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 Protocol-Lattice · Agent Tool · ★ 254
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is go-agent safe to install? View the security audit →
go-agent go-agent is a Go framework for building AI agents with pluggable LLM providers, memory, file context, guardrails, UTCP tool orchestration, and multi-agent coordination. Use it when you want agent runtime pieces that stay idiomatic in Go: A small core with , , and Provider adapters for Gemini, OpenAI, Anthropic, Ollama, and a local dummy model Short-term memory plus vector-store backed long-term memory ADK modules for wiring models, memory, tools, sub-agents, CodeMode, and UTCP Agent-as-tool patterns for specialist agents and hierarchical workflows Input/output guardrails and checkpoint/restore support Install For this repository: The module currently targets Go .
| Stars | 254 |
| Forks | 33 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 75.5278116933617/100 |
| Last Updated | 2026-08-29 |
| Created | 2025-10-13 |
| Platforms | gemini, go |
| Est. Tokens | ~18k |
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go-agent is An agent framework for Go with graph-aware memory, UTCP-native tools, and multi-agent orchestration. Built for production.. It is categorized as a Agent Tool with 254 GitHub stars.
go-agent is primarily written in Go. It covers topics such as agents, anthropic, codemode.
You can find installation instructions and usage details in the go-agent GitHub repository at github.com/Protocol-Lattice/go-agent. The project has 254 stars and 33 forks, indicating an active community.
go-agent is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to go-agent on Agent Skills Hub include SearChat, SearChat, kea-research. 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: