Posted by mikehostetler 6 hours ago
I'm the author of an Elixir Agent Framework called Jido. We reached our 2.0 release this week, shipping a production-hardened framework to build, manage and run Agents on the BEAM.
Jido now supports a host of Agentic features, including:
- Tool Calling and Agent Skills - Comprehensive multi-agent support across distributed BEAM processes with Supervision - Multiple reasoning strategies including ReAct, Chain of Thought, Tree of Thought, and more - Advanced workflow capabilities - Durability through a robust Storage and Persistence layer - Agentic Memory - MCP and Sensors to interface with external services - Deep observability and debugging capabilities, including full stack OTel
I know Agent Frameworks can be considered a bit stale, but there hasn't been a major release of a framework on the BEAM. With a growing realization that the architecture of the BEAM is a good match for Agentic workloads, the time was right to make the announcement.
My background is enterprise engineering, distributed systems and Open Source. We've got a strong and growing community of builders committed to the Jido ecosystem. We're looking forward to what gets built on top of Jido!
Come build agents with us!
Just a heads up, some of your code samples seem to be having an issue with entity escaping.
name: "my_agent",
description: "A simple agent",Congrats on the release!
https://github.com/openai/symphony
I'm not very familiar with the space, I follow Elixir goings on more than some of the AI stuff.
It is curious... and refreshing... to see Elixir & the BEAM popping up for these sorts of orchestration type workloads.
https://web.archive.org/web/20260305161030/https://jido.run/
I’ve found the hardest part with agent frameworks isn’t model plumbing, it’s operational boundaries: how you isolate tools, enforce time/budget limits, and recover from partial failures when an agent call chain fans out.
BEAM’s supervision model feels like a genuinely strong fit for that, especially if each tool execution can be treated as a supervised unit with clear restart/escalation semantics. Curious whether you’ve seen teams default to many small specialized agents vs fewer general agents with stricter policies.
Agree on operational boundaries - it took a long time to land where we did with the 2.0 release
Too much to say about this in a comment, but take a look at the "Concepts: Executor" section - it digs into the model here
I just LLM-built an A2A package which is a GenServer-like abstraction. I however missed that there already was another A2A implementation for Elixir. Anyway, I decided to leave it up because the package semantics were different enough. Here it is if anyone is interested: https://github.com/actioncard/a2a-elixir
The future is going to be wild
I used Claude to learn & refine the patterns, but it couldn’t write this level of OTP code at that time.
As models got better, I used them to find bugs and simplify - but the bones are roughly the same from that original design.