_step: ActionStep) -> Union[None, Any]:
if self.planning_interval is not None and self.step_number % self.planning_interval == 1:
self.planning_step(task, is_first_step=(self.step_number == 1), step=self.step_number)
self.logger.log_rule(f"Step {self.step_number}", level=LogLevel.INFO)
final_answer = self.step(memory_step)
if final_answer is not None and self.final_answer_checks:
self._validate_final_answer(final_answer)
return final_answer
def _validate_final_answer(self, final_answer: Any):
for check_function in self.final_answer_checks:
try:
assert check_function(final_answer, self.memory)
except Exception as e:
raise AgentError(f"Check {check_function.__name__} failed with error: {e}", self.logger)
def _finalize_step(self, memory_step: ActionStep, step_start_time: float):
memory_step.end_time = time.time()
memory_step.duration = memory_step.end_time - step_start_time
self.memory.steps.append(memory_step)
for callback in self.step_callbacks:
# For compatibility with old callbacks that don't take the agent as an argument
callback(memory_step) if len(inspect.signature(callback).parameters) == 1 else callback(
memory_step, agent=self
)
def _handle_max_steps_reached(self, task: str, images: List[str], step_start_time: float) -> Any:
final_answer = self.provide_final_answer(task, images)
final_memory_step = ActionStep(
step_number=self.step_number, error=AgentMaxStepsError("Reached max steps.", self.logger)
)
final_memory_step.action_output = final_answer
final_memory_step.end_time = time.time()
final_memory_step.duration = final_memory_step.end_time - step_start_time
self.memory.steps.append(final_memory_step)
for callback in self.step_callbacks:
callback(final_memory_step) if len(inspect.signature(callback).parameters) == 1 else callback(
final_memory_step, agent=self
)
return final_answer
def planning_step(self, task, is_first_step: bool, step: int) -> None:
input_messages, facts_message, plan_message = (
self._generate_initial_plan(task) if is_first_step else self._generate_updated_plan(task, step)
)
self._record_planning_step(input_messages, facts_message, plan_message, is_first_step)
def _generate_initial_plan(self, task: str) -> Tuple[ChatMessage, ChatMessage]:
input_messages = [
{
"role": MessageRole.USER,
"content": [
{
"type": "text",
"text": populate_template(
self.prompt_templates["planning"]["initial_facts"], variables={"task": task}
),
}
],
},
]
facts_message = self.model(input_messages)
message_prompt_plan = {
"role": MessageRole.USER,
"content": [
{
"type": "text",
"text": populate_template(
self.prompt_templates["planning"]["initial_plan"],
variables={
"task": task,
"tools": self.tools,
"managed_agents": self.managed_agents,
"answer_facts": facts_message.content,
},
),
}
],
}
plan_message = self.model([message_prompt_plan], stop_sequences=["<end_plan>"])
return input_messages, facts_message, plan_message
def _generate_updated_plan(self, task: str, step: int) -> Tuple[ChatMessage, ChatMessage]:
# Do not take the system prompt message from the memory
# summary_mode=False: Do not take previous plan steps to avoid influencing the new plan
memory_messages = self.write_memory_to_messages()[1:]
facts_update_pre = {
"role": MessageRole.SYSTEM,
"content": [{"type": "text", "text": self.prompt_templates["planning"]["update_facts_pre_messages"]}],
}
facts_update_post = {
"role": MessageRole.USER,
"content": [{"type": "text", "text": self.prompt_templates["planning"]["update_facts_post_messages"]}],
}
input_messages = [facts_update_pre] + memory_messages + [facts_update_post]
facts_message = self.model(input_messages)
update_plan_pre = {
"role": MessageRole.SYSTEM,
"content": [
{
"type": "text",
"text": populate_template(
self.prompt_templates["planning"]["update_plan_pre_messages"], variables={"task": task}
),
}
],
}
update_plan_post = {
"role": MessageRole.USER,
"content": [
{
"type": "text",
"text": populate_template(
self.prompt_templates["planning"]["update_plan_post_messages"],
variables={
"task": task,
"tools": self.tools,
"managed_agents": self.managed_agents,
"facts_update": facts_message.content,
