aumontzey8765

aumontzey8765 / few-shot-agent-ts

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TypeScript few-shot learning for LLM agents: semantic example retrieval, SQLite/Postgres stores, Vercel AI SDK.

ai-agents embeddings few-shot-learning llm postgresql
20
0
100% credibility
Found Apr 18, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

A TypeScript framework for creating AI agents that learn specific tasks from a small set of input-output examples using similarity-based retrieval to build adaptive prompts.

How It Works

1
🔍 Discover the tool

You find a helpful kit that lets an AI learn new tasks just by showing it a few examples, like teaching a smart assistant tricks without long training.

2
💻 Set it up on your computer

You grab the kit and prepare it quickly on your machine so it's ready to use.

3
🔗 Connect an AI brain

You link it to a thinking service so the AI can understand and generate responses.

4
📖 Teach it with examples

You share a handful of real input-and-output pairs for your task, like 'question: answer' demos, and it remembers them smartly.

5
💬 Chat or serve it up

You pick chatting directly with it or turning it into a helper service others can use over the web.

6
❓ Give it new challenges

You hand it fresh inputs similar to your examples, and it pulls the best matches to craft perfect replies.

🎉 AI adapts instantly

Your assistant nails new tasks using only your few examples, saving time and getting smarter with each success.

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Star Growth

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AI-Generated Review

What is few-shot-agent-ts?

few-shot-agent-ts is a TypeScript library for building LLM agents that adapt to tasks using just a few examples, skipping fine-tuning entirely. You store input-output pairs in memory, SQLite, or Postgres, and it handles semantic retrieval via embeddings to pull the best matches and inject them into prompts powered by Vercel AI SDK. Developers get a ready-to-use agent with CLI for quick testing, HTTP API endpoints for /run and example management, and Docker for easy deployment—perfect for TypeScript GitHub actions or SDK integrations.

Why is it gaining traction?

It stands out with persistent example stores across local-first SQLite or scalable Postgres, plus optional online learning to append successful runs automatically. Semantic retrieval beats basic keyword matching, delivering consistent few-shot prompts with configurable top-k similarity thresholds and caching. The Vercel AI SDK backbone means seamless model swaps like OpenAI GPT-4o-mini, making it a lightweight TypeScript GitHub example for LLM agent prototyping without vendor lock-in.

Who should use this?

AI engineers crafting task-specific agents for JSON extraction, code generation, or data parsing in TypeScript GitHub workflows. Backend devs integrating few-shot learning into Vercel or Node apps needing example retrieval for dynamic prompts. Teams experimenting with LLM agents in GitHub Copilot extensions or API services, where quick CLI onboarding and REST endpoints speed up iteration.

Verdict

Grab it for early-stage TypeScript LLM agent experiments—solid docs, CLI, API, and tests make setup painless despite 20 stars and 1.0% credibility score. Maturity is low, so expect tweaks for production; still, a smart few-shot retrieval SDK for devs tired of static prompts.

(198 words)

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