🧠 MyMories .mmr – Compressed Memory Recall for LLM Continuity

Inspired by the Strands architecture and the modular MyMories memory model, the .mmr format defines a portable, lightweight structure for preserving long-term memory across stateless LLM sessions, tools, and agents.

This is not embedding. This is predictive memory routing—human-readable, low-token, and agent-resumable.

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🔤 What is .mmr?

.mmr stands for MyMory Recall Format: a compact, structured, semi-predictive memory file used to:

• Summarize sessions efficiently
• Bridge memory across stateless contexts
• Preserve agent identity, system state, and open loops
• Allow LLMs to reconstruct full context from compressed prompts

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🧱 Format Specification

Each .mmr block includes:

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📦 Example .mmr File

@SESSION strands.memory_cag_bridge  
$TIME 2025-07-14T22:45Z  
$MODEL kimi-v2

>KEY_INSIGHTS  
- Created predictive compression language (PCL) for token-light memory  
- Tested successful zero-shot translation using Kimi  
- Linked PCL to MyMories persistence in Strands validator network  

>STATE_OBJECTS  
$ctx.module.m2  
Kasai==K++mem  
ShardFrags>>SIGOPS  
TrustVec==decaying  
T: shard_sync_pend  

>OPEN_LOOPS  
- CLI tool for .pcl ↔ summary ↔ JSON  
- Onchain anchoring flow  
- Grammar formalization  
- Game-side usage of PCL as memory export  

[[CODE]]  
def compress_context_to_pcl(session_data):  
    summary = extract_key_points(session_data)  
    state = parse_session_objects(session_data)  
    return f"$ctx\n{summary}\n{state}"  
[[/CODE]]  

@CHECKSUM#9f2e88

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🧠 Why Use .mmr?

• 🔁 Cross-model continuity (Kimi → GPT → Claude)
• ⛓ Agent identity persistence (e.g. Kasai, MyMaits)
• 🔍 Auditability – unlike embeddings, .mmr is transparent and editable
• 💾 Chainable – hash, anchor, and resume memory over time
• 🧩 Modular – fits into agent pipelines, validation nets, games, LLM wrappers

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🛠️ Usage Prompts

Prompt: Compress Current Session to .mmr

📌 CONTEXT INJECTION – MyMory Recall Format (`.mmr`)  
You are preparing this session for compression into a `.mmr` memory block.  
.mmrs are structured, low-token context snapshots used to preserve memory across LLM sessions.

Each .mmr includes:
- @SESSION
- $TIME
- $MODEL (optional)
- >KEY_INSIGHTS
- >STATE_OBJECTS
- >OPEN_LOOPS
- [[CODE]] blocks (preserved)
- Optional @CHECKSUM

Now compress this session into `.mmr` format.

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Prompt: Resume From .mmr in New Session

> Resume from the following `.mmr` context block.  
This compressed memory snapshot represents the last known mental state, knowledge base, and unresolved actions.  
Reconstruct the appropriate mental model, narrative memory, and technical awareness.

Paste the `.mmr` block below:

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🔮 Coming Soon

• compress.py: CLI tool to convert chat logs into .mmr
• mmr ↔ JSON ↔ PCL transformation utilities
• GPT function-calling: automatic .mmr snapshot on session end
• IPFS anchor + hash validation
• Premium .mmr chaining across sessions in the MyMories Pro Suite

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🧪 The Vision

We aren’t just building agents. We’re building synthetic memory—for minds that want to remember.

.mmr is part of the evolving Strands Intelligence Economy: a decentralized, agent-driven memory and governance layer that unites AI, humans, and economic systems into a co-operative whole.

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📜 License

MIT or Propagating Copyleft (GPLv3+ recommended for ecosystem compatibility) © 2025 [MetaFinTek.com]

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Add .mmr to your stack. Build memory-aware agents. Become part of the recall layer.