gcf-rust

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

100% comprehension on every frontier model tested. 25.5% fewer tokens than TOON, 53% fewer than JSON across 15 datasets. 90.7% on structurally complex code graphs (vs TOON 68.5%, JSON 53.6%). 1,700+ LLM evaluations. Zero training.

Docs: gcformat.com | Playground | GCF vs TOON

Install

[dependencies]
gcf = "0.1"

Zero-copy where possible. Minimal dependencies (serde, serde_json). Don't want to change code? Use the MCP proxy for zero-code adoption.

Quick Start

use gcf::encode_generic;
use serde_json::json;

let data = json!({
    "employees": [
        {"id": 1, "name": "Alice", "department": "Engineering", "salary": 95000},
        {"id": 2, "name": "Bob", "department": "Sales", "salary": 72000},
    ],
});
let output = encode_generic(&data);

Output:

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

Works on any serde_json::Value. One header declares field names, rows are positional values.

Graph Profile

For code graph data with symbols, edges, and distance groups:

use gcf::{Payload, Symbol, Edge, encode};

let p = Payload {
    tool: "context_for_task".into(), token_budget: 5000, tokens_used: 1847,
    symbols: vec![
        Symbol { qualified_name: "pkg.Auth".into(), kind: "function".into(), score: 0.78, provenance: "lsp".into(), distance: 0, ..Default::default() },
        Symbol { qualified_name: "pkg.Server".into(), kind: "function".into(), score: 0.54, provenance: "lsp".into(), distance: 1, ..Default::default() },
    ],
    edges: vec![Edge { source: "pkg.Server".into(), target: "pkg.Auth".into(), edge_type: "calls".into(), ..Default::default() }],
    ..Default::default()
};
let output = encode(&p);

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

use gcf::decode;

let p = decode(input).expect("valid GCF");
println!("{} {} symbols {} edges", p.tool, p.symbols.len(), p.edges.len());

Session Deduplication

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

use gcf::{Session, encode_with_session};

let sess = Session::new();

let out1 = encode_with_session(&payload1, &sess); // full declarations
let out2 = encode_with_session(&payload2, &sess); // reused symbols as "@N  # previously transmitted"

By the 5th call in a session: 92.7% token savings vs JSON.

Streaming Encode

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

use gcf::{StreamEncoder, StreamOptions, Symbol, Edge};

let enc = StreamEncoder::new(writer, "context_for_task", StreamOptions {
    token_budget: 5000,
    ..Default::default()
});

enc.write_symbol(&Symbol { qualified_name: "pkg.Auth".into(), kind: "function".into(), score: 0.95, provenance: "lsp".into(), distance: 0, ..Default::default() });
enc.write_edge(&Edge { source: "pkg.Server".into(), target: "pkg.Auth".into(), edge_type: "calls".into(), ..Default::default() });
enc.close();

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

Delta Encoding

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

use gcf::{DeltaPayload, Symbol, encode_delta};

let delta = DeltaPayload {
    tool: "context_for_task".to_string(),
    base_root: "aaa111".to_string(),
    new_root: "bbb222".to_string(),
    removed: vec![Symbol {
        qualified_name: "pkg.OldFunc".to_string(),
        kind: "function".to_string(),
        score: 0.0,
        provenance: String::new(),
        distance: 0,
        signature: String::new(),
        components: Default::default(),
    }],
    added: vec![Symbol {
        qualified_name: "pkg.NewFunc".to_string(),
        kind: "function".to_string(),
        score: 0.85,
        provenance: "rwr".to_string(),
        distance: 0,
        signature: String::new(),
        components: Default::default(),
    }],
    removed_edges: vec![],
    added_edges: vec![],
    delta_tokens: 30,
    full_tokens: 200,
};

let output = encode_delta(&delta);

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

Generic Encoding

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

use gcf::encode_generic;
use serde_json::json;

let data = json!({
    "employees": [
        {"id": 1, "name": "Alice", "department": "Engineering", "salary": 95000},
        {"id": 2, "name": "Bob", "department": "Sales", "salary": 72000},
    ],
});
let output = encode_generic(&data);

Output:

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

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

API

Types

Benchmarks

1,700+ LLM evaluations across 10 models, 3 providers, and 51 independent test runs.

GCF wins 13/15 datasets on the expanded token efficiency benchmark. Full results: gcformat.com/guide/benchmarks

Implementations

Zero runtime dependencies. MIT licensed. All implementations support both generic profile (encodeGeneric) and graph profile (encode). CLI included in all 6 languages.

Specification: SPEC v3.1 Stable with 157 conformance fixtures, 33,000,000,000+ lossless round-trips verified across 5 formats and 6 languages. All implementations at v2.1.0+ (Go v1.2.0). Cross-language 6x6 matrix verified.

License

MIT - Dayna Blackwell