Great question - This whole space is evolving quickly and has been a fantastic mental challenge to shed my old habits of how these apps get built - so this is my current perspective.

The “use an LLM assistant to execute a tool” trend of late benefits one party - LLM API providers. I’ve read over a LOT of repo’s, and have played with a lot of toys.

That’s why this foundational repo has ZERO LLM or AI code in it. At this foundational layer, LLM tool calling as a first class citizen didn’t feel right.

Don’t get me wrong, I wrote this to integrate with LLM’s - but the Elixir way is to build with pure data structures and minimal dependencies on OTP. That’s the purpose of this package.

As a sneak peek - here’s a Jido Action which implements a call to Anthropic Claude using instructor_ex to fetch a “Chat Completion” - aka agent chatting: https://github.com/agentjido/jido_ai/blob/main/lib/jido_ai/anthropic.ex#L6

Agents as GenServers is implemented and tested. I’m not entirely happy with the API yet so I didn’t include it in this official release. They each spin up a DynamicSupervisor and can manage delegating tasks to other agents, starting and stopping Signals and doing all sorts of other cool things.

I see a future where we each have thousands of Agents working for us constantly - like a swarm of ants. No other framework or platform comes close to making this happen - so I started from first principles with Elixir.

Enjoy!

EDIT: There will be many examples coming soon to demonstrate the scope of what Agents with Jido are capable of.

Correct - the foundational library can be used without LLM’s. During planning, I felt it was important to simplify down to the base data structures and interaction patterns - get those correct - and then build LLM interaction patterns on top of that.

You can see an initial implementation of a Jido.Action that calls an LLM in the jido_ai package here: https://github.com/agentjido/jido_ai/blob/main/lib/examples/politician.ex

This example uses instructor_ex - but langchain or any other Elixir AI package could easily be integrated for an LLM response.

Yes, exactly - or even Broadway. This problem space of data transformation pipelines has a lot of solutions.

Each solution has it’s strengths. I love and have used each of these tools many times.

Jido won’t be the best at raw throughput as it does not use GenStage - like Broadway or Flow. Jido does not assume a database, if you need a database backed Job Processing system, you should use Oban.

Jido is aimed at the space where a particular agent (or queue?) is built to facilitate a wide variety of output - because a LLM is typically involved. Low throughput, high variety.

Where the Agent space gets crazy-weird is when you let the LLM pick the next action. It’s not random … but it’s not idempotent either. Jido agents natively support a list of “allowed” actions (Agents — Jido v2.3.2) for this reason - it provides a small level of control over this use case.

I purpose built Directives to help provide some control over these advanced use cases.

This space evolves weekly - and I’m constantly learning and evolving my thinking here as well.

The primitives share similarities with Oban - but the use-cases are different.

Agent techniques like “Chain of Thought” (Chain-of-Thought Prompting | Prompt Engineering Guide<!-- -->) string together several actions with an LLM’s response deciding which next-action to choose. This simulates human reasoning leading to higher-quality outputs.

Jido was tailored towards these use cases. But, as I explained above, as I worked through my own prompting pipelines, I got annoyed with LLM’s being slow an flaky. When I set out to build Jido, I wanted to ensure the building blocks didn’t make assumptions about LLM’s being involved, but also supported classical AI planning algorithms - a currently neglected sector of artificial intelligence research.

Think about video game NPC’s - the AI used to power them are most often things like Behavior trees or Zipper trees.

Elixir has a solid base of these algorithms available as Hex packages now and there was even a talk at ElixirConf about them in 2018

• zipper | Hex
• GitHub - jschomay/elixir-behavior-tree: Elixir library for building AI's from composable behavior trees · GitHub
• https://www.youtube.com/watch?v=3sLYzxuKGXI

A lot of the latest AI research focus has gone towards LLM’s lately, but I find algorithms like behavior trees to be more practical in the real-world because they are more reliable in several ways.

Really appreciate the questions here- I’m adding clarifications to the README along the way to hopefully answer some of these questions for others in the future.