AIST – 950-token protocol for preserving AI session state
60x compression for AI context, but handoff format viability depends on LLM adoption.
TERSE - a hierarchical state language for humans and AI agents.
Token-efficient state format replacing Markdown for AI agent memory.
AI engineers and backend developers building agent memory systems
JSON · YAML · Protocol Buffers
I'm terse(greetings)
## Container
"A container may have one text block, single or multi-line"
Object(Semi-colon separated attributes; variables_like: 123; "Traditional unicode strings allowed here too.")
Another object with a name(multiple objects under a container)
## Further description of TERSE
"""
TERSE is a unified way to store state, with a defined way to query and mutate it that has been relentlessly refined for overall simplicity and token efficiency not just at rest, but in operation.
We realized state access was a low-level, common pattern that keeps getting reinvented. It goes deeper than just AI memory. Consider that Anthropic memory and others are a bunch of markdown files and in some respects, they got it right... simpler is better.
So REALLY the problem is settling on a format that is flexible to cover 95% of a domain. We can do so MUCH better than Markdown; TERSE is opinionated on that but it has very good reasons.
By being a line-ordered and line-identifiable structure (with special treatment for free-form blocks like this), now the whole state can be idempotently re-declared. Literally, this text of this post is a TERSE state declaration that an AI or you could make. Or, duh, just copy this to a .terse file!
So let's say you did that. To query this state:
> ? Hello World
To copy this whole state somewhere:
> # My new container
> ? Hello World //inline queries physically expand into the declaration
Directives are a [tail] with composable set of operations. For example, let's clean that up.. I declare thee removed!
> # My new container [REMOVED] // all contents too!
There's more. TERSE makes it exceeding easy for the AI to do stuff with state.
The MCP has one main op where these two things are sent *together*
1) state declarations
2) state queries (post-application of declarations and results unioned)
BTW, to query all state, wait for it...
> ? // Not that you want do this often!
TERSE has a full object model for (de)serialization. A reference Python implementation of the full specification is also included.
"""
## Example apps included(to help get you started; in the monorepo)
### TERSE MCP("store anything you want locally for your AI")
### TERSE Memory(a generalist; modify your own)
### TERSE Brain
"A Karpathy-brain compatible API but with TERSE as the backing store. 1/6th token use and 1/8th the number of tool calls!"
### Misc
TERSE Browser(query-response UI; great for testing) VS Code syntax highlighter(vsix included)
## Reminders
Terse is pre-release(alpha)
60x compression for AI context, but handoff format viability depends on LLM adoption.
Delegation chains with full visibility beat JWT, but switching costs are massive.
TOON format cuts LLM token costs 40-50%, but this is just Java support for an existing format.
KV-cache tool schemas once, reuse across requests: 29.2x speedup on 50 tools, flat 200ms TTFT.
Fuzzy opcode matching is a clever twist on standard esolang tokenization.
First LLM with per-token interpretability tracing input, concepts, and training provenance.