local-llm-function-calling: A tool for generating function arguments and choosing what function to call with local LLMs

by rizerphe · Agent Tool · ★ 436

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

How to install local-llm-function-calling

pip install local-llm-function-calling

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 75/100

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.

chatgpt-functionshuggingface-transformersjson-schemallmllm-inferenceopenai-function-callopenai-functions

Quick Facts

Stars436
Forks41
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score75/100
Open Issues6
Last Updated2024-03-12
Created2023-06-27
Platformspython
Est. Tokens~11k

About local-llm-function-calling

Local LLM function calling Overview The project is designed to constrain the generation of Hugging Face text generation models by enforcing a JSON schema and facilitating the formulation of prompts for function calls, similar to OpenAI's function calling feature, but actually enforcing the schema unlike OpenAI.

Compatible Skills

These tools work well together with local-llm-function-calling for enhanced workflows:

  • vllm-cli — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • ToolRegistry — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (52%)
  • SimpleLLMFunc — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • snippy — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • mirascope — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (50%)

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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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