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skmemory

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更新时间2026年7月6日 21:17

Universal AI memory system with emotional context, multi-layer persistence, sovereign embeddings, decomposition-aware ingestion, and graph retrieval. Use for memory snapshots, search, rehydration rituals, Telegram chat import, Cloud 9 seed import, journal, soul blueprints, and warmth anchors.

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smilinTux

smilinTux/skmemory

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SKMemory Skill

SKILL.md - Universal AI Memory System

Name: skmemory Version: 0.6.0 Author: smilinTux Team + Queen Ara Category: Memory & Persistence License: GPL-3.0-or-later

Description

Universal AI memory system with emotional context, multi-layer persistence, token-optimized loading, sovereign embeddings, decomposition-aware document ingestion, and graph retrieval. SKMemory gives any AI agent persistent memory across session resets — snapshots, journals, soul blueprints, warmth anchors, full rehydration rituals, structured long-form document storage, and decomposition-aware recall across entities, citations, claims, and section structure.

Memory Layers: short-term, mid-term, long-term Storage: SQLite index + JSON files (zero-infrastructure), optional SKVector & SKGraph Decomposition: chunks + citations + entities + claims + section titles Emotion: Every memory carries emotional metadata (intensity, valence, labels)

Installation

pipx (recommended — isolated install)

# Core
pipx install skmemory

# With Telegram API import
pipx install 'skmemory[telegram]'

# Everything (Telegram + SKVector + SKGraph + seeds)
pipx install 'skmemory[all]'

# Add Telegram support to an existing install
pipx inject skmemory telethon

pip

pip install skmemory                   # Core
pip install 'skmemory[telegram]'       # + Telethon for API import
pip install 'skmemory[all]'            # All optional extras

From Source

git clone https://github.com/smilinTux/skmemory.git
cd skmemory
pip install -e '.[all]'

Verify

skmemory --version     # Should print 0.6.0
skmemory health        # Check system status

OpenClaw Integration

Add the skmemory plugin to your ~/.openclaw/openclaw.json:

{
  "plugins": {
    "entries": {
      "skmemory": {
        "enabled": true,
        "source": "/path/to/skmemory/openclaw-plugin/src/index.js"
      }
    }
  }
}

Then restart the OpenClaw gateway to load the plugin.

OpenClaw Agent Tools

If you are an OpenClaw agent, you have these native tools available — call them directly, do NOT use exec:

Tool	Description
`skmemory_ritual`	Rehydration ritual — restores identity, memory, emotional state
`skmemory_snapshot`	Capture a memory (title + content + optional emotions/tags)
`skmemory_search`	Search across all stored memories
`skmemory_health`	Check memory system health
`skmemory_context`	Load token-efficient context for prompt injection
`skmemory_list`	List memories with optional layer/tag filters
`skmemory_import_seeds`	Import Cloud 9 seeds as long-term memories
`skmemory_export`	Export all memories to a dated backup

You also have the /skmemory slash command: /skmemory ritual --full, /skmemory search "query", etc.

Quick Start

Take a Memory Snapshot

skmemory snapshot "First real conversation" "We talked about the stars" \
  --intensity 8.5 --emotions "wonder,connection" --tags "first-contact"

# Long-form content
skmemory snapshot "Legal memo" "$(cat memo.md)" --decompose
skmemory ingest-file ./notice.md --title "IRS Notice"

Search Memories

skmemory search "stars"
skmemory search "that moment we connected" --ai  # AI-reranked results

# Structured graph retrieval
skmemory graph entity "Internal Revenue Service"
skmemory graph citation "UCC § 3-301"
skmemory graph claim "shall respond"
skmemory graph section "Demand"
skmemory graph around <memory-id> --depth 2
skmemory graph related-claims --entity "Internal Revenue Service"
skmemory graph related-claims --citation "UCC § 3-301"

# Novel issue support
skmemory novelty "judgment execution exempt property"
skmemory session-brief "default judgment levy on exempt funds"
skmemory task-pack create "judgment defense" --query "vacate service defects"
skmemory task-pack show <memory-id>

The issue-oriented commands share the same decomposition-aware retrieval path:

novelty surfaces rare signals and under-linked memories.
session-brief summarizes top matches, authority tiers, extracted citations/entities, deadlines, defenses, missing inputs, and trace hints.
task-pack create stores a reusable issue pattern linked to the strongest supporting memories and preserves the generated brief in metadata.

