Token Savior

Token Savior

One MCP server. One profile. 97.9% on tsbench at -80% tokens.

Structural code navigation, persistent memory, and Bash command rewriting for AI coding agents.

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[mibayy.github.io/token-savior](https://mibayy.github.io/token-savior/) -- project site + benchmark landing Benchmark source + fixtures: not currently published (see *Reproducing the score* below)

v4.23 in 31 seconds, measured on 2026-10-07 · watch the full video (MP4, sound on)

Benchmark -- 96 real coding tasks (Claude Opus 4.7, May 2026)

Reproduces with the optimized profile (single env var). The harness that produced these numbers is described below; its repository is not public at the moment, so take the figures as reported rather than as independently verifiable.

A re-measurement was published here on 2026-08-09 and has been withdrawn on 2026-08-10. It reported new-token savings from a small replacement harness. Those numbers did not measure this server at all: across 143 benchmark sessions, exactly one called a Token Savior tool. The client running the harness had MCP deferred-tool loading enabled, so all 18 tools sat behind a ToolSearch lookup instead of appearing in the model's manifest. The model never saw them and fell back to Grep and Read — 66 greps, 30 reads, one MCP call. What varied between the "profiles" was the size of the cached prefix, not what the agent did.

The lesson is worth more than the numbers were: a benchmark of an MCP server must assert that its tools were actually called. Ours did not, so it happily compared two identical agents. That assertion now exists in the harness.

The headline figures above therefore stand as reported and unverified, as stated in the previous paragraph.

Re-measured on 2026-10-07, with the tools actually called. Same 8 tasks on a frozen copy of this repo (commit 9d15a1e, no CLAUDE.md), Claude Haiku 4.5, plain Claude Code against the auto profile with alwaysLoad. The harness checks that the Token Savior sessions really called the server.

All 48 sessions succeeded. v4.22.0, measured once, saved 14% and used the tools in 5 sessions out of 8. Editing, measured on 2026-10-08 on the same repository (3 tasks in files of 1 900 to 3 700 lines, graded by running the code and the module's tests, 4 passes): every session of both arms succeeded, and cost is too noisy to call a gain (from -54% to +16% per pass). Native Edit only needs a partial Read, so there is little left to remove there.

Wall time is worse because each benchmark session starts a fresh server that indexes the repo first; a long-lived session pays that once. What moved from v4.22.0 to v4.23.0 is mostly wording: search_codebase now names the function holding each hit, and the tool descriptions say plainly which native call they replace.

Who starred this repo?

On June 30, 2026 GitHub restricted stargazer and watcher lists to repo admins and collaborators, which broke every "who starred my repo" tool at once. I rebuilt one that still works, precisely because it only reads repos you own or can push to: [starscope](https://starscope.duckdns.org) ranks the people who starred or forked your repo by influence, and surfaces their social accounts when their GitHub profile declares them.

Numbers on this very repo, computed with it: 1,147 people, 27% with a public social account, and the most followed carries 18,922 followers. The named list is visible to the repo owner and to nobody else — the public page shows aggregates only.

What's new

Release notes live where they can't drift out of sync with the code:

• CHANGELOG.md for the full history
• Releases for the tagged builds
• PyPI for what pip will actually give you

Quick start

pip install "token-savior-recall[mcp]"

Add to your MCP config (e.g. Claude Code):

{

"mcpServers": { "token-savior-recall": { "command": "/path/to/venv/bin/token-savior", "alwaysLoad": true, "env": { "WORKSPACE_ROOTS": "/path/to/project1,/path/to/project2", "TOKEN_SAVIOR_CLIENT": "claude-code", "TOKEN_SAVIOR_PROFILE": "optimized" } } } }

`"alwaysLoad": true` matters on Claude Code (2.1.28x and later): without it, every tool of the server sits behind ToolSearch and the model reaches for Read and Grep, which are already loaded. Measured on this project's benchmark before the option existed: one session in 143 called a Token Savior tool. With it, the manifest costs a few thousand cached tokens per session.

That's it. `TOKEN_SAVIOR_PROFILE=optimized` ships the Pareto-optimum config that wins tsbench. It bundles:

• tiny_plus (15 hot tools manifest)
• thin inputSchema (-44% manifest)
• capture sandbox disabled
• memory hooks gated for cross-project safety

No other tuning needed.

