Modular Python framework for LLM agents with multiple agent patterns, memory, RAG, MCP, and context engineering.
- capability exposure inferred + 35
- tool safety inferred + 5
- trust mitigators mixed − 3
inferred mixed
The A–E grade is our heuristic synthesis — a "review this" prompt, not a verdict. Each factor is tagged by what backs it: attested (a verifiable record), reported (a third party's claim), or inferred (our own heuristic, e.g. permissions). See methodology.
grade last moved 6d ago · see ecosystem CVEs →
- C · 40 → C · 37
- C · 52 → C · 40
No known CVEs for this server.
- medium dangerous code
dynamic exec: eval()/exec()
analyzed commit 23704a7 · analyzer v33 · 2w ago
skills & prompt files 1
- agent-rules Ling-ye-Lingye_Agent-23704a7/AGENTS.md
danger signals1
- dynamic code execution eval()/exec() Ling-ye-Lingye_Agent-23704a7/tests/test_mcp_server.py :24
result = eval(expression)
[](https://mcpobservatory.com/servers/github:Ling-ye/Lingye_Agent/security) Heuristic, inferred signals — false positives (legitimately powerful tools, forks, language ports) are expected. Treat each as "review this", not a verdict. See the ecosystem-wide picture on the security hub, or the fleet security of Ling-ye.