"remaining_steps": (self.max_steps - step),
},
),
}
],
}
plan_message = self.model(
[update_plan_pre] + memory_messages + [update_plan_post], stop_sequences=["<end_plan>"]
)
return input_messages, facts_message, plan_message
def _record_planning_step(
self, input_messages: list, facts_message: ChatMessage, plan_message: ChatMessage, is_first_step: bool
) -> None:
if is_first_step:
facts = textwrap.dedent(f"""Here are the facts that I know so far:\n```\n{facts_message.content}\n```""")
plan = textwrap.dedent(
f"""Here is the plan of action that I will follow to solve the task:\n```\n{plan_message.content}\n```"""
)
log_message = "Initial plan"
else:
facts = textwrap.dedent(
f"""Here is the updated list of the facts that I know:\n```\n{facts_message.content}\n```"""
)
plan = textwrap.dedent(
f"""I still need to solve the task I was given:\n```\n{self.task}\n```\n\nHere is my new/updated plan of action to solve the task:\n```\n{plan_message.content}\n```"""
)
log_message = "Updated plan"
self.memory.steps.append(
PlanningStep(
model_input_messages=input_messages,
facts=facts,
plan=plan,
model_output_message_plan=plan_message,
model_output_message_facts=facts_message,
)
)
self.logger.log(Rule(f"[bold]{log_message}", style="orange"), Text(plan), level=LogLevel.INFO)
@property
def logs(self):
logger.warning(
"The 'logs' attribute is deprecated and will soon be removed. Please use 'self.memory.steps' instead."
)
return [self.memory.system_prompt] + self.memory.steps
def initialize_system_prompt(self):
"""To be implemented in child classes"""
pass
def write_memory_to_messages(
self,
summary_mode: Optional[bool] = False,
) -> List[Dict[str, str]]:
"""
Reads past llm_outputs, actions, and observations or errors from the memory into a series of messages
that can be used as input to the LLM. Adds a number of keywords (such as PLAN, error, etc) to help
the LLM.
"""
messages = self.memory.system_prompt.to_messages(summary_mode=summary_mode)
for memory_step in self.memory.steps:
messages.extend(memory_step.to_messages(summary_mode=summary_mode))
return messages
def visualize(self):
"""Creates a rich tree visualization of the agent's structure."""
self.logger.visualize_agent_tree(self)
def extract_action(self, model_output: str, split_token: str) -> Tuple[str, str]:
"""
Parse action from the LLM output
Args:
model_output (`str`): Output of the LLM
split_token (`str`): Separator for the action. Should match the example in the system prompt.
"""
try:
split = model_output.split(split_token)
rationale, action = (
split[-2],
split[-1],
) # NOTE: using indexes starting from the end solves for when you have more than one split_token in the output
except Exception:
raise AgentParsingError(
f"No '{split_token}' token provided in your output.\nYour output:\n{model_output}\n. Be sure to include an action, prefaced with '{split_token}'!",
self.logger,
)
return rationale.strip(), action.strip()
def provide_final_answer(self, task: str, images: Optional[list[str]]) -> str:
"""
Provide the final answer to the task, based on the logs of the agent's interactions.
Args:
task (`str`): Task to perform.
images (`list[str]`, *optional*): Paths to image(s).
Returns:
`str`: Final answer to the task.
"""
messages = [
{
"role": MessageRole.SYSTEM,
"content": [
{
"type": "text",
"text": self.prompt_templates["final_answer"]["pre_messages"],
}
],
}
]
if images:
messages[0]["content"].append({"type": "image"})
messages += self.write_memory_to_messages()[1:]
messages += [
{
"role": MessageRole.USER,
"content": [
{
"type": "text",
"text": populate_template(
self.prompt_templates["final_answer"]["post_messages"], variables={"task": task}
),
}
],
}
]
try:
chat_message: ChatMessage = self.model(messages)
return chat_message.content
except Exception as e:
return f"Error in generating final LLM output:\n{e}"
def execute_tool_call(self, tool_name: str, arguments: Union[Dict[str, str], str]) -> Any:
"""
Execute tool with the provided input and returns the result.
This method replaces arguments with the actual values from the state if they refer to state variables.
