No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Hmbown · MCP Server · ★ 210
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is aleph safe to install? View the security audit →
Aleph Aleph is an MCP server and skill for Recursive Language Models (RLMs). It keeps working state — search indexes, code execution, evidence, recursion — in a Python process outside the prompt window, so the LLM reasons iteratively over large codebases, long-lived projects, logs, documents, and data without burning context on raw content. Why Aleph: Load once, reason many times. Data lives in Aleph memory, not the prompt. Compute server-side. runs code over the full context and returns only derived results. For JS/TS repos, and provide a persistent Node.js runtime over the same . Recurse. Sub-queries and recipes split complex work across multiple reasoning passes. Keep workspaces warm. Bind contexts back to files or generated workspace manifests, refresh them, and resume long inves
| Stars | 210 |
| Forks | 24 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73.7442594116775/100 |
| Open Issues | 3 |
| Last Updated | 2026-04-11 |
| Created | 2025-12-15 |
| Platforms | mcp, python |
| Est. Tokens | ~527k |
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aleph is Skill + MCP server to turn your agent into an RLM. Load context, iterate with search/code/think tools, converge on answers.. It is categorized as a MCP Server with 210 GitHub stars.
aleph is primarily written in Python. It covers topics such as llm, mcp, recursive.
You can find installation instructions and usage details in the aleph GitHub repository at github.com/Hmbown/aleph. The project has 210 stars and 24 forks, indicating an active community.
aleph is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to aleph on Agent Skills Hub include julia-mcp, Network-AI, agentic-radar. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: