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 MohitGoyal09 · MCP Server · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AgentForge safe to install? View the security audit →
AgentForge AgentForge is a terminal-based AI coding-agent harness built in Python for learning how modern coding agents are structured. It is not just a chatbot wrapper: the project is organized around the core harness concerns that make coding agents reliable, inspectable, and safe. Quick Start Install AgentForge from PyPI, create your provider config, verify the setup, then start the terminal UI: For an isolated install: The project currently supports OpenRouter, OpenAI, Anthropic, and custom OpenAI-compatible model providers, plus streaming model re
| Stars | 57 |
| Forks | 5 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73.5833389658912/100 |
| Open Issues | 1 |
| Last Updated | 2026-06-27 |
| Created | 2026-04-29 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~21k |
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AgentForge is Open-source terminal AI coding-agent harness for studying agent loops, tools, MCP, skills, safety, and persistence.. It is categorized as a MCP Server with 57 GitHub stars.
AgentForge is primarily written in Python. It covers topics such as agent-framework, agentic-ai, agents.
You can find installation instructions and usage details in the AgentForge GitHub repository at github.com/MohitGoyal09/AgentForge. The project has 57 stars and 5 forks, indicating an active community.
AgentForge is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AgentForge on Agent Skills Hub include tokentop, AICodeBot, SimplerLLM. 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: