AutoGPT: Deprecate MessageHistory

autogpts/autogpt/autogpt/agents/agent.py

@@ -18,12 +18,11 @@
 from autogpt.llm.utils import count_string_tokens
 from autogpt.logs.log_cycle import (
     CURRENT_CONTEXT_FILE_NAME,
-    FULL_MESSAGE_HISTORY_FILE_NAME,
     NEXT_ACTION_FILE_NAME,
     USER_INPUT_FILE_NAME,
     LogCycleHandler,
 )
-from autogpt.models.agent_actions import (
+from autogpt.models.action_history import (
     Action,
     ActionErrorResult,
     ActionInterruptedByHuman,
@@ -113,22 +112,12 @@ def construct_base_prompt(self, *args, **kwargs) -> ChatSequence:
                 kwargs["append_messages"] = []
             kwargs["append_messages"].append(budget_msg)
 
-        # # Include message history in base prompt
-        # kwargs["with_message_history"] = True
-
         return super().construct_base_prompt(*args, **kwargs)
 
     def on_before_think(self, *args, **kwargs) -> ChatSequence:
         prompt = super().on_before_think(*args, **kwargs)
 
         self.log_cycle_handler.log_count_within_cycle = 0
-        self.log_cycle_handler.log_cycle(
-            self.ai_config.ai_name,
-            self.created_at,
-            self.cycle_count,
-            self.message_history.raw(),
-            FULL_MESSAGE_HISTORY_FILE_NAME,
-        )
         self.log_cycle_handler.log_cycle(
             self.ai_config.ai_name,
             self.created_at,
@@ -148,11 +137,6 @@ def execute(
 
         if command_name == "human_feedback":
             result = ActionInterruptedByHuman(feedback=user_input)
-            self.message_history.add(
-                "user",
-                "I interrupted the execution of the command you proposed "
-                f"to give you some feedback: {user_input}",
-            )
             self.log_cycle_handler.log_cycle(
                 self.ai_config.ai_name,
                 self.created_at,
@@ -207,26 +191,6 @@ def execute(
                 elif result.status == "error":
                     result.reason = plugin.post_command(command_name, result.reason)
 
-        # Check if there's a result from the command append it to the message
-        if result.status == "success":
-            self.message_history.add(
-                "system",
-                f"Command {command_name} returned: {result.outputs}",
-                "action_result",
-            )
-        elif result.status == "error":
-            message = f"Command {command_name} failed: {result.reason}"
-
-            # Append hint to the error message if the exception has a hint
-            if (
-                result.error
-                and isinstance(result.error, AgentException)
-                and result.error.hint
-            ):
-                message = message.rstrip(".") + f". {result.error.hint}"
-
-            self.message_history.add("system", message, "action_result")
-
         # Update action history
         self.event_history.register_result(result)
 

autogpts/autogpt/autogpt/agents/base.py

@@ -15,8 +15,7 @@
 from autogpt.llm.base import ChatSequence, Message
 from autogpt.llm.providers.openai import OPEN_AI_CHAT_MODELS, get_openai_command_specs
 from autogpt.llm.utils import count_message_tokens, create_chat_completion
-from autogpt.memory.message_history import MessageHistory
-from autogpt.models.agent_actions import EpisodicActionHistory, ActionResult
+from autogpt.models.action_history import EpisodicActionHistory, ActionResult
 from autogpt.prompts.generator import PromptGenerator
 from autogpt.prompts.prompt import DEFAULT_TRIGGERING_PROMPT
 
@@ -92,11 +91,6 @@ def __init__(
 
         self.event_history = EpisodicActionHistory()
 
-        self.message_history = MessageHistory(
-            model=self.llm,
-            max_summary_tlength=summary_max_tlength or self.send_token_limit // 6,
-        )
-
         # Support multi-inheritance and mixins for subclasses
         super(BaseAgent, self).__init__()
 
@@ -168,7 +162,6 @@ def construct_base_prompt(
         prepend_messages: list[Message] = [],
         append_messages: list[Message] = [],
         reserve_tokens: int = 0,
-        with_message_history: bool = False,
     ) -> ChatSequence:
         """Constructs and returns a prompt with the following structure:
         1. System prompt
@@ -196,24 +189,6 @@ def construct_base_prompt(
             [Message("system", self.system_prompt)] + prepend_messages,
         )
 
-        if with_message_history:
-            # Reserve tokens for messages to be appended later, if any
-            reserve_tokens += self.message_history.max_summary_tlength
-            if append_messages:
-                reserve_tokens += count_message_tokens(append_messages, self.llm.name)
-
-            # Fill message history, up to a margin of reserved_tokens.
-            # Trim remaining historical messages and add them to the running summary.
-            history_start_index = len(prompt)
-            trimmed_history = add_history_upto_token_limit(
-                prompt, self.message_history, self.send_token_limit - reserve_tokens
-            )
-            if trimmed_history:
-                new_summary_msg, _ = self.message_history.trim_messages(
-                    list(prompt), self.config
-                )
-                prompt.insert(history_start_index, new_summary_msg)
-
         if append_messages:
             prompt.extend(append_messages)
 
@@ -372,24 +347,9 @@ def on_response(
             The parsed command name and command args, if any, and the agent thoughts.
         """
 
