Reasoning Agent#

Agents are a major use-case for language models. Agents are systems that use LLMs to determine what actions to take and what inputs to use for those actions. Some LLMs support reasoning, and can be used for Reasoning Agents.

Features#

Pre-built Tools: Leverages core library agent and tools.
Reasoning on top of an Agent: Leverages an underlying function, and performs reasoning on top.
Custom Plugin System: Developers can bring in new tools using plugins.
Agentic Workflows: Fully configurable via YAML for flexibility and productivity.
Ease of Use: Simplifies developer experience and deployment.

*onfiguration#

The Reasoning Agent may be utilized as a Workflow or a Function.

Example `config.yml`#

In your YAML file, to use the Calling Agent as a workflow:

workflow:
  _type: reasoning_agent
  llm_name: deepseek_r1_model
  augmented_fn: react_agent
  verbose: true

Configurable Options:#

llm_name: The LLM the agent should use. The LLM must be configured in the YAML file
verbose: Defaults to False (useful to prevent logging of sensitive data). If set to True, the Agent will log input, output, and intermediate steps.
augmented_fn: The function to reason on. The function should be an agent and must be defined in the config YAML.

reasoning_prompt_template: The prompt used in the first step of the reasoning agent. Defaults to:

"You are an expert reasoning model task with creating a detailed execution plan"
" for a system that has the following description:\n\n"
"**Description:** \n{augmented_function_desc}\n\n"
"Given the following input and a list of available tools, please provide a detailed step-by-step plan"
" that an instruction following system can use to address the input. Ensure the plan includes:\n\n"
"1. Identifying the key components of the input.\n"
"2. Determining the most suitable tools for each task.\n"
"3. Outlining the sequence of actions to be taken.\n\n"
"**Input:** \n{input_text}\n\n"
"**Tools and description of the tool:** \n{tools}\n\n"
"An example plan could look like this:\n\n"
"1. Call tool A with input X\n"
"2. Call tool B with input Y\n"
"3. Interpret the output of tool A and B\n"
"4. Return the final result"
"\n\n **PLAN:**\n"

instruction_prompt_template: The prompt used in the final step of the reasoning agent. Defaults to:

"Answer the following question based on message history: {input_text}"
"\n\nHere is a plan for execution that you could use to guide you if you wanted to:"
"\n\n{reasoning_output}"
"\n\nNOTE: Remember to follow your guidance on how to format output, etc."
"\n\n You must respond with the answer to the original question directly to the user."

How the Reasoning Agent Works#

The Reasoning Agent is an AI system that directly invokes an underlying function while performing reasoning on top. Unlike ReAct agents, it does not reason between steps but instead through planning ahead of time.

Step-by-Step Breakdown of a Reasoning Agent#

User Query – The agent receives an input or problem to solve.
Reasoning on top of Function – The agent reasons the best plan of action to take, based on the input and the augmented underlying function.
Instruction / Plan Execution – The agent invokes the underlying function, passing its plan of action along to it.

Comparing ReAct Agent With and Without the Reasoning Agent#

ReAct Agent Without Reasoning Agent#

Running Workflows

ReAct Agent With Reasoning Agent#