gcf-kotlin

July 18, 2026 · View on GitHub

gcf-kotlin

Kotlin/JVM implementation of GCF, the most token-efficient wire format for LLMs. A drop-in alternative to JSON and TOON for any structured data.

gcf-kotlin

Built for the agentic loop, where the same structured context crosses the model boundary turn after turn. A single payload is 50-92% smaller than JSON, but GCF also deduplicates repeated structure across turns and sends only deltas when context changes, so by the 5th overlapping call each response costs 99% fewer tokens than JSON, and a 10-call session runs 94.4% cheaper than re-sending JSON every turn. Session dedup and delta both need local IDs and a multi-turn design that neither JSON nor TOON has.

100% comprehension on every frontier model, zero training. 29% fewer tokens than TOON and 56% fewer than JSON across 16 datasets; 91.2% on structurally complex code graphs (vs TOON 68.8%, JSON 54.1%).
Proven lossless across 43,000,000,000+ round-trips in 5 formats and 6 languages. Zero runtime dependencies.
One format, four properties no other single format holds at once: schema-free, lossless, token-compact (50-92% vs JSON), and model-readable with zero training. JSON is verbose, Protobuf needs a schema, MessagePack is binary, and TOON isn't reliably lossless.

2,500+ LLM evaluations. Full benchmarks.

Docs: gcformat.com · Playground · GCF vs TOON

Install

Add the JitPack repository, then the dependency:

Gradle (Kotlin DSL)

repositories {
    maven("https://jitpack.io")
}

dependencies {
    implementation("com.github.blackwell-systems:gcf-kotlin:v2.4.0")
}

Gradle (Groovy)

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.blackwell-systems:gcf-kotlin:v2.4.0'
}

Maven

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependency>
    <groupId>com.github.blackwell-systems</groupId>
    <artifactId>gcf-kotlin</artifactId>
    <version>v2.4.0</version>
</dependency>

Don't want to change code? Use the MCP proxy for zero-code adoption.

Quick Start

import com.blackwellsystems.gcf.*

val output = encodeGeneric(mapOf(
    "employees" to listOf(
        mapOf("id" to 1, "name" to "Alice", "department" to "Engineering", "salary" to 95000),
        mapOf("id" to 2, "name" to "Bob", "department" to "Sales", "salary" to 72000),
    )
))

Output:

## employees [2]{department,id,name,salary}
Engineering|1|Alice|95000
Sales|2|Bob|72000

Graph Profile

val payload = Payload(
    tool = "context_for_task", tokenBudget = 5000, tokensUsed = 1847,
    symbols = listOf(
        Symbol(qualifiedName = "pkg.Auth", kind = "function", score = 0.78, provenance = "lsp", distance = 0),
        Symbol(qualifiedName = "pkg.Server", kind = "function", score = 0.54, provenance = "lsp", distance = 1),
    ),
    edges = listOf(Edge(source = "pkg.Server", target = "pkg.Auth", edgeType = "calls"))
)
val output = encode(payload)

Output:

GCF tool=context_for_task budget=5000 tokens=1847 symbols=2 edges=1
## targets
@0 fn pkg.Auth 0.78 lsp
## related
@1 fn pkg.Server 0.54 lsp
## edges [1]
@0<@1 calls

Decode

val p = decode(input)
println("${p.tool} ${p.symbols.size} symbols ${p.edges.size} edges")

Throws DecodeException on invalid input.

Session Deduplication

Track transmitted symbols across multiple tool responses. Previously-sent symbols become bare references instead of full declarations:

val session = Session()

val out1 = encodeWithSession(payload1, session) // full declarations
val out2 = encodeWithSession(payload2, session) // reused symbols as "@N  # previously transmitted"

By the 5th call in a session: 86% fewer tokens than JSON from dedup alone, 99% stacked with delta encoding.

Streaming Encode

Write GCF output incrementally as symbols and edges arrive. Zero buffering, O(1) memory per row:

val enc = StreamEncoder(writer, "context_for_task", StreamOptions(tokenBudget = 5000))

enc.writeSymbol(Symbol(qualifiedName = "pkg.Auth", kind = "function", score = 0.95, provenance = "lsp", distance = 0))
enc.writeEdge(Edge(source = "pkg.Server", target = "pkg.Auth", edgeType = "calls"))
enc.close()  // emits ##! summary trailer

Output uses [?] deferred counts and ##! summary trailer. Standard decode() handles streaming output with no changes. Thread-safe via @Synchronized.

