LLM Agent#

The basic agent class in MS-Agent is LLMAgent. MS-Agent’s conversational capabilities and tool invocation are all handled through this class. The diagram is as follows:

png

LLMAgent initializes config during construction. Then it proceeds sequentially:

  • Register callbacks configured in config

  • Initialize LLM, tools, message management, rag

  • Read message history cache (if any)

  • Prepare messages, such as performing rag queries, adding plans, etc.

  • Enter loop, attempt to compress messages

  • Prepare tool list, call LLM

  • Call tools based on LLM results, restart loop

Loop termination conditions:

  1. The model made a reply but without any tool calls

  2. Reached the number of rounds specified by max_chat_round in config

callbacks#

Developers can customize the Agent execution flow through callbacks. Callbacks can be configured as follows:

  • custom_callback.py

from ms_agent.callbacks import Callback

class CustomCallback(Callback):

    def on_generate_response(self, runtime,
                                   messages):
        ...

  • agent.yaml

callbacks:
  - custom_callback

This means there is a custom_callback.py file at the same level as the yaml file. This file contains subclasses that inherit from the Callback class and implement certain specific methods. Supported callbacks include:

  • on_task_begin: Called when task begins execution

  • on_generate_response: Called before calling LLM

  • on_tool_call: Called before calling tool

  • after_tool_call: Called after calling tool

  • on_task_end: Called after task completion

As an auxiliary mechanism, callbacks have low readability, so it’s recommended to execute less complex processes in callbacks, such as controlling process termination, printing logs, etc. If you need to customize the Agent, please consider directly inheriting from LLMAgent.

Custom Agent#

If more customization is needed, you can consider inheriting from the LLMAgent class and overriding certain methods. Defining a new Agent class follows the same process as defining callbacks above:

  • custom_agent.py


from ms_agent.agent import LLMAgent

class CustomAgent(LLMAgent):

    # For example, override the condense memory function
    async def condense_memory(self, messages):
        ...

  • agent.yaml

code_file: custom_agent

In this case, agent.yaml will use your custom agent class instead of loading the LLMAgent class for execution.

Examples#

  1. A basic agent.yaml: https://www.modelscope.cn/models/ms-agent/simple_agent

  2. Agent with external code: https://www.modelscope.cn/models/ms-agent/simple_agent_code

  3. Using callbacks to complete complex workflows: https://github.com/modelscope/ms-agent/tree/main/projects/code_genesis