Resources // TERSE

Getting Started

TERSE is a language and a protocol, not a framework — which means there are three on-ramps, from zero-install to full agentic stacks. Start where you are. When in doubt, the cheatsheet and playground are one click away.

01In a prompt — zero install

TERSE was designed to be learned by the model, not compiled by a toolchain. Drop the written guide (or the compact prompt-reference pack) into any system prompt, keep your working state in a TERSE block, and every framework on this page — and every one that doesn't exist yet — is already supported.

system prompt
# paste from the spec repo:
# terse-spec/TERSE.md, or the compact
# terse-spec/prompt-reference/ pack

You keep all working state in TERSE.
Read with ? queries; change state by
declaration. What you do not restate,
you do not change.
state.terse — lives in context
# Cases
## 4412 outage(priority: high; open)
reporter(name: "Ada Reyes")

? Cases.* [WHERE Its.priority == "high"]
## 4412 outage(+resolved; -open)
This is the on-ramp most people should take first: no dependencies, works in a chat window, and it's how you'll develop an instinct for terse-ific modeling before you wire anything up.

02In your code — terse-py & the products

The reference implementation is Python. One clone gets you the parser, the shared MCP server, and the products built on top. All Apache-2.0.

shell — install
# the language + the shared MCP server
$ git clone https://github.com/terse-lang/terse && cd terse
$ pip install -e ./terse-py -e ./terse-mcp

# terse-memory — durable agent memory (one-shot setup)
$ pip install -e ./apps/terse-memory
$ terse-memory setup            # init → wire → doctor, one report

# terse-brain — the self-updating librarian
$ pip install -e ./apps/terse-brain
$ terse-brain init && terse-brain wire && terse-brain doctor
$ terse-brain register-source ./notes/paper.pdf --kind article
$ terse-brain lint              # six drift checks, any time
The store is a file. Open it, diff it, git it — the same CLI the agent uses is the one you debug with. Memory disputes end with a query, not a support ticket.

03In your agent stack — one MCP server, every host

terse-mcp serves any number of stores under named namespaces (brain, memory, per-project, …). Every MCP-speaking host that connects is told at handshake time where the state lives and how to read it — no per-host adapter required.

shell — wire a namespace
$ terse-mcp map brain ./brain.terse     # namespace → store
# hosts pick it up at the FastMCP handshake — restart the host,
# and agents call terse_command with namespace="brain"

Where TERSE slots into the stacks you already run. TERSE doesn't compete with your orchestrator — it's the state and memory layer underneath it. These are integration patterns, not shipped adapters: the language is deliberately framework-agnostic, and the pattern is the same everywhere — state in a store, read with queries, write by declaration.

MCP hosts — Claude Code · Cowork · any client Native today

First-class support via terse-mcp. terse-brain and terse-memory ship host plugins with agent skills; CodeBoss orchestrates Claude Code headless against the same stores.

Claude Agent SDK Native today

Agents connect to terse-mcp like any MCP server. The [primer] handshake tells every session where memory lives — no lifecycle hooks needed.

LangGraph Pattern

Keep graph state in a TERSE store instead of opaque JSON blobs: checkpoint nodes read with ? queries, write by declaration — and your state history becomes diffable, auditable text.

OpenAI Agents SDK Pattern

Pass TERSE state across handoffs so each agent inherits organized context, not a transcript. Retrieval size guards pair naturally with the SDK's guardrail model.

Microsoft Agent Framework Pattern

Strict state management is the whole pitch on both sides: TERSE files as the durable, legible state layer under graph-based workflows in Azure-native environments.

Google ADK Pattern

Hierarchical agents, hierarchical state: mirror the agent tree in # containers so each sub-agent owns its branch and parents see the whole.

LlamaIndex Workflows Pattern

Run TERSE as the structured sidecar to your index: extracted claims cite their sources, disputes are flagged not overwritten, and syntheses compound across runs — the terse-brain pattern, in your pipeline.

CrewAI Pattern

Role-based crews share one store: each role reads its own container, writes by declaration, and silence preserves keeps agents from trampling each other's state.

Building an adapter for one of these stacks? Tell us — the spec is Apache-2.0 precisely so you can. Want one built for your enterprise? That's what our services team does.