ThinkLang vs Ax

A feature comparison between ThinkLang and Ax (ax-llm/ax).

Feature Comparison

Feature	ThinkLang	Ax
Structured Output	think<T>() with Zod or JSON Schema	gen() with schema
Type Safety	Full TypeScript generics	Partial
Agents	agent() + multi-agent orchestration	Multi-agent pipelines
Tool Calling	defineTool() with Zod input	Function calling
Providers	9+ built-in (Anthropic, OpenAI, Gemini, Groq, DeepSeek, Mistral, Together, OpenRouter, Ollama)	15+ providers
Custom Providers	OpenAICompatibleProvider base class	Adapter pattern
Prompt Optimization	optimizedThink() with few-shot examples	DSPy-style optimization
Vector Store / RAG	InMemoryVectorStore + indexText()	Built-in RAG pipeline
Observability	OpenTelemetry spans on all operations	OpenTelemetry support
Big Data	batch(), mapThink(), reduceThink(), Dataset	N/A
Streaming	streamThink(), streamInfer()	Streaming support
Testing Framework	Built-in .test.tl runner with semantic assertions	N/A
Language	.tl files with think keyword	N/A
Cost Tracking	Built-in per-call and aggregate cost tracking	N/A
Guards	Declarative output validation with retry	N/A
Confidence	Confident<T> type with .expect(), .or()	N/A

Code Comparison

Structured Output

ThinkLang:

import { think, zodSchema } from "thinklang";
import { z } from "zod";

const Sentiment = z.object({
  label: z.enum(["positive", "negative", "neutral"]),
  score: z.number(),
});

const result = await think<z.infer<typeof Sentiment>>({
  prompt: "Analyze sentiment",
  ...zodSchema(Sentiment),
  context: { text },
});

Ax:

import Anthropic from "@anthropic-ai/sdk";
import { AxAI, AxChainOfThought } from "@ax-llm/ax";

const ai = new AxAI({ name: "anthropic", apiKey: process.env.ANTHROPIC_API_KEY });
const gen = new AxChainOfThought(ai, "text -> label:string, score:number");
const result = await gen.forward({ text });

Agents

ThinkLang:

import { agent, defineTool } from "thinklang";

const result = await agent({
  prompt: "Research this topic",
  tools: [searchTool, writeTool],
  agents: [researcher, writer],
  maxTurns: 10,
});

What ThinkLang Does Differently

1. Type Safety First — Generic returns (think<T>(), agent<T>()) give you compile-time type checking on AI outputs.

2. Testing Framework — Write .test.tl files with semantic assertions. Snapshot fixtures enable deterministic replay without API calls.

3. The .tl Language — A dedicated programming language where think, infer, and reason are first-class keywords. The compiler catches type errors before you spend tokens.

4. Big Data Pipeline — batch(), mapThink(), reduceThink(), and Dataset make it easy to process thousands of items through AI with concurrency control and cost budgets.

5. Built-in Cost Tracking — Every call is tracked. Run thinklang cost-report to see aggregate spend by model and operation.