🐝 BeeAI agent

1import { Actor, log } from 'apify';
2import { BeeAgent } from 'bee-agent-framework/agents/bee/agent';
3import { UnconstrainedMemory } from 'bee-agent-framework/memory/unconstrainedMemory';
4import { z } from 'zod';
5import { LangChainChatModel } from 'bee-agent-framework/adapters/langchain/backend/chat';
6import { ChatOpenAI } from '@langchain/openai';
7import { OpenAIChatModel } from 'bee-agent-framework/adapters/openai/backend/chat';
8import { CalculatorSumTool } from './tools/calculator.js';
9import { InstagramScrapeTool } from './tools/instagram.js';
10import { StructuredOutputGenerator } from './structured_response_generator.js';
11
12// This is an ESM project, and as such, it requires you to specify extensions in your relative imports.
13// Read more about this here: https://nodejs.org/docs/latest-v18.x/api/esm.html#mandatory-file-extensions
14// Note that we need to use `.js` even when inside TS files
15// import { router } from './routes.js';
16
17// Actor input schema
18interface Input {
19    query: string;
20    modelName: string;
21    debug?: boolean;
22}
23
24// The init() call configures the Actor for its environment. It's recommended to start every Actor with an init().
25await Actor.init();
26
27// Charge for Actor start
28await Actor.charge({ eventName: 'actor-start' });
29
30// Handle input
31const {
32    // The query default value is provided only for template testing purposes.
33    // You can remove it.
34    query,
35    modelName,
36    debug,
37} = await Actor.getInput() as Input;
38if (debug) {
39    log.setLevel(log.LEVELS.DEBUG);
40}
41if (!query) {
42    throw new Error('An agent query is required.');
43}
44
45/**
46 * Actor code
47*/
48// Create a ReAct agent that can use tools.
49// See https://i-am-bee.github.io/bee-agent-framework/#/agents?id=bee-agent
50// In order to use PPE, the LangChain adapter must be used
51// otherwise, the token usage is not tracked.
52log.debug(`Using model: ${modelName}`);
53const llm = new LangChainChatModel(
54    new ChatOpenAI({ model: modelName }),
55);
56// The LangChain adapter does not work with the structured output generation
57// for some reason.
58// Create a separate LLM for structured output generation.
59const llmStructured = new OpenAIChatModel(modelName);
60const agent = new BeeAgent({
61    llm,
62    memory: new UnconstrainedMemory(),
63    tools: [new CalculatorSumTool(),
64        new InstagramScrapeTool()],
65});
66
67// Store tool messages for later structured output generation.
68// This can be removed if you don't need structured output.
69const structuredOutputGenerator = new StructuredOutputGenerator(llmStructured);
70
71// Prompt the agent with the query.
72// Debug log agent status updates, e.g., thoughts, tool calls, etc.
73const response = await agent
74    .run({ prompt: query })
75    .observe((emitter) => {
76        emitter.on('update', async ({ update }) => {
77            log.debug(`Agent (${update.key}) 🤖 : ${update.value}`);
78
79            // Save tool messages for later structured output generation.
80            // This can be removed if you don't need structured output.
81            if (['tool_name', 'tool_output', 'tool_input'].includes(update.key as string)) {
82                structuredOutputGenerator.processToolMessage(
83                    update.key as 'tool_name' | 'tool_output' | 'tool_input',
84                    update.value,
85                );
86            }
87            // End of tool message saving.
88        });
89    });
90
91log.info(`Agent 🤖 : ${response.result.text}`);
92
93// Hacky way to get the structured output.
94// Using the stored tool messages and the user query to create a structured output.
95const structuredResponse = await structuredOutputGenerator.generateStructuredOutput(query,
96    z.object({
97        totalLikes: z.number(),
98        totalComments: z.number(),
99        mostPopularPosts: z.array(z.object({
100            url: z.string(),
101            likes: z.number(),
102            comments: z.number(),
103            timestamp: z.string(),
104            caption: z.string().nullable().optional(),
105            alt: z.string().nullable().optional(),
106        })),
107    }));
108log.debug(`Structured response: ${JSON.stringify(structuredResponse)}`);
109
110// Charge for task completion
111await Actor.charge({ eventName: 'task-completed' });
112
113// Push results to the dataset.
114await Actor.pushData({
115    query,
116    response: response.result.text,
117    // This can be removed if you don't need structured output.
118    structuredResponse: structuredResponse.object,
119});
120log.info('Pushed the data into the dataset!');
121
122// Gracefully exit the Actor process. It's recommended to quit all Actors with an exit().
123await Actor.exit();