Building Agents

This system was designed to be extensible allowing you to design new agents to be utilized by orchestrator or other tool agents. This is achieved using recap.agents.base.BaseAgent class.

Here is an example:

from recap.agents.base import BaseAgent
from pydantic import BaseModel, Field

class ExampleInputs(BaseModel):
    query: str = Field(
        description="Natural language query that will be parsed to filter and search the Meta-Review Dataset"
    )
    another_param: int = Field(description="This is another required example parameter")

class NewAgent
    description = "Description of agent to be used by orchestrator. Describe inputs, expected outputs, when to use, etc.
    args_schema = ExampleInputs
    name = "new_agent"

    def __init__(self, model, tools: list=None) -> None:
        super().__init__(model=model, tools=tools)

    ...

    # Invoke method must match args_schema
    def invoke(self, query: str, another_param) -> str:
        ...
        # Do something
        ...
        return json_string

To implement base image class there are 4 requirements:

• name: This is used to identify the tool on frontend and load assocatied prompt.md

• description: A description of the agent used as tool description for orchestrator agent

• args_schema: Pydantic BaseModel with the inputs to the tool call

• invoke: Python function used as tool for .run_as_tool(). The inputs must match schema defintion and it is expected to return a json string.

Once you have your agent created you can run your agent directly:

# Create model for agent to use if necessary
from langchain_ollama import ChatOllama
model = ChatOllama(
    model="gemma4:e4b",
    temperature=0,
    num_ctx=(2048 * 4),
    base_url=os.getenv("OLLAMA_BASE_URL"),
)

new_agent = NewAgent(model=model)
new_agent.invoke("This is my user query", 5)

Or use at as tool for another agent:

# Create model for agent to use if necessary
from langchain_ollama import ChatOllama
from orchestrator.a
from recap.agents import Orchestrator

model = ChatOllama(
    model="gemma4:e4b",
    temperature=0,
    num_ctx=(2048 * 4),
    base_url=os.getenv("OLLAMA_BASE_URL"),
)

new_agent = NewAgent(model=model)
orchestrator = Orchestrator(model=model, tools=[new_agent.as_tool()])