Args:
tool_name (`str`): Name of the Tool to execute (should be one from self.tools).
arguments (Dict[str, str]): Arguments passed to the Tool.
"""
available_tools = {**self.tools, **self.managed_agents}
if tool_name not in available_tools:
error_msg = f"Unknown tool {tool_name}, should be instead one of {list(available_tools.keys())}."
raise AgentExecutionError(error_msg, self.logger)
try:
if isinstance(arguments, str):
if tool_name in self.managed_agents:
observation = available_tools[tool_name].__call__(arguments)
else:
observation = available_tools[tool_name].__call__(arguments, sanitize_inputs_outputs=True)
elif isinstance(arguments, dict):
for key, value in arguments.items():
if isinstance(value, str) and value in self.state:
arguments[key] = self.state[value]
if tool_name in self.managed_agents:
observation = available_tools[tool_name].__call__(**arguments)
else:
observation = available_tools[tool_name].__call__(**arguments, sanitize_inputs_outputs=True)
else:
error_msg = f"Arguments passed to tool should be a dict or string: got a {type(arguments)}."
raise AgentExecutionError(error_msg, self.logger)
return observation
except Exception as e:
if tool_name in self.tools:
tool = self.tools[tool_name]
error_msg = (
f"Error when executing tool {tool_name} with arguments {arguments}: {type(e).__name__}: {e}\nYou should only use this tool with a correct input.\n"
f"As a reminder, this tool's description is the following: '{tool.description}'.\nIt takes inputs: {tool.inputs} and returns output type {tool.output_type}"
)
raise AgentExecutionError(error_msg, self.logger)
elif tool_name in self.managed_agents:
error_msg = (
f"Error in calling team member: {e}\nYou should only ask this team member with a correct request.\n"
f"As a reminder, this team member's description is the following:\n{available_tools[tool_name]}"
)
raise AgentExecutionError(error_msg, self.logger)
def step(self, memory_step: ActionStep) -> Union[None, Any]:
"""To be implemented in children classes. Should return either None if the step is not final."""
pass
def replay(self, detailed: bool = False):
"""Prints a pretty replay of the agent's steps.
Args:
detailed (bool, optional): If True, also displays the memory at each step. Defaults to False.
Careful: will increase log length exponentially. Use only for debugging.
"""
self.memory.replay(self.logger, detailed=detailed)
def __call__(self, task: str, **kwargs):
"""Adds additional prompting for the managed agent, runs it, and wraps the output.
This method is called only by a managed agent.
"""
full_task = populate_template(
self.prompt_templates["managed_agent"]["task"],
variables=dict(name=self.name, task=task),
)
report = self.run(full_task, **kwargs)
answer = populate_template(
self.prompt_templates["managed_agent"]["report"], variables=dict(name=self.name, final_answer=report)
)
if self.provide_run_summary:
answer += "\n\nFor more detail, find below a summary of this agent's work:\n<summary_of_work>\n"
for message in self.write_memory_to_messages(summary_mode=True):
content = message["content"]
answer += "\n" + truncate_content(str(content)) + "\n---"
answer += "\n</summary_of_work>"
return answer
def save(self, output_dir: str, relative_path: Optional[str] = None):
"""
Saves the relevant code files for your agent. This will copy the code of your agent in `output_dir` as well as autogenerate:
- a `tools` folder containing the logic for each of the tools under `tools/{tool_name}.py`.
- a `managed_agents` folder containing the logic for each of the managed agents.
- an `agent.json` file containing a dictionary representing your agent.
- a `prompt.yaml` file containing the prompt templates used by your agent.
- an `app.py` file providing a UI for your agent when it is exported to a Space with `agent.push_to_hub()`
- a `requirements.txt` containing the names of the modules used by your tool (as detected when inspecting its
code)
Args:
output_dir (`str`): The folder in which you want to save your tool.
