Features
Mentor-like feedback for AI agents
Interrupts pattern inertia before wrong paths
Reduces over-engineering in agent workflows
Improves success rate by 27% (per study)
Halves harmful agent actions (per study)
Built on Anthropic's Model Context Protocol
Open source with 70k+ downloads
Listed in Anthropic's official MCP repo
PulseMCP most popular (Oct 2025)
Plug and play integration with MCP agents
KISS principle enforcement for agents
Supports coding, ambiguous, and high-risk tasks
Laguna XS.2 model (33B params, 3B active, on-device)
Laguna M.1 model (225B params, 23B active, via API)
256K context length for multi-step reasoning
Multi-agent orchestration with planning and tool use
Sandboxed agent execution environments
Developer surfaces: agents, TUI, IDE extensions, binaries
Custom foundation models deployed on-prem or in VPC
Data connectors to repos, databases, data warehouses
Role-based access control for humans and agents
Executive-grade governance and auditability
On-prem, VPC, and workstation deployment (defense only)
Forward Deployed Research Engineers embed with teams
Real-time observability with end-to-end traces
Air-gapped network support
Open-weight model weights available
