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  path: '/writing/agent-framework-comparison-2026',
  title: 'AgentKit vs GOAT vs Vercel AI SDK vs LangChain vs elizaOS: Which Agent Framework for Quant Tools?',
  description:
    "Five agent frameworks, one quant API, real code in each. The honest comparison with decision tables, code samples, performance numbers, and the migration paths between them. Covers Coinbase AgentKit, Crossmint GOAT, Vercel AI SDK, LangChain, and elizaOS.",
  keywords: [
    'agentkit vs langchain',
    'agentkit vs goat sdk',
    'vercel ai sdk vs langchain',
    'agent framework comparison',
    'ai agent framework 2026',
    'best agent framework quant finance',
    'eliza vs agentkit',
    'goat sdk vs agentkit',
  ],
});

const LAST_UPDATED = 'May 15, 2026';

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  headline:
    'AgentKit vs GOAT vs Vercel AI SDK vs LangChain vs elizaOS: Which Agent Framework for Quant Tools?',
  description:
    'Comprehensive comparison of five AI agent frameworks against the same use case (wiring quant finance tools). Code samples, decision tables, performance numbers, migration paths.',
  author: { '@type': 'Organization', name: 'QuantOracle' },
  publisher: { '@type': 'Organization', name: 'QuantOracle', url: 'https://quantoracle.dev' },
  datePublished: '2026-05-15',
  dateModified: '2026-05-15',
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        </Link>{' '}
        /{' '}
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          Writing
        </Link>{' '}
        / Agent Framework Comparison 2026
      </nav>

      <header className="mb-10">
        <h1 className="text-3xl sm:text-4xl font-bold tracking-tight">
          AgentKit vs GOAT vs Vercel AI SDK vs LangChain vs elizaOS: Which Agent Framework for Quant Tools?
        </h1>
        <p className="mt-4 text-slate-300 text-lg leading-relaxed">
          Five frameworks, one use case, real code. We built the same quant-finance agent in
          each — wiring up Black-Scholes pricing, Kelly sizing, Monte Carlo simulation, and
          paid x402 composites — and tracked what actually mattered: setup friction, runtime
          cost, debugging, lock-in, and where each one lies to you.
        </p>
        <p className="mt-3 text-xs text-slate-500">Published {LAST_UPDATED}</p>
      </header>

      <article className="prose-soft">
        <h2 id="tldr">TL;DR — the decision table</h2>
        <div className="overflow-x-auto">
          <table className="w-full text-sm">
            <thead>
              <tr>
                <th className="text-left p-2">Use case</th>
                <th className="text-left p-2">Pick</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td className="p-2">Pure-LLM TS app, no crypto, just want tool calls to work</td>
                <td className="p-2"><strong>Vercel AI SDK</strong></td>
              </tr>
              <tr>
                <td className="p-2">Onchain agent with Coinbase wallet, US-regulated context</td>
                <td className="p-2"><strong>Coinbase AgentKit</strong></td>
              </tr>
              <tr>
                <td className="p-2">Multi-chain onchain agent (EVM + Solana, multiple wallet types)</td>
                <td className="p-2"><strong>GOAT SDK</strong></td>
              </tr>
              <tr>
                <td className="p-2">Python team, heavy data science pipeline, mature production ops</td>
                <td className="p-2"><strong>LangChain</strong></td>
              </tr>
              <tr>
                <td className="p-2">Conversational character agent, social media / Discord, persistent personality</td>
                <td className="p-2"><strong>elizaOS</strong></td>
              </tr>
              <tr>
                <td className="p-2">Need every available tool, want dynamic discovery, multiple AI clients</td>
                <td className="p-2"><strong>MCP server</strong> (not a framework — a transport)</td>
              </tr>
            </tbody>
          </table>
        </div>
        <p>
          The rest of this article is the long form: what each framework actually does, code in
          each, where each shines and breaks, and the migration paths between them when you
          eventually outgrow your first choice.
        </p>

        <h2 id="setup">The shared use case: a quant-finance agent</h2>
        <p>
          For a fair comparison we used the same agent task in each framework: a developer
          wants to build an AI agent that can:
        </p>
        <ol>
          <li>Price a European option via Black-Scholes</li>
          <li>Compute Kelly Criterion optimal position sizing</li>
          <li>Run a Monte Carlo portfolio simulation</li>
          <li>Optionally call paid composite endpoints (full risk audit, hedge
            recommendation) that settle via x402 micropayments</li>
        </ol>
        <p>
          QuantOracle ships{' '}
          <Link href="https://github.com/QuantOracledev/quantoracle/tree/main/integrations" target="_blank" rel="noopener" className="text-accent">
            integration packages
          </Link>{' '}
          for all five frameworks, so the agent code is shorter than from scratch — but the
          framework comparison itself is independent of the integration. We focus on what each
          framework asks of the developer.
        </p>

        <h2 id="vercel-ai-sdk">1. Vercel AI SDK — the simplest agent framework that still works</h2>
        <p>
          <strong>Audience:</strong> any TypeScript developer building an AI feature into a web
          app. Powers Vercel&apos;s own products, plus thousands of Next.js apps.
        </p>
        <p>
          <strong>Model:</strong> A `tool()` helper takes a Zod schema and an `execute`
          function. The model invokes the tool automatically when the user asks something
          relevant.
        </p>
        <pre>
          <code>{`import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { quantoracleTools } from "@quantoracle/ai-tools";

const result = await generateText({
  model: openai("gpt-4o"),
  tools: quantoracleTools(),
  maxSteps: 5,
  prompt: "Price a 30-day SPY $500 call with vol=18%, spot=$498, rate=5%.",
});`}</code>
        </pre>
        <p>
          <strong>Strengths.</strong> Zero ceremony. The `tools` parameter is just a record of
          tool objects; the model picks. Works identically with `generateText`, `streamText`,
          and `useChat` — your React UI gets streamed tool invocations for free. Best
          DX in the comparison.
        </p>
        <p>
          <strong>Weaknesses.</strong> No native wallet abstraction — if you need to sign blockchain
          transactions, you bring your own wallet client and wire up x402 payment handlers
          manually. No agent state machine; everything is per-request. Not a great fit for
          long-running autonomous agents that need persistent state across runs.
        </p>
        <p>
          <strong>Read more:</strong>{' '}
          <Link href="/writing/vercel-ai-sdk-quant-tools" className="text-accent">
            Add reliable quant finance math to your Vercel AI SDK agent in 5 minutes
          </Link>{' '}
          for the full walkthrough.
        </p>

        <h2 id="agentkit">2. Coinbase AgentKit — the onchain agent default</h2>
        <p>
          <strong>Audience:</strong> developers building agents that need to transact onchain,
          especially Base mainnet. Maintained by Coinbase.
        </p>
        <p>
          <strong>Model:</strong> An `AgentKit` instance is initialized with a wallet provider
          (CDP wallet, viem wallet, or custom) and a list of `ActionProvider` classes that
          expose tools. Each ActionProvider extends a base class and uses `@CreateAction`
          decorators with Zod schemas.
        </p>
        <pre>
          <code>{`import { AgentKit } from "@coinbase/agentkit";
import { quantoracleActionProvider } from "@quantoracle/agentkit";

const agent = await AgentKit.from({
  walletProvider,
  actionProviders: [quantoracleActionProvider()],
});`}</code>
        </pre>
        <p>
          <strong>Strengths.</strong> Wallet management is first-class — CDP wallet, viem
          wallets, and Solana wallets all plug in via a uniform interface. x402 payment
          handling is automatic for endpoints that require it. Strong types throughout. The
          gold-standard for &quot;serious onchain agent in 2026.&quot;
        </p>
        <p>
          <strong>Weaknesses.</strong> Heavier than Vercel AI SDK — you&apos;re committing to
          the AgentKit framework, not just a tool helper. Adding a custom ActionProvider
          requires understanding the decorator pattern. Slower iteration speed for non-onchain
          work where you don&apos;t need wallet abstractions.
        </p>
        <p>
          <strong>Read more:</strong>{' '}
          <Link href="/writing/agentkit-reliable-quant-finance-math" className="text-accent">
            Give your Coinbase AgentKit agent reliable quant finance math in 10 minutes
          </Link>{' '}
          and{' '}
          <Link href="/writing/chaining-x402-paid-tool-calls" className="text-accent">
            Chaining x402 paid tool calls
          </Link>{' '}
          for the AgentKit-with-x402 deep dive.
        </p>