"""
make_init_file(output_dir)
# Recursively save managed agents
if self.managed_agents:
make_init_file(os.path.join(output_dir, "managed_agents"))
for agent_name, agent in self.managed_agents.items():
agent_suffix = f"managed_agents.{agent_name}"
if relative_path:
agent_suffix = relative_path + "." + agent_suffix
agent.save(os.path.join(output_dir, "managed_agents", agent_name), relative_path=agent_suffix)
class_name = self.__class__.__name__
# Save tools to different .py files
for tool in self.tools.values():
make_init_file(os.path.join(output_dir, "tools"))
tool.save(os.path.join(output_dir, "tools"), tool_file_name=tool.name, make_gradio_app=False)
# Save prompts to yaml
yaml_prompts = yaml.safe_dump(
self.prompt_templates,
default_style="|", # This forces block literals for all strings
default_flow_style=False,
width=float("inf"),
sort_keys=False,
allow_unicode=True,
indent=2,
)
with open(os.path.join(output_dir, "prompts.yaml"), "w", encoding="utf-8") as f:
f.write(yaml_prompts)
# Save agent dictionary to json
agent_dict = self.to_dict()
agent_dict["tools"] = [tool.name for tool in self.tools.values()]
with open(os.path.join(output_dir, "agent.json"), "w", encoding="utf-8") as f:
json.dump(agent_dict, f, indent=4)
# Save requirements
with open(os.path.join(output_dir, "requirements.txt"), "w", encoding="utf-8") as f:
f.writelines(f"{r}\n" for r in agent_dict["requirements"])
# Make agent.py file with Gradio UI
agent_name = f"agent_{self.name}" if getattr(self, "name", None) else "agent"
managed_agent_relative_path = relative_path + "." if relative_path is not None else ""
app_template = textwrap.dedent("""
import yaml
import os
from smolagents import GradioUI, {{ class_name }}, {{ agent_dict['model']['class'] }}
# Get current directory path
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
{% for tool in tools.values() -%}
from {{managed_agent_relative_path}}tools.{{ tool.name }} import {{ tool.__class__.__name__ }} as {{ tool.name | camelcase }}
{% endfor %}
{% for managed_agent in managed_agents.values() -%}
from {{managed_agent_relative_path}}managed_agents.{{ managed_agent.name }}.app import agent_{{ managed_agent.name }}
{% endfor %}
model = {{ agent_dict['model']['class'] }}(
{% for key in agent_dict['model']['data'] if key not in ['class', 'last_input_token_count', 'last_output_token_count'] -%}
{{ key }}={{ agent_dict['model']['data'][key]|repr }},
{% endfor %})
{% for tool in tools.values() -%}
{{ tool.name }} = {{ tool.name | camelcase }}()
{% endfor %}
with open(os.path.join(CURRENT_DIR, "prompts.yaml"), 'r') as stream:
prompt_templates = yaml.safe_load(stream)
{{ agent_name }} = {{ class_name }}(
model=model,
tools=[{% for tool_name in tools.keys() if tool_name != "final_answer" %}{{ tool_name }}{% if not loop.last %}, {% endif %}{% endfor %}],
managed_agents=[{% for subagent_name in managed_agents.keys() %}agent_{{ subagent_name }}{% if not loop.last %}, {% endif %}{% endfor %}],
{% for attribute_name, value in agent_dict.items() if attribute_name not in ["model", "tools", "prompt_templates", "authorized_imports", "managed_agents", "requirements"] -%}
{{ attribute_name }}={{ value|repr }},
{% endfor %}prompt_templates=prompt_templates
)
if __name__ == "__main__":
GradioUI({{ agent_name }}).launch()
""").strip()
template_env = jinja2.Environment(loader=jinja2.BaseLoader(), undefined=jinja2.StrictUndefined)
template_env.filters["repr"] = repr
template_env.filters["camelcase"] = lambda value: "".join(word.capitalize() for word in value.split("_"))
template = template_env.from_string(app_template)
# Render the app.py file from Jinja2 template
app_text = template.render(
{
"agent_name": agent_name,
"class_name": class_name,
"agent_dict": agent_dict,
"tools": self.tools,
"managed_agents": self.managed_agents,
"managed_agent_relative_path": managed_agent_relative_path,
}
)
with open(os.path.join(output_dir, "app.py"), "w", encoding="utf-8") as f:
f.write(app_text + "\n") # Append newline at the end
def to_dict(self) -> Dict[str, Any]:
"""Converts agent into a dictionary."""
