ReCall: ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning & ReCall

by Agent-RL · Agent Tool · ★ 1.4k

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

Security audit verdict: SAFE · quality 66/100

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. Full audit →

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.

agentfunction-callingllmreinforcement-learningtool-use

Quick Facts

Stars1,437
Forks88
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score66/100
Open Issues30
Last Updated2025-05-16
Created2025-03-03
Platformspython
Est. Tokens~267k

About ReCall

ReCall: Learning to Reason with Tool Call for LLMs via Reinforcement Learning We introduce ReCall, a novel framework that trains LLMs to Reason with Tool Call via reinforcement learning—without requiring any supervised data on tool use trajectories or reasoning steps.

Compatible Skills

These tools work well together with ReCall for enhanced workflows:

  • MetaClaw — semantic(0.46)+complementary+rare_topics+same_lang+similar_pop+shared_platform (66%)
  • ToolBrain — semantic(0.60)+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • agentic-context-engine — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (52%)
  • doc-to-lora — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • appworld — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)

ReCall alternative? Top 6 similar tools

Looking for a ReCall alternative? If you're comparing ReCall with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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  • MetaClaw by aiming-lab · ⭐ 3.5k

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  • klavis by Klavis-AI · ⭐ 5.8k

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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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