
Sophon
io.github.lacausecrypto由 GitHub 帳號 lacausecrypto 發布v0.5.4更新於 Oct 24, 2026
Honest token economics for MCP agents. 68% tokens saved, zero-ML Rust binary.
發布方自己的服務:Sophon Consulting
概覽
一個在本機執行的 Rust MCP 伺服器,以確定性方式壓縮提示詞、對話歷史、檔案讀取與指令輸出,為編碼代理降低 token 用量。
- 可以做什麼
- Sophon 透過 stdio 提供 11 個 MCP 工具,用來壓縮代理傳送給模型的文字。compress_prompt 只保留長提示詞中與查詢相關的段落,compress_history 產生摘要、事實與近期視窗,compress_output 以 21 個領域篩選器清除指令 stdout/stderr 的雜訊。其他工具負責檔案讀寫差異、重複樣板片段編碼、token 計數、工作階段節省統計,以及依查詢排序的程式碼庫摘要。查詢時不需要嵌入模型或 API 金鑰,以單一本機二進位檔執行。
- 適用情境
- 適合編碼代理工作流程中,重複讀取檔案、冗長指令輸出與不斷成長的對話歷史占主要 token 開銷的情境,尤其是搭配 Claude Code、Cursor 或 Cline 使用時。它疊加在供應商提示快取或記憶系統之前,而非取代它們。不適用於高召回率的對話記憶、大型文件的多跳推理、OCR 或 PDF 版面處理,以及非常小的輸入。
- 執行需求
- 以本機程序透過 stdio 啟動,可透過 npm 套件 mcp-sophon 安裝(postinstall 會下載適用於 macOS arm64/x64、Linux arm64/x64 或 Windows x64 的預先建置二進位檔),也可用 Rust 1.75+ 從原始碼建置。選用功能需要額外的建置參數。選用環境變數包括 SOPHON_MEMORY_PATH、SOPHON_RETRIEVER_PATH、SOPHON_HYBRID、SOPHON_ROLLING_SUMMARY、SOPHON_CHUNK_TARGET、SOPHON_EMBEDDER 與 SOPHON_LLM_CMD。未宣告驗證機制。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Sophon,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
Sophon
Deterministic context compression for MCP agents. One Rust binary. Zero ML at query time. Reproducible benchmarks, real-data measurements.
[npm version] [npm total downloads] [GitHub release] [CI] [License: MIT] [Rust 1.75+] [MCP] [Tests]
Sophon is a deterministic context layer for agents speaking the Model Context Protocol. It compresses prompts, conversation memory, code digests, file deltas, and shell output — without an embedding model at query time, without a GPU, and without API keys.
Single 5.2 MB Rust binary. MCP-native. cl100k_base-accurate. Default build pulls no Python, no ML weights, no network.
What it does, in 30 seconds
11 MCP tools total (full table below).
Real numbers — measured on this repo's own dev cycle
We built four independent benches that each capture a different chunk of an agent's tool traffic. All four run against this repo's actual git history + working tree on the operator's machine. Reproducible byte-for-byte by anyone with cargo build --release.
real_session_holistic.py runs all four sub-benches with --json, parses them, and produces the weighted blend. Default weights reflect this repo's observed shape; pass --weights "history=0.4,..." to model your own workload.
USD economy on Claude Opus 4.7
Pass
--model sonnetor--model haikutoreal_session_deep_dive.pyif you're re-pricing for a cheaper tier.
Where each dimension falls short (we say it ourselves)
- history measures only what
gitcaptures (commits + diffs) — typically ~5-10 % of a real session's tool traffic. The 94.6 % is the upper bound, not the typical case. - shell mixes commands that compress well (
git diff95 %) with commands that don't (gh repo view --jsonadds tokens, −9 %). 84.4 % is a real-world average, not a curated highlight. - filereads uncovered that
compress_prompton raw source files compresses by budget cap, not by query routing — same file with 3 different queries → identical output. Section detection only fires on structured input (Markdown headers, XML tags). Documented inline in the bench. - search depends entirely on YOUR repo's state. A repo with no TODOs gets 0 % on
grep TODO.
The blended 84.7 % is napkin-math from a linear weighted average across four real measurements. Not a cherry-picked synthetic. Run the benches yourself to verify.
Other reproducible benchmarks (synthetic, on-thesis)
Why Sophon — "in front of X"
Sophon is not a memory platform, a recall system, an OCR stack, or a replacement for provider-side caching. It's a deterministic compressor that slots in front of whatever memory / cache / code-nav layer you already use, and attacks the tokens those layers can't.
In front of Anthropic / OpenAI prompt caching
Provider caching handles the static half of a request — system prompt, tool definitions, reused documents. It doesn't touch the dynamic half (growing conversation history, tool outputs). Sophon compresses exactly that half. The two stack cleanly.
+24 % tokens / +49 % $ saved on top of prompt caching on a 25-turn Claude session — because the uncached dynamic block is billed at 10× the cached rate. See
sophon_plus_prompt_caching.py.
In front of mem0 / Letta / Zep / Graphiti
Memory systems retrieve the right memories. Sophon shrinks what gets sent to the LLM after retrieval. If mem0 returns 2 kB of raw memories, compress_prompt keeps only the sections the query actually references.
