English wiki

DMC — Advanced Usage Guide

Dynamic Memory Chunk (DMC) is a dynamic library that lets you install specialized packages on demand. Every package in the DMC ecosystem follows the same core philosophy: compressed memory chunks — a format optimized for efficient storage, retrieval, and integrity verification of structured data.

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Installation

pip install dmc

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Package management

DMC pulls packages directly from the DMC-registry folder in the GitHub repository. No external registry, no configuration files — just .zip files hosted on GitHub.

# Install a package
dmc install --package "package-name"

# List installed and available packages
dmc list

# Get detailed info about a package
dmc info --package "package-name"

# Search the registry
dmc search keyword

# Update
dmc update --package "package-name"
dmc update --all

# Reinstall
dmc reinstall --package "package-name"

# Remove
dmc remove --package "package-name"

# Remove individual modules (only if they have no internal dependents)
dmc edit --package "package-name"

# Requirements
dmc freeze
dmc export --output requirements.txt
dmc import --file requirements.txt

# Download .zip without installing
dmc download --package "package-name"
dmc download --package "package-name" --export "/path/to/save/"

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Python usage

DMC uses lazy loading — packages are only imported into memory when first accessed. Installing multiple packages does not mean all of them load on import dmc.

import dmc

# Only loads the package you access
dmc.package_a.SomeClass()

# Other installed packages stay unloaded until needed

Both access patterns work:

# Attribute access
import dmc
dmc.package_a.SomeClass()

# Direct import
from dmc import package_a
package_a.SomeClass()

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Core philosophy — compressed memory chunks

Every DMC package stores data in chunks — discrete, compressed units of information. The format varies per package but the underlying principles are consistent across the ecosystem:

• Compression — chunks are compressed automatically (ZLIB for recent/small data, LZMA for older/larger data)
• Integrity — CRC32 per chunk on read; optional SHA-1 mirror block for full verification
• Recovery — optional XOR parity blocks allow reconstruction of a damaged chunk from its group
• Index — a flat index at the end of each file enables fast lookup without scanning the entire file
• Sessions — chunks can be grouped by session for contextual retrieval
• TTL — each chunk can have an expiration time; expired chunks are excluded from normal reads but preserved until explicitly purged
• Relationships — chunks can reference a parent chunk, enabling linked structures

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Integration with LLMs

DMC packages designed for LLMs store conversation history, context, and knowledge as compressed chunks. This data is injected into the prompt on each call — the LLM remains stateless, but from its perspective it has persistent memory.

import dmc
import ollama

mem = dmc.amcx.SmartMemory("chat.amcx")
mem.append("user: what did we discuss earlier?")

context = mem.get_recent(20)

response = ollama.chat(
    model="deepseek-r1",
    messages=[
        {"role": "system", "content": "\n".join(context)},
        {"role": "user",   "content": "Summarize our conversation."},
    ]
)

mem.append(f"assistant: {response['message']['content']}")
mem.flush()

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Integration with AI agents

DMC packages designed for agents go beyond conversation history — they track tasks, scheduled events, tool calls, errors, and agent state across sessions.

import dmc

agent = dmc.dmc_agent_format.AgentMemory(
    "agent.dmc",
    session_id   = 1,
    use_mirror   = True,
    use_recovery = True,
)

agent.log_message("user", "Schedule a meeting tomorrow at 10am")
task_id = agent.add_task("Send meeting invites", priority=dmc.dmc_agent_format.Priority.HIGH)

import time
agent.schedule_event("Team meeting", start=int(time.time()) + 86400, location="Room A")
agent.log_tool("calendar_api", {"action": "create"}, {"event_id": "evt_123"}, success=True)
agent.set_state({"goal": "schedule meeting", "step": "waiting for confirmation"})
agent.flush()

# Next session — recover context
state  = agent.get_state()
tasks  = agent.pending_tasks()
events = agent.upcoming_events(within_seconds=86400)
tl     = agent.timeline(limit=10)

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Integrity and recovery

For production use, enable mirror and recovery on any DMC package that supports it:

# SHA-1 mirror — detects modification or corruption per chunk
status = agent.verify_mirror()
if not status.all_ok:
    for problem in status.problems:
        print(f"Chunk {problem.chunk_id}: {problem.status.value}")

# XOR recovery — reconstructs a damaged chunk from its parity group
if agent.repair_chunk(damaged_chunk_id):
    print("Chunk recovered successfully.")

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Packages (Usage Guide)

amcx

info amcx

This package is a clone of the AMCX library, to know how to use the lib read the wiki. to use is normally used only at the beginning use '''dmc.amcx'''

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dmc_agent_format