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Zero dependencies.","maintainers":[{"name":"daynajblackwell","email":"dayna@blackwell-systems.com"}],"readme":"

\n \"Playground\"\n \"Benchmarks\"\n \"npm\"\n \"License\"\n

\n\n

\n \"gcf-typescript\"\n

\n\n# gcf-typescript\n\nTypeScript 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.\n\n

\n \"\"\n

\n\n

\n \"gcf-typescript\"\n

\n\n

\n \"\"\n

\n\n**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.\n\n- **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%).\n- **Proven lossless** across 43,000,000,000+ round-trips in 5 formats and 6 languages. Zero runtime dependencies.\n- **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.\n\n2,500+ LLM evaluations. [Full benchmarks](https://gcformat.com/guide/benchmarks.html).\n\nDocs: [gcformat.com](https://gcformat.com/) · [Playground](https://gcformat.com/playground.html) · [GCF vs TOON](https://gcformat.com/guide/vs-toon.html)\n\n## Install\n\n```\nnpm install @blackwell-systems/gcf\n```\n\nZero dependencies. TypeScript-first. Includes CLI. Don't want to change code? Use the [MCP proxy](https://github.com/blackwell-systems/gcf-proxy) for zero-code adoption.\n\n## CLI\n\n```bash\nnpx @blackwell-systems/gcf encode < payload.json # JSON to GCF\nnpx @blackwell-systems/gcf decode < payload.gcf # GCF to JSON\nnpx @blackwell-systems/gcf stats < payload.json # token comparison\n```\n\n```\nPayload: 50 symbols, 20 edges\n\n JSON ██████████████████████████████ 4,200 tokens\n GCF ████████░░░░░░░░░░░░░░░░░░░░░░ 1,150 tokens\n\n Savings: 73% fewer tokens with GCF\n```\n\nOr install globally: `npm install -g @blackwell-systems/gcf` then use `gcf` directly.\n\n## Library\n\n### Quick Start\n\n```typescript\nimport { encodeGeneric } from '@blackwell-systems/gcf';\n\nconst output = encodeGeneric({\n employees: [\n { id: 1, name: 'Alice', department: 'Engineering', salary: 95000 },\n { id: 2, name: 'Bob', department: 'Sales', salary: 72000 },\n ],\n});\n```\n\nOutput:\n```\n## employees [2]{id,name,department,salary}\n1|Alice|Engineering|95000\n2|Bob|Sales|72000\n```\n\n## Decode\n\n```typescript\nimport { decode } from '@blackwell-systems/gcf';\n\nconst p = decode(input);\nconsole.log(p.tool, p.symbols.length, 'symbols', p.edges.length, 'edges');\n```\n\n## Session Deduplication\n\nTrack transmitted symbols across multiple tool responses. Previously-sent symbols become bare references instead of full declarations:\n\n```typescript\nimport { Session, encodeWithSession } from '@blackwell-systems/gcf';\n\nconst sess = new Session();\n\nconst out1 = encodeWithSession(payload1, sess); // full declarations\nconst out2 = encodeWithSession(payload2, sess); // reused symbols as \"@N # previously transmitted\"\n```\n\nBy the 5th call in a session: 86% fewer tokens than JSON from dedup alone, 99% stacked with delta encoding.\n\n## Streaming Encode\n\nWrite GCF output incrementally as symbols and edges arrive. Zero buffering, O(1) memory per row. Ideal for MCP servers that walk large graphs or paginate results:\n\n```typescript\nimport { StreamEncoder } from '@blackwell-systems/gcf';\n\nconst enc = new StreamEncoder(writer, 'context_for_task', { tokenBudget: 5000 });\n\n// Symbols emit immediately as they're discovered.\nenc.writeSymbol({ qualifiedName: 'pkg.Auth', kind: 'function', score: 0.95, provenance: 'lsp', distance: 0 });\nenc.writeSymbol({ qualifiedName: 'pkg.Server', kind: 'function', score: 0.60, provenance: 'lsp', distance: 1 });\n\n// Edges emit immediately too.\nenc.writeEdge({ source: 'pkg.Server', target: 'pkg.Auth', edgeType: 'calls' });\n\n// Close emits the ##! summary trailer with final counts.\nenc.close();\n```\n\nOutput:\n```\nGCF tool=context_for_task budget=5000\n## targets\n@0 fn pkg.Auth 0.95 lsp\n## related\n@1 fn pkg.Server 0.60 lsp\n## edges [?]\n@0<@1 calls\n##! summary symbols=2 edges=1 counts=1,1,1\n```\n\nThe `writer` is any object with a `write(s: string)` method (Node.js streams, web WritableStreams, or a simple callback). Standard `decode()` handles streaming output with no changes.