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#110 in Machine learning

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Simplifying Ai Agents in Rust 🕵🏼

espionox is an attempt to make building Ai applications in Rust just as approachable as it is with other libraries such as LangChain.

Why would I use Espionox?

  • Making an LLM application in Rust
  • Experimenting with with complex 'prompt flows' such as Chain/Tree of thought

Getting started

First you need to initialize an Agent Agent::new accepts two arguments:

  1. Optional content of a system prompt, if this is left None your agent will have no system prompt
  2. A CompletionModel whichever provider you wish to use (As of writing, only OpenAi and Anthropic providers are supported).
use espionox::prelude::*;

let api_key = std::env::var("OPENAI_KEY").unwrap();
let agent = Agent::new(Some("This is the system message"), CompletionModel::default_openai(api_key));

Now, In order to prompt your agent you will call do_action on it

let response: String = agent
    .do_action(io_completion, (), Option::<ListenerTrigger>::None)

This may look scary at first, but lets look at do_action's signature:

pub async fn do_action<'a, F, Args, Fut, R>(
    &'a mut self,
    f: F,
    args: Args,
    trigger: Option<impl Into<ListenerTrigger>>,
) -> AgentResult<R>
    F: for<'l> FnOnce(&'a mut Agent, Args) -> Fut,
    Fut: Future<Output = AgentResult<R>>

do_action takes 4 arguments:

  1. the Agent which calls the method
  2. an async function which mutates the agent and returns an AgentResult, which can be coerced from an anyhow::Result. So as long as the function signature returns any AgentResult<T>, just make sure to call .into() on any error return and it should be valid.
  3. optionally arguments for the aformentioned function
  4. An optional trigger for a listener (We'll get to this)

So, in our call to do_action earlier, we passed the function io_completion, an empty argument and None. espionox provides the following helper functions for getting completions or embeddings:

  • io_completion
  • stream_completion
  • function_completion

We used one of these functions, but we could have just as easily defined our own io_completion function and passed it when we called do_action


One of Espionox's best offerings is the AgentListener trait:

pub trait AgentListener: std::fmt::Debug + Send + Sync + 'static {
    fn trigger<'l>(&self) -> ListenerTrigger;
    fn async_method<'l>(&'l mut self, _a: &'l mut Agent) -> ListenerCallReturn<'l> {
        Box::pin(async move { Err(ListenerError::NoMethod.into()) })
    fn sync_method<'l>(&'l mut self, _a: &'l mut Agent) -> AgentResult<()> {

You will notice 3 methods:

  1. trigger: this is how you define when the listener will be triggered. Think of it like an ID. ListenerTrigger has 2 variants:
    • ListenerTrigger::String(String)
    • ListenerTrigger::Int(i64) Remember the trigger argument to do_action? Ensure a listener is triggered when do_action is called by passing a matching ListenerTrigger.
  2. async_method. Which, if implemented, the function body must be wrapped in Box::pin(async move {})
  3. sync_method

Each async_method and sync_method are where you define WHAT the listener will actually do when it's triggered. THESE ARE MUTUALLY EXCLUSIVE, only ONE of these methods should be implemented. If both are implemented, the sync method will be the only one to trigger. Any struct implementing this trait can be inserted into an agent using Agent::insert_listener.

How do you even use a listener??

The utility of listeners may not be immediately obvious to you, but it can be used to create self consistency mechanisms, prompt chains or even RAG pipelines. Check the examples directory for more information on AgentListener

espionox is very early in development and everything in the API may be subject to change Please feel free to reach out with any questions, suggestions, issues or anything else :)


~528K SLoC