@celp/agent-fleet
Advanced tools
A powerful CLI tool for creating, managing, and deploying AI agents with MCP integration
A comprehensive platform for creating, managing, and deploying standalone AI agents with tool support and MCP server integration.
Create a .env file with your OpenAI API key:
cp .env.example .env
# Edit .env and add your OpenAI API key
git clone <repository>
npm install
npm run build
Core Functionality:
CLI Features:
# Create a standalone agent repository (dependencies auto-installed)
npm run dev -- agent create-repo -n "my-assistant" -m "gpt-5" -i "You are a helpful assistant" --mcp-servers "google-workspace"
# Ready to use immediately! Chat with your agent:
npm run dev -- chat --agent "my-assistant"
# Or run directly from agent directory:
cd ~/.agent-fleet/agents/my-assistant/
npm run dev # Interactive development mode
# Or build and deploy
npm run build
npm start # HTTP server mode
# Create agent in platform database (legacy)
npm run dev agent add --interactive
# Chat through platform
npm run dev chat --agent my-assistant
Once you're chatting with an agent, try these examples to see tool execution in action:
"Calculate 25 * 4 + 10" # Uses calculator tool
"Read the package.json file" # Uses file reading tool
"List files in the current directory" # Uses directory listing
"What's the current date?" # Uses shell command
"Search for TypeScript best practices" # Uses web search tool
Note: Tools are automatically executed when the agent determines they're needed. Use --verbose flag to see detailed tool inputs/outputs.
The platform uses a distributed architecture designed for scalability:
┌─ AI Agent CLI Platform ─┐ ┌─ ai-agent-runtime ─┐
│ ├─ Agent Builder │────▶│ ├─ AgentRuntime │
│ ├─ Template System │ │ ├─ Built-in Tools │
│ ├─ Database (SQLite) │ │ ├─ MCP Integration │
│ └─ Repository Manager │ │ └─ Tool-Aware AI │
└─────────────────────────┘ └────────────────────┘
│ │
┌───────▼───────┐ │
│ Configuration │ │
└───────────────┘ │
│
┌──────────────────────────────────▼──────────────────────┘
│
┌───────▼───────┐
│ Standalone │
│ Agents │
├─ Lambda │
├─ Docker │
├─ HTTP Server │
└─ Custom │
Create a .env file in the project root:
OPENAI_API_KEY=your-openai-api-key
AI_MODEL=gpt-5
Each agent has its own .env file:
# In ~/.agent-fleet/agents/my-agent/.env
OPENAI_API_KEY=your-openai-api-key
NODE_ENV=development
PORT=3000
To avoid setting up AWS credentials for each agent individually, create a base .env.lambda file in the project root:
# Copy the template and fill in your AWS credentials
cp .env.lambda.example .env.lambda
# In agent-fleet/.env.lambda
AWS_ACCESS_KEY_ID=your-aws-access-key-id
AWS_SECRET_ACCESS_KEY=your-aws-secret-access-key
AWS_REGION=us-east-1
AWS_ACCOUNT_ID=123456789012
ECR_REGISTRY=123456789012.dkr.ecr.us-east-1.amazonaws.com
LAMBDA_ROLE_ARN=arn:aws:iam::123456789012:role/lambda-execution-role
When you generate new agents, they'll automatically inherit these AWS credentials. Existing agents can sync credentials using:
cd ~/.agent-fleet/agents/my-agent
npm run sync:aws-creds
Add agent-specific tools in src/custom-tools.ts:
import { z } from 'zod';
import type { ToolDefinition } from 'ai-agent-runtime';
export async function getCustomTools(): Promise<ToolDefinition[]> {
return [
{
name: 'weather_check',
description: 'Check current weather for a location',
parameters: z.object({
location: z.string().describe('City name')
}),
execute: async ({ location }) => {
// Your weather API integration
return { temperature: 72, condition: 'sunny' };
}
}
];
}
# HTTP Server
npm run build && npm start
# Docker
npm run deploy:docker
# AWS Lambda
npm run deploy:lambda
# Development
npm run dev
Connect to external services using MCP servers:
# agent.yaml
mcpServers:
- name: filesystem
url: npx @modelcontextprotocol/server-filesystem /allowed/path
- name: database
url: node ./custom-mcp-server.js
env:
DB_CONNECTION: "postgresql://..."
# Build CLI platform
npm run build
npm run typecheck
# Build runtime package
cd packages/runtime/
npm run build
# In agent directory
cd ~/.agent-fleet/agents/my-agent/
# Development mode
npm run dev
# Build for production
npm run build
# Type checking
npm run typecheck
# Validate configuration
npm run validate
# Update runtime package
cd packages/runtime/
npm version minor
npm publish
# Update specific agents
cd ~/.agent-fleet/agents/my-agent/
npm update ai-agent-runtime
npm run build && npm run deploy
Available templates for quick agent creation:
# Create from built-in template
npm run dev -- agent create-repo --template "sample-developer-assistant" -n "my-agent"
# Interactive template selection
npm run dev -- agent create-repo --interactive
⚠️ Note: Template and migrate commands were removed from the CLI:
npm run dev -- template [subcommand] → DELETED (files removed)npm run dev -- migrate [subcommand] → DELETED (files removed)Use agent create-repo with built-in templates instead.
Platform team updates runtime:
cd packages/runtime/
# Add new tools/features
npm version minor # 1.0.0 → 1.1.0
npm publish
Agent owners update selectively:
cd ~/.agent-fleet/agents/my-agent/
npm update ai-agent-runtime@1.1.0
npm run build && npm run deploy
Bulk updates (optional):
npm run dev agent bulk-update --runtime-version 1.1.0
// Automatically included in each agent
import { createAgentRuntime, loadManifestFromFile } from 'ai-agent-runtime';
import { startServer } from './server.js';
const manifest = await loadManifestFromFile('agent.yaml');
const runtime = await createAgentRuntime(manifest, customTools);
await startServer(runtime, 3000);
# Deploy with streaming support
npm run deploy:lambda:complete
# Test your Lambda function
curl -X POST https://your-function-url.lambda-url.region.on.aws/chat \
-H "Content-Type: application/json" \
-d '{"message": "Hello, Lambda!"}'
# Automatically included in each agent
FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build
EXPOSE 3000
CMD ["npm", "start"]
Each agent includes these built-in tools:
Customize tool behavior in your agent.yaml:
# Enable specific tools
enabledTools:
- web_search
- calculator
- analyze_pdf
# Configure individual tools
toolConfigurations:
web_search:
model: gpt-5-mini # Override model for web searches
maxTokens: 3000 # Limit response length
reasoning_effort: medium # For reasoning models (minimal/low/medium/high)
analyze_pdf:
model: gpt-5 # Model for PDF analysis
maxTokens: 8000 # Token limit for analysis
Available configurations:
model (defaults to agent model), maxTokens (5000), reasoning_effortmodel (defaults to agent model), maxTokens📖 Complete configuration guide: AGENT-CONFIGURATION.md
Contributions are welcome! Please read the contributing guidelines before submitting PRs.
MIT License - see LICENSE file for details.
FAQs
A powerful CLI tool for creating, managing, and deploying AI agents with MCP integration
The npm package @celp/agent-fleet receives a total of 1 weekly downloads. As such, @celp/agent-fleet popularity was classified as not popular.
We found that @celp/agent-fleet demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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