Activation (Bash compaction + rewriting)

Bash compaction and the PreToolUse rewriter are opt-in. Two env vars and one CLI call:

export TS_BASH_COMPACT=1       # PostToolUse output compactors (34 of them)

export TS_BASH_REWRITE=1 # PreToolUse command rewriter (10 rules)

ts init --agent claude --yes # auto-merge hooks into ~/.claude/settings.json

ts init is idempotent. It detects existing hook entries, dedups by (matcher, command), prints a unified diff, and backs up settings.json to .bak-YYYYMMDD-HHMMSS (UTC) before writing. Supported agents: claude, cursor, gemini, codex, openclaw. Pass --dry-run to preview, or --global to write the user-level config.

Optional audit log of every rewrite:

export TS_BASH_REWRITE_LOG=$HOME/.local/state/token-savior/rewrites.jsonl

Compactor catalog (34)

Each compactor is a pure function (no I/O, no globals) returning a token-efficient rendering. The dispatcher returns None when no matcher fires, leaving the existing sandbox path untouched. Compound commands (cd ... && cmd) fall through to the last meaningful segment.

These run in PostToolUse, so they do not shrink the current turn. The

hook fires after the tool has returned; it can add context, not remove it.

The compact rendering is appended below the raw output, which stays. What

you gain is persistence: the full output goes to the capture sandbox and

outlives a context compaction. For an actual reduction of what reaches the

model, use the PreToolUse rewriter (TS_BASH_REWRITE=1) — it edits the

command before it runs.

ts_discover -- find missed TS opportunities

New MCP tool that scans your Claude Code transcripts for patterns where TS tools would have been cheaper than what the agent actually did.

ts_discover()                       # active project, last 30 days

ts_discover(project=None) # ALL transcript projects ts_discover(format="adoption") # TS vs native ratio per session ts_discover(format="adoption_json") # same, JSON

Findings: Read->Grep->Read chains, sequential find_symbol, edits without get_edit_context, memory_search without memory_index, native shell on code files. Args are pruned to load-bearing keys (PII-safe). Streams JSONL with mtime fast-skip.

ts init CLI

ts init --agent claude [--global] [--dry-run] [--yes]

ts init --agent cursor ts init --agent gemini ts init --agent codex ts init --agent openclaw

Detects the target agent's settings location, deep-merges the Token Savior hook config (PostToolUse + PreToolUse), preserves existing hooks, dedups, prints a unified diff. Backs up to settings.json.bak-YYYYMMDD-HHMMSS (UTC). Re-running is a no-op.

What it does

Claude Code reads whole files to answer questions about three lines, and forgets everything the moment a session ends. Token Savior fixes both, plus a third axis: it bounds the noisy Bash output that bloats turn budgets between code reads — by rewriting the command before it runs.

It indexes your codebase by symbol -- functions, classes, imports, call graph -- so the model navigates by pointer instead of by cat. Measured reduction: 97% fewer chars injected across 170+ real sessions.

On top of that sits a persistent memory engine. Every decision, bugfix, convention, guardrail and session rollup is stored in SQLite WAL + FTS5

• vector embeddings, ranked by Bayesian validity and ROI, and

re-injected as a compact delta at the start of the next session.

And on top of *that*, since v4.1, sit the Bash compactors and the PreToolUse rewriter. Bench numbers above.

Which of those two actually shrinks a turn, and which does not. Measured 2026-08-09, and worth stating plainly because the distinction is not obvious:

• The PreToolUse rewriter changes the command *before* it runs, so a smaller output is produced and a smaller output reaches the model. This is a real reduction in the current turn.
• The PostToolUse compactors run *after* the tool has returned. A PostToolUse hook can only *add* context; by the time it fires, the raw output has already been sent. The compact rendering is appended, it does not replace anything. What the compactors genuinely buy you is different and still valuable: the full output is preserved in the capture sandbox and survives a context compaction, so it can be queried later instead of being re-run.

If your goal is a smaller turn, TS_BASH_REWRITE=1 is the switch that does it. If your goal is to stop losing command o