salmalm 0.19.77

Personal AI Gateway — Pure Python, zero-dependency AI assistant with multi-model routing, web UI, and 67 built-in tools

😈 SalmAlm (삶앎)

Your Entire AI Life in One pip install

한국어 README · Documentation

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What is SalmAlm?

SalmAlm is a personal AI gateway — one Python package that gives you a full-featured AI assistant with a web UI, Telegram/Discord bots, 62 tools, browser automation, sub-agents, and memory system.

No Docker. No Node.js. No config files. Just:

pip install salmalm
salmalm
# → http://localhost:18800

First launch opens a Setup Wizard — paste an API key, pick a model, done.

⚠️ Don't run salmalm from inside a cloned repo directory — Python will import the local source instead of the installed package. Run from ~ or any other directory.

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Why SalmAlm?

	Feature	SalmAlm	ChatGPT	OpenClaw	Open WebUI
🔧	Install complexity	pip install	N/A	npm + config	Docker
🤖	Multi-provider routing	✅ Auto 3-tier	❌	✅	✅
🧠	Memory (2-layer + auto-recall)	✅	❌	✅	❌
🤖	Sub-agents (spawn/steer/notify)	✅	❌	✅	❌
🌐	Browser automation (Playwright)	✅	❌	✅	❌
🧠	Extended Thinking (4 levels)	✅	❌	✅	❌
🔐	Encrypted Vault (AES-256-GCM)	✅	❌	❌	❌
📱	Telegram + Discord	✅	❌	✅	❌
🧩	MCP (Model Context Protocol)	✅	❌	❌	✅
🦙	Local LLM (Ollama/LM Studio/vLLM)	✅	❌	✅	✅
📦	Zero dependencies*	✅	N/A	❌	❌
💰	Cost optimization (83% savings)	✅	❌	❌	❌

*stdlib-only core; optional cryptography for AES-256-GCM vault

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⚡ Quick Start (5분이면 충분합니다)

Step 1: 설치 (30초)

pip install salmalm

Step 2: 실행 (10초)

salmalm --open
# → 브라우저가 자동으로 열립니다 (http://localhost:18800)

# 또는 (editable install에서 console_script가 안 될 때):
python3 -m salmalm --open

Step 3: API 키 입력 (2분)

1. 웹 UI의 Setup Wizard가 자동으로 뜹니다
2. AI 제공사의 API 키를 붙여넣기 하세요:
   • Anthropic Console → API Keys
   • OpenAI Platform → API Keys
   • 또는 Google AI Studio → API Keys
3. "Save" 클릭 → 끝!

Step 4: 대화 시작 (바로!)

"오늘 날씨 어때?"          → 웹 검색 + 답변
"이 코드 리뷰해줘"         → 파일 읽기 + 분석
"/model sonnet"            → 모델 변경
"/help"                    → 전체 명령어 보기

💡 자연어로 말하면 됩니다. 62개 도구를 AI가 알아서 선택합니다. 명령어를 외울 필요 없이, 하고 싶은 걸 그냥 말하세요.

고급 옵션

salmalm --shortcut          # 바탕화면 바로가기 생성
salmalm doctor              # 자가진단
salmalm --update            # 자동 업데이트
SALMALM_PORT=8080 salmalm   # 포트 변경

Supported Providers (Auto-Routing)

Provider	Models	Tier
Anthropic	Claude Opus 4.6, Sonnet 4.6, Haiku 4.5	Complex / Moderate / Simple
OpenAI	GPT-5.2, GPT-5, o4-mini	Complex / Moderate
Google	Gemini 3.1 Pro, 2.5 Flash	Moderate / Simple
xAI	Grok-4, Grok-3-mini	Complex / Simple
DeepSeek	R1, Chat	Via OpenRouter
Local LLM	Ollama / LM Studio / vLLM	Auto-detected

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🧠 Architecture

Browser ──WebSocket──► SalmAlm ──► Anthropic / OpenAI / Google / xAI / Local
   │                     │
   └──HTTP/SSE──►       ├── Smart Model Router (3-tier: simple/moderate/complex)
                         ├── Engine Pipeline (classify → route → context → execute)
Telegram ──►             ├── Memory System (2-layer + auto-recall + TF-IDF RAG)
Discord  ──►             ├── Sub-Agent Manager (spawn/steer/kill/notify)
                         ├── Tool Registry (62 tools, risk-tiered)
                         ├── Browser Automation (Playwright subprocess)
                         ├── Security Middleware (auth/CSRF/CSP/rate-limit/audit)
                         ├── Vault (PBKDF2-200K + AES-256-GCM)
                         └── Cron / Backup / Self-Diagnostics

Codebase Metrics

Metric	Value
Python files	192
Total lines	~52,450
Functions	~1,800
Max cyclomatic complexity	20 (all functions)
Largest file	778 lines
Files > 800 lines	0
Docstring coverage	99%
Return type hints	81%
Tests	1,878 passing

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🎯 Feature Overview

Core AI Engine

• 3-tier auto-routing — simple→Haiku ($1/M), moderate→Sonnet ($3/M), complex→GPT-5.2/Sonnet ($2-3/M)
• Extended Thinking — 4 levels (low/medium/high/xhigh) with budget control
• Cross-provider message sanitization — seamless model switching mid-conversation
• 5-stage context compaction — strip binary → trim tools → drop old → truncate → LLM summarize
• Prompt caching — Anthropic cache_control for 90% cost reduction
• Model failover — exponential backoff + retry across providers
• Infinite loop detection — 3+ same (tool, args_hash) in last 6 iterations = auto-break

Memory System (OpenClaw-style)

