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GAIA Super Agent SDK: Build GAIA-Benchmark-Ready Super Agents in Seconds, Not Weeks

#ai #agents #llm #benchmarking

Most "AI agent frameworks" look cool in diagrams, but the moment you try to run a serious benchmark like GAIA, you realize how much glue work is still on you:

  • wiring 10+ external APIs
  • writing tool wrappers by hand
  • juggling browser automation, search, sandbox, memory…
  • maintaining your own benchmark runner & result logger

That's exactly the pain point GAIA Super Agent SDK tries to remove.

Repo: https://github.com/gaia-agent/gaia-agent

This post walks through what the SDK actually gives you, how it's structured, and how you can use it today for both production agents and GAIA Benchmark runs.

What is GAIA Super Agent SDK?

At its core, this repo is a TypeScript / Node.js SDK that ships a pre-configured "Super Agent" built on:

  • AI SDK v6 ToolLoopAgent (the new ai SDK tool-based agent loop)
  • ToolSDK.ai integration for pulling in more tools
  • A ReAct-style reasoning + acting pattern with planning and verification baked in

The project description is very explicit:

"GAIA-benchmark-ready super agent built on AI SDK v6 ToolLoopAgent"

So instead of giving you a generic agent playground, this SDK is tuned for one clear mission:

Help you build agents that can seriously compete on the GAIA Benchmark, while still being usable as real production assistants.

Key Features

The README is quite dense, so here's the feature set translated into plain English.

1. Zero-config, GAIA-ready agent

  • A "Super Agent" that's immediately usable for GAIA tasks
  • You don't start from a graph editor or a prompt; you start from a ready-to-run agent instance

This is very close to "install → add keys → run benchmark".

2. ReAct + Planning + Verification

The agent doesn't just call tools randomly:

  • Uses ReAct-style reasoning: think → act → observe → think again
  • Has a planning layer for multi-step tasks
  • Includes verification to sanity-check final answers before returning them

This combo is important for GAIA, where tasks often require multiple hops across search, browser, files, and code.

3. 18+ Built-in Tools with Official SDKs

Tools are grouped into categories in the README:

  • Core: calculator, HTTP requests
  • Planning: planner, verifier
  • Search: Tavily search, Exa search & content fetch
  • Sandbox: code execution via E2B or Sandock
  • Browser: Steel, BrowserUse, or AWS browser agent
  • Memory: Mem0 or AWS AgentCore

You get a serious "batteries-included" toolkit that covers most GAIA capabilities (search, browser, code, files, memory) without you having to wire everything manually.

4. Swappable Providers (One-Line Switch)

A nice design choice: providers are swappable, and the README gives both code and env-var ways to do it.

For example (simplified):

import { createGaiaAgent } from '@gaia-agent/sdk';

const agent = createGaiaAgent({
  providers: {
    search: 'exa',       // instead of Tavily
    sandbox: 'sandock',  // instead of E2B
    browser: 'browseruse'
  }
});
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Or via environment variables:

GAIA_AGENT_SEARCH_PROVIDER=exa
GAIA_AGENT_SANDBOX_PROVIDER=sandock
GAIA_AGENT_BROWSER_PROVIDER=browseruse
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This makes it very easy to experiment: e.g. "What if I swap Tavily to Exa for search quality?" without touching agent logic.

5. Tight GAIA Benchmark Integration

This is the part that most other agent repos don't have.

The SDK ships with a benchmark module and a set of pnpm scripts for running GAIA tasks with good ergonomics:

pnpm benchmark            # run validation set
pnpm benchmark --limit 10 # smoke test with 10 tasks
pnpm benchmark:files      # only file-based tasks
pnpm benchmark:code       # only code-execution tasks
pnpm benchmark:search     # search-heavy tasks
pnpm benchmark:browser    # browser automation tasks
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There's even a --stream mode to watch the agent "think" in real time while it solves GAIA tasks.

6. "Wrong Answers" Collection & Retry Loop

One clever feature I really like: the wrong-answers pipeline.

Workflow:

  1. Run benchmarks → wrong answers are automatically logged to benchmark-results/wrong-answers.json
  2. Inspect failures
  3. Retry only the failed tasks with: 
pnpm benchmark:wrong --verbose
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  1. Keep iterating until that file is empty ("No wrong answers!")

This turns GAIA from a one-shot evaluation into an iterative training ground for your agent architecture and prompts.

7. Rich Benchmark Result Schema

Benchmark results capture more than just "correct / incorrect":

  • task id, question, level
  • which tools were used
  • duration
  • number of steps / tool calls
  • per-step details

So you can analyze, for example:

"Where does my agent waste time?"
"Which tools are over/under-used?"
"Why does it fail certain levels or task types?"

8. TypeScript-First, Tree-Shaking-Friendly

The SDK is written in TypeScript, exports ESM modules, and is designed to be tree-shakable.

This matters if you want to:

  • ship it into a Next.js / Remix / edge environment
  • avoid bundling tools you don't use
  • keep everything typed end-to-end

Quick Start (From the README, With Commentary)

Installation (npm): 

npm install @gaia-agent/sdk ai @ai-sdk/openai zod
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Basic usage looks like this: 

import { createGaiaAgent } from '@gaia-agent/sdk';

const agent = createGaiaAgent(); // reads config from env

const result = await agent.generate({
  prompt: 'Calculate 15 * 23 and search for the latest AI papers',
});

console.log(result.text);
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Environment variables are used to wire your providers, e.g.: 

OPENAI_API_KEY=<your-openai-api-key>
TAVILY_API_KEY=<your-tavily-api-key>      # search
E2B_API_KEY=<your-e2b-api-key>            # sandbox
STEEL_API_KEY=<your-steel-api-key>        # browser
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There's also a dedicated Environment Variables Guide linked from the README if you want more combinations (Mem0, Exa, Sandock, BrowserUse, AWS AgentCore, etc.).

Extending the Agent (Custom Tools & ToolSDK)

The SDK doesn't lock you into its default toolset.

Custom tools

You can grab the default tool set and add your own:

import { createGaiaAgent, getDefaultTools } from '@gaia-agent/sdk';
import { tool } from 'ai';
import { z } from 'zod';

const agent = createGaiaAgent({
  tools: {
    ...getDefaultTools(),
    weatherTool: tool({
      description: 'Get weather',
      inputSchema: z.object({ city: z.string() }),
      async execute({ city }) {
        // your own logic here
        return { temp: 24, condition: 'cloudy' };
      },
    }),
  },
});
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ToolSDK.ai ecosystem

The README also shows how to plug into ToolSDK.ai, so you can pull tools from its packages and expose them as AI SDK tools inside GAIA Agent.

That essentially turns this SDK into a hub: GAIA agent loop on top, tools from official providers + ToolSDK ecosystem underneath.

Docs & Developer Experience

The repo already ships with a pretty rich docs structure:

  • Quick Start Guide
  • ReAct + Planning Guide
  • Reflection Guide
  • Environment Variables
  • GAIA Benchmark setup & tips
  • Improving GAIA scores
  • Provider comparison
  • Testing guide (Vitest)
  • Advanced usage and API reference

There's also an automated NPM publish workflow: merge to main → tests → version bump → publish → changelog. So the package on npm should stay relatively aligned with main.

License: Apache 2.0.

When Should You Use GAIA Super Agent SDK?

This project makes the most sense if:

  • You want a serious GAIA benchmark agent without building everything from scratch
  • You want a production-grade multi-tool assistant with browser + search + sandbox already wired
  • You like the AI SDK v6 ecosystem and want an opinionated "super agent" built on top of its ToolLoopAgent
  • You want to iterate on prompts / tools / providers rather than infra

If you're just experimenting with agents for the first time, this might actually simplify the path: you get a working system on day 1, then you peel layers and customize from there.

Final Thoughts

I really like that this repo is not "yet another framework," but a concrete, GAIA-oriented super agent:

  • Batteries-included tools
  • Strong defaults
  • Real benchmark runner
  • Wrong-answer analysis loop
  • Provider swapping via one line or env vars

If you're playing with the GAIA Benchmark or building your own "do-anything" assistant with serious tooling, it's absolutely worth a try.

Repo: https://github.com/gaia-agent/gaia-agent

If you ship something cool with it (e.g. a leaderboard entry, a product demo, or internal tooling), definitely tag the project or share a write-up — the GAIA agent ecosystem is still very young and fast-moving.

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