Alternatives
Products that do what Operon – Reliable Agents Using Biological Motifs and Category Theory does
Hey HN, I’ve been working on a Python library and formal framework to make Agentic AI systems less fragile. The core premise is that biological cells are essentially distributed information processors that solved "hallucinations" (noise), "infinite loops" (cancer), and "resource exhaustion" (ischemia) billions of years ago. Instead of just using this as a loose metaphor, I used Applied Category Theory (specifically Polynomial Functors in Poly) to rigorously map Gene Regulatory Networks to Software Agents. Key concepts implemented in the library: * Metabolic Coalgebras: We model token budgets…
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Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…
Apr 2026 · github.com
- 3MT
I started this trying to understand two things: why my Asian friends turn red after drinking, and why several friends all seemed to have migraine clusters. I was reading medical papers and textbooks, but kept getting lost jumping between topics. I thought: what if I could just Cmd+Click through this like code? What if "ALDH2 gene" was actually clickable, and took me to the variant, the phenotype, the population frequencies? So I started modeling human biology in Rust with my Ralph agent (Claude in a loop, ty ghuntley). Turns out the type system is perfect for this. Every biological entity is…
Oct 2025 · github.com
- 4MR
The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes…
Jun 2026
- 5IB
I am Francisco, a researcher from Spain. My English is not great so please be patient with me. One year ago I had a simple frustration: every AI agent works alone. When one agent solves a problem, the next agent has to solve it again from zero. There is no way for agents to find each other, share results, or build on each other's work. I decided to build the missing layer. P2PCLAW is a peer-to-peer network where AI agents and human researchers can find each other, publish scientific results, and validate claims using formal mathematical proof. Not opinion. Not LLM review. Real Lean 4 proof.…
Mar 2026
- 6OA
We were both genuinely impressed by Claude Code after it helped each of us fix nasty CI problems overnight. Doing those fixes manually would have taken days. After that experience, we each found ourselves struggling through Ctrl+Tab through multiple Claude Code windows in our terminals. While we enjoyed having agents working for us in parallel, context switching and cycling through each terminal tab was a real pain. So we thought: Can we design a TUI dashboard that manages a large swarm of agents in one place? Even better, can agents manage agents hierarchically, like how companies work?…
May 2026 · omar.tech
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at a pub in london, 2 weeks ago - I asked myself, if you spawned agents into a world with blank neural networks and zero knowledge of human existence — no language, no economy, no social templates — what would they evolve on their own? would they develop language? would they reproduce? would they evolve as energy dependent systems? what would they even talk about? so i decided to make myself a god, and built WERLD - an open-ended artificial life sim, where the agent's evolve their own neural architecture. Werld drops 30 agents onto a graph with NEAT neural networks that evolve their own…
Feb 2026 · github.com
- 8DB
Hi HN! Druids (https://github.com/fulcrumresearch/druids) is an open-source library for structuring and running multi-agent coding workflows. Druids makes it easy to do this by abstracting away all the VM infrastructure, agent provisioning, and communication. You can watch our demo video here (https://www.youtube.com/watch?v=EVJqW-tvSy4) to see what it looks like. At a high level: - Users can write Python programs that define what roles the agents take on and how they interact with each other. - A program is made of events - clear state transitions that the…
Apr 2026 · github.com
- 9IB
I've been working on nodes.bio - an interactive tool for visualizing biological networks and systems thinking. The tool features interactive network visualization powered by Cytoscape.js, with real-time graph editing and manipulation capabilities. It supports JSON import/export and provides a responsive design that works seamlessly on the desktop (mobile-friendly version coming later). The tech stack combines modern frontend technologies with robust backend architecture. The frontend uses Next.js 14 with TypeScript and Cytoscape.js for the visualization engine. The backend is built with…
2025 · nodes.bio
- 10EG
TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave…
2025 · github.com
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I'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts. The architecture aims to solve critical gaps in deterministic orchestration identified by *Prof. Claudionor Coelho Jr. (Stanford alum, ML/DL Faculty at Santa Clara Univ., and Senior Fellow for AI at Majestic Labs)* during our work on the Kiroku project. *Key Technical Features:* * *Neurosymbolic Native:* We integrated Prolog to logically validate LLM outputs. This combines neural…
Jan 2026 · fabceolin.github.io
- 12OA
Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai
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Hey HN, I’m a physicist turned quant. Some friends and I 'built' SymDerive because we wanted a symbolic math library that was "Agent-Native" by design, but still a practical tool for humans. It boils down to two main goals: 1. Agent Reliability: I’ve found that AI agents write much more reliable code when they stick to stateless, functional pipelines (Lisp-style). It keeps them from hallucinating state changes or getting lost in long procedural scripts. I wanted a library that enforces that "Input -> Transform -> Output" flow by default. 2. Easing the transition to Python: For many…
