llm-apex-agents: Run Large Language Model Agents in Salesforce apex

by callawaycloud · Agent Tool · ★ 54

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 69/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

apexchatgptgpt-4llmopenaisalesforce

Quick Facts

Stars54
Forks26
LanguageApex
CategoryAgent Tool
LicenseMIT
Quality Score69/100
Open Issues7
Last Updated2023-04-26
Created2023-04-07
Est. Tokens~18k

About llm-apex-agents

llm-apex-agents Run Large Language Model (GPT) agents in Salesforce apex. Watch Full Demo Here :shipit: What is an Agent? An “Agent” is a technique for instilling the ability for an LLM to “Reason” and take “Action”. This approach is introduced by the ReAct Paper (Reason → Act) and used in popular libraries like langchain and auto-gpt. Library Terminology : LLM/GTP model.

Compatible Skills

These tools work well together with llm-apex-agents for enhanced workflows:

  • aiAgentStudio — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop (60%)
  • MCP-Salesforce — semantic(0.48)+complementary+rare_topics+similar_pop (51%)
  • mcp-server-salesforce — semantic(0.46)+complementary+rare_topics+similar_pop (51%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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