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@@ -10,7 +10,7 @@ Follow [@getcodegraph](https://x.com/getcodegraph) on X for updates.
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### Supercharge Claude Code, Cursor, Codex, OpenCode, Hermes Agent, Gemini, Antigravity, and Kiro with Semantic Code Intelligence
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-**~16% cheaper · ~58% fewer tool calls · 100% local**
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+**Surgical context · fewer tool calls · faster answers · 100% local**
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### [Documentation & Website →](https://colbymchenry.github.io/codegraph/)
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@@ -111,27 +111,31 @@ codegraph uninstall
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## Why CodeGraph?
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-When Claude Code explores a codebase, it spawns **Explore agents** that scan files with grep, glob, and Read — consuming tokens on every tool call.
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+When an AI agent needs to understand code — to answer a question or make a change — it discovers structure the slow way: grep, glob, and Read, one file at a time, rebuilding call paths and dependencies by hand. That's a pile of tool calls and round-trips before it even starts the real work.
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-**CodeGraph gives those agents a pre-indexed knowledge graph** — symbol relationships, call graphs, and code structure. Agents query the graph instantly instead of scanning files.
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+**CodeGraph hands the agent the exact code it needs in one call.** It's a pre-built knowledge graph of every symbol, call edge, and dependency in your codebase — so instead of crawling files, the agent asks one question and gets back the relevant source, the call paths between those symbols (including dynamic-dispatch hops grep can't follow), and the blast radius of a change. **Surgical context, not a file-by-file search** — which means fewer tool calls and faster answers on every codebase, large or small.
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+
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+> **A note on cost:** CodeGraph's win on *every* codebase is precision and speed — fewer tool calls, faster answers. It cuts token and dollar cost too, but those savings are **scale-dependent**: small and noisy on a modest codebase, and material only once a repo is large and tangled — at the scale of a Google or Microsoft monorepo, multiplied by a whole team's daily agent usage — for them to compound into a real line item. On a 500-file project, adopt CodeGraph for the speed; the cost savings show up when the codebase (and the team) gets big.
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### Benchmark Results
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-Tested across **7 real-world open-source codebases** spanning 7 languages, comparing an agent (Claude Code, headless) answering one architecture question **with** and **without** CodeGraph. Each cell is the savings at the **median of 4 runs per arm**. _Re-validated on Opus 4.8 (2026-06-02), on the current build (`codegraph_explore` as the primary tool)._
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+Tested across **7 real-world open-source codebases** spanning 7 languages, comparing an agent (Claude Code, headless) answering one architecture question **with** and **without** CodeGraph, at the **median of 4 runs per arm**. _Re-validated on Opus 4.8 (2026-06-02), on the current build (`codegraph_explore` as the primary tool)._
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+
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+> **The universal win — every repo, every size: 58% fewer tool calls · 22% faster · file reads cut to ~zero.**
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-> **Average: 16% cheaper · 47% fewer tokens · 22% faster · 58% fewer tool calls**
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+The reliable, universal payoff is **surgical context and speed**: CodeGraph collapses the agent's grep/find/Read crawl into a few direct queries — returning the exact methods you asked about even when they're buried in a multi-thousand-line file — so it answers with **near-zero file reads** while the no-CodeGraph agent spends its budget on discovery. The **Tokens** and **Cost** columns are real too, but — as noted above — they're **scale-dependent**: small and noisy per query, compounding into real money only at large-codebase, high-volume scale.
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-| Codebase | Language | Cost | Tokens | Time | Tool calls |
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-|----------|----------|------|--------|------|------------|
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-| **VS Code** | TypeScript · ~10k files | 18% cheaper | 64% fewer | 11% faster | 81% fewer |
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-| **Excalidraw** | TypeScript · ~640 | even | 25% fewer | 27% faster | 40% fewer |
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-| **Django** | Python · ~3k | 8% cheaper | 60% fewer | 13% faster | 77% fewer |
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-| **Tokio** | Rust · ~790 | even | 38% fewer | 18% faster | 57% fewer |
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-| **OkHttp** | Java · ~645 | 25% cheaper | 54% fewer | 31% faster | 50% fewer |
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-| **Gin** | Go · ~110 | 19% cheaper | 23% fewer | 24% faster | 44% fewer |
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-| **Alamofire** | Swift · ~110 | 40% cheaper | 64% fewer | 33% faster | 58% fewer |
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+| Codebase | Language | Tool calls | Time | File reads | Tokens | Cost |
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+|----------|----------|------------|------|------------|--------|------|
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+| **VS Code** | TypeScript · ~10k files | 81% fewer | 11% faster | 0 vs 9 | 64% fewer | 18% cheaper |
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+| **Excalidraw** | TypeScript · ~640 | 40% fewer | 27% faster | 0 vs 7 | 25% fewer | even |
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+| **Django** | Python · ~3k | 77% fewer | 13% faster | 0 vs 9 | 60% fewer | 8% cheaper |
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+| **Tokio** | Rust · ~790 | 57% fewer | 18% faster | 0 vs 8 | 38% fewer | even |
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+| **OkHttp** | Java · ~645 | 50% fewer | 31% faster | 0 vs 4 | 54% fewer | 25% cheaper |
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+| **Gin** | Go · ~110 | 44% fewer | 24% faster | 1 vs 6 | 23% fewer | 19% cheaper |
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+| **Alamofire** | Swift · ~110 | 58% fewer | 33% faster | 0 vs 9 | 64% fewer | 40% cheaper |
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-CodeGraph cuts **tokens, tool calls, and wall-clock time on every repo** — across small, medium, and large codebases — and answers them with **near-zero file reads**, while the no-CodeGraph agent spends its budget on grep/find/Read discovery. `codegraph_explore` shows the answer in full — the mechanism plus the exact methods you asked about, even when they're buried in a multi-thousand-line file — while collapsing redundant interchangeable implementations to signatures, so the response is sized to the *answer* rather than the file count. **Cost stays flat-to-cheaper everywhere** — largest on the small repos (Alamofire, OkHttp), roughly break-even on the most response-heavy ones (Excalidraw, Tokio), where CodeGraph trades the no-CodeGraph agent's many small grep/read round-trips for a few large, cache-heavy tool responses.
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+<sub>**File reads** = median files the agent opened **with** vs **without** CodeGraph — the surgical-context win in one column. **Tokens** and **Cost** are the same with-vs-without deltas; they're directional (they move run-to-run) and, per query, small in absolute terms — which is why they only become a line item at scale. `codegraph_explore` also collapses redundant interchangeable implementations to signatures, so a response is sized to the *answer* rather than the file count.</sub>
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<details>
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<summary><strong>Per-repo breakdown — WITH vs WITHOUT (median of 4)</strong></summary>
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@@ -234,7 +238,7 @@ CodeGraph cuts **tokens, tool calls, and wall-clock time on every repo** — acr
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-| **Smart Context Building** | One tool call returns entry points, related symbols, and code snippets — no expensive exploration agents |
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+| **Surgical Context** | One tool call returns entry points, related symbols, and code snippets — no slow file-by-file exploration |
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| **Full-Text Search** | Find code by name instantly across your entire codebase, powered by FTS5 |
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| **Impact Analysis** | Trace callers, callees, and the full impact radius of any symbol before making changes |
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| **Always Fresh** | File watcher uses native OS events (FSEvents/inotify/ReadDirectoryChangesW) with debounced auto-sync — the graph stays current as you code, zero config |
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