Multi-agent orchestration for Claude Code CLI.
Run specialized AI agents in parallel — research, critique, analyze, fix, and discuss.
Built on Claude Code with MCP tool integration.
Quick Start · Agents · Conductor · Architecture · Custom Agents
Most AI agent frameworks (CrewAI, AutoGen, LangGraph) treat LLMs as API endpoints — you manage tokens, tools, and prompts yourself. Agent Fleet takes a different approach: each agent is a full Claude Code session that can natively read files, edit code, run commands, and use MCP tools — just like a human developer.
- Dual auth — works with your Claude subscription (personal) or API key (commercial)
- Native tool use — agents read, write, and execute code directly (not through function-calling hacks)
- MCP ecosystem — plug in any MCP server for web search, code analysis, GitHub, and more
- Parallel execution — Conductor runs 3+ agents simultaneously, they discuss and synthesize
- No token counting — use your existing Claude Pro/Max plan, or set
ANTHROPIC_API_KEYfor API access
| Agent | Role | Tools |
|---|---|---|
| Research | Deep research with 8 modes (general, vision, tech, product, competitor, paper, idea, news) | Tavily, Context7, WebSearch |
| Critic | Devil's advocate — challenges ideas, plans, and reports | Tavily, WebSearch |
| Analyst | Code archaeologist — analyzes projects, finds patterns, health checks | CodePathfinder, Read/Glob/Grep |
| Discovery | Code scanner with 7 focus areas (security, dead-code, types, errors, patterns, debt) | CodePathfinder, Read/Glob/Grep |
| Repair | Automated bug fixer — takes Discovery findings and fixes them | CodePathfinder, Read/Edit/Write |
| CTO | The one who actually fixes — live code changes during discussions | CodePathfinder, Read/Edit/Write |
| Conductor | Multi-agent discussion orchestrator — parallel rounds + synthesis | All of the above |
# Prerequisites
npm install -g @anthropic-ai/claude-code # Claude Code CLI
claude auth login # Authenticate
# Setup
git clone https://github.com/studiomeyer-io/agent-fleet.git
cd agent-fleet
npm install
# Optional: Copy and configure .env
cp .env.example .env# Research
npm run research -- "AI agent frameworks 2026"
npm run research -- --tech "Model Context Protocol"
npm run research -- --product "AI code review tools"
npm run research -- --competitor "LLM orchestration frameworks"
npm run research -- --idea "MCP marketplace for agent tools"
# Critic (Devil's Advocate)
npm run critic -- "We should build a SaaS product"
npm run critic -- --idea "AI-powered code review service"
npm run critic -- --plan "Migrate from SQLite to PostgreSQL"
npm run critic -- --report 2026-03-14-research-ai-agents.md
# Analyst (Code Health)
npm run analyst -- /path/to/project
npm run analyst -- --health /path/to/project
npm run analyst -- --patterns /path/to/project
npm run analyst -- --compare /path/a /path/b
# Discovery (Code Scanner)
npm run discovery -- --project /path/to/project
npm run discovery -- --project /path/to/project --focus security
npm run discovery -- --project /path/to/project --focus dead-code --quick
# Repair (Bug Fixer)
npm run repair -- --project /path/to/project --issue "fix all any types"
npm run repair -- --project /path/to/project --report discovery-report --dry-run
# CTO (Live Fixer)
npm run cto -- --project /path/to/project --issue "add error handling to API routes"
npm run cto -- --project /path/to/project --report discovery-reportThe Conductor runs 3 agents (Research, Critic, Analyst) in parallel discussion rounds. Each agent uses its own tools independently, then they react to each other's findings.
# Open discussion
npm run conductor -- "Should we migrate to a monorepo?"
# Structured debate
npm run conductor -- --debate "PostgreSQL vs SQLite for agent memory"
# Review a report
npm run conductor -- --review research-report.md
# Improvement brainstorm
npm run conductor -- --improve "Our CI/CD pipeline"
# With CTO (live code fixes during discussion)
npm run conductor -- --with-cto "Fix all issues from last discovery scan"
# Options
npm run conductor -- --rounds 3 "Topic" # 3 rounds (default: 2, max: 4)
npm run conductor -- --sonnet "Topic" # Use Sonnet (faster, cheaper)All agents default to claude-opus-4-6. Override with flags:
npm run research -- --sonnet "quick topic" # Sonnet (faster)
npm run research -- --haiku "simple topic" # Haiku (fastest)agents/
lib/
base-agent.ts # Core: Claude CLI subprocess + MCP config + file/DB output
mcp-config.ts # MCP server registry — pickMcp() for type-safe selection
db.ts # Optional PostgreSQL persistence (no-ops without DATABASE_URL)
research-agent.ts # 8 research modes, parallel search
critic-agent.ts # Devil's advocate with independent verification
analyst-agent.ts # Code analysis, pattern finding, health checks
discovery-agent.ts # Code scanning with 7 focus areas
repair-agent.ts # Automated fixes from discovery findings
cto-agent.ts # Live code fixes, blast radius checking
conductor.ts # Multi-agent parallel discussion orchestrator
reports/ # Markdown reports with YAML frontmatter
- Each agent is a Claude CLI subprocess (
claude -p --model X --max-turns Y) - Agents get MCP servers for tool access (web search, code analysis, etc.)
- Agents get Claude Code tools (Read, Edit, Write, Glob, Grep, Bash)
- Output is parsed, cleaned, and saved as Markdown reports
- Optionally persisted to PostgreSQL (no-ops without DATABASE_URL)
Agents use these MCP servers (all via npx, no local installation needed):
| Server | Purpose |
|---|---|
@anthropic/code-pathfinder-mcp |
Call analysis, symbol finding |
@upstash/context7-mcp |
Library documentation |
@nicholasarner/context-mcp |
Package search & docs |
@anthropic/github-mcp |
GitHub integration |
@anthropic/sequential-thinking-mcp |
Reasoning chains |
tavily-mcp |
Deep web research (needs API key) |
Add your own in agents/lib/mcp-config.ts.
Reports are always saved to reports/ as Markdown files. For structured persistence:
# Create the database
createdb agent_fleet
psql -d agent_fleet -f schema.sql
# Set the connection string
echo 'DATABASE_URL=postgresql://user:pass@localhost:5432/agent_fleet' >> .envEdit agents/lib/mcp-config.ts:
export const mcpServers = {
// ... existing servers ...
'my-server': {
command: 'node',
args: ['/path/to/my-server/dist/server.js'],
},
};Then use in agents: pickMcp('my-server', 'code-pathfinder').
import { runAgent, type AgentConfig } from './lib/base-agent.js';
import { pickMcp } from './lib/mcp-config.js';
const config: AgentConfig = {
name: 'My Agent',
type: 'custom',
defaultModel: 'claude-opus-4-6',
maxTurns: 20,
mcpServers: pickMcp('tavily', 'code-pathfinder'),
extraTools: ['Read', 'Glob', 'Grep', 'WebSearch'],
};
const result = await runAgent(config, {
topic: 'My research topic',
prompt: 'Your detailed prompt here...',
tags: ['custom'],
});- Node.js >= 18
- Claude Code CLI —
npm install -g @anthropic-ai/claude-code - Claude Pro or Max Plan — for personal use via Claude Code CLI
- Anthropic API Key — for commercial/production use (
ANTHROPIC_API_KEYenv var) - Tavily API Key — optional, for deep web research
MIT
Built by StudioMeyer