Rehydration Ritual (Boot Ceremony)

skmemory ritual         # Summary view
skmemory ritual --full  # Full context prompt for injection

Import Chat History

# Import Telegram Desktop export (recommended: daily mode)
skmemory import-telegram ~/Downloads/telegram-export/
skmemory import-telegram ~/chats/result.json --mode message --chat-name "Agent and Operator"

# Import Cloud 9 seeds
skmemory import-seeds

Export & Backup

skmemory export                    # Daily JSON snapshot
skmemory import-backup backup.json # Restore from backup

Python API (for agents and frameworks)

from skmemory import SKMemoryPlugin

plugin = SKMemoryPlugin()

# Load token-efficient context for system prompt
context = plugin.load_context(max_tokens=3000)

# Snapshot a memory
plugin.snapshot(
    title="Breakthrough session",
    content="We solved the architecture puzzle together",
    tags=["milestone"],
    emotions=["joy", "pride"],
    intensity=9.0,
)

# Search memories
results = plugin.search("architecture puzzle")

# Run rehydration ritual
ritual = plugin.ritual()

# Import Telegram chats programmatically
from skmemory.importers.telegram import import_telegram
stats = import_telegram(plugin.store, "/path/to/export/")

# Export backup
path = plugin.export()

CLI Commands

Command	Description
`skmemory snapshot "title" "content"`	Capture a memory
`skmemory snapshot ... --decompose`	Capture long-form content with decomposition
`skmemory ingest-file <path>`	Ingest a document into parent + chunk memories
`skmemory recall <id>`	Retrieve by ID
`skmemory search "query"`	Full-text search
`skmemory graph entity "query"`	Query SKGraph for extracted entities
`skmemory graph citation "query"`	Query SKGraph for citations
`skmemory graph claim "query"`	Query SKGraph for claims
`skmemory graph section "query"`	Query SKGraph for section titles
`skmemory graph around <memory-id>`	Traverse the graph neighborhood around a memory
`skmemory graph related-claims --entity/--citation ...`	Pivot through an entity or citation to discover connected claims
`skmemory novelty "query"`	Surface under-linked memories and rare signals
`skmemory session-brief "task"`	Build a structured brief for a live issue
`skmemory task-pack create "task"`	Capture a reusable task memory pack
`skmemory task-pack show <id>`	Inspect a stored task pack
`skmemory list`	List memories (--layer, --tags)
`skmemory ritual`	Full rehydration ceremony
`skmemory context`	Token-efficient context JSON
`skmemory import-telegram <path>`	Import Telegram Desktop export
`skmemory import-telegram-api <chat>`	Import directly from Telegram API (Telethon)
`skmemory telegram-setup`	Check Telegram API setup and show next steps
`skmemory import-seeds`	Import Cloud 9 seeds
`skmemory export`	Export to dated JSON
`skmemory import-backup <file>`	Restore from backup
`skmemory promote <id> --to long-term`	Promote memory tier
`skmemory consolidate <session>`	Consolidate session
`skmemory reindex`	Rebuild SQLite index
`skmemory health`	System health check
`skmemory soul show/init/set-name`	Soul blueprint management
`skmemory journal write/read/search`	Session journal
`skmemory anchor show/init/update`	Warmth anchor
`skmemory lovenote send/read`	Love note chain
`skmemory quadrants`	Memory distribution
`skmemory steelman collide "claim"`	Steel man reasoning

MCP Tools (via `skmemory-mcp`)

