
Agent Lightning: Microsoft’s “Trainer Gym” for AI Agents ⚡
Microsoft’s Agent Lightning is an open-source trainer layer for AI agents, using RL and fine-tuning to turn static LangChain/OpenAI agents into learning systems.
TOON is a compact, human-readable data format designed to reduce token usage and lower costs in AI and LLM workflows.

When we talk about data serialization formats, JSON tends to walk into the room wearing sunglasses like a rockstar — it’s everywhere, everyone supports it, and it works well enough. But the world has changed. We now feed structured data to Large Language Models (LLMs), and suddenly every token matters: cost, speed, context window… all are strategic constraints.
Enter TOON — Token-Oriented Object Notation — a compact and elegant data notation tailored for LLM interactions.
Think of it as the data format for conversations, not storage.
🪄 TOON reduces token usage without sacrificing structure or clarity.

{ "products": [
{
"id": 1,
"name": "Phone",
"price": 699
},
{
"id": 2,
"name": "Laptop",
"price": 1299
}
]
}
products[2]{id,name,price}:
1,Phone,699
2,Laptop,1299
Notice the philosophy:
It’s structured like JSON
But compact like CSV
And read like plain text
A rather elegant bridge
<name>[<count>]{<fields>}:
<row1 values>
<row2 values>
...
Yes — that’s nearly the entire grammar in one breath.
TOON works best when:
In other words, TOON loves tables.
When sending data to LLMs, each character turns into tokens, which affect:
TOON cuts token usage by removing redundancy.
It’s not uncommon to see 20%–60% token savings on large datasets.
This can translate to:
This is practical efficiency — not theoretical.
https://github.com/toon-format/toon
https://dev.to/sreeni5018/toon-vs-json-a-modern-data-format-showdown-2ooc
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