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Tool-calling Agent ​

Build a reusable tool-calling agent using createAgent and tool(). The agent automatically loops through think → tool calls → observation until it produces a final answer.

Plugins used: createAgent, tool() (from flowneer/presets/agent)

The code ​

typescript
import "dotenv/config";
import { OpenAI } from "openai";
import { tool, createAgent } from "flowneer/presets/agent";
import type { LlmAdapter, AgentState } from "flowneer/presets/agent";

// ─── Tools ───────────────────────────────────────────────────────────────────

const calculator = tool(
  ({ expression }: { expression: string }) => {
    // Safe arithmetic only — replace with a proper parser in production
    return Function(`"use strict"; return (${expression})`)();
  },
  {
    name: "calculator",
    description: "Evaluate a mathematical expression",
    params: {
      expression: {
        type: "string",
        description: "A JavaScript arithmetic expression, e.g. '(12 * 4) / 2'",
        required: true,
      },
    },
  },
);

const getTime = tool(() => new Date().toUTCString(), {
  name: "get_time",
  description: "Get the current UTC date and time",
  params: {},
});

const webSearch = tool(
  async ({ query }: { query: string }) => {
    // Replace with a real search API call
    return `[mock search results for: ${query}]`;
  },
  {
    name: "web_search",
    description: "Search the web for up-to-date information",
    params: {
      query: { type: "string", description: "Search query", required: true },
    },
  },
);

// ─── LLM adapter (OpenAI) ────────────────────────────────────────────────────

const openai = new OpenAI();

const callLlm: LlmAdapter = async (messages, toolDefs) => {
  const res = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    messages: messages as any,
    tools: toolDefs.map((t) => ({ type: "function" as const, function: t })),
    tool_choice: "auto",
  });

  const msg = res.choices[0]!.message;

  if (msg.tool_calls?.length) {
    return {
      toolCalls: msg.tool_calls.map((tc: any) => ({
        id: tc.id,
        name: tc.function.name,
        args: JSON.parse(tc.function.arguments),
      })),
    };
  }

  return { text: msg.content ?? "" };
};

// ─── Agent (create once, reuse forever) ──────────────────────────────────────

const agent = createAgent({
  tools: [calculator, getTime, webSearch],
  callLlm,
  systemPrompt:
    "You are a helpful assistant. Use the available tools when needed. " +
    "Always show your reasoning before giving a final answer.",
  maxIterations: 8,
});

// ─── Run ─────────────────────────────────────────────────────────────────────

const state: AgentState = {
  input: "What is 1337 * 42, and what time is it right now?",
  messages: [],
};

await agent.run(state);
console.log(state.output);

if (state.__reactExhausted) {
  console.warn("Agent hit the iteration limit without finishing.");
}

How it works ​

  1. tool() wraps each function with a name, description, and parameter schema.
  2. createAgent() returns a FlowBuilder<AgentState> that wires .withTools() + .withReActLoop() internally.
  3. On each iteration the callLlm adapter receives the full conversation history and tool schemas. If the model returns tool_calls, Flowneer dispatches them and injects the results into state.messages before the next iteration.
  4. When the model returns plain text with no tool calls, the loop ends and state.output is set.

Variations ​

Swap the model — change "gpt-4o-mini" to "gpt-4o" or any Claude / Gemini adapter.

Add memory — call .withMemory(new BufferWindowMemory(10)) on the returned FlowBuilder to persist context across runs.

Cap cost — add .withTokenBudget(...) to stop the loop when the token budget is exceeded.

Stream chunks — replace agent.run(state) with:

typescript
for await (const event of agent.stream(state)) {
  if (event.type === "chunk") process.stdout.write(String(event.data));
}

See also ​