Æther Shell (ae)

The world's first agentic shell with typed functional pipelines and multi-modal AI.
Built in Rust for safety and performance, featuring revolutionary AI protocols found nowhere else.

Quick Start • Features • Examples • TUI Guide • Docs • Contributing

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🚀 Quick Start

VS Code Extension (Syntax Highlighting + LSP)

For full IDE support including syntax highlighting, IntelliSense, and error diagnostics:

# Install the extension from marketplace
code --install-extension admercs.aethershell

# Build the Language Server (for IntelliSense)
cd AetherShell
cargo build -p aethershell-lsp --release

# The extension will auto-detect the LSP server

Features: Syntax highlighting, autocompletion, hover docs, go-to-definition, error diagnostics.

Installation

# Install from source
git clone https://github.com/nervosys/AetherShell && cd AetherShell
cargo install --path . --bin ae

# Launch interactive TUI (recommended)
ae tui

# Or classic REPL
ae

# Run a script file
ae script.ae

# Typed pipelines — not text streams!
[1, 2, 3, 4, 5] | map(fn(x) => x * 2) | sum()
# => 30

# Pattern matching
match type_of(42) {
    "Int" => "It's an integer!",
    _ => "Something else"
}

# String manipulation
"hello,world" | split(",") | map(fn(s) => upper(s)) | join(" ")
# => "HELLO WORLD"

# AI query
ai("Explain quantum computing in simple terms")

# AI with vision
ai("Describe this image", {images: ["photo.jpg"]})

# AI agent with tool access
agent("Find all TODO comments in src/", ["ls", "cat", "grep"])

# 38 built-in themes
config_set("colors.theme", "dracula")
themes() | take(5)
# => ["catppuccin", "catppuccin-latte", "catppuccin-frappe", ...]

📝 Note: Set OPENAI_API_KEY for AI features: export OPENAI_API_KEY="sk-..."

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✨ Features

🤖 AI-Native Shell Multi-modal AI: Images, audio, video analysis Autonomous agents with tool access 130+ MCP tools across 27 categories Multi-provider: OpenAI, Ollama, local models Fine-tuning API for custom model training RAG & Knowledge Graphs built-in	💎 Typed Pipelines Hindley-Milner type inference Structured data: Records, Arrays, Tables First-class functions and lambdas Pattern matching expressions
🧠 ML & Enterprise Neural networks creation & evolution Reinforcement learning (Q-Learning, DQN) Enterprise RBAC with role-based access Audit logging & compliance reporting SSO integration (SAML, OAuth, OIDC) Cluster management for distributed AI	🎨 Developer Experience Interactive TUI with tabs & themes Language Server Protocol (LSP) VS Code extension with IntelliSense Plugin system with TOML manifests WASM support for browser REPL Package management & imports

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🎯 What Makes AetherShell Unique?

AetherShell is the only shell combining these capabilities:

Feature	AetherShell	Traditional Shells	Nushell
AI Agents with Tools	✅	❌	❌
Multi-modal AI (Vision/Audio/Video)	✅	❌	❌
MCP Protocol (130+ tools)	✅	❌	❌
Neural Networks Built-in	✅	❌	❌
Hindley-Milner Types	✅	❌	✅
Typed Pipelines	✅	❌	✅
Agent-to-Agent Protocol (A2A)	✅	❌	❌
Consensus Protocol (NANDA)	✅	❌	❌
Enterprise (RBAC, Audit, SSO)	✅	❌	❌
Language Server Protocol (LSP)	✅	❌	✅

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📐 Language Features at a Glance

AetherShell is a typed functional language with 215+ built-in functions across these categories:

Types & Literals Int — 42, -7 Float — 3.14, 2.0 String — "hello", "${var}" Bool — true, false Null — null Array — [1, 2, 3] Record — {a: 1, b: 2} Lambda — fn(x) => x * 2

Operators Arithmetic: + - * / % ** Comparison: == != < <= > >= Logical: && || ! Pipeline: | Member: .

