feat: add context compress (#590)

* feat:Add context compress

* feat: Add unit test

* feat: add unit test for context manager

* feat: add postprocessor param && code format

* feat: add configuration guide

* fix: fix the configuration_guide

* fix: fix the unit test

* fix: fix the default value

* feat: add test and log for context_manager

src/graph/nodes.py

@@ -6,16 +6,17 @@ import logging
 import os
 from typing import Annotated, Literal
 
-from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
 from langchain_core.runnables import RunnableConfig
 from langchain_core.tools import tool
 from langchain_mcp_adapters.client import MultiServerMCPClient
 from langgraph.types import Command, interrupt
+from functools import partial
 
 from src.agents import create_agent
 from src.config.agents import AGENT_LLM_MAP
 from src.config.configuration import Configuration
-from src.llms.llm import get_llm_by_type
+from src.llms.llm import get_llm_by_type, get_llm_token_limit_by_type
 from src.prompts.planner_model import Plan
 from src.prompts.template import apply_prompt_template
 from src.tools import (
@@ -26,6 +27,7 @@ from src.tools import (
 )
 from src.tools.search import LoggedTavilySearch
 from src.utils.json_utils import repair_json_output
+from src.utils.context_manager import ContextManager
 
 from ..config import SELECTED_SEARCH_ENGINE, SearchEngine
 from .types import State
@@ -283,13 +285,22 @@ def reporter_node(state: State, config: RunnableConfig):
         )
     )
 
+    observation_messages = []
     for observation in observations:
-        invoke_messages.append(
+        observation_messages.append(
             HumanMessage(
                 content=f"Below are some observations for the research task:\n\n{observation}",
                 name="observation",
             )
         )
+
+    # Context compression
+    llm_token_limit = get_llm_token_limit_by_type(AGENT_LLM_MAP["reporter"])
+    compressed_state = ContextManager(llm_token_limit).compress_messages(
+        {"messages": observation_messages}
+    )
+    invoke_messages += compressed_state.get("messages", [])
+
     logger.debug(f"Current invoke messages: {invoke_messages}")
     response = get_llm_by_type(AGENT_LLM_MAP["reporter"]).invoke(invoke_messages)
     response_content = response.content
@@ -469,11 +480,20 @@ async def _setup_and_execute_agent_step(
                     f"Powered by '{enabled_tools[tool.name]}'.\n{tool.description}"
                 )
                 loaded_tools.append(tool)
-        agent = create_agent(agent_type, agent_type, loaded_tools, agent_type)
+
+        llm_token_limit = get_llm_token_limit_by_type(AGENT_LLM_MAP[agent_type])
+        pre_model_hook = partial(ContextManager(llm_token_limit, 3).compress_messages)
+        agent = create_agent(
+            agent_type, agent_type, loaded_tools, agent_type, pre_model_hook
+        )
         return await _execute_agent_step(state, agent, agent_type)
     else:
         # Use default tools if no MCP servers are configured
-        agent = create_agent(agent_type, agent_type, default_tools, agent_type)
+        llm_token_limit = get_llm_token_limit_by_type(AGENT_LLM_MAP[agent_type])
+        pre_model_hook = partial(ContextManager(llm_token_limit, 3).compress_messages)
+        agent = create_agent(
+            agent_type, agent_type, default_tools, agent_type, pre_model_hook
+        )
         return await _execute_agent_step(state, agent, agent_type)