llm-orchestra 0.12.2

Multi-agent LLM communication system with ensemble orchestration

LLM Orchestra

A multi-agent LLM communication system for ensemble orchestration and intelligent analysis.

Overview

LLM Orchestra lets you coordinate multiple AI agents for complex analysis tasks. Run code reviews with security and performance specialists, analyze architecture decisions from multiple angles, or get systematic coverage of any multi-faceted problem.

Mix expensive cloud models with free local models - use Claude for strategic insights while Llama3 handles systematic analysis tasks.

Key Features

• Multi-Agent Ensembles: Coordinate specialized agents with flexible dependency graphs
• Agent Dependencies: Define which agents depend on others for sophisticated orchestration patterns
• Script Agent Integration: Execute custom scripts alongside LLM agents with JSON I/O communication
• Model Profiles: Simplified configuration with named shortcuts for model + provider combinations
• Cost Optimization: Mix expensive and free models based on what each task needs
• Streaming Output: Real-time progress updates during ensemble execution
• CLI Interface: Simple commands with piping support (cat code.py | llm-orc invoke code-review)
• Secure Authentication: Encrypted API key storage with easy credential management
• YAML Configuration: Easy ensemble setup with readable config files
• Usage Tracking: Token counting, cost estimation, and timing metrics
• Artifact Management: Automatic saving of execution results with timestamped persistence

Installation

Option 1: Homebrew (macOS - Recommended)

# Add the tap
brew tap mrilikecoding/llm-orchestra

# Install LLM Orchestra
brew install llm-orchestra

# Verify installation
llm-orc --version

Option 2: pip (All Platforms)

# Install from PyPI
pip install llm-orchestra

# Verify installation
llm-orc --version

Option 3: Development Installation

# Clone the repository
git clone https://github.com/mrilikecoding/llm-orc.git
cd llm-orc

# Install with development dependencies
uv sync --dev

# Verify installation
uv run llm-orc --version

Updates

# Homebrew users
brew update && brew upgrade llm-orchestra

# pip users
pip install --upgrade llm-orchestra

Quick Start

1. Set Up Authentication

Before using LLM Orchestra, configure authentication for your LLM providers:

# Interactive setup wizard (recommended for first-time users)
llm-orc auth setup

# Or add providers individually
llm-orc auth add anthropic --api-key YOUR_ANTHROPIC_KEY
llm-orc auth add google --api-key YOUR_GOOGLE_KEY

# OAuth for Claude Pro/Max users
llm-orc auth add anthropic-claude-pro-max

# List configured providers
llm-orc auth list

# Remove a provider if needed
llm-orc auth remove anthropic

Security: API keys are encrypted and stored securely in ~/.config/llm-orc/credentials.yaml.

2. Configuration Options

LLM Orchestra supports both global and local configurations:

Global Configuration

Create ~/.config/llm-orc/ensembles/code-review.yaml:

name: code-review
description: Multi-perspective code review ensemble

agents:
  - name: security-reviewer
    model_profile: free-local
    system_prompt: "You are a security analyst. Focus on identifying security vulnerabilities, authentication issues, and potential attack vectors."

  - name: performance-reviewer
    model_profile: free-local
    system_prompt: "You are a performance analyst. Focus on identifying bottlenecks, inefficient algorithms, and scalability issues."

  - name: quality-reviewer
    model_profile: free-local
    system_prompt: "You are a code quality analyst. Focus on maintainability, readability, and best practices."

  - name: senior-reviewer
    model_profile: default-claude
    depends_on: [security-reviewer, performance-reviewer, quality-reviewer]
    system_prompt: |
      You are a senior engineering lead. Synthesize the security, performance,
      and quality analysis into actionable recommendations.
    output_format: json

Local Project Configuration

For project-specific ensembles, initialize local configuration:

# Initialize local configuration in your project
llm-orc config init

# This creates .llm-orc/ directory with:
# - ensembles/   (project-specific ensembles)
# - models/      (shared model configurations)
# - scripts/     (project-specific scripts)
# - config.yaml  (project configuration)

View Current Configuration

# Check configuration status with visual indicators
llm-orc config check

3. Using LLM Orchestra

Basic Usage

# List available ensembles
llm-orc list-ensembles

# List available model profiles
llm-orc list-profiles

# Get help for any command
llm-orc --help
llm-orc invoke --help

Invoke Ensembles

# Analyze code from a file (pipe input)
cat mycode.py | llm-orc invoke code-review

# Provide input directly
llm-orc invoke code-review --input "Review this function: def add(a, b): return a + b"

# JSON output for integration with other tools
llm-orc invoke code-review --input "..." --output-format json

# Use specific configuration directory
llm-orc invoke code-review --config-dir ./custom-config

# Enable streaming for real-time progress (enabled by default)
llm-orc invoke code-review --streaming

Output Formats

LLM Orchestra supports three output formats for different use cases:

Rich Interface (Default)

Interactive format with real-time progress updates and visual dependency graphs:

llm-orc invoke code-review --input "def add(a, b): return a + b"

JSON Output

Structured data format for integration and automation:

llm-orc invoke code-review --output-format json --input "code to review"

Returns complete execution data including events, results, metadata, and dependency information.

Text Output

Clean, pipe-friendly format for command-line workflows:

llm-orc invoke code-review --output-format text --input "code to review"

Plain text results perfect for piping and scripting: llm-orc invoke ... | grep "security"

Configuration Management

# Initialize local project configuration
llm-orc config init --project-name my-project

# Check configuration status with visual indicators
llm-orc config check                # Global + local status with legend
llm-orc config check-global        # Global configuration only  
llm-orc config check-local         # Local project configuration only

# Reset configurations with safety options
llm-orc config reset-global        # Reset global config (backup + preserve auth by default)
llm-orc config reset-local         # Reset local config (backup + preserve ensembles by default)

# Advanced reset options
llm-orc config reset-global --no-backup --reset-auth       # Complete reset including auth
llm-orc config reset-local --reset-ensembles --no-backup   # Reset including ensembles

Script Management

LLM Orchestra includes powerful script agent integration for executing custom scripts alongside LLM agents:

# List available scripts in your project
llm-orc scripts list

# Show detailed information about a script
llm-orc scripts show file_operations/read_file.py

# Test a script with parameters
llm-orc scripts test file_operations/read_file.py --parameters '{"filepath": "example.txt"}'

# Scripts are discovered from .llm-orc/scripts/ directories
# Results are automatically saved to .llm-orc/artifacts/ with timestamps

Script agents use JSON I/O for seamless integration with LLM agents, enabling powerful hybrid workflows where scripts provide data and context for LLM analysis.

MCP Server

LLM Orchestra includes a Model Context Protocol (MCP) server that exposes ensembles, artifacts, and metrics as MCP resources. This enables integration with MCP clients like Claude Code, Claude Desktop, and other tools.

Quick Start

1. Add .mcp.json to your project root:

{
  "mcpServers": {
    "llm-orc": {
      "command": "uv",
      "args": ["run", "llm-orc", "mcp", "serve"]
    }
  }
}

2. Restart Claude Code - MCP tools appear as mcp__llm-orc__*

3. Try it:

mcp__llm-orc__get_help              # Get full documentation
mcp__llm-orc__get_provider_status   # Check which models are available
mcp__llm-orc__list_ensembles        # See available ensembles

Resources (Read-Only Data)

Resource	Description
llm-orc://ensembles	List all available ensembles with metadata
llm-orc://ensemble/{name}	Get specific ensemble configuration
llm-orc://profiles	List model profiles
llm-orc://artifacts/{ensemble}	List execution artifacts for an ensemble
llm-orc://artifact/{ensemble}/{id}	Get individual artifact details
llm-orc://metrics/{ensemble}	Get aggregated metrics (success rate, cost, duration)

Tools (25 Total)

Core Execution

Tool	Description
invoke	Execute ensemble with streaming progress, saves artifacts automatically
list_ensembles	List all ensembles from local/library/global sources
validate_ensemble	Check config validity, profile availability, and dependencies
update_ensemble	Modify ensemble config (supports dry-run and backup)
analyze_execution	Analyze execution artifact data

Provider Discovery - Check what's available before running

Tool	Description
get_provider_status	Show available providers and Ollama models
check_ensemble_runnable	Check if ensemble can run, suggest local alternatives