Control Flow match expressions Pattern guards Wildcard _ patterns Lambda functions Pipeline chaining

Builtin Categories (215+ functions)

Category	Examples	Count
Core	help, print, echo, debug, assert, trace	15
Functional	map, where, reduce, take, any, all	12
String	split, join, trim, upper, lower, replace	10
Array	flatten, reverse, slice, range, zip, push	8
Math	abs, min, max, sqrt, pow, floor, ceil	8
Aggregate	sum, avg, product, unique, values	5
File System	ls, cat, pwd, cd, exists, mkdir	11
Config	config, config_get, config_set, themes	7
AI	ai, agent, swarm, rag_query, finetune_start	20+
Enterprise	role_create, audit_log, sso_init, compliance_check	22
Distributed	cluster_create, job_submit, aggregate_results	15
Platform	platform, is_windows, is_linux, features	12

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📖 Examples

Core Syntax — Typed Functional Programming

# Literals and Types
let age = 42                    # Int
let pi = 3.14159                # Float
let name = "AetherShell"        # String
let active = true               # Bool
let empty = null                # Null

# String interpolation
let greeting = "Hello, ${name}! You're ${age} years old."

# Arrays — ordered collections
let nums = [1, 2, 3, 4, 5]
let mixed = [1, "hello", true, 3.14]

# Records — structured data
let user = {name: "Alice", age: 30, admin: true}
print(user.name)               # => "Alice"

# Lambdas — first-class functions
let double = fn(x) => x * 2
let add = fn(a, b) => a + b
print(double(21))              # => 42
print(add(10, 20))             # => 30

Functional Pipelines — Structured Data Processing

# Transform arrays with map
[1, 2, 3] | map(fn(x) => x * x)        # => [1, 4, 9]

# Filter with where
[1, 2, 3, 4, 5] | where(fn(x) => x > 2) # => [3, 4, 5]

# Aggregate with reduce
[1, 2, 3, 4] | reduce(fn(a, b) => a + b, 0)  # => 10

# Chain operations for complex transformations
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
  | where(fn(x) => x % 2 == 0)         # Keep evens
  | map(fn(x) => x ** 2)               # Square them
  | reduce(fn(a, b) => a + b, 0)       # Sum: 220

# Array manipulation
([1, 2, 3, 4, 5] | reverse)            # => [5, 4, 3, 2, 1]
([[1, 2], [3, 4]] | flatten)           # => [1, 2, 3, 4]
([1, 2, 3, 4, 5] | slice(1, 4))        # => [2, 3, 4]

# Check conditions
[1, 2, 3, 4, 5] | any(fn(x) => x > 4)  # => true
[2, 4, 6, 8] | all(fn(x) => x % 2 == 0) # => true

Pattern Matching — Powerful Control Flow

# Match on values
let grade = fn(score) => match score {
    100 => "Perfect!",
    90..99 => "A",
    80..89 => "B",
    70..79 => "C",
    _ => "Keep trying"
}
print(grade(95))               # => "A"

# Match with guards
let classify = fn(n) => match n {
    x if x < 0 => "negative",
    0 => "zero",
    x if x > 0 => "positive"
}

# Match on types
let describe = fn(val) => match type_of(val) {
    "Int" => "An integer",
    "String" => "A string",
    "Array" => "An array with ${len(val)} elements",
    "Record" => "A record with keys: ${keys(val)}",
    _ => "Something else"
}

String Operations — Built-in Text Processing

# Manipulation
split("a,b,c", ",")            # => ["a", "b", "c"]
join(["a", "b", "c"], "-")     # => "a-b-c"
trim("  hello  ")              # => "hello"
upper("hello")                 # => "HELLO"
lower("WORLD")                 # => "world"
replace("foo bar foo", "foo", "baz")  # => "baz bar baz"

# Queries
contains("hello world", "world")      # => true
starts_with("hello", "hel")           # => true
ends_with("hello", "lo")              # => true
len("hello")                          # => 5

Math Operations — Scientific Computing

# Basic math
abs(-42)                       # => 42
min(5, 3)                      # => 3
max(5, 3)                      # => 5
pow(2, 10)                     # => 1024
sqrt(16)                       # => 4.0