\n\n## Delta Encoding\n\nWhen the consumer already has a prior context pack, send only what changed:\n\n```typescript\nimport { encodeDelta, type DeltaPayload } from '@blackwell-systems/gcf';\n\nconst delta: DeltaPayload = {\n tool: 'context_for_task',\n baseRoot: 'aaa111',\n newRoot: 'bbb222',\n removed: [{ qualifiedName: 'pkg.OldFunc', kind: 'function', score: 0, provenance: '', distance: 0 }],\n added: [{ qualifiedName: 'pkg.NewFunc', kind: 'function', score: 0.85, provenance: 'rwr', distance: 0 }],\n removedEdges: [],\n addedEdges: [],\n deltaTokens: 30,\n fullTokens: 200,\n};\n\nconst output = encodeDelta(delta);\n```\n\n81.2% savings on re-queries where the pack changed slightly.\n\n## Generic Encoding\n\nEncode any JS value (not just graph payloads) into GCF tabular format:\n\n```typescript\nimport { encodeGeneric } from '@blackwell-systems/gcf';\n\nconst output = encodeGeneric({\n employees: [\n { id: 1, name: 'Alice', department: 'Engineering', salary: 95000 },\n { id: 2, name: 'Bob', department: 'Sales', salary: 72000 },\n ],\n});\n```\n\nOutput:\n```\n## employees [2]{id,name,department,salary}\n1|Alice|Engineering|95000\n2|Bob|Sales|72000\n```\n\nWorks on objects, arrays, and primitives. Arrays of uniform objects get tabular rows. Nested objects use `## key` section headers.\n\n## Generic-Profile Delta (multi-turn)\n\nIn 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):\n\n```typescript\nimport {\n diffGenericSets,\n encodeGenericDelta,\n verifyGenericDelta,\n type GenericSet,\n} from '@blackwell-systems/gcf';\n\nconst base: GenericSet = {\n key: 'id',\n fields: ['id', 'status'],\n rows: [\n { id: 1001, status: 'pending' },\n { id: 1002, status: 'shipped' },\n ],\n};\nconst next: GenericSet = {\n key: 'id',\n fields: ['id', 'status'],\n rows: [\n { id: 1001, status: 'shipped' }, // changed\n { id: 1003, status: 'pending' }, // added (1002 removed)\n ],\n};\n\nconst d = diffGenericSets(base, next); // GenericDeltaPayload\nconst wire = encodeGenericDelta(d); // ## added / ## changed / ## removed\nconst held = verifyGenericDelta(base, d, d.newRoot); // atomic apply + new_root verification\n```\n\nOpt-in and bilateral, keyed on content-addressed pack roots. By the 5th overlapping call, ~97% fewer tokens than re-sending JSON.\n\n### Re-anchor session helper\n\n`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.\n\n```typescript\nimport { GenericDeltaSession, sizeGuard } from '@blackwell-systems/gcf';\n\nconst sess = new GenericDeltaSession(base, 'orders', sizeGuard());\nlet wire = sess.currentFull(); // transmit the base once to establish it\nfor (const snapshot of stream) { // each turn's current GenericSet\n const { wire, isFull } = sess.next(snapshot); // a compact delta, or a periodic full re-anchor\n}\n```\n\n`fixedN(15)` re-anchors every N turns; `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.\n\n## API\n\n| Function | Description |\n|----------|-------------|\n| `encode(p: Payload): string` | Encode a graph payload to GCF text |\n| `encodeGeneric(data: unknown): string` | Encode any value to GCF tabular format |\n| `decode(input: string): Payload` | Parse GCF text back to a Payload |\n| `encodeWithSession(p: Payload, s: Session): string` | Encode with session deduplication |\n| `new StreamEncoder(w, tool, opts)` | Create a streaming encoder (zero-buffering) |\n| `encodeDelta(d: DeltaPayload): string` | Encode a delta (added/removed only) |\n| `diffGenericSets(base, next): GenericDeltaPayload` | Diff two keyed record sets (generic profile) |\n| `encodeGenericDelta(d): string` / `decodeGenericDelta(s)` | Generic-profile delta wire (§10a) |\n| `verifyGenericDelta(base, d, root): GenericSet` | Atomic apply + `new_root` verification |\n| `new GenericDeltaSession(base, tool, policy)` | Producer-side re-anchor cadence helper (§10a.8) |\n| `new Session()` | Create a new session tracker |\n\n## Types\n\n| Type | Purpose |\n|------|---------|\n| `Payload` | Full GCF payload: tool, budget, symbols, edges, pack root |\n| `Symbol` | Graph node: qualified name, kind, score, provenance, distance |\n| `Edge` | Directed relationship: source, target, edge type |\n| `DeltaPayload` | Diff between two packs: added/removed symbols and edges |\n| `GenericSet` / `GenericDeltaPayload` | Keyed record set and its generic-profile diff (§10a) |\n| `GenericDeltaSession` | Stateful producer that schedules delta vs full re-anchor (§10a.8) |\n| `Session` | Tracker for multi-call deduplication |\n| `KIND_ABBREV` / `KIND_EXPAND` | Bidirectional kind abbreviation maps |\n\n## Benchmarks\n\n2,500+ LLM evaluations across 11 models, 4 providers, and 50+ independent test runs.\n\n| | GCF | TOON | JSON |\n|---|---|---|---|\n| **Comprehension** (23 runs, 10 models) | **91.2%** | 68.8% | 54.1% |\n| **Generation** (28 runs, 9 models) | **5/5** | 1.0/5 | 5.0/5 |\n| **Input tokens** (500 symbols) | **11,090** | 16,378 | 53,341 |\n| **Output tokens** (100 symbols) | **5,976** | 8,937 | 16,121 |\n\nGCF wins 15/16 datasets on the expanded [token efficiency benchmark](https://github.com/blackwell-systems/toon/tree/gcf-comparison). Full results: [gcformat.com/guide/benchmarks](https://gcformat.com/guide/benchmarks.html)\n\n## Implementations\n\n| Language | Package | Repository |\n|----------|---------|-----------|\n| Go | `go get github.com/blackwell-systems/gcf-go` | [gcf-go](https://github.com/blackwell-systems/gcf-go) |\n| TypeScript | `npm install @blackwell-systems/gcf` | [gcf-typescript](https://github.com/blackwell-systems/gcf-typescript) |\n| Python | `pip install gcf-python` | [gcf-python](https://github.com/blackwell-systems/gcf-python) |\n| Rust | `cargo add gcf` | [gcf-rust](https://github.com/blackwell-systems/gcf-rust) |\n| Swift | Swift Package Manager | [gcf-swift](https://github.com/blackwell-systems/gcf-swift) |\n| Kotlin | JitPack | [gcf-kotlin](https://github.com/blackwell-systems/gcf-kotlin) |\n| MCP Proxy | `pip install gcf-proxy` | [gcf-proxy](https://github.com/blackwell-systems/gcf-proxy) (bidirectional, session dedup, HTTP frontend) |\n| Claude Code Plugin | `/plugin install` | [gcf-claude-plugin](https://github.com/blackwell-systems/gcf-claude-plugin) (one-command install, session stats hook) |\n| Codex Plugin | `codex plugin add` | [gcf-codex-plugin](https://github.com/blackwell-systems/gcf-codex-plugin) (one-command install, session stats hook) |\n| VS Code | `ext install blackwell-systems.gcf-vscode` | [gcf-vscode](https://marketplace.visualstudio.com/items?itemName=blackwell-systems.gcf-vscode) (syntax highlighting) |\n| n8n | `npm install n8n-nodes-gcf` | [gcf-n8n-nodes](https://github.com/blackwell-systems/gcf-n8n-nodes) (workflow encode/decode) |\n| Tree-sitter | `npm install tree-sitter-gcf` | [tree-sitter-gcf](https://github.com/blackwell-systems/tree-sitter-gcf) |\n\n**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.\n\n**Specification:** [SPEC v3.5.3 Stable](https://github.com/blackwell-systems/gcf/blob/main/SPEC.md) with 279 conformance fixtures, 43,000,000,000+ lossless round-trips verified across 5 formats and 6 languages. Current versions: Go v1.7.0, TypeScript v2.6.0, Python v2.6.0, Rust v3.0.0, Swift v2.7.0, Kotlin v2.6.0, .NET v0.2.0. Cross-language conformance verified across all seven SDKs.\n\n## Adopted by\n\n| Project | |\n|---------|--|\n| **[Chrome DevTools MCP](https://github.com/ChromeDevTools/chrome-devtools-mcp)** | 47K★ · the Google Chrome DevTools team's MCP server; exposes live browser state (DOM, network, console, performance) to AI coding agents |\n| **[Speakeasy](https://speakeasy.com)** | OpenAPI tooling (customers include Google, Verizon, Mistral AI, DocuSign, Vercel); GCF is a native output format in their `oq` CLI |\n| **[OmniRoute](https://omniroute.online)** | 17K★ · AI gateway, registry, and proxy between AI clients and model providers; GCF vendored into its compression engine |\n| **[NetClaw](https://github.com/automateyournetwork/netclaw)** | 610★ · AI-powered network automation (113 skills, 66 MCP integrations); replaced TOON with GCF across every MCP server |\n| **[ctx](https://github.com/stevesolun/ctx)** | 552★ · real-time context selector for Claude Code; surfaces only the relevant tools from a 103K-node knowledge graph |\n| **[Lynkr](https://github.com/Fast-Editor/Lynkr)** | 531★ · local LLM gateway for AI coding clients; GCF as a drop-in tool-result compressor alongside TOON |\n| **[Open Data Products SDK](https://opendataproducts.org/sdk/)** | Linux Foundation · Python toolkit and MCP server for data-product standards; GCF sidecars for agent context |\n| **[NeuroNest](https://neuronest.cc)** | agent-first IDE; first commercial GCF adoption, across four encoding surfaces with session dedup and delta |\n| **[Raycast](https://raycast.com/blackwell-systems/json-to-gcf-converter)** | JSON-to-GCF Converter extension in the Raycast Store, for the macOS productivity launcher |\n\n[See all adopters →](https://gcformat.com/ecosystem/adopters.html)\n\n## License\n\nMIT - [Dayna Blackwell](https://github.com/blackwell-systems)\n","readmeFilename":"README.md"}