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 trade-memorygit 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/trade-memory)<a href="https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/trade-memory"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/trade-memory/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/trade-memory"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/trade-memory.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 | $0.00068 | $0.02133 |
| Opus 5 | $0.00034 | $0.01066 |
| Sonnet 5 | $0.00014 | $0.00427 |
| Haiku 4.5 | $0.00007 | $0.00213 |
Grade A, and why
trade-memory 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 10d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TradeMemory — Decision Audit Trail for AI Trading Agents
Every Binance skill executes trades. None of them record why.
TradeMemory is the compliance layer. When your AI agent opens a position using the Spot or Futures skill, TradeMemory captures the full decision context: what conditions triggered the signal, which filters passed or blocked, the market indicators at that moment, risk state, and execution details. Every record is SHA-256 hashed for tamper detection.
This matters because regulators now require it. MiFID II Article 17 mandates algorithmic trading audit trails. The EU AI Act (August 2025) requires high-risk AI systems to maintain systematic logging of every action and decision path. ESMA's February 2026 supervisory briefing specifically targets AI-driven trading. Non-compliance fines reach up to 15M EUR or 3% of global turnover.
What TradeMemory Records
For every trading decision your agent makes:
| Field | Description |
|---|---|
timestamp |
UTC decision time |
agent_id |
Which agent/EA made the decision |
model_version |
Software version at decision time |
decision_type |
ENTRY, EXIT, HOLD, SKIP |
strategy |
Strategy name (e.g. VolBreakout) |
conditions |
Entry conditions evaluated (passed/failed with thresholds) |
filters |
Risk filters checked (spread gate, regime gate, portfolio limits) |
indicators |
Market snapshot (ATR, EMA, spread, session range) |
execution |
Ticket, price, slippage, latency |
regime |
Market regime at decision time (trending/ranging/transitioning) |
risk_state |
Consecutive losses, cooldown status, daily P&L |
memory_context |
Past trades recalled via Outcome-Weighted Memory |
data_hash |
SHA-256 of all inputs for tamper detection |
Real Decision Event
This is a real decision event from a XAUUSD trading system running three automated strategies. The AI agent detected a SHORT breakout signal but the sell_allowed filter blocked execution:
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.
- 10d ago First seen · 219 lines · 68 tokens per session scan A 7c9a33892165
trade-memory is a skill published in the GitHub repository mnemox-ai/tradememory-protocol (1,416 stars, last pushed today), licensed MIT. It adds 68 tokens to every session and 2,133 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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