Delta Encoding

When the consumer already has a prior context pack, send only what changed:

val delta = DeltaPayload(
    tool = "context_for_task",
    baseRoot = "aaa111",
    newRoot = "bbb222",
    removed = listOf(Symbol(qualifiedName = "pkg.OldFunc", kind = "function")),
    added = listOf(Symbol(qualifiedName = "pkg.NewFunc", kind = "function", score = 0.85, provenance = "rwr")),
    deltaTokens = 30,
    fullTokens = 200
)

val output = encodeDelta(delta)

81.2% savings on re-queries where the pack changed slightly.

Generic Encoding

Encode any value (not just graph payloads) into GCF tabular format:

val data = mapOf(
    "employees" to listOf(
        mapOf("id" to 1, "name" to "Alice", "department" to "Engineering", "salary" to 95000),
        mapOf("id" to 2, "name" to "Bob", "department" to "Sales", "salary" to 72000),
    )
)
val output = encodeGeneric(data)

Output:

## employees [2]{department,id,name,salary}
Engineering|1|Alice|95000
Sales|2|Bob|72000

Works on maps, lists, and primitives. Arrays of uniform maps get tabular rows. Nested maps use ## key section headers.

Generic-Profile Delta (multi-turn)

In an agent loop the same keyed table gets re-queried turn after turn. Instead of re-sending the whole table each time, send only the changed rows (SPEC §10a):

val base = GenericSet(key = "id", fields = listOf("id", "status"), rows = listOf(
    mapOf("id" to 1001, "status" to "pending"),
    mapOf("id" to 1002, "status" to "shipped"),
))
val next = GenericSet(key = "id", fields = listOf("id", "status"), rows = listOf(
    mapOf("id" to 1001, "status" to "shipped"),   // changed
    mapOf("id" to 1003, "status" to "pending"),   // added (1002 removed)
))

val d = diffGenericSets(base, next)              // the blessed producer path
val wire = encodeGenericDelta(d)                 // ## added / ## changed / ## removed
val held = verifyGenericDelta(base, d, d.newRoot) // atomic apply + new_root verification

Opt-in and bilateral, keyed on content-addressed pack roots. By the 5th overlapping call, ~97% fewer tokens than re-sending JSON. Byte-for-byte interoperable with the Go, Python, TypeScript, Rust, and Swift SDKs.

Re-anchor session helper

GenericDeltaSession manages the delta/re-anchor cadence for you: each next() returns either a compact delta or, on its cadence, a full re-anchor (which re-grounds the consumer), updating its held base.

val sess = GenericDeltaSession(base, tool = "orders", policy = ReanchorPolicy.SizeGuard)
val establish = sess.currentFull()               // transmit the base once to establish it
for (snapshot in stream) {                        // each turn's current GenericSet
    val (wire, isFull) = sess.next(snapshot)      // a compact delta, or a periodic full re-anchor
}

ReanchorPolicy.FixedN(15) re-anchors every N turns; ReanchorPolicy.SizeGuard (recommended) re-anchors once the cumulative delta reaches a full payload's size. It introduces no new wire syntax and the decoder stays cadence-agnostic, so a re-anchor is just the protocol's "full" outcome on a schedule. A schema change forces a full (§10a.7).

API

Function	Description
`encode(payload: Payload): String`	Encode a graph payload to GCF text
`encodeGeneric(data: Any?): String`	Encode any value to GCF tabular format
`decode(input: String): Payload`	Parse GCF text back to a Payload
`encodeWithSession(payload: Payload, session: Session?): String`	Encode with session deduplication
`encodeDelta(delta: DeltaPayload): String`	Encode a graph delta (added/removed only)
`diffGenericSets(base: GenericSet, next: GenericSet): GenericDeltaPayload`	Diff two keyed record sets (generic profile)
`encodeGenericDelta(d: GenericDeltaPayload): String` / `decodeGenericDelta(s: String): GenericDeltaPayload`	Generic-profile delta wire (§10a)
`verifyGenericDelta(base: GenericSet, d: GenericDeltaPayload, root: String): GenericSet`	Atomic apply + `new_root` verification
`GenericDeltaSession(base, tool, policy)`	Producer-side re-anchor cadence helper (§10a.8)
`Session()`	Create a new session tracker (thread-safe)

