by mnemox-ai · MCP Server · ★ 1.4k
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 | 1,408 |
| Forks | 165 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.4893625489717/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-11 |
| Created | 2026-02-23 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~18k |
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 165 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.