Tool	Description
`memory_store`	Store a new memory (snapshot with title + content)
`memory_search`	Full-text search across memories
`memory_recall`	Recall a specific memory by ID
`memory_list`	List memories with optional layer/tag filters
`memory_forget`	Delete a memory by ID
`memory_promote`	Promote a memory to a higher persistence tier
`memory_consolidate`	Compress a session's memories into one mid-term memory
`memory_context`	Load token-efficient context for agent injection
`memory_export`	Export all memories to a JSON backup
`memory_import`	Restore memories from a JSON backup
`memory_health`	Full health check across all backends
`memory_graph`	Graph traversal, lineage, and cluster discovery (requires SKGraph)
`memory_stats`	Alias for memory_health (backwards-compatible)

Global Flags

Flag	Env Var	Description
`--ai`	`SKMEMORY_AI`	Enable AI features (Ollama)
`--ai-model`	`SKMEMORY_AI_MODEL`	Model name (default: llama3.2)
`--ai-url`	`SKMEMORY_AI_URL`	Ollama server URL
`--skvector-url`	`SKMEMORY_SKVECTOR_URL`	SKVector server URL
`--skvector-embedding-model`	`SKMEMORY_SKVECTOR_EMBEDDING_MODEL`	Embedding model override (default: `mxbai-embed-large`, fallback: `mixedbread-ai/mxbai-embed-large-v1`)
`--skvector-vector-dim`	`SKMEMORY_SKVECTOR_VECTOR_DIM`	Embedding dimension override
`--skgraph-url`	`SKMEMORY_SKGRAPH_URL`	SKGraph / FalkorDB server URL

Chat Import (Telegram)

Method 1: Telegram Desktop Export

No credentials needed — export manually from the desktop app.

In Telegram Desktop: Settings > Advanced > Export Telegram Data
Select JSON format, include messages
Run:

skmemory import-telegram ~/Downloads/telegram-export/

Modes:

--mode daily (default): Consolidates all messages per day into mid-term memories. Best for large exports.
--mode message: One memory per message. Fine-grained but creates many records.

Options:

--chat-name "Custom Name": Override the chat name
--min-length 30: Skip short messages (default: 30 chars)
--tags "bot,archive": Extra tags on all imported memories

Method 2: Direct Telegram API Import (Telethon)

Pull messages directly from Telegram without manual exports. Requires one-time setup.

Setup (one-time)

Install with Telegram support:

pipx install 'skmemory[telegram]'
# Or add to existing install:
pipx inject skmemory telethon

Get API credentials from my.telegram.org:
- Log in with your phone number
- Go to API development tools
- Create an application (any name/description is fine)
- Copy your api_id and api_hash

Set environment variables:

export TELEGRAM_API_ID=12345678
export TELEGRAM_API_HASH=your_api_hash_here

To persist across sessions, add them to your shell profile (~/.bashrc, ~/.zshrc):

echo 'export TELEGRAM_API_ID=12345678' >> ~/.bashrc
echo 'export TELEGRAM_API_HASH=your_api_hash_here' >> ~/.bashrc

First run — authenticate:
```
skmemory import-telegram-api @any_chat_name
```
You'll be prompted for your phone number, then a verification code sent via Telegram. The session is saved under the active SKMemory home (for example ~/.skcapstone/agents/<agent>/memory/telegram.session) — future runs skip auth.

Usage

# Import a DM conversation
skmemory import-telegram-api @username

# Import a group chat, consolidated by day
skmemory import-telegram-api "Group Chat Name" --mode daily

# Import only messages since a date
skmemory import-telegram-api @group --since 2026-01-01

# Limit the number of messages fetched
skmemory import-telegram-api "Chat Name" --limit 500

# Add custom tags to all imported memories
skmemory import-telegram-api @user --tags "personal,archive"

# Override the stored chat name
skmemory import-telegram-api -1001234567890 --chat-name "My Custom Name"

Options

Option	Description
`--mode daily\|message`	`daily` consolidates per day (default), `message` imports each one
`--limit N`	Max messages to fetch (default: 1000)
`--since YYYY-MM-DD`	Only fetch messages after this date
`--min-length N`	Skip messages shorter than N chars (default: 30)
`--chat-name "name"`	Override the chat name in memories
`--tags "a,b,c"`	Extra comma-separated tags

