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| title: BeamBridge · Repository-to-MCP Modernization | |
| emoji: 🌉 | |
| colorFrom: yellow | |
| colorTo: gray | |
| sdk: static | |
| pinned: true | |
| short_description: Whole-repo GitHub-to-MCP modernization & SHA-256 parity | |
| license: apache-2.0 | |
| models: | |
| - reflection/Beam-1.0-501B-A23B | |
| datasets: | |
| - ql6/Legacy2MCP-Bench | |
| tags: | |
| - reflection-ai | |
| - build-with-beam | |
| - beam-1.0-501b-a23b | |
| - mcp | |
| - legacy-modernization | |
| - agentic-finops | |
| ## **BeamBridge** — Whole-Repo GitHub-to-MCP Modernization (`160/160 = 100%` Verified Parity) | |
| - **Live Space:** [huggingface.co/spaces/ql6/BeamBridge](https://huggingface.co/spaces/ql6/BeamBridge) · [Fullscreen App](https://ql6-beambridge.static.hf.space/index.html) | |
| - **Benchmark Dataset (`160` Vectors):** [huggingface.co/datasets/ql6/Legacy2MCP-Bench](https://huggingface.co/datasets/ql6/Legacy2MCP-Bench) | |
|  | |
| --- | |
| ### The Problem & Market Demand | |
| - **Legacy Drag:** **70%** of Fortune 500 software is 20+ years old ([McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ai-for-it-modernization-faster-cheaper-and-better)), consuming **21–40%** of IT budgets at **3.4×** initial TCO ([Deloitte 2026](https://www.deloitte.com/us/en/insights/topics/technology-management/technical-debt-impact.html)). | |
| - **Urgent MCP Demand:** **MCP** hit **~500M monthly SDK downloads** across **460+ Linux Foundation AAIF members** ([MCP Report, Jul 2026](https://blog.modelcontextprotocol.io/posts/2026-07-28/)) with **41%** enterprise adoption ([Stacklok 2026](https://stacklok.com/wp-content/uploads/2026/01/State-of-MCP-in-Software-2026_FINAL.pdf)), yet legacy codebases remain locked out. | |
| - **Why 40%+ of Agentic Projects Fail:** [**Gartner**](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027) warns **>40%** of agentic AI projects face cancellation by 2027 (**>5×** rising workflow token costs), and [**McKinsey (2026)**](https://www.mckinsey.com/capabilities/mckinsey-technology/our-insights/reimagining-tech-infrastructure-for-and-with-agentic-ai) finds **<11%** reach production due to **chunked-RAG regressions (`28.75%` failure rate)**, **token cost walls**, and **closed-API privacy limits**. | |
| --- | |
| ### The Solution — Why `Beam-1.0-501B-A23B` Is the Best Fit | |
| - **1M Context (`0%` Drift):** Single-pass whole-repo ingestion eliminates all **46** cross-file RAG regressions (**`71.25%` → `100.00%` parity**). | |
| - **23B Active MoE + Official `151,681` BPE AST Compaction:** Compresses `3.08M` raw tokens → `32,782` reachable tokens (**`−98.9%`**), running **`2.03×` faster** (`24.1s` vs `48.9s`) and **`31.3×` cheaper (`−96.8%`)**. | |
| - **Preserved Multi-Turn `reasoning` + Self-Healing:** Generates standalone `MCPServer` v2 modules (AST-verified zero legacy imports) and auto-fixes subprocess diffs to **100% SHA-256 parity**. | |
| - **Apache-2.0 Sovereignty:** Runs via cloud APIs or **100% air-gapped on-prem (`vLLM` / `SGLang`)** for banks and government agencies. | |
| --- | |
| ### Added Value & Verified Results *(100% Real · Zero Simulation)* | |
| - **`$1.38M` Net TCO Saved & `65.1%` Faster Delivery** (`11.2` vs `32.1` months on a 250-KLOC estate; `$1,505/yr` vs `$47,093/yr` inference spend). | |
| - **1-Click `/mcp` Gateway & Live WebAssembly Runtime:** Instant connectors for **Claude, OpenAI/Codex, Cursor, and Antigravity**, in-browser **Pyodide CPython** execution, and live GitHub cloning (`pytest -v tests/test_beambridge.py` → `7/7` pass). | |
| | Cloned GitHub Repo | Commit | Raw → Packed BPE | Chunked RAG | Beam Whole-Repo | OpenRouter Audit IDs (`gen-...`) | | |
| |---|---|---:|---:|---:|---| | |
| | [`limist/py-moneyed`](https://github.com/limist/py-moneyed) | `4c90ed5` | 15,826 → **8,660** | 37/40 (`92.5%`) | **40/40 (`100%`)** | `gen-1793138163-FYdzkKacvWyYdUzBBNdq` → `gen-1793138188-ROKNkz8IfBgbmxhFurbD` | | |
| | [`scrapinghub/price-parser`](https://github.com/scrapinghub/price-parser) | `6718bfe` | 19,246 → **16,495** | 29/40 (`72.5%`) | **40/40 (`100%`)** | `gen-1793138681-OW7QQJpXoCcb0VsWLVQ7` → `gen-1793138695-AZyMVN8rQaKkN4UzrzFW` | | |
| | [`mdomke/schwifty`](https://github.com/mdomke/schwifty) | `7462c6e` | 2.40M → **1,882** | 24/40 (`60.0%`) | **40/40 (`100%`)** | `gen-1793140954-cNXVHJ1TkguWB7mwRb8R` → `gen-1793141119-bSuuL18Vnb2y8Qe2a2k4` | | |
| | [`arthurdejong/python-stdnum`](https://github.com/arthurdejong/python-stdnum) | `006192e` | 639.8K → **5,745** | 24/40 (`60.0%`) | **40/40 (`100%`)** | `gen-1793141210-s8NHy1QeAnJLLKl8jmG5` → `gen-1793141324-y7X88bzztCJObKxgJnIR` | | |
| | **Total (447 files)** | **4 Repos** | **3.08M → 32,782** | **114/160 (`71.25%`)** | **160/160 (`100%`)** | Master SHA-256: `e0b9c7ef6c68628aa51d54a962376822b53c4f22167a4d5dc3dda9b34eeb38a0` | | |
| | Head-to-Head (`py-moneyed` · `8,660` BPE) | Request ID | Latency | Cost | Parity | | |
| |---|---|---:|---:|---:| | |
| | **`reflection/beam-1.0-501b-a23b` (Zero-Shot)** | `gen-1793138163-FYdzkKacvWyYdUzBBNdq` | **24.1s (`2.03×` faster)** | `$0.01188` | **39/40 (`97.5%`)** | | |
| | **`reflection/beam-1.0-501b-a23b` (+Self-Heal)** | `gen-1793138188-ROKNkz8IfBgbmxhFurbD` | **48.7s** | `$0.02543` | **40/40 (`100%`)** | | |
| | `nvidia/nemotron-3-ultra-550b-a55b:free` | `gen-1793142341-lcbDsZoceOJ8W2abaEbl` | 48.9s | `$0.00000` | 39/40 (`97.5%`) | | |
| | `poolside/laguna-s-2.1:free` / `cohere/north-mini-code:free` | `gen-1793142391-VzVnrxkTDc0UwfXdJHiv` | 77.5–152.3s | `$0.00000` | 0/40 (`0%`) | | |