Function executeAgentBlock

packages/runtime-core/src/agent-handler.ts:148–247

(block: AgentBlock, context: AgentBlockContext)

}

export async function executeAgentBlock(block: AgentBlock, context: AgentBlockContext): Promise<AgentBlockResult> {
  const openai = createOpenAI({
    apiKey: context.openAiToken,
    baseURL: process.env.OPENAI_BASE_URL,
  })

  const modelName =
    block.metadata.deepnote_agent_model !== 'auto'
      ? block.metadata.deepnote_agent_model
      : (process.env.OPENAI_MODEL ?? 'gpt-5')
  const maxTurns = 10

  // Use the Responses API for direct OpenAI access (supports reasoning
  // summaries), but fall back to Chat Completions for custom base URLs
  // since most OpenAI-compatible providers don't implement the Responses API.
  const baseURL = process.env.OPENAI_BASE_URL
  const model = baseURL ? openai.chat(modelName) : openai(modelName)

  const blockMcpServers = block.metadata.deepnote_mcp_servers ?? []
  const mergedMcpConfig = mergeMcpConfigs(context.mcpServers, blockMcpServers)

  const mcpClients = await Promise.all(
    mergedMcpConfig.map(s =>
      createMCPClient({
        transport: new Experimental_StdioMCPTransport({
          command: s.command,
          args: s.args,
          env: resolveEnvVars(s.env),
          stderr: 'pipe',
        }),
      })
    )
  )

  const addCodeBlockTool = tool({
    description:
      'Add a Python code block to the notebook and execute it. Returns the combined output text or an error message.',
    inputSchema: z.object({
      code: z.string().describe('Python code to execute'),
    }),
    execute: context.addAndExecuteCodeBlock,
  })

  const addMarkdownBlockTool = tool({
    description: 'Add a markdown block to the notebook for explanations, section headers, or documentation.',
    inputSchema: z.object({
      content: z.string().describe('Markdown content'),
    }),
    execute: context.addMarkdownBlock,
  })

  const mcpToolSets = await Promise.all(mcpClients.map(client => client.tools()))
  const mcpTools: Record<string, unknown> = Object.assign({}, ...mcpToolSets)

  const agent = new ToolLoopAgent({
    model,
    instructions: buildSystemPrompt(context.notebookContext, context.integrations),
    tools: {