Jido – Run 10k agents at 25KB each (Elixir)

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Hi HN! I'm Mike Hostetler and I built Jido, an Agent SDK in Elixir that lets you run thousands of agents without heavy infrastructure. Repo: https://github.com/agentjido/jido Getting Started: https://hexdocs.pm/jido/getting-started.html Why another framework? After using several popular Agent frameworks and platforms, I had two key challenges: - Running multiple agents required process-heavy infrastructure like Docker or K8s. Running 50,000 agents in parallel was costly and diminished the benefits of agentic programming. - Today's agents require too much human intervention when building workflows. Why couldn't agents manage their own WDLC (Workflow Design Life Cycle)? This felt like a major missing piece. Agentic frameworks were written for humans. LLMs working with this code were constantly working around human work-style assumptions. So, I wrote a framework specifically for LLMs to code and operate their own agentic flows. Elixir was a natural choice because of it's functional nature, rock-solid concurrency primitives and "let-it-crash" philosophy with dynamic error compensation. Hot code reloading was a bonus. Agents in Jido use 25Kb of memory at rest and can easily serialize then hibernate for long-lived access. Agents possess the APIs to dynamically start and manage their own sub-agents or any other Elixir process utilizing Elixir's OTP architecture. Jido Actions are functional primitives that Agents can dynamically orchestrate into workflows. Generated code can either run in a separate process in the current VM or in another BEAM VM that's linked and hardened before introduction into the Agent VM. I'm excited to help enable a world where thousands of agents work seamlessly on behalf of their human operators. Thanks!

Visit product View on Hacker NewsDec 30, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, 000, io · Missing: https docs, just released, exist

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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