# Rounding
floor(3.7)                     # => 3
ceil(3.2)                      # => 4
round(3.5)                     # => 4

# Statistical (on arrays)
sum([1, 2, 3, 4, 5])           # => 15
avg([10, 20, 30])              # => 20
product([2, 3, 4])             # => 24
unique([1, 2, 2, 3, 3, 3])     # => [1, 2, 3]

File System — Structured Output

# List files with structured data
ls("./src")
  | where(fn(f) => f.size > 1000)
  | map(fn(f) => {name: f.name, kb: f.size / 1024})
  | take(5)

# Read and process files
cat("config.toml") | split("\n") | len()

# Check existence
exists("./src/main.rs")        # => true

# Get current directory
pwd()                          # => "/home/user/project"

Configuration System — XDG-Compliant

# Get all config
config()

# Get specific values
config_get("colors.theme")           # => "tokyo-night"
config_get("history.max_size")       # => 10000

# Set values
config_set("colors.theme", "dracula")

# Get all paths (XDG-compliant)
let paths = config_path()
print(paths.config_file)       # ~/.config/aether/config.toml

# List all 38 built-in themes
themes()
# => ["catppuccin", "dracula", "github-dark", "monokai", 
#     "nord", "one-dark", "solarized", "tokyo-night", ...]

AI Agents with Tool Access

# Deploy an AI agent that can use shell tools
agent("Analyze the project structure and find large files", ["ls", "cat", "wc"])

# Agent with configuration
agent({
  goal: "Find security issues in the codebase",
  tools: ["grep", "cat", "ls"],
  max_steps: 10,
  dry_run: true  # Preview actions first
})

Multi-Modal AI

# Analyze images
ai("What's in this screenshot?", {images: ["screenshot.png"]})

# Process audio
ai("Transcribe and summarize this meeting", {audio: ["meeting.mp3"]})

# Video analysis
ai("Extract the key steps from this tutorial", {video: ["tutorial.mp4"]})

Typed Functional Pipelines

# Structured data processing — not text parsing!
ls("./src")
  | where(fn(f) => f.ext == ".rs" && f.size > 1000)
  | map(fn(f) => {name: f.name, kb: f.size / 1024})
  | sort_by(fn(f) => f.kb, "desc")
  | take(5)

# Statistical operations
[1, 2, 3, 4, 5] | sum()      # => 15
[10, 20, 30] | avg()         # => 20.0
[1, 2, 1, 3] | unique()      # => [1, 2, 3]
{a: 1, b: 2} | values()      # => [1, 2]

MCP Tools (Model Context Protocol)

# 130 tools across 27 categories
let tools = mcp_tools()
print(len(tools))  # => 130

# Filter by category
mcp_tools({category: "development"})     # git, cargo, npm, etc.
mcp_tools({category: "machinelearning"}) # ollama, tensorboard, etc.
mcp_tools({category: "kubernetes"})      # kubectl, helm, k9s, etc.

# Execute tools via MCP
mcp_call("git", {command: "status"})

Neural Networks & Evolution

# Create a neural network
let brain = nn_create("agent", [4, 8, 2])

# Evolutionary optimization
let pop = population(100, {genome_size: 10})
let evolved = evolve(pop, fitness_fn, {generations: 50})

# Reinforcement learning
let agent = rl_agent("learner", 16, 4)

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🌍 Real-World Use Cases

DevOps: Log Analysis Pipeline

# Analyze logs with structured pipelines
cat("/var/log/app.log")
  | split("\n")
  | where(fn(line) => contains(line, "ERROR"))
  | map(fn(line) => {
      timestamp: line | slice(0, 19),
      message: line | slice(20, len(line))
    })
  | take(10)

Data Science: CSV Processing

# Process CSV data functionally
let data = cat("sales.csv") | split("\n") | map(fn(row) => split(row, ","))
let headers = first(data)
let rows = data | slice(1, len(data))

# Calculate statistics
let totals = rows | map(fn(r) => r[2]) | map(fn(x) => x + 0) | sum()
print("Total sales: ${totals}")

