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Give AI trading agents auditable memory, weighted recall, and tamper detection.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "tradememory-protocol" yet — see the docs or source repo.
Read an AI trading agent’s memory records from the last week and reconstruct the decision process behind one losing trade in timeline order. Highlight the signals used, reasoning steps, execution actions, and final outcome, then identify the step most likely responsible for the loss.
A structured trade audit report showing the decision chain, outcome, and likely failure point.
Analyze the agent’s historical memory for patterns shared by high-profit and high-loss trades. Using outcome-weighted recall, summarize which past patterns should be prioritized under similar market conditions and provide actionable rules.
A weighted summary of successful and failed patterns, plus recall and decision rules for future trades.
Run an integrity check on the specified trading agent’s memory archive. Use SHA-256 verification to detect modified, missing, or out-of-order records, and produce an auditable anomaly summary.
An integrity verification report listing suspicious records, anomaly types, and audit conclusions.
Add governed cross-agent memory with retrieval and sync for coding agents.
Expose trading analytics tools for indicators, risk, portfolio, and backtest insights.
Give AI agents persistent memory, shared reasoning, and auditable collaboration.
Give AI agents persistent, verifiable memory with blockchain-backed integrity proofs.
Give AI coding agents persistent local shared memory across agents.
Build a self-evolving memory graph for coding agents with semantic search.