Honest caveat: on very short retrieved blocks (< ~200 tokens) Sophon's wrapper adds overhead and you should pass through. The bench reports this directly.
In front of Claude Code / Cursor / Cline
Primary use case. Every repeat file read becomes a read_file_delta; every shell command output goes through compress_output; every repeated boilerplate block gets a fragment_cache token. Install transparently with sophon hook install --agent claude --global.
In front of a RAG pipeline
navigate_codebase produces a PageRanked repo digest that a RAG retriever would otherwise spend expensive embedding calls to build. Tree-sitter / regex symbol extraction over 11 languages, sub-second.
When NOT to use Sophon
- Long-form conversational recall above 80 % — Sophon caps at ~40 % on LOCOMO and we don't chase it. Run mem0 / Letta / Zep for recall, then optionally pipe their output through Sophon.
- Multi-hop reasoning on massive documents — that's HippoRAG or GraphRAG.
- OCR / PDF layout — out of scope. Use Docling / Marker / Unstructured upstream.
- Very small inputs (< ~200 tokens) — Sophon's section scaffolding can cost more than it saves.
Quick start
Install via npm (recommended)
The postinstall script downloads the right prebuilt binary for your platform from the GitHub Releases page. Supported: macOS arm64/x64, Linux arm64/x64, Windows x64.
Build from source
Optional features:
Requires Rust 1.75+.
Wire it into an MCP client
Most clients accept this snippet (Claude Desktop, Claude Code, Cursor, Cline, Continue):
Run sophon doctor to print the right config path for your client.
Recommended runtime setup
Quick CLI
Programmatic (one-shot JSON-RPC)
What the binary ships
11 MCP tools, all stdio:
Binary sizes by feature set:
MCP protocol: 2025-06-18. notifications/cancelled actually drops the response (since v0.5.4). Structured JSON-RPC error codes (-32000..-32099 reserved for Sophon). Infallible dispatcher — a malformed request can't kill the stdio loop.
Filesystem confinement (opt-in)
For clients running untrusted prompts, set SOPHON_ALLOWED_ROOT to an existing workspace directory. read_file_delta, write_file_delta, and navigate_codebase reject resolved paths outside this directory. By default, trusted local integrations retain unrestricted filesystem access. This is a path-level defense, not a hardened OS sandbox: although known symlinks and dangling symlinks are checked, a concurrent actor who can modify directory links may swap a path between authorization and the subsequent file operation (TOCTOU). Do not expose these tools to hostile users with write access to the same filesystem; use operating-system isolation for strict security boundaries. New files require an existing parent directory when confinement is enabled.
Configuration
Run sophon doctor to see every SOPHON_* env var currently set with validation warnings. Full catalogue (24 flags) lives in runtime_flags.rs. The flags worth knowing:
Deprecated v0.4.0 recall-chasing flags — SOPHON_HYDE, SOPHON_FACT_CARDS, SOPHON_ENTITY_GRAPH, SOPHON_ADAPTIVE, SOPHON_LLM_RERANK, SOPHON_TAIL_SUMMARY, SOPHON_REACT, SOPHON_GRAPH_MEMORY, SOPHON_MULTIHOP_LLM — chase LOCOMO recall, an axis we no longer optimise. Still functional but sophon doctor flags them. Removed in a future major.
Honest limitations
The full list lives in BENCHMARK.md § 8. Headlines:
- LOCOMO conversational recall caps at ~40 %. mem0 / HippoRAG hit 80-90 % with neural retrieval at query time — we chose determinism + sub-100 ms p99 instead. Pipe mem0 in front of Sophon if you need that recall.
- HashEmbedder is keyword-bound. "favorite food" ↔ "weakness for ginger snaps" doesn't match. Activate BGE (
SOPHON_EMBEDDER=bge) for semantic recall — costs +25 MB binary + model load. - No multimodal ingestion. Images / PDFs / audio out of scope. Run Docling / Marker / Unstructured upstream.
- Rolling summary doesn't help on small sessions. When the un-summarised tail fits the budget, the rolling cache is a no-op. Useful for long-running sessions with
SOPHON_LLM_CMDset. - Some commands don't compress.
gh repo view --jsonadds tokens,git log --onelinesaves 0.4 %. Sophon's job isn't to compress already-compact output — it's to compress redundant verbose output. The benches name the gaps explicitly.
Project layout
Contributing
PRs welcome. Run the test suite:
Every benchmark claim is reproducible — pointers to the scripts live in BENCHMARK.md. If a number doesn't reproduce on your machine, open an issue.
Particularly welcome:
- TypeScript bindings (Python bindings ship in
sophon-py/) ghfamily filter (gh run list,gh pr list,gh repo view --json) — the bench shows this is currently a gapSOPHON_EMBEDDER_CMDshell-out plugin pattern (mirror ofSOPHON_LLM_CMD) for Voyage / OpenAI / Cohere- Multi-repo
real_session_holistic.pyruns against popular open-source repos
License
MIT. See LICENSE.
來源:README.md,提交 e4bc0b4
工具
0版本歷史
1- v0.5.4最新Sep 16, 2026
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