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# gcf-rust
Rust implementation of [GCF](https://gcformat.com/) -- the most token-efficient wire format for LLMs. A drop-in alternative to JSON and TOON for any structured data.
**79% fewer input tokens than JSON. 63% fewer output tokens. 90.5% average comprehension accuracy across 10 models and 3 providers (four models hit 100%). 1,300+ LLM evaluations. Zero training.**
## Install
```toml
[dependencies]
gcf = "0.1"
```
Zero-copy where possible. Minimal dependencies (serde, serde_json). Don't want to change code? Use the [MCP proxy](https://github.com/blackwell-systems/gcf-proxy) for zero-code adoption.
## Quick Start
```rust
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}
```
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:
```rust
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
```rust
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:
```rust
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:
```rust
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:
```rust
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:
```rust
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}
```
Works on objects, arrays, and primitives. Arrays of uniform objects get tabular rows. Nested objects use `## key` section headers.
## API
| `encode(p: &Payload) -> String` | Encode a graph payload to GCF text |
| `encode_generic(data: &Value) -> String` | Encode any JSON value to GCF tabular format |
| `decode(input: &str) -> Result<Payload, DecodeError>` | Parse GCF text back to a Payload |
| `encode_with_session(p: &Payload, s: &Session) -> String` | Encode with session deduplication |
| `encode_delta(d: &DeltaPayload) -> String` | Encode a delta (added/removed only) |
| `Session::new() -> Session` | Create a new session tracker (thread-safe via Mutex) |
## Types
| `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 packs: added/removed symbols and edges |
| `Components` | Score breakdown: blast_radius, confidence, recency, distance |
| `Session` | Thread-safe tracker for multi-call deduplication |
| `DecodeError` | Enum of decode failure modes |
## Benchmarks
1,300+ LLM evaluations across 10 models, 3 providers, and 51 independent test runs.
| **Comprehension** (23 runs, 10 models) | **90.5%** | 68.5% | 53.6% |
| **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 all 6 datasets on [TOON's own benchmark](https://github.com/blackwell-systems/toon/tree/gcf-comparison). Full results: [gcformat.com/guide/benchmarks](https://gcformat.com/guide/benchmarks.html)
## Links
- [Documentation](https://gcformat.com/)
- [Playground](https://gcformat.com/playground.html)
- [Specification](https://github.com/blackwell-systems/gcf)
- [Go library](https://github.com/blackwell-systems/gcf-go)
- [TypeScript library](https://github.com/blackwell-systems/gcf-typescript)
- [Python library](https://github.com/blackwell-systems/gcf-python)
- [MCP Proxy](https://github.com/blackwell-systems/gcf-proxy) (zero-code adoption)
- [GCF vs TOON](https://gcformat.com/guide/vs-toon.html)
## License
MIT