Tools & Commands¶

NEAR AI supports function based tool calling where the LLM can decide to call one of the functions (Tools) that you pass it. You can register your own function or use any of the built-in tools (list_files, read_file, write_file, exec_command, query_vector_store, request_user_input).

The tool registry supports OpenAI style tool calling and Llama style. When a llama model is explicitly passed to completion(s)_and_run_tools a system message is added to the conversation. This system message contains the tool definitions and instructions on how to invoke them using <function> tags.

To tell the LLM about your tools and automatically execute them when selected by the LLM, call one of these environment methods:

• completion_and_run_tools: Allows tools to be passed and processes any returned tool_calls by running the tool
• completions_and_run_tools: Handles tool calls and returns the full llm response.

By default, these methods will add both the LLM response and tool invocation responses to the message list. You do not need to call env.add_message for these responses. This behavior allows the LLM to see its call then tool responses in the message list on the next iteration or next run. This can be disabled by passing add_to_messages=False to the method.

• get_tool_registry: returns the tool registry, a dictionary of tools that can be called by the agent. By default it is populated with the tools listed above for working with files and commands plus request_user_input. To register a function as a new tool, call register_tool on the tool registry, passing it your function.

  def my_tool():
      """A simple tool that returns a string. This docstring helps the LLM know when to call the tool."""
      return "Hello from my tool"
  
  tool_registry = env.get_tool_registry()
  tool_registry.register_tool(my_tool)
  tool_def = tool_registry.get_tool_definition('my_tool')
  response = env.completions_and_run_tools(messages, tools=[tool_def])
  

To pass all the built in tools plus any you have registered use the get_all_tool_definitions method.

all_tools = env.get_tool_registry().get_all_tool_definitions()
response = env.completions_and_run_tools(messages, tools=all_tools)

    tool_registry = env.get_tool_registry(new=True)
    tool_registry.register_tool(my_tool)
    tool_registry.register_tool(my_tool2)
    response = env.completions_and_run_tools(messages, tools=tool_registry.get_all_tool_definitions())

Terminal Commands¶

Agents have access to the local terminal through the environment, the following methods are available: