Show HN: Open sourcing our ERP (Sold $500k contracts, 7k stars)

Curious how you’re thinking about failure capture and agent evolution, feels like the hardest unsolved part.

I've been contributing heavily to Hive/Aden recently to help bridge the gap between "research framework" and "production platform."

My recent PRs have focused on improving Developer Experience and safety:

- Goal Decomposition Preview: I noticed a lot of "blind generation" in agent frameworks. I implemented a CLI feature (hive preview) that performs a lightweight LLM pass to decompose a goal into a directed graph structure (nodes & flow logic). It explicitly flags risks (e.g., ambiguous success criteria) and provides cost/complexity estimates before you generate a single line of scaffold code.

- Simulation Mode: To tighten the dev loop, I added a simulation harness that allows for dry-running agent logic against mocked inputs. This lets you test decision trees and retry mechanisms without burning real API credits or triggering side effects (like actually sending an SMS or writing to a DB).

- Enterprise Integrations: I’ve been fleshing out the MCP (Model Context Protocol) layer to support actual business workflows, including Microsoft SQL Server, Twilio (SMS/WhatsApp), Google Maps, n8n, and Zendesk.

- Persistent Memory: Just shipped integration with Memori to solve the statelessness problem, giving agents long-term context retention across sessions.

Happy to answer any questions on the implementation details.

Hi everyone,

What was the issue? While exploring Hive for real-world finance use cases, I noticed there wasn’t a clear, reusable structure for implementing credit-risk logic inside agents. This made experimentation harder and limited how easily risk-related workflows could scale across agents. How did I fix it? I contributed by working on a credit-risk–focused agent/module, improving the structure, documentation, and alignment with the existing agent pipeline. The goal was to make the logic more modular and easier to extend as new agents and use cases are added. Why does it matter? Credit risk is a core problem in many real-world applications (fintech, lending, B2B workflows). Making this logic modular and transparent helps Hive support more serious production use cases, while keeping the system understandable and contributor-friendly.

I’ve been contributing to Hive and opened Issue #3763 around production readiness specifically missing reliability controls (retry, recovery, persistence) and cost transparency (token and workflow-level visibility).

I proposed structured retry policies, crash-safe state persistence, and cost observability via CLI/TUI. Based on maintainer feedback, I broke this down into focused sub-issues under #3763 to make implementation incremental and aligned with Hive’s architecture. I also submitted PR #4398 from my fork to improve documentation around production hardening and cost visibility.

This matters because production agent workflows need reliability and predictable cost behavior otherwise deployment confidence and adoption suffer.

I also contributed to Issue #4131 by proposing a “Post-Quickstart Evaluation Walkthrough” to help developers validate agent behavior immediately after setup and improve onboarding clarity.

Hive’s event-loop architecture is solid these contributions focus on helping bridge the gap from experimentation → production deployment.

This is a really cool project and I've enjoyed exploring it. I've worked on a couple of things within this repo. My preference was to use local LLMs as opposed to LLM API keys, as I'm working on developing local LLM usage to address the enterprise concerns of data privacy. Hive supports the usage of Ollama for local model inference, which I felt was a good starting point. During development, I found the Hive docs to be primarily focused on using Claude Code and OpenAI LLM inference, and development on Linux OS. I absolutely use Linux frequently, and also understand that there are many devs using Windows as well. Issue: Hive docs needed some fleshing out to produce clear guides, walkthroughs, and examples using the above framework. How to fix: I proposed a fully fleshed out guide using Ollama models, and CLI, along with a breakdown for both Linux and Windows users. Though, WSL works, again, I wanted to appeal to less technical users that may have an interest in Hive, and suggested a less technical approach for simple PowerShell or Linux CLI instructions. Why does it matter?: There are several reasons this is important, but primarily it has to do with creating a platform that not only highly technical people can use, but also beginner or intermediate users as well. This not only broadens the audience, but also demonstrates professionalism.

I've really enjoyed using the Hive framework to build some local LLM inference projects (currently using Hive for a short term/long term memory agentic system to address context window limitations, attention deficit, and drift in long conversations.