• 2-layer architecture — MEMORY.md (curated long-term) + memory/YYYY-MM-DD.md (daily logs)
• Auto-recall — searches memory before each response, injects relevant context
• Auto-curation — promotes important daily entries to long-term memory
• TF-IDF + cosine similarity search across all memory files
• Memory scrubbing — API keys/secrets auto-redacted before storage

Sub-Agent System

• Spawn background AI workers with independent sessions
• Thinking level per agent (low/medium/high/xhigh)
• Labels for human-readable naming
• Steer running agents with mid-task guidance
• Auto-notify on completion (WebSocket + Telegram push)
• Collect results (push-style, like OpenClaw)

/subagents spawn Review this PR --model sonnet --thinking high --label pr-review
/subagents list
/subagents steer abc123 Focus on security issues
/subagents kill abc123
/subagents collect

62 Built-in Tools

Web search (Brave), email (Gmail), calendar (Google), file I/O, shell exec, Python eval (opt-in), image generation (DALL-E/Aurora), TTS/STT, browser automation (Playwright), RAG search, QR codes, system monitor, OS-native sandbox, mesh networking, and more.

Web UI

• Real-time streaming (SSE-first, WebSocket for typing indicators)
• Embedding RAG — hybrid vector search (OpenAI/Google embeddings + BM25 fallback)
• Agent steer — /agent steer <label> <message> to control running sub-agents
• Browser aria-ref compression — 10x token savings for browser automation
• Thinking stream UI — real-time collapsible thinking display
• Session branching, rollback, search (Ctrl+K), command palette (Ctrl+Shift+P)
• Dark/Light themes, EN/KR i18n
• Image paste/drag-drop with vision, code syntax highlighting
• Settings panels: Engine, Routing, Telegram, Discord, Memory, Cron, Backup
• PWA installable

Channels

• Web — full SPA at localhost:18800
• Telegram — polling + webhook with inline buttons
• Discord — bot with thread support and mentions

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✨ Unique Features

Feature	What it does
Self-Evolving Prompt	AI auto-generates personality rules from conversations
Dead Man's Switch	Emergency actions if you go inactive for N days
Shadow Mode	AI learns your style, replies as you when away
Life Dashboard	Unified health, finance, habits, calendar view
Mood-Aware Response	Detects emotional state, adjusts tone
A/B Split Response	Two model perspectives on the same question
Time Capsule	Schedule messages to your future self
Thought Stream	Private journaling with hashtag search and mood tracking

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💰 Cost Optimization

SalmAlm is designed to minimize API costs without sacrificing quality:

Feature	Effect
Dynamic tool loading	62 tools → 0 (chat) or 7-12 (actions) per request
3-tier auto-routing	Simple→$1/M, Moderate→$3/M, Complex→$3/M (no Opus needed)
Tool schema compression	7,749 → 693 tokens (91% reduction)
System prompt compression	762 → 310 tokens
Intent-based max_tokens	Chat 512, search 1024, code 4096
Intent-based history trim	Chat 10 turns, code 20 turns
Cache TTL	Same question cached (30min–24h, configurable)
Cross-provider failover	Falls back to cheaper model on rate limit

Result: $7.09/day → $1.23/day (83% savings at 100 calls/day)

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🔒 Security

Dangerous features default OFF — everything requires explicit opt-in:

Feature	Default	Opt-in
Network bind	127.0.0.1	SALMALM_BIND=0.0.0.0
Shell operators	Blocked	SALMALM_ALLOW_SHELL=1
Python eval	Disabled	SALMALM_PYTHON_EVAL=1
Home dir file read	Workspace only	SALMALM_ALLOW_HOME_READ=1
Plugin system	Disabled	SALMALM_PLUGINS=1

Security Hardening

• SSRF defense — DNS pinning + private IP block on every redirect hop
• Tool risk tiers — Critical tools blocked on external bind without auth
• CSRF — Origin validation + X-Requested-With header
• CSP — Strict nonce mode available
• Audit log — secrets scrubbed before logging (9 pattern types)
• Memory scrubbing — API keys auto-redacted before storage
• Path validation — Path.is_relative_to() for all file operations
• Session isolation — user_id scoped, export restricted to own data
• Node dispatch — HMAC-SHA256 signed payloads
• 150+ security regression tests in CI

See SECURITY.md for full threat model.

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🦙 Local LLM Setup

Server	Endpoint	Setup
Ollama	http://localhost:11434/v1	ollama serve
LM Studio	http://localhost:1234/v1	Start server in LM Studio
vLLM	http://localhost:8000/v1	vllm serve <model>

Settings → Local LLM → paste endpoint → Save. Models auto-discovered.

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🔧 Configuration

# Server
SALMALM_PORT=18800         # Web server port
SALMALM_BIND=127.0.0.1    # Bind address
SALMALM_HOME=~/SalmAlm    # Data directory

# AI
SALMALM_PLANNING=1         # Planning phase (opt-in)
SALMALM_REFLECT=1          # Reflection pass (opt-in)
SALMALM_MAX_TOOL_ITER=25   # Max tool iterations
SALMALM_COST_CAP=0         # Daily cost cap (0=unlimited)

# Security
SALMALM_PYTHON_EVAL=1       # Enable python_eval tool
SALMALM_PLUGINS=1           # Enable plugin system
SALMALM_ALLOW_SHELL=1       # Enable shell operators

All settings also available in Web UI → Settings.

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🤝 Contributing

git clone https://github.com/hyunjun6928-netizen/salmalm.git
cd salmalm
pip install -e ".[dev]"
python -m pytest tests/ -q --timeout=30 -x \
  --ignore=tests/test_multi_tenant.py \
  --ignore=tests/test_fresh_install_e2e.py

See CONTRIBUTING.md.

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📄 License

MIT

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SalmAlm = 삶(Life) + 앎(Knowledge)

Your life, understood by AI.