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feat: Add support for multiple LLM output formats beyond ReAct
- Implement extensible parser architecture with BaseOutputParser abstract class - Add OutputFormatRegistry for automatic format detection and parsing - Support OpenAI Harmony format with analysis and commentary channels - Maintain full backward compatibility with existing ReAct format - Add comprehensive tests for both formats and automatic detection - Zero breaking changes - existing ReAct code continues to work unchanged Addresses issue #3508: Support for Multiple LLM Output Formats Beyond ReAct Co-Authored-By: João <joao@crewai.com>
This commit is contained in:
Devin AI
@@ -1,11 +1,15 @@
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"""Agent output parsing module for ReAct-style LLM responses.
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"""Agent output parsing module for multiple LLM response formats.
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This module provides parsing functionality for agent outputs that follow
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the ReAct (Reasoning and Acting) format, converting them into structured
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AgentAction or AgentFinish objects.
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different formats (ReAct, OpenAI Harmony, etc.), converting them into structured
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AgentAction or AgentFinish objects with automatic format detection.
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"""
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import Dict, Union
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import re
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import json
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from json_repair import repair_json
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@@ -17,6 +21,11 @@ from crewai.agents.constants import (
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MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
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MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
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UNABLE_TO_REPAIR_JSON_RESULTS,
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HARMONY_START_PATTERN,
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HARMONY_ANALYSIS_CHANNEL,
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HARMONY_COMMENTARY_CHANNEL,
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HARMONY_FINAL_ANSWER_ERROR_MESSAGE,
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HARMONY_MISSING_CONTENT_ERROR_MESSAGE,
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)
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from crewai.utilities import I18N
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@@ -60,77 +69,180 @@ class OutputParserException(Exception):
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super().__init__(error)
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class BaseOutputParser(ABC):
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"""Abstract base class for output parsers."""
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@abstractmethod
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def can_parse(self, text: str) -> bool:
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"""Check if this parser can handle the given text format."""
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pass
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@abstractmethod
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def parse_text(self, text: str) -> Union[AgentAction, AgentFinish]:
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"""Parse the text into AgentAction or AgentFinish."""
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pass
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class OutputFormatRegistry:
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"""Registry for managing different output format parsers."""
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def __init__(self):
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self._parsers: Dict[str, BaseOutputParser] = {}
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def register(self, name: str, parser: BaseOutputParser) -> None:
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"""Register a parser for a specific format."""
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self._parsers[name] = parser
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def detect_and_parse(self, text: str) -> Union[AgentAction, AgentFinish]:
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"""Automatically detect format and parse with appropriate parser."""
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for name, parser in self._parsers.items():
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if parser.can_parse(text):
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return parser.parse_text(text)
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return self._parsers.get('react', ReActParser()).parse_text(text)
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class ReActParser(BaseOutputParser):
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"""Parser for ReAct format outputs."""
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def can_parse(self, text: str) -> bool:
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"""Check if text follows ReAct format."""
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return (
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FINAL_ANSWER_ACTION in text or
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ACTION_INPUT_REGEX.search(text) is not None or
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ACTION_REGEX.search(text) is not None
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)
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def parse_text(self, text: str) -> Union[AgentAction, AgentFinish]:
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"""Parse ReAct format text."""
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thought = _extract_thought(text)
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includes_answer = FINAL_ANSWER_ACTION in text
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action_match = ACTION_INPUT_REGEX.search(text)
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if includes_answer:
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final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
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if final_answer.endswith("```"):
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count = final_answer.count("```")
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if count % 2 != 0:
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final_answer = final_answer[:-3].rstrip()
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return AgentFinish(thought=thought, output=final_answer, text=text)
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elif action_match:
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action = action_match.group(1)
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clean_action = _clean_action(action)
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action_input = action_match.group(2).strip()
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tool_input = action_input.strip(" ").strip('"')
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safe_tool_input = _safe_repair_json(tool_input)
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return AgentAction(
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thought=thought, tool=clean_action, tool_input=safe_tool_input, text=text
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)
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if not ACTION_REGEX.search(text):
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raise OutputParserException(
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f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{_I18N.slice('final_answer_format')}",
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)
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elif not ACTION_INPUT_ONLY_REGEX.search(text):
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raise OutputParserException(
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MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
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)
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else:
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err_format = _I18N.slice("format_without_tools")
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error = f"{err_format}"
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raise OutputParserException(error)
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class HarmonyParser(BaseOutputParser):
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"""Parser for OpenAI Harmony format outputs."""
