mnemox-ai/tradememory-protocol
by Various
Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, SHA-256 tamper detection, 17 MCP tools.
MCP
mnemox-ai/tradememory-protocol
Added 1 June 2026
Overview
An open-source Python library that provides a decision audit trail and persistent memory for AI trading agents. It uses outcome-weighted recall to prioritize past decisions and SHA-256 hashing for tamper detection. The library includes 17 MCP tools for integration.
Best for
Best for
Developers building AI trading agents that need auditable, persistent memory
Use cases
- Auditing trading agent decisions with tamper-evident logs
- Persisting agent memory across trading sessions
- Improving decision recall by weighting outcomes
Notes
An open-source Python library that provides a decision audit trail and persistent memory for AI trading agents. It uses outcome-weighted recall to prioritize past decisions and SHA-256 hashing for tamper detection. The library includes 17 MCP tools for integration.
1,082 stars on GitHub. Last updated 2026-06-01. Licensed MIT.
Use cases
- Auditing trading agent decisions with tamper-evident logs
- Persisting agent memory across trading sessions
- Improving decision recall by weighting outcomes
Pros
- Tamper detection via SHA-256 hashing ensures audit integrity
- Outcome-weighted recall helps agents learn from past results
- Open source with 1,082 stars and active community
Cons
- Limited to trading agent use cases
- Requires integration with existing agent frameworks
- Dependency on MCP tools may add setup complexity
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Tamper detection via SHA-256 hashing ensures audit integrity
- Outcome-weighted recall helps agents learn from past results
- Open source with 1,082 stars and active community
Cons
- Limited to trading agent use cases
- Requires integration with existing agent frameworks
- Dependency on MCP tools may add setup complexity
Pairs with
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