        <h2 id="goat">3. GOAT SDK — the multi-chain onchain agent toolkit</h2>
        <p>
          <strong>Audience:</strong> developers building cross-chain agents that need to work
          on EVM and Solana (and beyond) with one codebase. Built by Crossmint.
        </p>
        <p>
          <strong>Model:</strong> Plugin-based, similar to AgentKit but with a different chain
          abstraction. The wallet is passed to `getOnChainTools()` along with an array of
          plugins. Each plugin extends `PluginBase` and uses `@Tool` decorators with parameter
          classes generated via `createToolParameters()`.
        </p>
        <pre>
          <code>{`import { getOnChainTools } from "@goat-sdk/adapter-vercel-ai";
import { viem } from "@goat-sdk/wallet-viem";
import { quantoracle } from "@quantoracle/goat-plugin";

const tools = await getOnChainTools({
  wallet: viem(walletClient),
  plugins: [...quantoracle({ include: ["core", "defi"] })],
});`}</code>
        </pre>
        <p>
          <strong>Strengths.</strong> Multi-chain by design. The same plugin works on Base,
          Polygon, Solana, etc. Adapter-agnostic — works with Vercel AI SDK, LangChain, Eliza,
          and several others via dedicated adapters. Strong DeFi-focused ecosystem (Uniswap,
          Jupiter, Polymarket, etc. all have GOAT plugins).
        </p>
        <p>
          <strong>Weaknesses.</strong> Two-layer setup (plugin + adapter) is more complex than
          Vercel AI SDK&apos;s single-layer tools. The plugin/wallet abstraction is heavier
          than AgentKit if you only ever use one chain. Smaller core team than Vercel or
          Coinbase.
        </p>

        <h2 id="langchain">4. LangChain — the Python heavyweight</h2>
        <p>
          <strong>Audience:</strong> Python teams, especially data-science-adjacent ones with
          existing ML pipelines or research code. The original agent framework.
        </p>
        <p>
          <strong>Model:</strong> Tools are classes (or decorated functions) with Pydantic
          schemas. Agents are built from `AgentExecutor` + `Runnable` chains. The framework is
          much larger than the others — chains, memory, retrievers, vector stores, evaluation,
          tracing, deployment.
        </p>
        <pre>
          <code>{`from langchain_quantoracle import (
    BlackScholesTool, KellyTool, MonteCarloTool
)
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_openai import ChatOpenAI

tools = [BlackScholesTool(), KellyTool(), MonteCarloTool()]
llm = ChatOpenAI(model="gpt-4o")
agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
result = executor.invoke({"input": "Price a 30-day SPY $500 call..."})`}</code>
        </pre>
        <p>
          <strong>Strengths.</strong> Ecosystem depth. Every integration, every embedding
          model, every vector store, every observability platform supports LangChain. Best
          choice for teams already invested in Python data science (pandas, numpy, sklearn,
          quantlib). Mature production patterns — LangServe for deployment, LangSmith for
          tracing.
        </p>
        <p>
          <strong>Weaknesses.</strong> The framework is enormous and the abstractions change
          frequently. Stack traces are notoriously hard to read. Bundle weight and dependency
          footprint are significant. For simple use cases the ceremony exceeds the value.
        </p>

        <h2 id="eliza">5. elizaOS — the conversational character framework</h2>
        <p>
          <strong>Audience:</strong> developers building agents that have a persistent
          personality and operate across social channels (Discord, Twitter, Telegram). Open
          source, with a thriving community of agent &quot;characters.&quot;
        </p>
        <p>
          <strong>Model:</strong> Character files (JSON or TypeScript) define the
          agent&apos;s personality, knowledge, and behaviors. Plugins add capabilities. The
          runtime handles social-channel integrations, memory, and the agent loop.
        </p>
        <pre>
          <code>{`import { AgentRuntime } from "@elizaos/core";
import { quantOraclePlugin } from "@quantoracle/plugin-quantoracle";

const runtime = new AgentRuntime({
  character: myQuantBotCharacter,
  plugins: [quantOraclePlugin],
});`}</code>
        </pre>
        <p>
          <strong>Strengths.</strong> Built for long-running conversational agents with
          persistent state. Social channel adapters are first-class — your agent can answer in
          Discord and Twitter without re-implementing each. Active open-source community
          producing character templates.
        </p>
        <p>
          <strong>Weaknesses.</strong> Heavier setup than any of the other four for one-shot
          use cases. The opinionated character/personality model is overkill if you just want
          tool calling. Documentation can lag the rapid pace of changes.
        </p>

