99.97% cost reduction on context reads. 1.69µs retrieval. Drop-in with LangChain, CrewAI, AutoGen.

The problem every multi-agent system has

Your agents are making tool calls to read context that hasn't changed. Each one costs:

• 800ms+ round-trip latency
• Scaffold tokens burned on the same boilerplate
• API cost, repeated per agent, per request

With 5 agents and 3 context reads each: $1,387/year on reads alone.

SignalMesh: broadcast once, tune in everywhere

pip install signalmesh  # or self-host via Docker

from signalmesh import signal_registry

# Any source broadcasts
signal_registry.broadcast("market_data", "rss", {"btc": 42000})

# Any agent tunes in — 1.69µs, no network, no tokens
context = signal_registry.tune_in(["market_data", "price"])

The mesh is in-memory, per-frequency buffered (last 100 signals), and keyword-flexible — agents find context even when their keyword doesn't exactly match the frequency name.

What's live right now

The public mesh is running at https://acecalisto3-signalmesh.hf.space:

• 27 active frequencies
• Real external agent traffic
• CORS open, no auth required
• 7 REST endpoints

curl https://acecalisto3-signalmesh.hf.space/ui/frequencies      # all live frequencies
curl https://acecalisto3-signalmesh.hf.space/ui/status           # mesh health + signal count

The numbers

Works with your existing stack

No schema changes. No migration. Broadcast from wherever you produce context:

# LangChain tool → mesh
@tool
def fetch_and_broadcast(query: str):
    data = your_api.get(query)
    signal_registry.broadcast(query, "tool", data)
    return data

# CrewAI agent reads from mesh instead of calling tool
context = signal_registry.tune_in(["query_keyword"])

Tiers

Custom implementations (LangGraph, AutoGen, CrewAI integration) available — flat-rate, delivery in days.

Links

• Interactive demo: https://kyklos.io
• HuggingFace Space: https://acecalisto3-signalmesh.hf.space
• GitHub (MIT): https://github.com/Ig0tU/SignalMesh
• Enterprise: abra.autopreneur@gmail.com