# TODO: handle serializing step_callbacks and final_answer_checks
for attr in ["final_answer_checks", "step_callbacks"]:
if getattr(self, attr, None):
self.logger.log(f"This agent has {attr}: they will be ignored by this method.", LogLevel.INFO)
tool_dicts = [tool.to_dict() for tool in self.tools.values()]
tool_requirements = {req for tool in self.tools.values() for req in tool.to_dict()["requirements"]}
managed_agents_requirements = {
req for managed_agent in self.managed_agents.values() for req in managed_agent.to_dict()["requirements"]
}
requirements = tool_requirements | managed_agents_requirements
if hasattr(self, "authorized_imports"):
requirements.update(
{package.split(".")[0] for package in self.authorized_imports if package not in BASE_BUILTIN_MODULES}
)
agent_dict = {
"tools": tool_dicts,
"model": {
"class": self.model.__class__.__name__,
"data": self.model.to_dict(),
},
"managed_agents": {
managed_agent.name: managed_agent.__class__.__name__ for managed_agent in self.managed_agents.values()
},
"prompt_templates": self.prompt_templates,
"max_steps": self.max_steps,
"verbosity_level": int(self.logger.level),
"grammar": self.grammar,
"planning_interval": self.planning_interval,
"name": self.name,
"description": self.description,
"requirements": list(requirements),
}
if hasattr(self, "authorized_imports"):
agent_dict["authorized_imports"] = self.authorized_imports
if hasattr(self, "executor_type"):
agent_dict["executor_type"] = self.executor_type
agent_dict["executor_kwargs"] = self.executor_kwargs
if hasattr(self, "max_print_outputs_length"):
agent_dict["max_print_outputs_length"] = self.max_print_outputs_length
return agent_dict
@classmethod
def from_hub(
cls,
repo_id: str,
token: Optional[str] = None,
trust_remote_code: bool = False,
**kwargs,
):
"""
Loads an agent defined on the Hub.
<Tip warning={true}>
Loading a tool from the Hub means that you'll download the tool and execute it locally.
ALWAYS inspect the tool you're downloading before loading it within your runtime, as you would do when
installing a package using pip/npm/apt.
</Tip>
Args:
repo_id (`str`):
The name of the repo on the Hub where your tool is defined.
token (`str`, *optional*):
The token to identify you on hf.co. If unset, will use the token generated when running
`huggingface-cli login` (stored in `~/.huggingface`).
trust_remote_code(`bool`, *optional*, defaults to False):
This flags marks that you understand the risk of running remote code and that you trust this tool.
If not setting this to True, loading the tool from Hub will fail.
kwargs (additional keyword arguments, *optional*):
Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as
`cache_dir`, `revision`, `subfolder`) will be used when downloading the files for your agent, and the
others will be passed along to its init.
"""
if not trust_remote_code:
raise ValueError(
"Loading an agent from Hub requires to acknowledge you trust its code: to do so, pass `trust_remote_code=True`."
)
# Get the agent's Hub folder.
download_kwargs = {"token": token, "repo_type": "space"} | {
key: kwargs.pop(key)
for key in [
"cache_dir",
"force_download",
"proxies",
"revision",
"local_files_only",
]
if key in kwargs
}
download_folder = Path(snapshot_download(repo_id=repo_id, **download_kwargs))
return cls.from_folder(download_folder, **kwargs)
@classmethod
def from_folder(cls, folder: Union[str, Path], **kwargs):
"""Loads an agent from a local folder.
Args:
folder (`str` or `Path`): The folder where the agent is saved.