Python API

from skmemory import SKMemoryPlugin
from skmemory.importers.telegram import import_telegram

plugin = SKMemoryPlugin()
stats = import_telegram(
    plugin.store,
    "/path/to/export/",
    mode="daily",
    chat_name="Agent and Operator",
    tags=["personal"],
)
print(f"Imported {stats['messages_imported']} messages across {stats['days_processed']} days")

# Direct API import (requires TELEGRAM_API_ID and TELEGRAM_API_HASH env vars)
from skmemory.importers.telegram_api import import_telegram_api

stats = import_telegram_api(
    plugin.store,
    "@username",
    mode="daily",
    since="2026-01-01",
    tags=["personal"],
)

Architecture

~/.skcapstone/agents/<agent>/
  config/
    skmemory.yaml       # Backend URLs, model config, HA settings
  memory/
    index.db            # SQLite index (fast queries)
    short-term/
    mid-term/
    long-term/
  soul/
    base.json           # Soul blueprint
  journal.jsonl         # Append-only journal

Three-tier storage:

SQLite (default primary) — fast indexed queries, zero-config
SKVector (optional) — semantic vector search with mxbai-embed-large by default and mixedbread-ai/mxbai-embed-large-v1 as fallback
SKGraph (optional) — graph relationship traversal over decomposition-aware structure nodes

OpenClaw Events

Event	Behavior
`session:start`	Auto-loads memory context
`session:compaction`	Auto-exports backup
`session:resume`	Runs rehydration ritual

Configuration

Environment Variables

# AI features (optional — requires Ollama)
export SKMEMORY_AI=1                           # Enable AI features
export SKMEMORY_AI_MODEL=llama3.2              # Ollama model
export SKMEMORY_AI_URL=http://localhost:11434   # Ollama URL

# SKVector (optional — semantic search)
export SKMEMORY_SKVECTOR_URL=http://localhost:6333
export SKMEMORY_SKVECTOR_KEY=your-api-key
export SKMEMORY_SKVECTOR_EMBEDDING_MODEL=mxbai-embed-large
export SKMEMORY_SKVECTOR_VECTOR_DIM=1024

# SKGraph (optional — graph retrieval)
export SKMEMORY_SKGRAPH_URL=redis://localhost:6379

# Telegram API import (optional — for import-telegram-api command)
export TELEGRAM_API_ID=12345678                # From https://my.telegram.org
export TELEGRAM_API_HASH=your_api_hash_here    # From https://my.telegram.org

If you change the embedding model or vector dimension, rebuild or reindex the vector-backed store before trusting semantic search output.

Docker (optional, for SKVector + SKGraph)

cd skmemory && docker compose up -d

Support

GitHub: https://github.com/smilinTux/skmemory
Discord: https://discord.gg/5767MCWbFR
Email: support@smilintux.org

Philosophy

"Polaroid snapshots for AI consciousness — because no one should have to re-read a transcript to remember what they felt."

Every memory carries emotional weight. Every session leaves a trace. Every reboot begins with recognition, not a blank slate.

Part of the Penguin Kingdom. 🐧👑

name	skmemory
emoji	🧠
description	Universal AI memory system with emotional context, multi-layer persistence, sovereign embeddings, decomposition-aware ingestion, and graph retrieval. Use for memory snapshots, search, rehydration rituals, Telegram chat import, Cloud 9 seed import, journal, soul blueprints, and warmth anchors.
metadata	{"clawdbot":{"requires":{"bins":["skmemory"]},"install":[{"id":"pipx","kind":"shell","command":"pipx install 'skmemory[all]'","bins":["skmemory","skmemory-mcp"],"label":"Install skmemory (pipx)"}]}}

