hermes-agent/architecture/agents.md

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# Agents
Agents can be viewed as an FSM using an LLM to generate inputs into the system that operates over a DAG.
What this really means is that the agent is just a function without memory that uses text inputs and outputs in a
defined order.
```python
def my_agent(*args, **kwargs) -> str:
# do whatever you want!
return "Hi I'm an agent!"
```
Now obviously, that's like saying water's wet, but we're going to be using that definition to inform our design of the
library, namely, that we should *not* store agent state outside the function call.
## The Agent Class
So we don't have state, why are we using a class?
Well, we want to initialize things, we want to have some configuration, and we want to have some helper functions.
Preferably all in a single place.
```python
class BaseAgent:
def agent_primitives(self) -> list[BaseAgent]:
# Returns a list of Agents that are utilized by this agent to generate inputs
# We use agent primitives here instead of subagents because these are going to be part
# of the message graph, not a subagent tool call.
raise NotImplementedError
def tools(self) -> list[BaseTool]:
# Returns a list of tools that the agent needs to run
raise NotImplementedError
def run(self, config, *args, **kwargs) -> ConversationGraph:
llm = get_llm(config)
tools = self.tools()
for agent in self.agent_primitives():
tools.extend(agent.tools())
tools = remove_duplicates(tools)
tools = initialize_tools(tools, config)
return self(llm, tools, config, *args, **kwargs)
@staticmethod
def __call__(self, llm, tools, config, *args, **kwargs) -> ConversationGraph:
# Returns a ConversationGraph that can be parsed to get the output of the agent
# Use w/e args/kwargs you want, as long as llm/tools/config are satisfied.
raise NotImplementedError
```
Doesn't seem too bad (I hope), it is a bit annoying that we don't initialize everything in the constructor, but
hopefully we all kinda like it :)