Ensemble Management

Tool	Description
create_ensemble	Create new ensemble from scratch or template
delete_ensemble	Delete ensemble (requires confirmation)

Profile Management

Tool	Description
list_profiles	List profiles with optional provider filter
create_profile	Create new model profile
update_profile	Update existing profile
delete_profile	Delete profile (requires confirmation)

Script Management

Tool	Description
list_scripts	List primitive scripts by category
get_script	Get script source and metadata
test_script	Test script with sample input
create_script	Create new primitive script
delete_script	Delete script (requires confirmation)

Library Operations

Tool	Description
library_browse	Browse library ensembles and scripts
library_copy	Copy from library to local project
library_search	Search library by keyword
library_info	Get library metadata and statistics

Artifact Management

Tool	Description
delete_artifact	Delete individual execution artifact
cleanup_artifacts	Delete old artifacts (supports dry-run)

Help

Tool	Description
get_help	Get comprehensive docs: directory structure, schemas, workflows

Example Workflow

# 1. Check what's available
mcp__llm-orc__get_provider_status
# → Shows Ollama running with llama3, mistral models

# 2. Find an ensemble
mcp__llm-orc__library_search query="code review"
# → Found: code-analysis/security-review

# 3. Check if it can run locally
mcp__llm-orc__check_ensemble_runnable ensemble_name="security-review"
# → Shows which profiles need local alternatives

# 4. Copy and adapt
mcp__llm-orc__library_copy source="code-analysis/security-review"
mcp__llm-orc__update_ensemble ensemble_name="security-review" changes={"agents": [...]}

# 5. Run it
mcp__llm-orc__invoke ensemble_name="security-review" input_data="Review this code..."

CLI Usage

# Start MCP server (stdio transport for MCP clients)
llm-orc mcp serve

# Start with HTTP transport for debugging
llm-orc mcp serve --transport http --port 8080

Ensemble Library

Looking for pre-built ensembles? Check out the LLM Orchestra Library - a curated collection of analytical ensembles for code review, research analysis, decision support, and more.

Library CLI Commands

LLM Orchestra includes built-in commands to browse and copy ensembles from the library:

# Browse all available categories
llm-orc library categories
llm-orc l categories  # Using alias

# Browse ensembles in a specific category
llm-orc library browse code-analysis

# Show detailed information about an ensemble
llm-orc library show code-analysis/security-review

# Copy an ensemble to your local configuration
llm-orc library copy code-analysis/security-review

# Copy an ensemble to your global configuration
llm-orc library copy code-analysis/security-review --global

Library Source Configuration

By default, LLM Orchestra fetches library content from the remote GitHub repository. For development purposes, you can use a local copy of the library:

# Use remote GitHub library (default)
llm-orc library browse research-analysis

# Use local library for development
export LLM_ORC_LIBRARY_SOURCE=local
llm-orc library browse research-analysis  # Uses local submodule
llm-orc init                              # Copies from local submodule

# Switch back to remote
unset LLM_ORC_LIBRARY_SOURCE

When to use local library:

• Testing changes to library ensembles before publishing
• Working on feature branches of the llm-orchestra-library
• Offline development (when remote access unavailable)
• Custom ensemble development and testing

Requirements for local library:

• The llm-orchestra-library submodule must be initialized and present
• Clear error messages guide you if the local library is not found

Use Cases

Code Review

Get systematic analysis across security, performance, and maintainability dimensions. Each agent focuses on their specialty while synthesis provides actionable recommendations.

Architecture Review

Analyze system designs from scalability, security, performance, and reliability perspectives. Identify bottlenecks and suggest architectural patterns.

Product Strategy

Evaluate business decisions from market, financial, competitive, and user experience angles. Get comprehensive analysis for complex strategic choices.

Research Analysis

Systematic literature review, methodology evaluation, or multi-dimensional analysis of research questions.

Model Support

• Claude (Anthropic) - Strategic analysis and synthesis
• Gemini (Google) - Multi-modal and reasoning tasks
• Ollama - Local deployment of open-source models (Llama3, etc.)
• Custom models - Extensible interface for additional providers

Configuration

Model Profiles

Model profiles simplify ensemble configuration by providing named shortcuts for complete agent configurations including model, provider, system prompts, and timeouts:

# In ~/.config/llm-orc/config.yaml or .llm-orc/config.yaml
model_profiles:
  free-local:
    model: llama3
    provider: ollama
    cost_per_token: 0.0
    system_prompt: "You are a helpful assistant that provides concise, accurate responses for local development and testing."
    timeout_seconds: 30

  default-claude:
    model: claude-sonnet-4-20250514
    provider: anthropic-claude-pro-max
    system_prompt: "You are an expert assistant that provides high-quality, detailed analysis and solutions."
    timeout_seconds: 60

  high-context:
    model: claude-3-5-sonnet-20241022
    provider: anthropic-api
    cost_per_token: 3.0e-06
    system_prompt: "You are an expert assistant capable of handling complex, multi-faceted problems with detailed analysis."
    timeout_seconds: 120

  small:
    model: claude-3-haiku-20240307
    provider: anthropic-api
    cost_per_token: 1.0e-06
    system_prompt: "You are a quick, efficient assistant that provides concise and accurate responses."
    timeout_seconds: 30

Profile Benefits:

• Complete Agent Configuration: Includes model, provider, system prompts, and timeout settings
• Simplified Configuration: Use model_profile: default-claude instead of explicit model + provider + system_prompt + timeout
• Consistency: Same profile names work across all ensembles with consistent behavior
• Cost Tracking: Built-in cost information for budgeting
• Flexibility: Local profiles override global ones, explicit agent configs override profile defaults

Usage in Ensembles:

agents:
  - name: bulk-analyzer
    model_profile: free-local     # Complete config: model, provider, prompt, timeout
  - name: expert-reviewer
    model_profile: default-claude # High-quality config with appropriate timeout
  - name: document-processor
    model_profile: high-context   # Large context processing with extended timeout
    system_prompt: "Custom prompt override"  # Overrides profile default

Override Behavior: Explicit agent configuration takes precedence over model profile defaults:

agents:
  - name: custom-agent
    model_profile: free-local
    system_prompt: "Custom prompt"  # Overrides profile system_prompt
    timeout_seconds: 60            # Overrides profile timeout_seconds

Ensemble Configuration

Ensemble configurations support:

• Model profiles for simplified, consistent model selection
• Agent specialization with role-specific prompts
• Agent dependencies using depends_on for sophisticated orchestration
• Dependency validation with automatic cycle detection and missing dependency checks
• Timeout management per agent with performance configuration
• Mixed model strategies combining local and cloud models
• Output formatting (text, JSON) for integration
• Streaming execution with real-time progress updates

Agent Dependencies

The new dependency-based architecture allows agents to depend on other agents, enabling sophisticated orchestration patterns:

agents:
  # Independent agents execute in parallel
  - name: security-reviewer
    model_profile: free-local
    system_prompt: "Focus on security vulnerabilities..."

  - name: performance-reviewer  
    model_profile: free-local
    system_prompt: "Focus on performance issues..."

  # Dependent agent waits for dependencies to complete
  - name: senior-reviewer
    model_profile: default-claude
    depends_on: [security-reviewer, performance-reviewer]
    system_prompt: "Synthesize the security and performance analysis..."

Benefits:

• Flexible orchestration: Create complex dependency graphs beyond simple coordinator patterns
• Parallel execution: Independent agents run concurrently for better performance
• Automatic validation: Circular dependencies and missing dependencies are detected at load time
• Better maintainability: Clear, explicit dependencies instead of implicit coordinator relationships

Configuration Status Checking

LLM Orchestra provides visual status checking to quickly see which configurations are ready to use:

# Check all configurations with visual indicators
llm-orc config check

Visual Indicators:

• 🟢 Ready to use - Profile/provider is properly configured and available
• 🟥 Needs setup - Profile references unavailable provider or missing authentication

Provider Availability Detection:

• Authenticated providers - Checks for valid API credentials
• Ollama service - Tests connection to local Ollama instance (localhost:11434)
• Configuration validation - Verifies model profiles reference available providers

Example Output:

Configuration Status Legend:
🟢 Ready to use    🟥 Needs setup

=== Global Configuration Status ===
📁 Model Profiles:
🟢 local-free (llama3 via ollama)
🟢 quality (claude-sonnet-4 via anthropic-claude-pro-max)  
🟥 high-context (claude-3-5-sonnet via anthropic-api)

🌐 Available Providers: anthropic-claude-pro-max, ollama

=== Local Configuration Status: My Project ===
📁 Model Profiles:
🟢 security-auditor (llama3 via ollama)
🟢 senior-reviewer (claude-sonnet-4 via anthropic-claude-pro-max)

Configuration Reset Commands

LLM Orchestra provides safe configuration reset with backup and selective retention options:

# Reset global configuration (safe defaults)
llm-orc config reset-global        # Creates backup, preserves authentication

# Reset local configuration (safe defaults)  
llm-orc config reset-local         # Creates backup, preserves ensembles

# Advanced reset options
llm-orc config reset-global --no-backup --reset-auth           # Complete global reset
llm-orc config reset-local --reset-ensembles --no-backup       # Complete local reset
llm-orc config reset-local --project-name "My Project"         # Set project name

Safety Features:

• Automatic backups - Creates timestamped .backup directories by default
• Authentication preservation - Keeps API keys and credentials safe by default
• Ensemble retention - Preserves local ensembles by default
• Confirmation prompts - Prevents accidental data loss

Available Options:

Global Reset:

• --backup/--no-backup - Create backup before reset (default: backup)
• --preserve-auth/--reset-auth - Keep authentication (default: preserve)

Local Reset:

• --backup/--no-backup - Create backup before reset (default: backup)
• --preserve-ensembles/--reset-ensembles - Keep ensembles (default: preserve)
• --project-name - Set project name (defaults to directory name)

Configuration Hierarchy

LLM Orchestra follows a configuration hierarchy:

1. Local project configuration (.llm-orc/ in current directory)
2. Global user configuration (~/.config/llm-orc/)
3. Command-line options (highest priority)

Library Path Configuration

Control where llm-orc init finds primitive scripts using environment variables or project-specific configuration:

# Option 1: Custom library location via environment variable
export LLM_ORC_LIBRARY_PATH="/path/to/your/custom-library"
llm-orc init

# Option 2: Project-specific configuration via .llm-orc/.env
mkdir -p .llm-orc
echo 'LLM_ORC_LIBRARY_PATH=/path/to/your/custom-library' > .llm-orc/.env
llm-orc init

# Option 3: Use local submodule (development default)
export LLM_ORC_LIBRARY_SOURCE=local
llm-orc init

# Option 4: Auto-detect library in current directory (no configuration needed)
# Looks for: ./llm-orchestra-library/scripts/primitives/
llm-orc init

Priority order:

1. LLM_ORC_LIBRARY_PATH environment variable - Explicit custom location (highest priority)
2. .llm-orc/.env file - Project-specific configuration
3. LLM_ORC_LIBRARY_SOURCE=local - Package submodule
4. ./llm-orchestra-library/ - Current working directory auto-detection
5. No scripts installed (graceful fallback)

Note: Environment variables always take precedence over .env file settings, allowing temporary overrides without modifying project files.

This allows developers to maintain their own script libraries while still using llm-orc's orchestration features.

XDG Base Directory Support

Configurations follow the XDG Base Directory specification:

• Global config: ~/.config/llm-orc/ (or $XDG_CONFIG_HOME/llm-orc/)
• Automatic migration from old ~/.llm-orc/ location

Cost Optimization

• Local models (free) for systematic analysis tasks
• Cloud models (paid) reserved for strategic insights
• Usage tracking shows exactly what each analysis costs
• Intelligent routing based on task complexity

Development

# Run tests
uv run pytest

# Run linting and formatting
uv run ruff check .
uv run ruff format --check .

# Type checking
uv run mypy src/llm_orc

Research

This project includes comparative analysis of multi-agent vs single-agent approaches. See docs/ensemble_vs_single_agent_analysis.md for detailed findings.

Philosophy

Reduce toil, don't replace creativity. Use AI to handle systematic, repetitive analysis while preserving human creativity and strategic thinking.

License

MIT License - see LICENSE for details.