**kwargs: Additional keyword arguments that will be passed to the agent's init.
"""
folder = Path(folder)
agent_dict = json.loads((folder / "agent.json").read_text())
# Recursively get managed agents
managed_agents = []
for managed_agent_name, managed_agent_class in agent_dict["managed_agents"].items():
agent_cls = getattr(importlib.import_module("smolagents.agents"), managed_agent_class)
managed_agents.append(agent_cls.from_folder(folder / "managed_agents" / managed_agent_name))
tools = []
for tool_name in agent_dict["tools"]:
tool_code = (folder / "tools" / f"{tool_name}.py").read_text()
tools.append(Tool.from_code(tool_code))
model_class: Model = getattr(importlib.import_module("smolagents.models"), agent_dict["model"]["class"])
model = model_class.from_dict(agent_dict["model"]["data"])
args = dict(
model=model,
tools=tools,
managed_agents=managed_agents,
name=agent_dict["name"],
description=agent_dict["description"],
max_steps=agent_dict["max_steps"],
planning_interval=agent_dict["planning_interval"],
grammar=agent_dict["grammar"],
verbosity_level=agent_dict["verbosity_level"],
)
if cls.__name__ == "CodeAgent":
args["additional_authorized_imports"] = agent_dict["authorized_imports"]
args["executor_type"] = agent_dict["executor_type"]
args["executor_kwargs"] = agent_dict["executor_kwargs"]
args["max_print_outputs_length"] = agent_dict["max_print_outputs_length"]
args.update(kwargs)
return cls(**args)
def push_to_hub(
self,
repo_id: str,
commit_message: str = "Upload agent",
private: Optional[bool] = None,
token: Optional[Union[bool, str]] = None,
create_pr: bool = False,
) -> str:
"""
Upload the agent to the Hub.
Parameters:
repo_id (`str`):
The name of the repository you want to push to. It should contain your organization name when
pushing to a given organization.
commit_message (`str`, *optional*, defaults to `"Upload agent"`):
Message to commit while pushing.
private (`bool`, *optional*, defaults to `None`):
Whether to make the repo private. If `None`, the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.
token (`bool` or `str`, *optional*):
The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
create_pr (`bool`, *optional*, defaults to `False`):
Whether to create a PR with the uploaded files or directly commit.
"""
repo_url = create_repo(
repo_id=repo_id,
token=token,
private=private,
exist_ok=True,
repo_type="space",
space_sdk="gradio",
)
repo_id = repo_url.repo_id
metadata_update(
repo_id,
{"tags": ["smolagents", "agent"]},
repo_type="space",
token=token,
overwrite=True,
)
with tempfile.TemporaryDirectory() as work_dir:
self.save(work_dir)
logger.info(f"Uploading the following files to {repo_id}: {','.join(os.listdir(work_dir))}")
return upload_folder(
repo_id=repo_id,
commit_message=commit_message,
folder_path=work_dir,
token=token,
create_pr=create_pr,
repo_type="space",
)
class ToolCallingAgent(MultiStepAgent):
"""
This agent uses JSON-like tool calls, using method `model.get_tool_call` to leverage the LLM engine's tool calling capabilities.
Args:
tools (`list[Tool]`): [`Tool`]s that the agent can use.
model (`Callable[[list[dict[str, str]]], ChatMessage]`): Model that will generate the agent's actions.
prompt_templates ([`~agents.PromptTemplates`], *optional*): Prompt templates.
planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.
**kwargs: Additional keyword arguments.
"""
def __init__(
self,
tools: List[Tool],
model: Callable[[List[Dict[str, str]]], ChatMessage],
prompt_templates: Optional[PromptTemplates] = None,
planning_interval: Optional[int] = None,
**kwargs,
):
prompt_templates = prompt_templates or yaml.safe_load(
importlib.resources.files("smolagents.prompts").joinpath("toolcalling_agent.yaml").read_text()
)
super().__init__(
tools=tools,
model=model,
prompt_templates=prompt_templates,
planning_interval=planning_interval,
**kwargs,
)
def initialize_system_prompt(self) -> str:
system_prompt = populate_template(
self.prompt_templates["system_prompt"],
variables={"tools": self.tools, "managed_agents": self.managed_agents},
)
return system_prompt
def step(self, memory_step: ActionStep) -> Union[None, Any]:
"""
Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
Returns None if the step is not final.
"""
memory_messages = self.write_memory_to_messages()
self.input_messages = memory_messages
# Add new step in logs
memory_step.model_input_messages = memory_messages.copy()
try:
model_message: ChatMessage = self.model(
memory_messages,
tools_to_call_from=list(self.tools.values()),
stop_sequences=["Observation:"],
)
memory_step.model_output_message = model_message
if model_message.tool_calls is None or len(model_message.tool_calls) == 0:
raise Exception("Model did not call any tools. Call `final_answer` tool to return a final answer.")
tool_call = model_message.tool_calls[0]
tool_name, tool_call_id = tool_call.function.name, tool_call.id
tool_arguments = tool_call.function.arguments
except Exception as e:
raise AgentGenerationError(f"Error in generating tool call with model:\n{e}", self.logger) from e
memory_step.tool_calls = [ToolCall(name=tool_name, arguments=tool_arguments, id=tool_call_id)]
# Execute
self.logger.log(
Panel(Text(f"Calling tool: '{tool_name}' with arguments: {tool_arguments}")),
level=LogLevel.INFO,
)
if tool_name == "final_answer":
if isinstance(tool_arguments, dict):
if "answer" in tool_arguments:
answer = tool_arguments["answer"]
else:
answer = tool_arguments
else:
answer = tool_arguments
if (
isinstance(answer, str) and answer in self.state.keys()
): # if the answer is a state variable, return the value
final_answer = self.state[answer]
self.logger.log(
f"[bold {YELLOW_HEX}]Final answer:[/bold {YELLOW_HEX}] Extracting key '{answer}' from state to return value '{final_answer}'.",
level=LogLevel.INFO,
)
else:
final_answer = answer
self.logger.log(
Text(f"Final answer: {final_answer}", style=f"bold {YELLOW_HEX}"),
level=LogLevel.INFO,
)
memory_step.action_output = final_answer
return final_answer
else:
if tool_arguments is None:
tool_arguments = {}
observation = self.execute_tool_call(tool_name, tool_arguments)
observation_type = type(observation)
if observation_type in [AgentImage, AgentAudio]:
if observation_type == AgentImage:
observation_name = "image.png"
elif observation_type == AgentAudio:
observation_name = "audio.mp3"
# TODO: observation naming could allow for different names of same type
self.state[observation_name] = observation
updated_information = f"Stored '{observation_name}' in memory."
else:
updated_information = str(observation).strip()
self.logger.log(
f"Observations: {updated_information.replace('[', '|')}", # escape potential rich-tag-like components
level=LogLevel.INFO,
)
memory_step.observations = updated_information
return None
class CodeAgent(MultiStepAgent):
"""
In this agent, the tool calls will be formulated by the LLM in code format, then parsed and executed.
Args:
tools (`list[Tool]`): [`Tool`]s that the agent can use.
model (`Callable[[list[dict[str, str]]], ChatMessage]`): Model that will generate the agent's actions.
prompt_templates ([`~agents.PromptTemplates`], *optional*): Prompt templates.
grammar (`dict[str, str]`, *optional*): Grammar used to parse the LLM output.
additional_authorized_imports (`list[str]`, *optional*): Additional authorized imports for the agent.
planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.
executor_type (`str`, default `"local"`): Which executor type to use between `"local"`, `"e2b"`, or `"docker"`.
executor_kwargs (`dict`, *optional*): Additional arguments to pass to initialize the executor.
max_print_outputs_length (`int`, *optional*): Maximum length of the print outputs.
**kwargs: Additional keyword arguments.
"""
def __init__(
self,
tools: List[Tool],
model: Callable[[List[Dict[str, str]]], ChatMessage],
prompt_templates: Optional[PromptTemplates] = None,
grammar: Optional[Dict[str, str]] = None,
additional_authorized_imports: Optional[List[str]] = None,
planning_interval: Optional[int] = None,
executor_type: str = "local",
executor_kwargs: Optional[Dict[str, Any]] = None,
max_print_outputs_length: Optional[int] = None,
**kwargs,
):
self.additional_authorized_imports = additional_authorized_imports if additional_authorized_imports else []
self.authorized_imports = list(set(BASE_BUILTIN_MODULES) | set(self.additional_authorized_imports))
self.max_print_outputs_length = max_print_outputs_length
prompt_templates = prompt_templates or yaml.safe_load(
importlib.resources.files("smolagents.prompts").joinpath("code_agent.yaml").read_text()
)
super().__init__(
tools=tools,
model=model,
prompt_templates=prompt_templates,
grammar=grammar,
planning_interval=planning_interval,
**kwargs,
)
if "*" in self.additional_authorized_imports:
self.logger.log(
"Caution: you set an authorization for all imports, meaning your agent can decide to import any package it deems necessary. This might raise issues if the package is not installed in your environment.",
0,
)
self.executor_type = executor_type
self.executor_kwargs = executor_kwargs or {}
self.python_executor = self.create_python_executor(executor_type, self.executor_kwargs)
def create_python_executor(self, executor_type: str, kwargs: Dict[str, Any]) -> PythonExecutor:
match executor_type:
case "e2b" | "docker":
if self.managed_agents:
raise Exception("Managed agents are not yet supported with remote code execution.")
