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.
No outside reports on file
This tool is safe for local development and testing, but connecting it to real trading accounts or untrusted data sources creates high financial risk because the AI can execute trades based on data that might be forged or outdated.
Based on public adoption and transparency signals as of Oct 22, 2026. Our opinion, not a code audit or security guarantee.
What it can access
- Local filesNo local folder access notedLOW
- Command executionNo shell command runner notedLOW
- Network reachConnects to outside service APINOTED
- CredentialsNo API token requiredLOW
- Package sourceGitHub (mnemox-ai/tradememory-protocol)PUBLIC
Inferred from this listing’s public package details and description. Check the project’s source before installing.
Install
No npm or PyPI package is listed. Install from source: github.com/mnemox-ai/tradememory-protocol ↗
Everything known
We found nothing about this project in the sources we read (last checked 2026-10-30). That does not mean it has no problems.
0 incidents · 0 advisories · 0 news articles · 0 community threads · checked 2026-10-30
Popularity grade: C (from public signals such as stars and downloads, not a code review). Graded 2026-10-22.
Sources we read: OSV, GitHub Advisory Database, CISA Known Exploited Vulnerabilities, 44 security news feeds and our incident library. Last checked 2026-10-30.
About Tradememory Protocol
This MCP connector adds decision tracking and memory features for AI agents that make trading decisions. It records outcomes, weights past decisions by their results, and uses cryptographic timestamping to create a tamper-proof audit trail so you can see exactly what choices the AI made and why.
Who it’s for
Developers building AI trading systems who need to track agent decisions, audit trading choices, or build systems that learn from past outcomes. Teams that need compliance records or want to understand why their trading AI made specific decisions.
Alternatives
- Langchain memory modules — simpler persistent memory for AI agents without trading-specific features. - OpenAI Assistants with retrieval — uses vector search to recall past context but less tamper-evident. - Custom logging + PostgreSQL — build your own audit trail with full control but more engineering work. - Temporal workflows — tracks agent decision history with built-in replay and audit for general use cases.
Setup
Check these steps against the project’s own instructions before running anything.
Follow the project's README. The tool is published on GitHub but shows no npm download activity, so setup instructions from the official repository are recommended.
Why this grade
Signal-based grade (3/7 points): well starred on GitHub, public source code. This reflects adoption and transparency, not a code review.
Popularity and upkeep
- GitHub stars
- 1.4k
- Listing last refreshed
- 2026-10-29
These are public popularity signals, not a maintenance check. Look at the repository’s commit history and open issues.
Risks to watch for
- Trade execution access: If connected to a live trading account, the agent can place real trades based on stored decisions and patterns, risking real money loss. - Malicious memory injection: Attackers who compromise the audit trail database could insert fake past outcomes to trick the AI into repeating bad trades. - Cryptographic assumptions: While SHA-256 chains prevent accidental tampering, a compromised system can re-create valid chains; the security depends entirely on how the keys are protected. - Data leakage in logs: Decision history, trade amounts, and strategy details stored in the audit trail could expose trading patterns or portfolio composition if logs are accessed. - Timestamp spoofing: RFC 3161 anchoring helps but only if the timestamp server itself is trusted; a compromised or tricked timestamp service weakens the chain.
How to lower the risk
Least-privilege permissions
- Run the agent with read-only access to audit logs; only allow writes to a separate sandbox environment.
- Limit the agent's ability to actually execute trades to a small test account first.
Network isolation
- Keep the audit database on a machine disconnected from the internet during sensitive operations.
- Use a VPN or private network if the agent must talk to a trading API.
Monitoring
- Log every decision and outcome the agent records; review suspicious patterns weekly.
- Set alerts if the agent suddenly changes strategy or makes trades much larger than normal.
Red flags to watch for
- Audit chain gaps or timestamps that jump backward.
- Outcomes that seem too good to be true or exactly match a pattern you didn't code.
- Unexpected new trades placed when the agent was supposed to be paused.
Questions
How do I install Tradememory Protocol?
Tradememory Protocol does not publish a standalone npm or PyPI package. Clone or follow the setup instructions in its source repository at github.com/mnemox-ai/tradememory-protocol.
Are there security reports about Tradememory Protocol?
No reports found for this package in our research (last checked 2026-10-30). Tradememory Protocol currently holds Grade C (Moderate risk signals) based on public adoption and transparency signals. Key risk areas to watch include trade execution access.
Is Tradememory Protocol actively maintained?
Tradememory Protocol has 1.4k GitHub stars. Our directory data was last refreshed on 2026-10-29. Check recent commit activity, release tags, and open issues at github.com/mnemox-ai/tradememory-protocol before pinning it in production.
What are alternatives to Tradememory Protocol?
Notable alternatives to Tradememory Protocol include Langchain memory modules (simpler persistent memory for AI agents without trading-specific features. - **OpenAI Assistants with retrieval** — uses vector search to recall past context but less tamper-evident. - **Custom logging + PostgreSQL** — build your own review trail with full control but more engineering work. - **Temporal workflows** — tracks agent decision history with built-in replay and review for general use cases). You can also compare Tradememory Protocol side by side with other MCP servers in the Related tools section on this page.
README badge and team notes
Maintain or use Tradememory Protocol? Show its live grade in your GitHub README, docs site, or run a check from the terminal before you install.
GitHub README Markdown:
[](https://agentarmorhq.com/skills/mnemox-ai-tradememory-protocol)HTML embed:
<a href="https://agentarmorhq.com/skills/mnemox-ai-tradememory-protocol"><img src="https://agentarmorhq.com/api/badge/mnemox-ai-tradememory-protocol" alt="AgentArmorHQ Risk Grade for Tradememory Protocol" height="20" /></a>Terminal check:
curl -fsSL https://agentarmorhq.com/api/cli | node --input-type=module - check "mnemox-ai-tradememory-protocol"- Compare Tradememory Protocol →
- Add to a team allowlist
- Claude and terminal guide →
- Download the PDF guide
Share with your security lead
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### AgentArmorHQ Security Review Memo: tradememory-protocol
- **Listing URL:** https://agentarmorhq.com/skills/mnemox-ai-tradememory-protocol
- **Type:** App connector
- **Risk Grade:** Grade C (Moderate risk signals)
- **Outside Security Advisories on File:** 0
- **Public Adoption:** 1.4k GitHub stars
#### Permission & Blast-Radius Signals
- **Local files:** No local folder access noted
- **Command execution:** No shell command runner noted
- **Network reach:** Connects to outside service API
- **Credentials:** No API token required
- **Package source:** GitHub (mnemox-ai/tradememory-protocol)
#### Recommended Hardened Setup
- **Source Repo:** https://github.com/mnemox-ai/tradememory-protocol
- **Controls:** Pin an exact version in `.mcp.json`, scope access to a single workspace folder, and use a least-privilege read-only token where supported.
_Based on public adoption and transparency signals as of Oct 22, 2026. Our opinion, not a code audit or security guarantee._Key facts
- Type
- App connector
- Publisher
- mnemox-ai
- Source code
- github.com/mnemox-ai/tradememory-protocol
- Est. context load
- ~550 tokens / turn (~3 tool schemas, 0.27% of 200k context)
- Popularity
- 1.4k GitHub stars
- Last updated
- 2026-10-29
- Last graded
- 2026-10-22
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