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def can_parse(self, text: str) -> bool:
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"""Check if text follows OpenAI Harmony format."""
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return HARMONY_START_PATTERN.search(text) is not None
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def parse_text(self, text: str) -> Union[AgentAction, AgentFinish]:
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"""Parse OpenAI Harmony format text."""
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matches = HARMONY_START_PATTERN.findall(text)
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if not matches:
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raise OutputParserException(HARMONY_MISSING_CONTENT_ERROR_MESSAGE)
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channel, tool_name, content = matches[-1]
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content = content.strip()
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if channel == HARMONY_ANALYSIS_CHANNEL:
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return AgentFinish(
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thought=f"Analysis: {content}",
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output=content,
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text=text
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)
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elif channel == HARMONY_COMMENTARY_CHANNEL and tool_name:
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thought_content = content
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tool_input = content
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try:
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json_match = re.search(r'\{.*\}', content, re.DOTALL)
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if json_match:
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tool_input = json_match.group(0)
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thought_content = content[:json_match.start()].strip()
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if not thought_content:
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thought_content = f"Using tool {tool_name}"
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except:
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pass
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safe_tool_input = _safe_repair_json(tool_input)
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return AgentAction(
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thought=thought_content,
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tool=tool_name,
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tool_input=safe_tool_input,
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text=text
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)
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else:
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raise OutputParserException(HARMONY_FINAL_ANSWER_ERROR_MESSAGE)
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_format_registry = OutputFormatRegistry()
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_format_registry.register('react', ReActParser())
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_format_registry.register('harmony', HarmonyParser())
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def parse(text: str) -> AgentAction | AgentFinish:
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"""Parse agent output text into AgentAction or AgentFinish.
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Expects output to be in one of two formats.
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If the output signals that an action should be taken,
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should be in the below format. This will result in an AgentAction
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being returned.
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Automatically detects the format (ReAct, OpenAI Harmony, etc.) and uses
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the appropriate parser. Maintains backward compatibility with existing ReAct format.
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Supports multiple formats:
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ReAct format:
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Thought: agent thought here
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Action: search
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Action Input: what is the temperature in SF?
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If the output signals that a final answer should be given,
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should be in the below format. This will result in an AgentFinish
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being returned.
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Or for final answers:
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Thought: agent thought here
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Final Answer: The temperature is 100 degrees
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OpenAI Harmony format:
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<|start|>assistant<|channel|>analysis<|message|>The temperature is 100 degrees<|end|>
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Or for tool actions:
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<|start|>assistant<|channel|>commentary to=search<|message|>{"query": "temperature in SF"}<|call|>
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Args:
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text: The agent output text to parse.
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Returns:
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AgentAction or AgentFinish based on the content.
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Raises:
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OutputParserException: If the text format is invalid.
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OutputParserException: If the text format is invalid or unsupported.
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"""
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thought = _extract_thought(text)
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includes_answer = FINAL_ANSWER_ACTION in text
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action_match = ACTION_INPUT_REGEX.search(text)
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if includes_answer:
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final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
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# Check whether the final answer ends with triple backticks.
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if final_answer.endswith("```"):
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# Count occurrences of triple backticks in the final answer.
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count = final_answer.count("```")
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# If count is odd then it's an unmatched trailing set; remove it.
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if count % 2 != 0:
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final_answer = final_answer[:-3].rstrip()
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return AgentFinish(thought=thought, output=final_answer, text=text)
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elif action_match:
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action = action_match.group(1)
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clean_action = _clean_action(action)
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action_input = action_match.group(2).strip()
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tool_input = action_input.strip(" ").strip('"')
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safe_tool_input = _safe_repair_json(tool_input)
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return AgentAction(
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thought=thought, tool=clean_action, tool_input=safe_tool_input, text=text
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)
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if not ACTION_REGEX.search(text):
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raise OutputParserException(
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f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{_I18N.slice('final_answer_format')}",
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)
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elif not ACTION_INPUT_ONLY_REGEX.search(text):
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raise OutputParserException(
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MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
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)
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else:
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err_format = _I18N.slice("format_without_tools")
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error = f"{err_format}"
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raise OutputParserException(
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error,
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)
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return _format_registry.detect_and_parse(text)
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def _extract_thought(text: str) -> str:
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