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 mnemox-ai · MCP Server · ★ 1.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is tradememory-protocol safe to install? View the security audit →
TradeMemory Protocol A Mnemox Project — MCP server that gives AI trading agents persistent, outcome-weighted memory. Works with: Claude Desktop · Claude Code · Cursor · Windsurf · any MCP client The Problem Your AI trading agent has no memory. Every session starts from zero — same mistakes, same blown setups, no learning. The Fix Session 1: Agent loses $200 → remembertrade stores context + outcome Session 2: Agent calls recallmemories → "Asian breakouts: 0% win rate, -$590" Agent
| Stars | 1,417 |
| Forks | 167 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.4893625489717/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-14 |
| Created | 2026-02-23 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~18k |
Looking for a tradememory-protocol alternative? If you're comparing tradememory-protocol with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowl
MaverickMCP - Personal Stock Analysis MCP Server
Open-source MCP server for LinkedIn. Give Claude and any MCP-compatible AI agent access to profiles, companies
Backtrader-powered backtesting framework for algorithmic trading, featuring 20+ strategies, multi-market suppo
Official remote MCP server for Atlassian. Securely connect Jira, Confluence, Jira Service Management, Bitbucke
Alpaca’s official MCP Server lets you trade stocks, ETFs, crypto, and options, run data analysis, and build st
Explore other popular mcp server tools:
tradememory-protocol is Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, tamper-evident SHA-256 chain with RFC 3161 anchoring, 20 MCP tools.. It is categorized as a MCP Server with 1.4k GitHub stars.
tradememory-protocol is primarily written in Python. It covers topics such as agentic-trading, ai-agents, audit-trail.
You can find installation instructions and usage details in the tradememory-protocol GitHub repository at github.com/mnemox-ai/tradememory-protocol. The project has 1.4k stars and 167 forks, indicating an active community.
tradememory-protocol is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to tradememory-protocol on Agent Skills Hub include mcp-memory-service, maverick-mcp, linkedin-mcp-server. 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: