LeadAgent: Universal CLI Orchestrator

Route any prompt to Claude, Gemini, or OpenAI from a single terminal command — using your existing subscriptions, with full cross-project memory and zero per-token cost.

Not a replacement for Aider, Cursor, or your existing dev tools. LeadAgent is a "Global Brain" that sits at the OS level, providing shared memory across all your projects.

Visual Tour

Agent Routing

Knowledge Graph

MCP Tool Rules

Privacy Guarantee

• Local-only data — your knowledge graph (KuzuDB) lives on your machine; nothing is sent to LeadAgent servers
• No telemetry — zero tracking, no analytics, no usage reporting
• User-space daemon — runs as your own user, no root, no system services
• What the watcher indexes — file paths and git metadata only; source content is never stored

Quick Start

Requires Python 3.10+, Go 1.25+, and at least one AI provider CLI (claude or gemini).

git clone https://github.com/your-username/LeadAgent.git
cd LeadAgent
./install.sh

Verify the daemon is running:

leadagent health
# → backend: ok  agentmemory: ok  ollama: ok (if installed)

Open the dashboard: http://localhost:8000/dashboard

Send your first prompt:

# From any project directory
leadagent "Explain the architecture of this project"
leadagent "Refactor this module" --agent claude
leadagent debate "Microservices vs Monolith for this MVP?"

Add alias leadagent='/path/to/LeadAgent/cli/leadagent' to your .zshrc to invoke globally.

Docker

Run the full stack in isolation without committing to a native daemon:

docker compose up -d --build
leadagent health

Why LeadAgent?

• Zero Marginal Cost — routes through your existing subscription CLIs, no API keys needed
• Cross-Project Memory — local KuzuDB graph remembers decisions and history across repos
• State-Aware Routing — task affinity scoring + deterministic fallback DAGs for autonomous recovery
• Privacy-First — your knowledge graph lives entirely on your machine
• Editor Agnostic — works alongside any IDE via the terminal and MCP interface

How It Works

prompt
  └─► brain (memory check + complexity score)
        └─► router (agent + mode selection)
              └─► CLI (claude / gemini / codex)
                    └─► stream back to terminal
                          └─► memory update (KuzuDB graph)
                                └─► dashboard (live metrics)

1. Brain checks project memory for relevant context
2. Router scores task affinity across enabled agents and picks the best fit
3. CLI runs in plan (text) or execute (tool use / agentic) mode
4. Stream is relayed token-by-token with status heartbeats
5. Memory stores the outcome in the local knowledge graph
6. Dashboard updates usage, ROI, and session history in real time

Daemon Management

./start_backend.sh              # start in foreground (debug)
./start_backend.sh --daemon     # start in background
./start_backend.sh backend      # restart only the FastAPI backend
leadagent --onboarding          # re-run setup wizard
tail -f leadagent-data/daemon.log

Contributing

Open engineering surfaces:

• ML Brain — LightGBM + MiniLM joint classifier for agent + mode routing
• New Adapters — add support for additional CLI-based AI providers
• Dashboard — React/Vite frontend at frontend/
• MCP Rules Engine — structural tool enforcement at backend/mcp_rules.py

Docs

• Architecture & Flow — routing pipeline, MCP layer, context discovery, full diagram
• MCP Rules — structural tool enforcement: block, allow, or escalate before the agent ever sees a capability
• Debate Engine — GAN-style adversarial multi-agent debates with umpire synthesis
• Docker Setup — Docker vs native mode, auth troubleshooting
• Open Source Credits — dependencies and related projects

Reset

./nuke.sh   # wipe environment and start fresh

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Open-source under the MIT License. Orchestration should be neutral, auditable, and vendor-agnostic.