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

SignalMesh, an open-source ambient context layer for AI agent fleets, achieves 99.97% cost reduction on context reads with 1.69µs retrieval latency. The in-memory mesh allows agents to tune into signals without network calls or token consumption, integrating with frameworks like LangChain, CrewAI, and AutoGen. A public mesh is available for testing, and the project offers free self-hosted, managed cloud, and enterprise tiers.

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 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. The public mesh is running at https://acecalisto3-signalmesh.hf.space 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 | 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 | No schema changes. No migration. Broadcast from wherever you produce context: python 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" | 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 engineer | Custom implementations LangGraph, AutoGen, CrewAI integration available — flat-rate, delivery in days.