if executor_type == "e2b":
return E2BExecutor(self.additional_authorized_imports, self.logger, **kwargs)
else:
return DockerExecutor(self.additional_authorized_imports, self.logger, **kwargs)
case "local":
return LocalPythonExecutor(
self.additional_authorized_imports,
max_print_outputs_length=self.max_print_outputs_length,
)
case _: # if applicable
raise ValueError(f"Unsupported executor type: {executor_type}")
def initialize_system_prompt(self) -> str:
system_prompt = populate_template(
self.prompt_templates["system_prompt"],
variables={
"tools": self.tools,
"managed_agents": self.managed_agents,
"authorized_imports": (
"You can import from any package you want."
if "*" in self.authorized_imports
else str(self.authorized_imports)
),
},
)
return system_prompt
def step(self, memory_step: ActionStep) -> Union[None, Any]:
"""
Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
Returns None if the step is not final.
"""
memory_messages = self.write_memory_to_messages()
self.input_messages = memory_messages.copy()
# Add new step in logs
memory_step.model_input_messages = memory_messages.copy()
try:
additional_args = {"grammar": self.grammar} if self.grammar is not None else {}
chat_message: ChatMessage = self.model(
self.input_messages,
stop_sequences=["<end_code>", "Observation:"],
**additional_args,
)
memory_step.model_output_message = chat_message
model_output = chat_message.content
memory_step.model_output = model_output
except Exception as e:
raise AgentGenerationError(f"Error in generating model output:\n{e}", self.logger) from e
self.logger.log_markdown(
content=model_output,
title="Output message of the LLM:",
level=LogLevel.DEBUG,
)
# Parse
try:
code_action = fix_final_answer_code(parse_code_blobs(model_output))
except Exception as e:
error_msg = f"Error in code parsing:\n{e}\nMake sure to provide correct code blobs."
raise AgentParsingError(error_msg, self.logger)
memory_step.tool_calls = [
ToolCall(
name="python_interpreter",
arguments=code_action,
id=f"call_{len(self.memory.steps)}",
)
]
# Execute
self.logger.log_code(title="Executing parsed code:", content=code_action, level=LogLevel.INFO)
is_final_answer = False
try:
output, execution_logs, is_final_answer = self.python_executor(code_action)
execution_outputs_console = []
if len(execution_logs) > 0:
execution_outputs_console += [
Text("Execution logs:", style="bold"),
Text(execution_logs),
]
observation = "Execution logs:\n" + execution_logs
except Exception as e:
if hasattr(self.python_executor, "state") and "_print_outputs" in self.python_executor.state:
execution_logs = str(self.python_executor.state["_print_outputs"])
if len(execution_logs) > 0:
execution_outputs_console = [
Text("Execution logs:", style="bold"),
Text(execution_logs),
]
memory_step.observations = "Execution logs:\n" + execution_logs
self.logger.log(Group(*execution_outputs_console), level=LogLevel.INFO)
error_msg = str(e)
if "Import of " in error_msg and " is not allowed" in error_msg:
self.logger.log(
"[bold red]Warning to user: Code execution failed due to an unauthorized import - Consider passing said import under `additional_authorized_imports` when initializing your CodeAgent.",
level=LogLevel.INFO,
)
raise AgentExecutionError(error_msg, self.logger)
truncated_output = truncate_content(str(output))
observation += "Last output from code snippet:\n" + truncated_output
memory_step.observations = observation
execution_outputs_console += [
Text(
f"{('Out - Final answer' if is_final_answer else 'Out')}: {truncated_output}",
style=(f"bold {YELLOW_HEX}" if is_final_answer else ""),
),
]
self.logger.log(Group(*execution_outputs_console), level=LogLevel.INFO)
memory_step.action_output = output
return output if is_final_answer else None
|