Streaming

import { ChatOpenAI } from '@langchain/openai'
import { MCPAgent, MCPClient } from 'mcp-use'

async function stepStreamingExample() {
    // Setup agent
    const config = {
        mcpServers: {
            playwright: {
                command: 'npx',
                args: ['@playwright/mcp@latest']
            }
        }
    }

    const client = new MCPClient(config)
    const llm = new ChatOpenAI({ model: 'gpt-4o' })
    const agent = new MCPAgent({ llm, client })

    // Stream the agent's steps
    console.log('🤖 Agent is working...')
    console.log('-'.repeat(50))

    for await (const step of agent.stream('Search for the latest Python news and summarize it')) {
        console.log(`\n🔧 Tool: ${step.action.tool}`)
        console.log(`📝 Input: ${JSON.stringify(step.action.toolInput)}`)
        // Note: step.observation is empty when the step is yielded
        // Tool results are tracked internally but not included in the step object
    }

    console.log('\n🎉 Done!')
    await client.closeAllSessions()
}

stepStreamingExample().catch(console.error)

import { ChatOpenAI } from '@langchain/openai'
import { MCPAgent, MCPClient } from 'mcp-use'

async function basicStreamingExample() {
    // Setup agent
    const config = {
        mcpServers: {
            playwright: {
                command: 'npx',
                args: ['@playwright/mcp@latest']
            }
        }
    }

    const client = new MCPClient(config)
    const llm = new ChatOpenAI({ model: 'gpt-4o' })
    const agent = new MCPAgent({ llm, client })

    // Stream the agent's response
    console.log('Agent is working...')

    for await (const event of agent.streamEvents('Search for the latest Python news and summarize it')) {
        if (event.event === 'on_chat_model_stream') {
            // Stream LLM output token by token
            // Note: chunk property may be 'text' or 'content' depending on LLM provider
            const text = event.data?.chunk?.text || event.data?.chunk?.content
            if (text) {
                process.stdout.write(text)
            }
        }
    }

    console.log('\n\nDone!')
    await client.closeAllSessions()
}

basicStreamingExample().catch(console.error)

import { MCPAgent } from 'mcp-use'

async function prettyStreamExample() {
    const agent = new MCPAgent({
        llm: 'openai/gpt-4o',
        mcpServers: {
            filesystem: {
                command: 'npx',
                args: ['-y', '@modelcontextprotocol/server-filesystem', './']
            }
        }
    })

    // Pretty streaming with automatic formatting and colors
    for await (const _ of agent.prettyStreamEvents({
        prompt: 'List all TypeScript files and count the total lines of code',
        maxSteps: 20
    })) {
        // Just iterate - formatting is handled automatically
    }

    await agent.close()
}

prettyStreamExample().catch(console.error)

for await (const _ of agent.prettyStreamEvents({
    prompt: 'Your query here',
    maxSteps: 10,
    schema: MyZodSchema  // Optional: for structured output
})) {
    // Pretty formatted output to console
}