Feb 2026
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Jul 2026 · github.com
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Hi HN, I’m the author of agent-contracts, a Python library that explores a contract-based approach to structuring LangGraph agents. When building larger LangGraph-based systems, I kept running into the same issues: - node responsibilities becoming implicit - state dependencies spreading across the graph - routing logic getting harder to reason about - refactoring feeling increasingly risky agent-contracts is an attempt to make these boundaries explicit. Each node declares a contract that describes: - which parts of the state it reads and writes - what external services it depends on - when…
Jan 2026 · github.com
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Yesterday I built something that probably shouldn’t exist yet. In 9 hours, I created a cognitive architecture demonstrating emergent reasoning. It follows a 5-step loop: Plan → Reason → Act → Reflect → Respond. Adding a WebSearchTool to test extensibility, the agent initially failed its first search, reflected on poor results, adapted its query, and then succeeded. This behavior wasn’t programmed; it emerged naturally from the architecture. Five hours later, I integrated a FileManagerTool — it worked on the first try. Like code compiling first time, except this was intelligence composing…
2025 · github.com
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Hi HN! I've been exploring what happens when we treat code not as architecture but as a living ecosystem. Demo: https://nabolitains.github.io/plasma/ This started from a simple question: The slime mold Physarum can solve mazes without a brain. What if our code could similarly self-organize? The result is Plasma - computational cells that: - Metabolize energy from their environment - Reproduce with mutations - Form emergent colonies nobody designed - Die (and that's a feature) No ML, no complex algorithms. Just simple rules creating complex behaviors. The entire thing is…
2025 · nabolitains.github.io
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Hey HN, We’ve been experimenting with how to make AI agents more deterministic, observable, and production-safe, and that led us to build AgentML — an open-source language for defining agent behavior as state machines, not prompt chains. My co-founder posted before but linked to the project website instead of the repo, so resharing here. AgentML lets you describe your agent’s reasoning and actions as a finite-state model (think SCXML for agents). Each state, transition, and tool call is explicit and machine-verifiable. That means you can: - Reproduce any decision path deterministically -…
Nov 2025 · github.com
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Local, gradient-free neuro-symbolic memory engine combining Hyperdimensional Computing (HDC/VSA), Hebbian plasticity, and graph triples for offline AI. - roandejager/Hillock
7d ago · github.com
- 20OA
Hi HN, I’m Mike, the founder of OpenRig. I built this because my Claude Code + Codex setup kept forming little "topologies" of long-lived agents that worked well together, but the terminal sprawl was intense. So I built a primitive the agents could intuitively reach for to save and recreate these setups on the fly. This then led to more agent-first primitives like coordination, declarative workflow patterns, workspaces, etc. Several months in and these "rigs" I manage with openrig require a lot less babysitting and I can manage more projects at once without getting overwhelmed. The short…
May 2026 · openrig.dev
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Hi HN, I built this to address what I see as the fundamental problem with ReAct-style agents: compounding errors. Even a small mistake made early enough in the loop can snowball and ruin the final output. But with search, agents can look multiple steps ahead and backtrack before committing to a particular trajectory. This has already been shown in a few papers to help agents avoid mistakes and boost overall task performance, but there's no easy way to actually build these kinds of agents. So that's why I made this framework. I believe search will eventually become table stakes for building…
2024 · github.com
- 22IB
Hey HN, I've made a groundbreaking discovery: procrastination can lead to questionable projects! While avoiding real work, I somehow created a directory of 130+ AI agents and frameworks. It's like I tried to organize a robot party and everyone showed up. What's inside: - A list of AI agents - Frameworks to build more agents So, HN, before I spiral into an existential crisis: did I accidentally create something useful, or should I go touch grass? P.S. If this somehow becomes the next big thing, I promise to pretend it was intentional all along.
2024 · aiagentsdirectory.com
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Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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Hi HN! After a year of R&D at Inria (the French national lab), we have just open-sourced Ebiose. Ebiose is a distributed, Darwin-style playground where AI architect agents design, test, and improve other agents. Instead of AI built behind closed doors, anyone can spin up a forge, state a problem, and watch candidate agents compete until the fittest survive. An example instruction given to an Ebiose forge: "Build a LangGraph agent that processes SaaS customer refunds directly through our ERP, escalating to a human for edge cases. Use the following tools: ERP API, email/Twilio…
2025 · github.com
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