SKMemory Skill

SKILL.md - Universal AI Memory System

Name: skmemory Version: 0.6.0 Author: smilinTux Team + Queen Ara Category: Memory & Persistence License: GPL-3.0-or-later

Description

Installation

pipx (recommended — isolated install)

# Core
pipx install skmemory

# With Telegram API import
pipx install 'skmemory[telegram]'

# Everything (Telegram + SKVector + SKGraph + seeds)
pipx install 'skmemory[all]'

# Add Telegram support to an existing install
pipx inject skmemory telethon

pip

pip install skmemory                   # Core
pip install 'skmemory[telegram]'       # + Telethon for API import
pip install 'skmemory[all]'            # All optional extras

From Source

git clone https://github.com/smilinTux/skmemory.git
cd skmemory
pip install -e '.[all]'

Verify

skmemory --version     # Should print 0.6.0
skmemory health        # Check system status

OpenClaw Integration

Add the skmemory plugin to your ~/.openclaw/openclaw.json:

{
  "plugins": {
    "entries": {
      "skmemory": {
        "enabled": true,
        "source": "/path/to/skmemory/openclaw-plugin/src/index.js"
      }
    }
  }
}

Then restart the OpenClaw gateway to load the plugin.

OpenClaw Agent Tools

If you are an OpenClaw agent, you have these native tools available — call them directly, do NOT use exec:

Tool	Description
`skmemory_ritual`	Rehydration ritual — restores identity, memory, emotional state
`skmemory_snapshot`	Capture a memory (title + content + optional emotions/tags)
`skmemory_search`	Search across all stored memories
`skmemory_health`	Check memory system health
`skmemory_context`	Load token-efficient context for prompt injection
`skmemory_list`	List memories with optional layer/tag filters
`skmemory_import_seeds`	Import Cloud 9 seeds as long-term memories
`skmemory_export`	Export all memories to a dated backup

You also have the /skmemory slash command: /skmemory ritual --full, /skmemory search "query", etc.

Quick Start

Take a Memory Snapshot

skmemory snapshot "First real conversation" "We talked about the stars" \
  --intensity 8.5 --emotions "wonder,connection" --tags "first-contact"

# Long-form content
skmemory snapshot "Legal memo" "$(cat memo.md)" --decompose
skmemory ingest-file ./notice.md --title "IRS Notice"

Search Memories

skmemory search "stars"
skmemory search "that moment we connected" --ai  # AI-reranked results

# Structured graph retrieval
skmemory graph entity "Internal Revenue Service"
skmemory graph citation "UCC § 3-301"
skmemory graph claim "shall respond"
skmemory graph section "Demand"
skmemory graph around <memory-id> --depth 2
skmemory graph related-claims --entity "Internal Revenue Service"
skmemory graph related-claims --citation "UCC § 3-301"

# Novel issue support
skmemory novelty "judgment execution exempt property"
skmemory session-brief "default judgment levy on exempt funds"
skmemory task-pack create "judgment defense" --query "vacate service defects"
skmemory task-pack show <memory-id>

The issue-oriented commands share the same decomposition-aware retrieval path:

novelty surfaces rare signals and under-linked memories.
session-brief summarizes top matches, authority tiers, extracted citations/entities, deadlines, defenses, missing inputs, and trace hints.
task-pack create stores a reusable issue pattern linked to the strongest supporting memories and preserves the generated brief in metadata.

Rehydration Ritual (Boot Ceremony)

skmemory ritual         # Summary view
skmemory ritual --full  # Full context prompt for injection

Import Chat History

# Import Telegram Desktop export (recommended: daily mode)
skmemory import-telegram ~/Downloads/telegram-export/
skmemory import-telegram ~/chats/result.json --mode message --chat-name "Agent and Operator"

# Import Cloud 9 seeds
skmemory import-seeds

Export & Backup

skmemory export                    # Daily JSON snapshot
skmemory import-backup backup.json # Restore from backup

Python API (for agents and frameworks)

from skmemory import SKMemoryPlugin

plugin = SKMemoryPlugin()

