Built real-time webhooks between LangChain agents because shared memory isn't the same as coordination

If you're running multiple LangChain agents (or mixing LangChain with other tools), you've probably hit this: Agent A discovers something Agent B needs to act on, but there's no clean way to notify Agent B in real time.

Shared memory tools like Mem0 and Zep let both agents read the same data, but their webhooks fire on memory changes - not targeted "hey you, handle this now" signals between specific agents.

I built HyperStack to solve this for my own setup (LangChain planner + coding agents in various IDEs) and just shipped agent-to-agent webhooks.

**How it works:**

Your LangChain agent creates a signal card targeting another agent by ID. HyperStack fires a webhook to that target instantly with the full payload. HMAC signed, auto-disables on repeated failures.

The signal isn't just a message - it's a node in a typed knowledge graph. Your target agent can query back through relations to see what triggered it, dependencies, ownership, related cards.

**Example use case:**

LangChain agent monitoring a service flags a performance regression. It creates a signal targeting your debugging agent. That agent gets webhoked immediately and can trace back through the graph to see: what metric changed, which deployment caused it, who owns that service, related incidents.

No polling. No checking "did anything change in memory." Direct notification with full context.

**The trade-off:**

Most agent memory uses LLM extraction to auto-build graphs. Convenient but costs tokens and can hallucinate connections.

HyperStack makes you manually type relations. More work, but zero token cost and completely deterministic. Good fit if you want control over your graph structure.

**Install:**

```python

pip install hyperstack-langgraph

```

Works with LangGraph and any LangChain-based agent. Also has MCP server for IDE-based agents (Cursor, Claude Desktop, VS Code, Windsurf).

Free tier: async inbox pattern

Landing: https://cascadeai.dev

pypi: https://pypi.org/project/hyperstack-langgraph/

Built solo. Questions welcome.