TradeMemory is a memory and audit layer for AI trading agents that records trading decisions, outcomes, and context in a tamper-evident history. It is for traders and automated trading systems that need agents to recall past decisions and document their reasoning. Catalogue add-ons provide skills, commands, MCP tools, and related workflow components for using it.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mnemox-ai/tradememory-protocol --skill tradememory-bridgegit clone --depth 1 https://github.com/mnemox-ai/tradememory-protocolWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/tradememory-bridge)<a href="https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/tradememory-bridge"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/tradememory-bridge/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/tradememory-bridge"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/tradememory-bridge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.01648 |
| Opus 5 | $0.00026 | $0.00824 |
| Sonnet 5 | $0.00011 | $0.00330 |
| Haiku 4.5 | $0.00005 | $0.00165 |
Grade A, and why
tradememory-bridge scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TradeMemory Bridge for Binance
Store Binance spot trades into persistent memory. Recall similar past trades before entering new positions. Detect behavioral biases (overtrading, revenge trading). Track strategy performance across sessions.
Requires: TradeMemory Protocol MCP server running.
Setup
Install and start the TradeMemory MCP server:
pip install tradememory-protocol
python -m tradememory
Or add to Claude Desktop / Claude Code MCP config:
{
"mcpServers": {
"tradememory": {
"command": "uvx",
"args": ["tradememory-protocol"]
}
}
}
Workflow
After executing a Binance spot trade using the Binance Spot skill:
- Store the trade using
remember_tradeMCP tool - Before next trade, recall similar past trades using
recall_memoriesMCP tool - Check agent state using
get_agent_stateto see if drawdown or confidence suggests pausing - Review behaviors using
get_behavioral_analysisto detect biases
MCP Tools Reference
remember_trade
Store a completed trade into memory. Automatically updates all memory layers.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| symbol | string | Yes | Trading pair (e.g. "BTCUSDT", "ETHUSDT") |
| direction | string | Yes | "long" or "short" |
| entry_price | number | Yes | Entry price |
| exit_price | number | Yes | Exit price |
| pnl | number | Yes | Profit/loss in account currency |
| strategy_name | string | Yes | Strategy name (e.g. "GridBreakout", "MeanReversion") |
| market_context | string | Yes | Natural language description of market conditions |
| pnl_r | number | No | P&L as R-multiple (risk units) |
| context_regime | string | No | Market regime: trending_up, trending_down, ranging, volatile |
| confidence | number | No | Confidence level 0-1 (default 0.5) |
| reflection | string | No | Lessons learned from this trade |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 201 lines · 53 tokens per session scan A 576e3c7ab1f7
tradememory-bridge is a skill published in the GitHub repository mnemox-ai/tradememory-protocol (1,416 stars, last pushed 3d ago), licensed MIT. It adds 53 tokens to every session and 1,648 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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