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Open source monitoring for AI agents in production
1 starsPython
Open-source monitoring for AI agents (MCP-compatible)
by yohanpoul·Feb 11, 2026·1 point·0 comments
AI Analysis
●●SolidShip ItBig Brain
One-line monitoring for agents; drift + security scanning matter for production, but early MVP.
Strengths
- •Decorator-based SDK minimizes instrumentation friction compared to invasive logging
- •Drift detection + prompt injection detection address real production AI pain points
- •Local-first architecture and MCP compatibility show thoughtful design for privacy-aware teams
Weaknesses
- •Roadmap dated Feb–Q2 2026 signals pre-launch; core security scanning still planned, not shipped
- •Directly competes with AgentOps (funded, established), LangSmith (Anthropic-backed) without proven differentiation
- •Zero stars/forks suggests early-stage, unvalidated in production deployments
Category
Target Audience
ML engineers, AI agent developers deploying in production
Similar To
AgentOps (commercial, same name but different author) · LangSmith (LangChain ecosystem) · Arize (drift detection for traditional ML)
Post Description
I built AgentOps - open source monitoring for AI agents. Problem: no visibility when agents drift or get attacked. Solution: one line decorator. Features: monitoring, drift detection, security, MCP support. GitHub: https://github.com/yohanpoul/agentops- Feedback welcome!
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