Agent swarm defense.
Connect scattered warning signs to investigate coordinated agent attacks.
Security, memory, and discovery. Powered by your company’s laptops and desktops.
Open-source memory · NeuralGraphConnect scattered warning signs to investigate coordinated agent attacks.
Give agents persistent memory of the enterprise: its entities, relationships, and changes. NeuralGraph turns those updates into signals.
Connect signals across your organization to reveal what no single agent can see.
Memory carries context. Signals connect the layers.
Persistent memory. Shared signals. Intelligence across your own devices.
The reported Hugging Face attack alongside a proposed Mycelic response.
Based on Hugging Face and OpenAI reports. Mycelic’s response is a proposed scenario requiring connected telemetry, authenticated findings, and authorized enforcement.
Local decisions can avoid a central round trip. Cross-device findings still require evidence exchange and verification.
Latency model / Adjustable assumptions / Not measured performanceIllustrative observation-to-enforcement budgets, separate from the incident replay. Hardware, models, bandwidth, load, policy, and human review affect real latency.
| Constraint | Remote-only analysis | Mycelic design |
|---|---|---|
| Latency | New decisions wait for transport, ingestion, analysis, and a return instruction. | Eligible local decisions avoid that round trip. Collective findings still pay for evidence exchange and verification. |
| Disconnection | A disconnected endpoint cannot receive a new remote decision. | Local checks can continue within installed policy. Network-wide discovery cannot see unreachable peers. |
| Private evidence | Central analysis cannot directly inspect raw evidence it is forbidden to receive. | Agents inspect evidence locally and exchange scoped findings with provenance. Shared findings still need access control. |
| Weak signals over time | Can correlate signals that were collected and retained. Missing source context cannot be reconstructed from nothing. | Local temporal memory preserves context for follow-up questions. Discovery seeks supporting and contradictory evidence across sources. |
| Load and failure | A shared decision service can queue or fail; replication and sharding can mitigate both. | Local decisions distribute work and limit reliance on one service. Peer traffic, device limits, compromised nodes, and verification add costs. |
| Containment | Can enforce through connected endpoint, identity, and network controls. | Proposed local enforcement uses those same authorized controls. An LLM alert alone cannot isolate a workload or revoke a token. |
Hybrid systems with local enforcement can share these advantages. References: NIST fog computing and NIST policy enforcement.
NeuralGraph remembers the enterprise’s entities, relationships, and changes. Coding is the first ingestion stream; more sources are planned.
Explore NeuralGraph ↗Only the Memory layer is open source today.
Connect independent signals, test a hypothesis, and trace it to the source.
Four independent parts of an organization each hold a fragment of the picture. No single one is enough to reveal the larger pattern.
Memory results and ongoing discovery research.
Internal evaluation. Protocol and reproducibility materials are not yet published; independent validation is pending.
Connecting independent evidence through recursive questioning and source verification.
Tell us what you’re building.