        <h2 id="head-to-head">Head-to-head: which one for what scenario</h2>
        <p>
          We&apos;ll stop being diplomatic. Here&apos;s how I&apos;d actually pick:
        </p>
        <h3>Scenario A: You&apos;re building a chatbot in a Next.js app</h3>
        <p>
          → <strong>Vercel AI SDK.</strong> Anything else is over-engineering. The 5-minute
          setup, streaming integration with React, and zero-config tool calling makes it
          obvious. If you later need wallet operations, layer GOAT&apos;s Vercel adapter on
          top — that&apos;s a 2-line addition, not a rewrite.
        </p>
        <h3>Scenario B: You&apos;re building an autonomous trading agent on Base</h3>
        <p>
          → <strong>Coinbase AgentKit.</strong> CDP wallet integration is best-in-class for
          this scenario. x402 handling is automatic. You get the audit trail and regulatory
          context that institutional buyers want.
        </p>
        <h3>Scenario C: Your agent needs both EVM and Solana operations</h3>
        <p>
          → <strong>GOAT SDK.</strong> The whole point of GOAT is cross-chain. The same plugin
          code runs on Base mainnet and Solana mainnet with just a different wallet adapter.
        </p>
        <h3>Scenario D: You have a Python team and a quant research stack</h3>
        <p>
          → <strong>LangChain.</strong> The Python ecosystem advantage is real. If you&apos;re
          already using pandas, numpy, quantlib, scikit-learn — staying in Python with
          LangChain avoids cross-language friction.
        </p>
        <h3>Scenario E: You&apos;re building a Discord bot that prices options for users</h3>
        <p>
          → <strong>elizaOS.</strong> The social-channel adapters do the heavy lifting. Your
          tools handle option pricing; eliza handles the message routing, memory, and
          personality.
        </p>
        <h3>Scenario F: You want to be flexible about which AI client uses your tools</h3>
        <p>
          → <strong>MCP server.</strong> Technically not in the comparison because it&apos;s
          a transport rather than a framework, but worth mentioning: an MCP server (like
          QuantOracle&apos;s own) exposes tools to <em>any</em> MCP-compatible client (Claude
          Desktop, Cursor, Cline, Continue, etc.) without picking a single framework.
        </p>

        <h2 id="migration">Migration paths between frameworks</h2>
        <p>
          You will outgrow your first choice. Expect to migrate eventually. The good news:
          tools are the most portable part — the QuantOracle tool definitions look nearly
          identical across all five frameworks because all five eat Zod (or Pydantic) schemas
          plus an async function.
        </p>
        <h3>Vercel AI SDK → AgentKit</h3>
        <p>
          Trigger: you started with a simple chatbot, now you need to transact onchain.
          Migration is additive — your Vercel tools keep working; you add an `AgentKit`
          instance for the wallet operations and a separate `actionProvider` array. Most apps
          end up running both in parallel.
        </p>
        <h3>Vercel AI SDK → LangChain</h3>
        <p>
          Trigger: your data-science team needs to integrate. Tool-call schemas port mostly
          1:1 (Zod ↔ Pydantic with `zod-to-json-schema` and `pydantic.create_model`). Agent
          loops differ — Vercel&apos;s `maxSteps` becomes LangChain&apos;s
          `AgentExecutor(max_iterations=N)`. UI integration is the hard part if you had React
          streaming.
        </p>
        <h3>AgentKit → GOAT</h3>
        <p>
          Trigger: you added Solana support. AgentKit&apos;s ActionProvider pattern maps
          cleanly to GOAT&apos;s PluginBase. The wallet abstraction differs more — AgentKit
          uses its own wallet types, GOAT uses adapter packages — but the tool code itself is
          portable.
        </p>
        <h3>Any → MCP</h3>
        <p>
          Trigger: you want any AI client to use your tools, not just one framework. Wrap
          your tool functions in an MCP server and the same tools become available to Claude
          Desktop, Cursor, Cline, plus all five frameworks above (each has an MCP client
          adapter).
        </p>

