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 SolaceLabs · MCP Server · ★ 4.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is solace-agent-mesh safe to install? View the security audit →
Solace Agent Mesh Open-source framework for building event driven multi-agent AI systems Star ⭐️ this repo to stay updated as we ship new features and improvements. Key Features • Quickstart • Next Steps • Docs Solace Agent Mesh is a framework that supports building AI applications where multiple specialized AI agents work together to solve complex problems. It uses the event messaging of [Solace Platform](https://solac
| Stars | 4,930 |
| Forks | 276 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 65.2623796871528/100 |
| Open Issues | 99 |
| Last Updated | 2026-09-13 |
| Created | 2025-01-10 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
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solace-agent-mesh is An event-driven framework designed to build and orchestrate multi-agent AI systems. It enables seamless integration of AI agents with real-world data sources and systems, facilitating complex, multi-s. It is categorized as a MCP Server with 4.9k GitHub stars.
solace-agent-mesh is primarily written in Python. It covers topics such as a2a, agentframework, agentic.
You can find installation instructions and usage details in the solace-agent-mesh GitHub repository at github.com/SolaceLabs/solace-agent-mesh. The project has 4.9k stars and 276 forks, indicating an active community.
solace-agent-mesh is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to solace-agent-mesh on Agent Skills Hub include voltagent, nexent, awesome_ai_agents. 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: