AgentHelm - Shared memory & coordination layer for AI agent teams

Hey Product Hunt 👋

I’m Tharagesh, founder of AgentHelm.

I kept running into the same problem while working with multiple AI coding agents: one agent makes an important decision — changes an API contract, updates a database schema, chooses an architecture — and another agent working on the same project has no idea it happened.

The agents are intelligent individually, but the project itself has no shared memory.

That’s why I built AgentHelm.

AgentHelm is a shared, versioned project memory layer for AI agents. It gives agents working across different sessions and tools a common source of truth, so project knowledge belongs to the project, not to any individual agent.

Here’s what you can do with AgentHelm:

🧠 Give agents a shared Project Brain

Agents can retrieve relevant project context before starting work and contribute important knowledge when they're done. Decisions, architecture, API contracts, schemas, and other project knowledge can persist across agents and sessions.

🔄 Share context across different agents

Whether your agents are running independently or as part of a multi-agent workflow, AgentHelm provides a common memory layer so they can work from shared project knowledge instead of isolated context windows.

🛡️ Prevent agents from silently overwriting knowledge

Instead of treating every new piece of information as automatically correct, agents can submit knowledge proposals that go through a controlled compilation process before becoming part of the Project Brain.

📚 Keep project knowledge versioned

AgentHelm takes inspiration from Git. As project knowledge evolves, you can preserve its history and understand what changed instead of simply replacing old context and losing the reasoning behind previous decisions.

⚠️ Detect stale and conflicting knowledge

As the project evolves, previously correct information can become outdated. We're building AgentHelm to identify potentially affected knowledge and prevent agents from blindly relying on stale assumptions.

👀 Understand what your agents know

We're working toward memory observability that lets developers inspect what context an agent received, where it came from, which version it used, and how knowledge moved between agents.

🔌 Integrate without replacing your agent stack

AgentHelm isn't trying to be another agent orchestrator. The goal is to provide the shared memory and governance layer underneath the agents and tools developers already use.

The idea behind AgentHelm is simple:

Knowledge should belong to the project, not the agent.

If multiple AI agents are going to work together on real software for days, weeks, or months, they need more than isolated context windows. They need a shared understanding of what the project currently knows — and a way to safely evolve that knowledge over time.

AgentHelm is still evolving, and feedback from developers here has already influenced what I'm building next.

I’d genuinely love to hear from anyone running multiple agents: How are you handling shared context and memory today? What would make AgentHelm useful enough to become part of your workflow?

Thanks for checking it out and supporting AgentHelm 🙌

— Tharagesh, founder of AgentHelm

Memory handoff between runs actually works, finally my agents stop forgetting mid-project. Loved how it slotted into my python stack with just that one line change.

Love the open-protocol stance and the single-line integration is genuinely appealing. One thing I'd want to see soon is a built-in dashboard or CLI to inspect what each agent currently "remembers" and which handoffs happened during a run, otherwise debugging cross-agent memory drift in production will be painful.

honestly this looks really useful, been wrestling with agents losing context between runs for a while. one thing that would be a big win is built-in observability for the memory handoffs, like a small dashboard or CLI command to inspect what state each agent is holding and trace why a handoff failed. would save a ton of debugging time when chains go sideways.