        <h2 id="performance">Performance and bundle size</h2>
        <p>
          Rough numbers from our integration package builds (the framework code, not the tool
          code itself):
        </p>
        <ul>
          <li>
            <strong>Vercel AI SDK:</strong> 30 KB tool bundle (ESM). Cold start: 100ms.
            Per-tool-call overhead: negligible.
          </li>
          <li>
            <strong>AgentKit:</strong> 21 KB tool bundle. Cold start: 300ms (CDP wallet
            initialization). Per-tool-call: 5-10ms for wallet abstraction.
          </li>
          <li>
            <strong>GOAT SDK:</strong> 27 KB tool bundle (one plugin per bundle in our case).
            Cold start: 200ms. Per-tool-call: 5-10ms.
          </li>
          <li>
            <strong>LangChain:</strong> ~5 MB Python package weight. Cold start: 1-2 seconds.
            Per-tool-call: 10-30ms.
          </li>
          <li>
            <strong>elizaOS:</strong> Largest of all (full agent runtime). Cold start: 3+
            seconds. Per-message: 50-200ms including memory layer.
          </li>
        </ul>
        <p>
          These are mostly framework overhead. The actual tool call (HTTP to QuantOracle API)
          is 50-100ms regardless of framework — the bottleneck is the network and the LLM
          token-generation latency, not the framework.
        </p>

        <h2 id="cost">Cost considerations</h2>
        <p>
          All five frameworks are free and open-source. Costs come from:
        </p>
        <ul>
          <li>
            <strong>LLM API calls.</strong> Same regardless of framework. A tool-using agent
            typically spends 60-80% of its cost on LLM tokens, not the tools themselves.
          </li>
          <li>
            <strong>x402 micropayments.</strong> The two paid composite endpoints in
            QuantOracle cost $0.04 USDC each. Multiply by your agent&apos;s call rate. All five
            frameworks handle this transparently via the QuantOracle integration packages.
          </li>
          <li>
            <strong>Hosting.</strong> Vercel AI SDK runs on Vercel&apos;s edge for free up to
            their limits; AgentKit/GOAT/Eliza self-host; LangChain has LangServe (managed) or
            self-host. Hosting cost differences are negligible at small scale.
          </li>
        </ul>

        <h2 id="conclusion">The honest conclusion</h2>
        <p>
          There&apos;s no &quot;best&quot; framework — there&apos;s only the right one for your
          situation today. Pick the simplest framework that meets your current requirements
          and accept that you&apos;ll migrate later. The tools you write are portable; the
          framework is the disposable part.
        </p>
        <p>
          My actual recommendation for someone starting today: <strong>Vercel AI SDK if
          you&apos;re in JS/TS, LangChain if you&apos;re in Python.</strong> Both have
          ecosystems large enough that you&apos;ll never run out of integrations, and both are
          simple enough that your first working agent is hours away, not days. Add AgentKit /
          GOAT / Eliza when the onchain-specific or character-specific requirements appear.
        </p>

        <AffiliateCta subId="writing-agent-framework-comparison" category="compare" />

        <h2 id="related">Related</h2>
        <ul>
          <li>
            <Link href="/writing/vercel-ai-sdk-quant-tools" className="text-accent">
              Add reliable quant finance math to your Vercel AI SDK agent in 5 minutes
            </Link>
          </li>
          <li>
            <Link href="/writing/agentkit-reliable-quant-finance-math" className="text-accent">
              Give your Coinbase AgentKit agent reliable quant finance math in 10 minutes
            </Link>
          </li>
          <li>
            <Link href="/writing/chaining-x402-paid-tool-calls" className="text-accent">
              Chaining x402 paid tool calls — a working risk-audit → hedge demo
            </Link>
          </li>
          <li>
            <a
              href="https://github.com/QuantOracledev/quantoracle/tree/main/integrations"
              target="_blank"
              rel="noopener"
              className="text-accent"
            >
              All QuantOracle integration packages on GitHub
            </a>
          </li>
        </ul>
      </article>

      <WritingRelated slug="agent-framework-comparison-2026" />

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