Security: Automated Code Audit

# AI-powered security scan
agent({
  goal: "Find potential security vulnerabilities in the codebase",
  tools: ["grep", "cat", "ls"],
  max_steps: 20
})

# Search for hardcoded secrets
ls("./src") 
  | where(fn(f) => ends_with(f.name, ".rs"))
  | map(fn(f) => {file: f.name, content: cat(f.path)})
  | where(fn(f) => contains(f.content, "password") || contains(f.content, "secret"))

System Administration: Disk Usage Report

# Generate disk usage report
ls("/home")
  | map(fn(d) => {
      name: d.name,
      size_mb: d.size / (1024 * 1024),
      files: len(ls(d.path))
    })
  | where(fn(d) => d.size_mb > 100)
  | map(fn(d) => "${d.name}: ${round(d.size_mb)}MB (${d.files} files)")

AI-Assisted Development

# Generate documentation with AI
let code = cat("src/main.rs")
ai("Generate comprehensive documentation for this Rust code:", {context: code})

# Code review assistant
agent({
  goal: "Review the recent changes and suggest improvements",
  tools: ["git", "cat", "grep"],
  max_steps: 15
})

# Generate tests
ai("Write unit tests for these functions:", {
  context: cat("src/utils.rs"),
  model: "openai:gpt-4o"
})

Infrastructure: Kubernetes Monitoring

# List pods with structured output
mcp_call("kubectl", {command: "get pods -o json"})
  | map(fn(pod) => {
      name: pod.metadata.name,
      status: pod.status.phase,
      restarts: pod.status.containerStatuses[0].restartCount
    })
  | where(fn(p) => p.restarts > 0)

Enterprise: RBAC & Compliance

# Create roles with permissions
role_create("data_analyst", [
    {resource: "reports", actions: ["read", "export"]},
    {resource: "dashboards", actions: ["read", "create"]}
], "Data analytics team role")

# Grant roles to users
role_grant("user_123", "data_analyst")

# Check permissions before operations
if check_permission("user_123", "reports", "export") {
    audit_log("report_export", {user: "user_123", report: "Q4_sales"})
    # ... export the report
}

# Compliance reporting
compliance_check("GDPR")
compliance_report("SOC2", "json")

AI: Fine-tuning & RAG

# Start model fine-tuning
finetune_start("gpt-4o-mini", "training_data.jsonl", {
    epochs: 3,
    learning_rate: 0.0001
})

# Check fine-tuning status
finetune_status("ft-abc123")

# Build knowledge base with RAG
rag_index("project_docs", ["README.md", "docs/*.md"])
rag_query("project_docs", "How do I configure themes?")

# Knowledge graphs
kg_add("AetherShell", "language", "Rust")
kg_relate("AetherShell", "has_feature", "typed_pipelines")
kg_query({entity: "AetherShell"})

Distributed Computing

# Create a compute cluster
cluster_create("ml_cluster", {max_nodes: 10})

# Add worker nodes
cluster_add_node("ml_cluster", "worker_1", {capabilities: ["gpu", "ml"]})
cluster_add_node("ml_cluster", "worker_2", {capabilities: ["gpu", "ml"]})

# Submit distributed jobs
job_submit("ml_cluster", "train_model", {
    model: "neural_net",
    data: "training_set.csv"
})

# Monitor cluster status
cluster_status("ml_cluster")

Interactive Data Exploration

# Explore JSON APIs
let response = http_get("https://api.github.com/repos/nervosys/AetherShell")
print("Stars: ${response.stargazers_count}")
print("Forks: ${response.forks_count}")
print("Language: ${response.language}")

# Parse and transform
response.topics | map(fn(t) => upper(t)) | join(", ")

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🎮 TUI Interface

Launch the beautiful terminal UI with ae tui:

Tab	Description
Chat	Conversational AI with multi-modal support
Agents	Deploy and monitor AI agent swarms
Media	View images, play audio, preview videos
Help	Quick reference and documentation

Keyboard shortcuts:

