altk-evolve: Self improving agents through iterations

by AgentToolkit · MCP Server · ★ 134

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

How to install altk-evolve

git clone https://github.com/agenttoolkit/altk-evolve.git
pip install altk-evolve

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 71/100

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.

agent-learningagent-memoryagentic-workflowsai-agentsclaude-codeclaude-code-pluginclaude-code-pluginscodexcontext-managementepisodic-memory

Quick Facts

Stars134
Forks19
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score71/100
Open Issues40
Last Updated2026-10-28
Created2025-12-12
Platformsclaude-code, codex, mcp, python
Est. Tokens~15k

About altk-evolve

Evolve: On‑the‑job learning for AI agents Blog posts: IBM announcement | Hugging Face blog Coding agents repeat the same mistakes because they start fresh every session. Evolve gives agents memory — they learn from what worked and what didn't, so each session is better than the last. Evolve is a system designed to help agents improve over time by learning from their trajectories.

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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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