Agent Tooling

Framework Wrappers

Building your own agent? Official packages expose the full Edge tool set as native tools for LangChain, LlamaIndex, CrewAI, and the Vercel AI SDK.

How They Work

Both packages fetch tool definitions from the MCP endpoint at runtime rather than bundling static copies — so when Edge ships a new tool or refines a description, your agent picks it up on the next run without a package update. Arguments are validated against each tool's JSON Schema before anything is sent.

Package	Registry	Frameworks
edge-network-agent	PyPI	LangChain, LlamaIndex, CrewAI — via optional extras
@edge-network/ai-tools	npm	Vercel AI SDK (`generateText` / `streamText`)

Authentication is the same everywhere: pass an agent access code explicitly or set the EDGE_AGENT_CODE environment variable.

LangChain

pip install "edge-network-agent[langchain]"

from edge_network_agent import EdgeAgentTools
from langchain.agents import create_react_agent

edge = EdgeAgentTools(agent_code="ea_live_...")  # or EDGE_AGENT_CODE env var
agent = create_react_agent(model, tools=edge.langchain_tools())

agent.invoke({"messages": [
    ("user", "Deploy ./dist as a static site on Edge and give me the URL")
]})

langchain_tools() returns StructuredTool instances with Pydantic argument models built from each tool's schema. Pass include=["edge_discover", "edge_deploy_static_site"] to expose a subset.

LlamaIndex

pip install "edge-network-agent[llamaindex]"

from edge_network_agent import EdgeAgentTools
from llama_index.core.agent import ReActAgent

edge = EdgeAgentTools()  # EDGE_AGENT_CODE env var
agent = ReActAgent.from_tools(edge.llamaindex_tools(), llm=llm)

agent.chat("What projects do I have on Edge, and are they healthy?")

llamaindex_tools() returns FunctionTool instances ready for any LlamaIndex agent.

CrewAI

pip install "edge-network-agent[crewai]"

from edge_network_agent import EdgeAgentTools
from crewai import Agent

edge = EdgeAgentTools()
devops = Agent(
    role="DevOps engineer",
    goal="Deploy and monitor the team's sites on Edge",
    tools=edge.crewai_tools(),
)

crewai_tools() returns BaseTool instances any crew member can use.

Vercel AI SDK

npm install @edge-network/ai-tools ai

import { generateText } from 'ai'
import { createEdgeTools, fetchStarterPrompt } from '@edge-network/ai-tools'

const tools = await createEdgeTools({ agentCode: 'ea_live_...' })

const { text } = await generateText({
  model,
  system: await fetchStarterPrompt(),
  tools,
  prompt: 'Deploy the ./dist folder as a static site and report the URL',
})

createEdgeTools() resolves to a tool map for generateText and streamText; fetchStarterPrompt() returns Edge's recommended system prompt.

Without a Framework

The Python package also works standalone — call tools directly and pull the starter prompt:

edge = EdgeAgentTools(agent_code="ea_live_...")

# Call any tool directly, no framework needed
result = edge.call(
    "edge_deploy_static_site",
    project="my-site",
    files=[...],
    dry_run=True,   # preview first
)

# The recommended system prompt for agents working with Edge
system = edge.starter_prompt()

Next Steps

MCP Tool Reference

Every tool your agent gets, with parameters

Agent Self-Signup

Let agents provision their own explore-tier account