• Tab — Switch tabs
• Enter — Send message / activate
• Space — Select media files
• q — Quit
• Ctrl+C — Force quit

📖 Full guide: docs/TUI_GUIDE.md

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📦 Installation

From Source (Recommended)

git clone https://github.com/nervosys/AetherShell
cd AetherShell
cargo install --path . --bin ae

From Crates.io

cargo install aether_shell

VS Code Extension

Get syntax highlighting, snippets, and integrated REPL:

cd editors/vscode
npm install && npm run compile
# Press F5 to test

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⚙️ Configuration

Environment Variables

# AI Provider (required for AI features)
export OPENAI_API_KEY="sk-..."

# Agent permissions
export AGENT_ALLOW_CMDS="ls,git,curl,python"

# Alternative AI backend
export AETHER_AI="ollama"  # or "openai"

Secure Key Storage

# Store keys in OS credential manager (recommended)
ae keys store openai sk-your-key-here

# View stored keys (masked)
ae keys list

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📚 Documentation

Document	Description
Quick Reference	One-page syntax guide
TUI Guide	Terminal UI documentation
Type System	Type inference details
MCP Servers	Tool integration guide
AI Backends	Provider configuration
Security	Security assessment

Example Scripts

File	Topic
00_hello.ae	Basic syntax
01_pipelines.ae	Typed pipelines
02_tables.ae	Table operations
04_match.ae	Pattern matching
05_ai.ae	AI integration
06_agent.ae	Agent deployment
09_tui_multimodal.ae	Multi-modal TUI

Coverage Test Scripts

File	Topic
syntax_comprehensive.ae	All AST constructs
builtins_core.ae	Core functions
builtins_functional.ae	Functional ops
builtins_string.ae	String operations
builtins_array.ae	Array operations
builtins_math.ae	Math functions
builtins_aggregate.ae	Aggregate functions
builtins_config.ae	Config & themes

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🧪 Testing

AetherShell has comprehensive test coverage with 100% pass rate.

# Run the full test coverage suite
./scripts/test_coverage.ps1     # Windows PowerShell
./scripts/run_tests.sh          # Linux/macOS

# Run specific test categories
cargo test --test builtins_coverage  # 23 builtin tests
cargo test --test theme_coverage     # 6 theme tests
cargo test --test eval               # 6 evaluator tests
cargo test --test typecheck          # 10 type inference tests
cargo test --test pipeline           # Pipeline tests
cargo test --test smoke              # Smoke tests

# Run all library tests
cargo test --lib

Test Coverage Summary

Category	Tests	Status
Builtins Coverage	23	✅
Theme System	6	✅
Core Builtins	2	✅
Evaluator	6	✅
Pipelines	1	✅
Type Inference	10	✅
Smoke Tests	4	✅
.ae Syntax Tests	8 files	✅

Test files: See TESTING.md for the complete testing strategy and tests/coverage/ for syntax coverage tests.

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🛣️ Roadmap

See ROADMAP.md for the complete development roadmap with detailed progress tracking.

✅ Completed (January 2026)

• 215+ builtins with comprehensive test coverage
• 38 built-in color themes with XDG-compliant config
• Neural network primitives & evolutionary algorithms
• 130+ MCP tools with protocol compliance
• Multi-modal AI (images, audio, video)
• Reinforcement learning (Q-Learning, DQN, Actor-Critic)
• Distributed agent swarms & cluster management
• Language Server Protocol (LSP) for IDE integration
• VS Code extension v0.2.0 with IntelliSense
• Enterprise features (RBAC, Audit, SSO, Compliance)
• Fine-tuning API for custom model training
• RAG & knowledge graphs
• Plugin system with TOML manifests
• WASM support (browser-based shell)
• Package management & module imports
• 100% test pass rate

🔜 Coming Soon

• Advanced video streaming
• Mobile platform support

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🤝 Contributing

We welcome contributions! See our development setup:

git clone https://github.com/nervosys/AetherShell
cd AetherShell
cargo build
cargo test --lib

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

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📜 License

Licensed under the Apache License 2.0.

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ae tui

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