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Druids – Build your own software factory

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 agents or clients can call to modify state. Each event gets exposed as an agent tool.- Druids provisions full VMs so that the agents can run continuously and communicate effectively.We made Druids because we were making lots of internal coding tools using agents and found it annoying to have to rearrange the wiring every time.As we were building Druids, we realized a lot of our internal tools were easier to express as an event-driven architecture – separating deterministic control flow from agent behavior – and this design also made it possible to have many agents work reliably.We had issues with scaling the number of concurrent agents within a run, so we decided to have each program run in an isolated sandbox program runtime, kind of the same way you run a Modal function. Each agent then calls the runtime with an agent token, which checks who can talk to who or send files across VMs, and then applies the tool call.Our early users have found the library useful for:- running many agents to do performance optimization- building custom automated software pipelines for eg code review, pentesting, large-scale migrations, etc...We've heard that the frontier labs have the infrastructure to quickly spin up 100 agents and have them coordinate with each other smoothly in various ways. We're hoping that Druids can be a starting point to make that infrastructure more accessible.

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FL score

69

out of 100

Verdict

BUILD

high confidence

Competition

15

competitors found, emerging market, funded players

Trend

No signal yet

An open-source library for building and orchestrating reliable, scalable multi-agent coding workflows by abstracting infrastructure.

The pain

Developers building multi-agent systems struggle with the complexity of provisioning VMs, wiring agents, managing communication, and ensuring reliability and scalability (context degradation, long execution times, difficulty integrating).

The gap

While many AI coding assistants and open-source agent frameworks exist, few provide a dedicated, abstracted layer for VM infrastructure, agent provisioning, and event-driven, sandbox-isolated communication specifically designed for building *custom, reliable multi-agent coding workflows*. The gap is in simplifying the 'factory' infrastructure for developers rather than just offering agents or basic frameworks.

Build angle

Create a managed service or premium extensions for Druids, focusing on specific high-value use cases (e.g., performance optimization, large-scale migrations) for teams that need to scale and reliably coordinate numerous autonomous agents without managing underlying infrastructure.

Strengths

  • Addresses a real, specific, and growing pain in multi-agent orchestration.
  • Strong creator-problem fit, born from personal experience.
  • Clear technical differentiation with infrastructure abstraction and event-driven design.
  • High market growth potential in AI-powered development tools.
  • Initial validation with early users finding the library useful.

Risks

  • Highly crowded market with many funded competitors and established open-source alternatives.
  • Complexity of building and maintaining a robust library that handles VM provisioning and agent orchestration for a solo builder.
  • Unclear monetization path for an open-source library; requires a well-defined commercial offering (e.g., SaaS).
  • Risk that developers prefer off-the-shelf agents or more comprehensive frameworks over building their own with a library.
  • High switching cost if users are already invested in other agent frameworks or manual setups.

Next steps

  1. Identify the most painful, specific use case for Druids that a small group of users would pay for as a managed service.
  2. Validate demand and pricing for a commercial offering (e.g., hosted Druids environment or premium features) with current and potential users.
  3. Focus marketing efforts on developer communities experiencing the specific pain points Druids solves for multi-agent workflows, potentially showcasing how it solves issues like Devin AI's context degradation or integration challenges.

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