SignalMesh: The Open Source Ambient Context Layer for AI Agent Fleets

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

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

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

pip install signalmesh  # or self-host via Docker
python
from signalmesh import signal_registry

signal_registry.broadcast("market_data", "rss", {"btc": 42000})

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.

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

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

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

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

context = signal_registry.tune_in(["query_keyword"])

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