---
title: AgentKit vs LangChain vs Direct HTTP — picking the right integration for paid agent APIs
published: true
description: I built the same agent three ways using a real x402 API. Same question, same answer, three different integration patterns. Here's the honest comparison and the decision rule.
tags: langchain, agentkit, ai, webdev
cover_image:
canonical_url: https://dev.to/quantoracle/agentkit-vs-langchain-vs-direct-http-picking-the-right-integration-for-paid-agent-apis-2582
---

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---

When you're plugging an LLM agent into an external API, you have three reasonable patterns: hand-rolled HTTP, AgentKit's action provider model, or LangChain's tool calling. They all work. They produce identical outputs against the same input.

So which one should you actually use?

I built the exact same agent three different ways — answering the same Kelly Criterion question — and the answer to "which one" depends on your stack, your team, and (most underrated) your wallet model. Here's the honest comparison.

## The test case

Question: *"I have a 55% win rate, $150 average win, $100 average loss. What's my Kelly fraction?"*

Answer: **f* = 17.5%** (full Kelly), or **8.75%** (half-Kelly — what most quant funds actually use).

The math doesn't care which integration computes it. Kelly is a 1956 formula that fits in a tweet:

```
f* = (p · b − q) / b
```

Where p = win probability, q = 1-p, b = avg_win/avg_loss.

What changes between integrations is everything around the math: how the agent discovers the tool, how it formats inputs, how it handles errors, and — for paid services — how it pays.

## Pattern 1 — Direct HTTP

The minimum-viable integration:

```bash
curl -s -X POST https://api.quantoracle.dev/v1/risk/kelly \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "discrete",
    "win_rate": 0.55,
    "avg_win": 150,
    "avg_loss": 100
  }'
```

Response:

```json
{
  "full_kelly": 0.175,
  "half_kelly": 0.0875,
  "quarter_kelly": 0.0438,
  "edge": 32.5,
  "payoff_ratio": 1.5,
  "recommended": "HALF_KELLY",
  "ms": 8.2
}
```

**Pros:** zero dependencies. Zero auth setup (this API has a 1K-calls/day free tier). Works from any language. Easy to cache, easy to mock for tests.

**Cons:** you handle everything yourself. Schema validation, error retries, rate limit handling, payment if there are paid tiers. The LLM doesn't know about the endpoint — you're putting raw HTTP into your agent loop.

**When this is right:** you're building a deterministic backtest pipeline, a CI script, or any pre-prompted workflow where the agent doesn't need to discover tools at runtime. Or you're building a thin proxy that wraps a paid API for resale.

## Pattern 2 — Coinbase AgentKit (TypeScript)

This is where it gets interesting if your agent has a wallet:

```ts
import { AgentKit, CdpEvmWalletProvider } from "@coinbase/agentkit";
import { quantoracleActionProvider } from "./quantoracle";

const walletProvider = await CdpEvmWalletProvider.configureWithWallet({
  apiKeyId: process.env.CDP_API_KEY_ID!,
  apiKeySecret: process.env.CDP_API_KEY_SECRET!,
  networkId: "base-mainnet",
});

const agentkit = await AgentKit.from({
  walletProvider,
  actionProviders: [quantoracleActionProvider()],
});

// LLM picks `calculate_kelly` because the Zod schema's .describe()
// text matches "Kelly fraction" / "win rate" / "payoff"
const tools = await getLangChainTools(agentkit);
```

The LLM doesn't see raw HTTP. It sees an action called `calculate_kelly` with parameters documented via Zod schemas. AgentKit handles the network call and returns the parsed result.

**The killer feature:** the same agent can call paid endpoints (e.g. `assess_portfolio_risk` at $0.04 USDC) and AgentKit's wallet handles payment automatically via x402. The LLM doesn't write any payment code. No API key. No signup. No billing system. The wallet just needs USDC.

**Pros:** clean tool model for the LLM. Wallet-native payment (huge if you're building agents that need to pay other agents). Type-safe via TypeScript + Zod. Built-in tracing through LangChain.

**Cons:** TypeScript-only as of today. Adds the @coinbase/agentkit dependency tree. Wallet provisioning is one more thing to set up (though `CdpEvmWalletProvider.configureWithWallet` is basically two env vars).

**When this is right:** Coinbase-stack agents, x402-native agents, EVM or Solana wallets, autonomous trading bots that need to pay for premium tools, anything where wallet-native auth is the natural model.

