mnemox-ai/tradememory-protocol — Decision audit trail + persistent memory for AI trading agents.

Description and use

**Brief Summary**
mnemox‑ai’s *tradememory‑protocol* provides a decision‑audit trail and tamper‑evident persistent memory for AI‑driven trading agents, combining outcome‑weighted recall with SHA‑256 integrity checks and a suite of 17 MCP (Modular Compute‑Protocol) tools. The open‑source Python library makes it easy to prototype, inspect, and integrate blockchain‑based trading workflows, wallet logic, or DeFi components.

**Value**

• **Transparency & Trust:** Every trading decision is recorded with cryptographic hashes, giving regulators, auditors, and developers an immutable view of AI actions.
• **Performance‑aware Recall:** The outcome‑weighted memory lets agents retrieve the most profitable historical contexts, improving strategy refinement without re‑training.
• **Tooling Ecosystem:** The 17 MCP utilities cover data ingestion, state sync, replay, and analytics, reducing the amount of custom code needed for Web3‑enabled trading pipelines.

**Practical Adoption Path**

1. **Evaluation:** Clone the repo, run the provided CLI to generate a sample audit trail, and inspect the generated SHA‑256 proofs.
2. **Integration:** Use the Python SDK to replace the existing state store of your AI trader; the SDK exposes simple store_decision() and query_memory() methods that accept any serializable payload.
3. **Extension:** Leverage the MCP tools (e.g., mcp‑sync, mcp‑replay) to connect the protocol to your preferred blockchain (Ethereum, Solana, etc.) or off‑chain data lake.
4. **Testing & Deployment:** Run the built‑in unit and integration tests, then containerize the service (Dockerfile is included) for CI/CD pipelines.

**Production Readiness**

• **Activity & Community:** 887 stars, 117 forks, recent commits (as of 2026‑05‑14), and active issue discussions indicate a healthy, engaged community.
• **Maturity:** The codebase is Python‑first, well‑documented, and includes a CLI, SDK, and API surface, making it straightforward to embed in existing micro‑service architectures.
• **Risk Profile:** No major metadata or licensing red flags have been identified, though a final security audit and verification of maintainer responsiveness are recommended before mission‑critical deployment.

Overall, *tradememory‑protocol* is a production‑grade OSS candidate for teams looking to add verifiable, memory‑augmented AI decision making to Web3 trading or DeFi applications.

Problem

mnemox-ai/tradememory-protocol helps prototype or inspect blockchain workflows with open implementation details.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• build Web3 workflows
• inspect blockchain integrations
• prototype wallet or DeFi features

Evidence

Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, SHA-256 tamper detection, 17 MCP tools.

Source