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mnemox-ai

tradememory-protocol

Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, tamper-evident SHA-256 chain with RFC 3161 anchoring, 20 MCP tools.

AI EngineeringGive agent toolsGive agent knowledgePython
Open on GitHub
Momentum
+0stars in 24 hours0.0%
1.43k
Stars
172
Forks
+5
This week
3
Contributors
Created 2026-02-23 · Updated 2026-10-13 · #10357 today
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zychenpengXxUs3rZer0xXalexander-schneider

mnemox-ai/tradememory-protocol is ranked 10,357 with 1,430 stars.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguagePython
CategoryAI Engineering
SubcategoryGive agent tools, Give agent knowledge
KeywordsMCP, Memory management, Retrieval, Trading
Stars1,430
Forks172
Contributors3
Created date2026-02-23
Updated date2026-10-13
Stars in 1 day0
Stars in 7 days5
Rank10,357
Previous rank10,358
Rank change1
7-day stars rank5,572
Previous 7-day stars rank5,585
7-day stars rank change13
Total stars rank8,409
Previous total stars rank8,397
Total stars rank change-12
README summary

** It is an open-source, local-first memory and brake for AI trading agents: it pulls in your fills, finds where your own history loses money, puts those losing trades in front of your agent before the next order, and can refuse an order that breaks rules you set, before it reaches the broker. Brokers now let AI agents trade over MCP, with different guardrails: Webull and tastytrade set size or buying-power limits, Interactive Brokers only lets the agent

draft an order for you to submit, and Robinhood and Public set no cap you can impose on an external agent (StockBrokers, 2026-09-30). None of them looks at how your own past trades went. Start with your own history Each closed trade is stored in memory once; running it again stores only new trades. Then it prints where your history loses money. The format, with illustrative numbers: These are descriptive statistics of your own past trades, not advice

about the next one. Hyperliquid's API serves only an address's recent fills, not its whole history, so the sooner it is synced, the more history is kept. Other venues: MT5 and Binance spot sync scripts are in , and any agent can record a trade with . Before the next order puts every losing trade ahead of the rest, the bigger losses in similar conditions first, with their size and P&L. Besides the symbol's most recent trades, it searches the symbol's

recent losing trades, so an older loss is not crowded out. The server tells connected agents to call it before proposing a trade. The default order ranks better outcomes higher (by R multiple, where one was recorded) and keeps at least 20% losses in the list. "* Claude Code / Cursor / Docker **Full walkthrough:** Getting Started (Trader Track + Developer Track) Put a brake in front of your broker (preview) The extra runs TradeMemory between your agent and your broker's MCP server (Alpaca

today). Every tool is forwarded unchanged except the order-placing ones, which Mnemox Control evaluates against a policy you own before they reach the broker. Every evaluation, allowed or refused, is recorded and chained into the audit log. An allowed order comes back with your losing trades from similar conditions first, and later fills in how each forwarded trade ended, in R when the entry carried a stop. The agent you already have keeps working; only one line of its

MCP config changes. Refused by default: symbols outside your list, orders above your notional and position limits, entries without a bracket stop, any new order after your daily-loss or drawdown limit, cancelling the protective stop of an open position, any tool the brake has not classified, and everything while you have run . Orders at or above wait for , which approves exactly the terms you read; the agent retries with the same and the same terms, and the proxy forwards

it at most once. returns the same decision without placing anything, for pre-checks and for advisory layers in other

Frequently asked questions

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mnemox-ai/tradememory-protocol has 1,430 stars and 172 forks.

What is the repository rank?

mnemox-ai/tradememory-protocol is ranked 10,357.

What language and category are recorded?

mnemox-ai/tradememory-protocol has 1,430 stars, uses Python and belongs to AI Engineering.

Which repository is closest in rank?

mnemox-ai/tradememory-protocol is compared with remorses/kimaki, ranked 10,346 with 1,432 stars.

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