## Pattern 3 — LangChain (Python)

For when you're in the Python ecosystem and want broad tool access:

```python
from langchain_quantoracle import QuantOracleToolkit
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

# All 73 tools from this API (63 calculators + 10 composites)
tools = QuantOracleToolkit().get_tools()

# Or filter
tools = QuantOracleToolkit(categories=["risk", "stats"]).get_tools()

llm = ChatOpenAI(model="gpt-4o", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "Use the provided tools for any math."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

response = executor.invoke({
    "input": "I have 55% win rate, $150 avg win, $100 avg loss — Kelly?"
})
```

LangChain's toolkit pattern is the established way to give an agent a curated set of tools. Pydantic schemas describe each tool to the LLM. The toolkit handles HTTP, retries, and error wrapping.

**Pros:** Python-native (huge for the quant + ML crowd). Composes with LangGraph for stateful workflows. Works with any LangChain-compatible LLM (OpenAI, Anthropic, local Llama, etc.). Exposes all 73 endpoints by default — useful when you don't want to pre-curate.

**Cons:** no native wallet integration — if you want paid endpoints, you handle x402 separately. The breadth (73 tools) can confuse smaller LLMs that aren't great at narrowing from many options.

**When this is right:** Python-native pipelines, multi-tool agents (combining this API with web search, file ops, vector DBs), LangGraph workflows, anywhere you want broad tool access without curating a subset.

## The decision rule

| Your situation | Use |
|---|---|
| Scripts / backtests / CI | Direct HTTP |
| Building on Coinbase / x402 / CDP wallets | AgentKit |
| Python, LangChain, or LangGraph workflows | LangChain Python |
| OpenAI custom GPT | GPT Actions (a 4th path I didn't cover here) |
| MCP client (Claude Desktop, Cursor) | MCP server (a 5th path) |
| Solana ecosystem, want sub-second x402 settlement | AgentKit with `SolanaKeypairWalletProvider` |

All paths hit the same underlying API. The math is byte-identical across integrations for the same inputs. **Pick by ergonomics and wallet model, not by capability.**

## Two patterns from production that combine these

These aren't mutually exclusive. In real systems you'll often use more than one:

### Backtest in Python, deploy in TypeScript

You develop your strategy in a Jupyter notebook with `langchain-quantoracle` (all 73 tools available, easy to explore). When you find an edge worth productionizing, you re-implement the agent in TypeScript with AgentKit (curated 5-tool subset, wallet-native payments). Same API answers both. Your research notebook and your production agent agree because they're hitting the same engine.

### Free tier for research, paid for production

The free tier (1K calls/IP/day) covers backtests across thousands of historical days. Once you've validated the strategy and want it running 24/7 as a paid signal service, you switch to the AgentKit + x402 pattern so the wallet pays per call. The economics scale linearly with usage instead of forcing a subscription decision.

## What this isn't about

This isn't about which framework is "better." All three are excellent. AgentKit's wallet integration is unique value if you need x402; LangChain's tool ecosystem is unique value if you're orchestrating many tools; raw HTTP is unique value when you want maximum control.

It's also not about quant finance specifically. The same decision rule applies to any external API your agent might use — weather, web search, on-chain data, image generation. The frameworks differ in how they help your agent *discover and pay for* tools, not in what those tools can do.

## My pick — and why

If I had to start from zero today:

1. **Python team, no wallet** → LangChain. Most flexible. Most existing tools.
2. **TypeScript team with crypto-native agents** → AgentKit. The wallet-paid x402 flow is genuinely magical when you have it working.
3. **Mixed team or no strong preference** → start with direct HTTP for the first integration, then add a framework when you have a second one. The HTTP version is your reference implementation either way.

The wrong move is "framework first, problem second." All three integrations work because the underlying API is well-designed. The framework is a thin layer on top. Pick the thin layer that matches the rest of your stack.

---

The QuantOracle API (the one I used for these examples) is at [quantoracle.dev](https://quantoracle.dev) — free tier of 1,000 calls per IP per day, no signup. All three integration paths are documented at [the repo](https://github.com/QuantOracledev/quantoracle/tree/main/integrations).

The AgentKit action provider files are [here](https://github.com/QuantOracledev/quantoracle/tree/main/integrations/agentkit). The Python LangChain toolkit is `pip install langchain-quantoracle`. The OpenAPI spec for the direct HTTP path is at [api.quantoracle.dev/openapi.json](https://api.quantoracle.dev/openapi.json).

What integration pattern are you using for your agent? Always curious how others land on the trade-off.