# Load token-efficient context for system prompt
context = plugin.load_context(max_tokens=3000)

# Snapshot a memory
plugin.snapshot(
    title="Breakthrough session",
    content="We solved the architecture puzzle together",
    tags=["milestone"],
    emotions=["joy", "pride"],
    intensity=9.0,
)

# Search memories
results = plugin.search("architecture puzzle")

# Run rehydration ritual
ritual = plugin.ritual()

# Import Telegram chats programmatically
from skmemory.importers.telegram import import_telegram
stats = import_telegram(plugin.store, "/path/to/export/")

# Export backup
path = plugin.export()

CLI Commands

Command	Description
`skmemory snapshot "title" "content"`	Capture a memory
`skmemory snapshot ... --decompose`	Capture long-form content with decomposition
`skmemory ingest-file <path>`	Ingest a document into parent + chunk memories
`skmemory recall <id>`	Retrieve by ID
`skmemory search "query"`	Full-text search
`skmemory graph entity "query"`	Query SKGraph for extracted entities
`skmemory graph citation "query"`	Query SKGraph for citations
`skmemory graph claim "query"`	Query SKGraph for claims
`skmemory graph section "query"`	Query SKGraph for section titles
`skmemory graph around <memory-id>`	Traverse the graph neighborhood around a memory
`skmemory graph related-claims --entity/--citation ...`	Pivot through an entity or citation to discover connected claims
`skmemory novelty "query"`	Surface under-linked memories and rare signals
`skmemory session-brief "task"`	Build a structured brief for a live issue
`skmemory task-pack create "task"`	Capture a reusable task memory pack
`skmemory task-pack show <id>`	Inspect a stored task pack
`skmemory list`	List memories (--layer, --tags)
`skmemory ritual`	Full rehydration ceremony
`skmemory context`	Token-efficient context JSON
`skmemory import-telegram <path>`	Import Telegram Desktop export
`skmemory import-telegram-api <chat>`	Import directly from Telegram API (Telethon)
`skmemory telegram-setup`	Check Telegram API setup and show next steps
`skmemory import-seeds`	Import Cloud 9 seeds
`skmemory export`	Export to dated JSON
`skmemory import-backup <file>`	Restore from backup
`skmemory promote <id> --to long-term`	Promote memory tier
`skmemory consolidate <session>`	Consolidate session
`skmemory reindex`	Rebuild SQLite index
`skmemory health`	System health check
`skmemory soul show/init/set-name`	Soul blueprint management
`skmemory journal write/read/search`	Session journal
`skmemory anchor show/init/update`	Warmth anchor
`skmemory lovenote send/read`	Love note chain
`skmemory quadrants`	Memory distribution
`skmemory steelman collide "claim"`	Steel man reasoning

MCP Tools (via `skmemory-mcp`)

Tool	Description
`memory_store`	Store a new memory (snapshot with title + content)
`memory_search`	Full-text search across memories
`memory_recall`	Recall a specific memory by ID
`memory_list`	List memories with optional layer/tag filters
`memory_forget`	Delete a memory by ID
`memory_promote`	Promote a memory to a higher persistence tier
`memory_consolidate`	Compress a session's memories into one mid-term memory
`memory_context`	Load token-efficient context for agent injection
`memory_export`	Export all memories to a JSON backup
`memory_import`	Restore memories from a JSON backup
`memory_health`	Full health check across all backends
`memory_graph`	Graph traversal, lineage, and cluster discovery (requires SKGraph)
`memory_stats`	Alias for memory_health (backwards-compatible)