Types

Type	Purpose
`Payload`	Full GCF payload: tool, budget, symbols, edges, pack root
`Symbol`	Graph node: qualified name, kind, score, provenance, distance
`Edge`	Directed relationship: source, target, edge type
`DeltaPayload`	Diff between two graph packs: added/removed symbols and edges
`GenericSet` / `GenericDeltaPayload`	Keyed record set and its generic-profile diff (§10a)
`GenericDeltaSession`	Stateful producer that schedules delta vs full re-anchor (§10a.8)
`ReanchorPolicy`	Re-anchor cadence: `FixedN(n)` or `SizeGuard`
`Session`	Thread-safe tracker for multi-call deduplication
`Components`	Score breakdown: blast radius, confidence, recency, distance
`DecodeException`	Thrown on invalid GCF input
`kindAbbrev` / `kindExpand`	Bidirectional kind abbreviation maps

Benchmarks

2,500+ LLM evaluations across 11 models, 4 providers, and 50+ independent test runs.

	GCF	TOON	JSON
Comprehension (23 runs, 10 models)	91.2%	68.8%	54.1%
Generation (28 runs, 9 models)	5/5	1.0/5	5.0/5
Input tokens (500 symbols)	11,090	16,378	53,341
Output tokens (100 symbols)	5,976	8,937	16,121

GCF wins 15/16 datasets on the expanded token efficiency benchmark. Full results: gcformat.com/guide/benchmarks

Implementations

Language	Package	Repository
Go	`go get github.com/blackwell-systems/gcf-go`	gcf-go
TypeScript	`npm install @blackwell-systems/gcf`	gcf-typescript
Python	`pip install gcf-python`	gcf-python
Rust	`cargo add gcf`	gcf-rust
Swift	Swift Package Manager	gcf-swift
Kotlin	JitPack	gcf-kotlin
MCP Proxy	`pip install gcf-proxy`	gcf-proxy (bidirectional, session dedup, HTTP frontend)
Claude Code Plugin	`/plugin install`	gcf-claude-plugin (one-command install, session stats hook)
Codex Plugin	`codex plugin add`	gcf-codex-plugin (one-command install, session stats hook)
VS Code	`ext install blackwell-systems.gcf-vscode`	gcf-vscode (syntax highlighting)
n8n	`npm install n8n-nodes-gcf`	gcf-n8n-nodes (workflow encode/decode)
Tree-sitter	`npm install tree-sitter-gcf`	tree-sitter-gcf

Zero runtime dependencies. Permanently. All six implementations depend only on their language's standard library. No transitive dependencies. No supply chain risk. This is a permanent commitment: GCF will never take on external runtime dependencies. MIT licensed. All implementations support both generic profile (encodeGeneric) and graph profile (encode). CLI included in all 6 languages.

Specification: SPEC v3.4.1 Stable with 204 conformance fixtures, 43,000,000,000+ lossless round-trips verified across 5 formats and 6 languages. All implementations at v2.4.0+ (Go v1.5.0). Cross-language 6x6 matrix verified.

Adopted by

Project
Chrome DevTools MCP	47K★ · the Google Chrome DevTools team's MCP server; exposes live browser state (DOM, network, console, performance) to AI coding agents
Speakeasy	OpenAPI tooling (customers include Google, Verizon, Mistral AI, DocuSign, Vercel); GCF is a native output format in their `oq` CLI
OmniRoute	17K★ · AI gateway, registry, and proxy between AI clients and model providers; GCF vendored into its compression engine
NetClaw	610★ · AI-powered network automation (113 skills, 66 MCP integrations); replaced TOON with GCF across every MCP server
ctx	552★ · real-time context selector for Claude Code; surfaces only the relevant tools from a 103K-node knowledge graph
Lynkr	531★ · local LLM gateway for AI coding clients; GCF as a drop-in tool-result compressor alongside TOON
Open Data Products SDK	Linux Foundation · Python toolkit and MCP server for data-product standards; GCF sidecars for agent context
NeuroNest	agent-first IDE; first commercial GCF adoption, across four encoding surfaces with session dedup and delta
Raycast	JSON-to-GCF Converter extension in the Raycast Store, for the macOS productivity launcher

See all adopters →

License

MIT - Dayna Blackwell