WIP: generated summary from documents split, could also create memgpt approach

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -1,6 +1,7 @@
 import time
 from typing import TYPE_CHECKING, Optional
 
+
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem

src/crewai/agents/executor.py

@@ -10,6 +10,10 @@ from langchain_core.exceptions import OutputParserException
 from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
 from pydantic import InstanceOf
+import tiktoken
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.summarize import load_summarize_chain
+from openai import BadRequestError
 
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.tools_handler import ToolsHandler
@@ -40,6 +44,8 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
     response_template: Optional[str] = None
+    retry_summarize: bool = False
+    retry_summarize_count: int = 2
 
     def _call(
         self,
@@ -120,6 +126,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
 
         Override this to take control of how the agent makes and acts on choices.
         """
+        for attempt in range(self.retry_summarize_count):
             try:
                 if self._should_force_answer():
                     error = self._i18n.errors("force_final_answer")
@@ -128,7 +135,39 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                     yield AgentStep(action=output, observation=error)
                     return
 
-            intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
+                intermediate_steps = self._prepare_intermediate_steps(
+                    intermediate_steps
+                )
+                if self.retry_summarize:
+                    encoding = tiktoken.encoding_for_model(self.llm.model_name)
+                    original_token_count = len(
+                        encoding.encode(intermediate_steps[0][1])
+                    )
+                    if original_token_count > 8000:
+                        print(
+                            "BEFORE AGENT PLAN TOKEN LENGTH",
+                            original_token_count,
+                        )
+                        text = intermediate_steps[0][1]
+
+                        text_splitter = RecursiveCharacterTextSplitter(
+                            separators=["\n\n", "\n"],
+                            chunk_size=8000,
+                            chunk_overlap=500,
+                        )
+                        docs = text_splitter.create_documents([text])
+                        print("DOCS", docs)
+                        print("DOCS length", len(docs))
+                        breakpoint()
+                        # TODO: store to vector db - using memgpt like strategy
+                        summary_chain = load_summarize_chain(
+                            self.llm, chain_type="map_reduce", verbose=True
+                        )
+                        summary = summary_chain.run(docs)
+
+                        print("SUMMARY:", summary)
+
+                        intermediate_steps[0] = (intermediate_steps[0][0], summary)
 
                 # Call the LLM to see what to do.
                 output = self.agent.plan(  # type: ignore #  Incompatible types in assignment (expression has type "AgentAction | AgentFinish | list[AgentAction]", variable has type "AgentAction")
@@ -185,10 +224,32 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                 yield AgentStep(action=output, observation=observation)
                 return
 
+            except BadRequestError as e:
+                print("bad request string str(e)", str(e))
+                if (
+                    "context_length_exceeded" in str(e)
+                    and attempt < self.retry_summarize_count - 1
+                ):
+                    print(
+                        f"Context length exceeded. Retrying with summarization (attempt {attempt + 1})..."
+                    )
+                    self.retry_summarize = True
+                    breakpoint()
+                    continue
+                else:
+                    print("Error now raising occurred in _iter_next_step:", e)
+                    raise e
+
+            except Exception as e:
+                print("Error occurred in _iter_next_step:", e)
+                raise e
+
             # If the tool chosen is the finishing tool, then we end and return.
             if isinstance(output, AgentFinish):
                 if self.should_ask_for_human_input:
-                human_feedback = self._ask_human_input(output.return_values["output"])
+                    human_feedback = self._ask_human_input(
+                        output.return_values["output"]
+                    )
 
                     if self.crew and self.crew._train:
                         self._handle_crew_training_output(output, human_feedback)
@@ -235,7 +296,10 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                     agent=self.crew_agent,
                     action=agent_action,
                 )
+
+                # print("tool_usage", tool_usage)
                 tool_calling = tool_usage.parse(agent_action.log)
+                # print("tool_calling", tool_calling)
 
                 if isinstance(tool_calling, ToolUsageErrorException):
                     observation = tool_calling.message
@@ -249,7 +313,9 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                     else:
                         observation = self._i18n.errors("wrong_tool_name").format(
                             tool=tool_calling.tool_name,
-                        tools=", ".join([tool.name.casefold() for tool in self.tools]),
+                            tools=", ".join(
+                                [tool.name.casefold() for tool in self.tools]
+                            ),
                         )
                 yield AgentStep(action=agent_action, observation=observation)
 

src/crewai/tools/tool_usage.py

@@ -16,7 +16,7 @@ try:
 except ImportError:
     agentops = None
 
-OPENAI_BIGGER_MODELS = ["gpt-4"]
+OPENAI_BIGGER_MODELS = ["gpt-4o"]
 
 
 class ToolUsageErrorException(Exception):