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
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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"])
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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
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The numbers
Metric Value tune_in() latency (single agent) 1.69 µs tune_in() latency (100 concurrent) ~1.25 ms Cost vs tool call architecture -99.97% Payload size impact on latency negligible (refs, not copies)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"])
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Tiers
Open Source Managed Cloud Enterprise Price Free (MIT) $299/mo Custom Nodes Unlimited (self-host) 500 Unlimited SLA — 99.9% 99.99% Support Community Email + Slack Dedicated engineerCustom 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: [email protected]
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