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Agent tooling · Framework wrappers

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.

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How they work#

Both packages expose the full Edge tool set and fetch tool definitions from the MCP endpoint at runtime rather than bundling static copies. 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#

Terminal
pip install "edge-network-agent[langchain]"
Python
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#

Terminal
pip install "edge-network-agent[llamaindex]"
Python
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#

Terminal
pip install "edge-network-agent[crewai]"
Python
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#

Terminal
npm install @edge-network/ai-tools ai
JavaScript
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:

Python
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

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