-        # Save assistant reply to message history
-        self.message_history.append(prompt[-1])
-        self.message_history.add(
-            "assistant", llm_response.content, "ai_response"
-        )  # FIXME: support function calls
-
-        try:
-            return self.parse_and_process_response(
-                llm_response, thought_process_id, prompt, instruction
-            )
-        except InvalidAgentResponseError as e:
-            # TODO: tune this message
-            self.message_history.add(
-                "system",
-                f"Your response could not be parsed: {e}"
-                "\n\nRemember to only respond using the specified format above!",
-            )
-            raise
+        return self.parse_and_process_response(
+            llm_response, thought_process_id, prompt, instruction
+        )
 
         # TODO: update memory/context
 
@@ -415,27 +375,3 @@ def parse_and_process_response(
             The parsed command name and command args, if any, and the agent thoughts.
         """
         pass
-
-
-def add_history_upto_token_limit(
-    prompt: ChatSequence, history: MessageHistory, t_limit: int
-) -> list[Message]:
-    current_prompt_length = prompt.token_length
-    insertion_index = len(prompt)
-    limit_reached = False
-    trimmed_messages: list[Message] = []
-    for cycle in reversed(list(history.per_cycle())):
-        messages_to_add = [msg for msg in cycle if msg is not None]
-        tokens_to_add = count_message_tokens(messages_to_add, prompt.model.name)
-        if current_prompt_length + tokens_to_add > t_limit:
-            limit_reached = True
-
-        if not limit_reached:
-            # Add the most recent message to the start of the chain,
-            #  after the system prompts.
-            prompt.insert(insertion_index, *messages_to_add)
-            current_prompt_length += tokens_to_add
-        else:
-            trimmed_messages = messages_to_add + trimmed_messages
-
-    return trimmed_messages

autogpts/autogpt/autogpt/agents/features/watchdog.py

@@ -3,7 +3,7 @@
 import logging
 from contextlib import ExitStack
 
-from autogpt.models.agent_actions import EpisodicActionHistory
+from autogpt.models.action_history import EpisodicActionHistory
 
 from ..base import BaseAgent
 

autogpts/autogpt/autogpt/agents/planning_agent.py

@@ -21,9 +21,8 @@
     USER_INPUT_FILE_NAME,
     LogCycleHandler,
 )
-from autogpt.models.agent_actions import (
+from autogpt.models.action_history import (
     ActionErrorResult,
-    EpisodicActionHistory,
     ActionInterruptedByHuman,
     ActionResult,
     ActionSuccessResult,
@@ -69,8 +68,6 @@ def __init__(
         self.log_cycle_handler = LogCycleHandler()
         """LogCycleHandler for structured debug logging."""
 
-        self.action_history = EpisodicActionHistory()
-
         self.plan: list[str] = []
         """List of steps that the Agent plans to take"""
 
@@ -90,12 +87,12 @@ def construct_base_prompt(
             plan_section += [f"{i}. {s}" for i, s in enumerate(self.plan, 1)]
 
             # Add the actions so far to the prompt
-            if self.action_history:
+            if self.event_history:
                 plan_section += [
                     "\n### Progress",
                     "So far, you have executed the following actions based on the plan:",
                 ]
-                for i, cycle in enumerate(self.action_history, 1):
+                for i, cycle in enumerate(self.event_history, 1):
                     if not (cycle.action and cycle.result):
                         logger.warn(f"Incomplete action in history: {cycle}")
                         continue
@@ -229,7 +226,7 @@ def on_before_think(self, *args, **kwargs) -> ChatSequence:
             self.ai_config.ai_name,
             self.created_at,
             self.cycle_count,
-            self.action_history.episodes,
+            self.event_history.episodes,
             "action_history.json",
         )
         self.log_cycle_handler.log_cycle(
@@ -285,7 +282,7 @@ def execute(
 
             result_tlength = count_string_tokens(str(result), self.llm.name)
             memory_tlength = count_string_tokens(
-                str(self.message_history.summary_message()), self.llm.name
+                str(self.event_history.fmt_paragraph()), self.llm.name
             )
             if result_tlength + memory_tlength > self.send_token_limit:
                 result = ActionErrorResult(
@@ -301,23 +298,6 @@ def execute(
                 elif result.status == "error":
                     result.reason = plugin.post_command(command_name, result.reason)
 
-        # Check if there's a result from the command append it to the message
-        if result.status == "success":
-            self.message_history.add(
-                "system",
-                f"Command {command_name} returned: {result.outputs}",
-                "action_result",
-            )
-        elif result.status == "error":
-            message = f"Command {command_name} failed: {result.reason}"
-            if (
-                result.error
-                and isinstance(result.error, AgentException)
-                and result.error.hint
-            ):
-                message = message.rstrip(".") + f". {result.error.hint}"
-            self.message_history.add("system", message, "action_result")
-
         return result
 
     def parse_and_process_response(

autogpts/autogpt/autogpt/logs/log_cycle.py

@@ -6,7 +6,6 @@
 from .config import LOG_DIR
 
 DEFAULT_PREFIX = "agent"
-FULL_MESSAGE_HISTORY_FILE_NAME = "full_message_history.json"
 CURRENT_CONTEXT_FILE_NAME = "current_context.json"
 NEXT_ACTION_FILE_NAME = "next_action.json"
 PROMPT_SUMMARY_FILE_NAME = "prompt_summary.json"

autogpts/autogpt/tests/unit/test_agent.py

@@ -3,7 +3,7 @@
 
 def test_agent_initialization(agent: Agent):
     assert agent.ai_config.ai_name == "Base"
-    assert agent.message_history.messages == []
+    assert agent.event_history.episodes == []
     assert agent.cycle_budget is None
     assert "You are Base" in agent.system_prompt