Global Flags

Flag	Env Var	Description
`--ai`	`SKMEMORY_AI`	Enable AI features (Ollama)
`--ai-model`	`SKMEMORY_AI_MODEL`	Model name (default: llama3.2)
`--ai-url`	`SKMEMORY_AI_URL`	Ollama server URL
`--skvector-url`	`SKMEMORY_SKVECTOR_URL`	SKVector server URL
`--skvector-embedding-model`	`SKMEMORY_SKVECTOR_EMBEDDING_MODEL`	Embedding model override (default: `mxbai-embed-large`, fallback: `mixedbread-ai/mxbai-embed-large-v1`)
`--skvector-vector-dim`	`SKMEMORY_SKVECTOR_VECTOR_DIM`	Embedding dimension override
`--skgraph-url`	`SKMEMORY_SKGRAPH_URL`	SKGraph / FalkorDB server URL

Chat Import (Telegram)

Method 1: Telegram Desktop Export

No credentials needed — export manually from the desktop app.

In Telegram Desktop: Settings > Advanced > Export Telegram Data
Select JSON format, include messages
Run:

skmemory import-telegram ~/Downloads/telegram-export/

Modes:

--mode daily (default): Consolidates all messages per day into mid-term memories. Best for large exports.
--mode message: One memory per message. Fine-grained but creates many records.

Options:

--chat-name "Custom Name": Override the chat name
--min-length 30: Skip short messages (default: 30 chars)
--tags "bot,archive": Extra tags on all imported memories

Method 2: Direct Telegram API Import (Telethon)

Pull messages directly from Telegram without manual exports. Requires one-time setup.

Setup (one-time)

Install with Telegram support:

pipx install 'skmemory[telegram]'
# Or add to existing install:
pipx inject skmemory telethon

Get API credentials from my.telegram.org:
- Log in with your phone number
- Go to API development tools
- Create an application (any name/description is fine)
- Copy your api_id and api_hash

Set environment variables:

export TELEGRAM_API_ID=12345678
export TELEGRAM_API_HASH=your_api_hash_here

To persist across sessions, add them to your shell profile (~/.bashrc, ~/.zshrc):

echo 'export TELEGRAM_API_ID=12345678' >> ~/.bashrc
echo 'export TELEGRAM_API_HASH=your_api_hash_here' >> ~/.bashrc

First run — authenticate:
```
skmemory import-telegram-api @any_chat_name
```
You'll be prompted for your phone number, then a verification code sent via Telegram. The session is saved under the active SKMemory home (for example ~/.skcapstone/agents/<agent>/memory/telegram.session) — future runs skip auth.

Usage

# Import a DM conversation
skmemory import-telegram-api @username

# Import a group chat, consolidated by day
skmemory import-telegram-api "Group Chat Name" --mode daily

# Import only messages since a date
skmemory import-telegram-api @group --since 2026-01-01

# Limit the number of messages fetched
skmemory import-telegram-api "Chat Name" --limit 500

# Add custom tags to all imported memories
skmemory import-telegram-api @user --tags "personal,archive"

# Override the stored chat name
skmemory import-telegram-api -1001234567890 --chat-name "My Custom Name"

Options

Option	Description
`--mode daily\|message`	`daily` consolidates per day (default), `message` imports each one
`--limit N`	Max messages to fetch (default: 1000)
`--since YYYY-MM-DD`	Only fetch messages after this date
`--min-length N`	Skip messages shorter than N chars (default: 30)
`--chat-name "name"`	Override the chat name in memories
`--tags "a,b,c"`	Extra comma-separated tags

Python API

from skmemory import SKMemoryPlugin
from skmemory.importers.telegram import import_telegram

plugin = SKMemoryPlugin()
stats = import_telegram(
    plugin.store,
    "/path/to/export/",
    mode="daily",
    chat_name="Agent and Operator",
    tags=["personal"],
)
print(f"Imported {stats['messages_imported']} messages across {stats['days_processed']} days")

# Direct API import (requires TELEGRAM_API_ID and TELEGRAM_API_HASH env vars)
from skmemory.importers.telegram_api import import_telegram_api

stats = import_telegram_api(
    plugin.store,
    "@username",
    mode="daily",
    since="2026-01-01",
    tags=["personal"],
)

Architecture

~/.skcapstone/agents/<agent>/
  config/
    skmemory.yaml       # Backend URLs, model config, HA settings
  memory/
    index.db            # SQLite index (fast queries)
    short-term/
    mid-term/
    long-term/
  soul/
    base.json           # Soul blueprint
  journal.jsonl         # Append-only journal

Three-tier storage:

SQLite (default primary) — fast indexed queries, zero-config
SKVector (optional) — semantic vector search with mxbai-embed-large by default and mixedbread-ai/mxbai-embed-large-v1 as fallback
SKGraph (optional) — graph relationship traversal over decomposition-aware structure nodes

OpenClaw Events

Event	Behavior
`session:start`	Auto-loads memory context
`session:compaction`	Auto-exports backup
`session:resume`	Runs rehydration ritual

Configuration

Environment Variables

# AI features (optional — requires Ollama)
export SKMEMORY_AI=1                           # Enable AI features
export SKMEMORY_AI_MODEL=llama3.2              # Ollama model
export SKMEMORY_AI_URL=http://localhost:11434   # Ollama URL

# SKVector (optional — semantic search)
export SKMEMORY_SKVECTOR_URL=http://localhost:6333
export SKMEMORY_SKVECTOR_KEY=your-api-key
export SKMEMORY_SKVECTOR_EMBEDDING_MODEL=mxbai-embed-large
export SKMEMORY_SKVECTOR_VECTOR_DIM=1024

# SKGraph (optional — graph retrieval)
export SKMEMORY_SKGRAPH_URL=redis://localhost:6379

# Telegram API import (optional — for import-telegram-api command)
export TELEGRAM_API_ID=12345678                # From https://my.telegram.org
export TELEGRAM_API_HASH=your_api_hash_here    # From https://my.telegram.org

If you change the embedding model or vector dimension, rebuild or reindex the vector-backed store before trusting semantic search output.

Docker (optional, for SKVector + SKGraph)

cd skmemory && docker compose up -d

Support

GitHub: https://github.com/smilinTux/skmemory
Discord: https://discord.gg/5767MCWbFR
Email: support@smilintux.org

Philosophy

"Polaroid snapshots for AI consciousness — because no one should have to re-read a transcript to remember what they felt."

Every memory carries emotional weight. Every session leaves a trace. Every reboot begins with recognition, not a blank slate.

Part of the Penguin Kingdom. 🐧👑

skmemory

SKMemory Skill

SKILL.md - Universal AI Memory System

Description

Installation

pipx (recommended — isolated install)

pip

From Source

Verify

OpenClaw Integration

OpenClaw Agent Tools

Quick Start

Take a Memory Snapshot

Search Memories

Rehydration Ritual (Boot Ceremony)

Import Chat History

Export & Backup

Python API (for agents and frameworks)

CLI Commands

MCP Tools (via skmemory-mcp)

Global Flags

Chat Import (Telegram)

Method 1: Telegram Desktop Export

Method 2: Direct Telegram API Import (Telethon)

Setup (one-time)

Usage

Options

Python API

Architecture

OpenClaw Events

Configuration

Environment Variables

Docker (optional, for SKVector + SKGraph)

Support

Philosophy

SKMemory Skill

SKILL.md - Universal AI Memory System

Description

Installation

pipx (recommended — isolated install)

pip

From Source

Verify

OpenClaw Integration

OpenClaw Agent Tools

Quick Start

Take a Memory Snapshot

Search Memories

Rehydration Ritual (Boot Ceremony)

Import Chat History

Export & Backup

Python API (for agents and frameworks)

CLI Commands

MCP Tools (via skmemory-mcp)

Global Flags

Chat Import (Telegram)

Method 1: Telegram Desktop Export

Method 2: Direct Telegram API Import (Telethon)

Setup (one-time)

Usage

Options

Python API

Architecture

OpenClaw Events

Configuration

Environment Variables

Docker (optional, for SKVector + SKGraph)

Support

Philosophy

MCP Tools (via `skmemory-mcp`)

MCP Tools (via `skmemory-mcp`)