TokenSave vs GrapeRoot

TokenSavevsGrapeRoot

GrapeRoot (formerly Dual-Graph) is the one tool here that disagrees with TokenSave at the root. TokenSave gives the agent tools and trusts it to ask. GrapeRoot decides what the agent should see and packs it into the prompt before the agent gets a turn. Almost every other difference follows from that one.

TokenSave v7.12.1 · GrapeRoot / graperoot 3.10.19 (PyPI, Sep 2026)

Prefill versus query

GrapeRoot is a prefill layer. It sits between you and your assistant as a launcher, ranks the files it believes are relevant, and loads them into the prompt. The stated goal is that "no exploration" happens — the agent never spends turns looking around because the answer is already in front of it.

TokenSave is a query surface. It assumes the agent is capable of asking good questions given good tools, and optimises for making each question cheap and precise.

Neither is obviously right, and the honest answer is that it depends on the model. A weaker model that picks tools badly benefits from having context chosen for it. A strong model that knows what it is looking for is held back by a fixed prefill guess and does better with a scalpel. As frontier models get better at tool selection, the prefill advantage narrows — but that is a trend, not a proof, and on a bad model today prefill genuinely wins.

At a glance

Dimension	TokenSave	GrapeRoot
Model	Agent queries the graph on demand	Files pre-loaded into the prompt
Implementation	Rust, single static binary	Python 3.10+ and Node.js 18+
Runtime deps	None	Two runtimes, ~80 MB+
License	MIT, auditable end to end	Apache-2.0 launcher, proprietary core
Unit of analysis	Symbol — functions, fields, call edges	File, plus symbols and imports
Call graph	Full inter-procedural	File-level neighbours
MCP tools	87	3 graph tools, prefill is the main path
Languages	60	12
Storage	libSQL with FTS5 and indexed lookups	JSON files — every lookup is a full scan
Exploration turns	Agent spends a call to ask	Zero — context arrives pre-loaded
Multi-branch	Yes, opt-in	No
Privacy	Anonymous token count, documented opt-out	Persistent install ID sent on launch

Where GrapeRoot is the better tool

Zero exploration turns

This is a real advantage and worth stating without hedging. Every TokenSave query is a round trip: the agent decides to ask, calls a tool, waits, reads. GrapeRoot pays that cost once, up front, outside the conversation. On a task where the relevant files are predictable, prefill is simply fewer turns.

It works when the agent chooses badly

TokenSave's value is contingent on the agent actually calling it and calling the right tool — which is precisely why TokenSave has to install hooks that block Explore agents and redirect greps. GrapeRoot needs no such enforcement, because the agent was never given the choice.

Compounding savings across a session

Because context is assembled before the prompt, savings accrue over a whole session rather than per call.

A simpler mental model

Nothing to learn, no tool vocabulary, no rules in CLAUDE.md telling the model what to prefer. You launch your assistant through it and it gets better context.

Where TokenSave is the better tool

You can read all of it

TokenSave is MIT-licensed Rust you can audit, fork and patch. GrapeRoot's launcher scripts are Apache-2.0, but the engine that actually consumes your code — the graperoot package — is proprietary and published as a closed binary. You cannot see what it does with your code graph. For some organisations that ends the evaluation on its own.

Symbol level, not file level

"Who calls this function?" and "what breaks if I change this struct?" are questions a file-level neighbour graph cannot answer at all. TokenSave answers them from indexed call edges.

Indexed storage

libSQL with FTS5, WAL-mode concurrent reads and O(1) lookups, against JSON files that must be scanned in full for every query. The gap widens with repo size.

One runtime — none, in fact

GrapeRoot needs both Python 3.10+ and Node.js 18+. TokenSave needs neither.

Prefill cannot answer the unexpected question

The fundamental limit of guessing ahead: when the agent needs something the ranker did not anticipate, there is no way to ask for it. TokenSave degrades gracefully because asking is always available.

Which should you use

Choose GrapeRoot if…

you would rather not think about it: you want better context with no tool vocabulary to learn and no dependence on the model choosing well, and a proprietary core is acceptable to you.

Choose TokenSave if…

you want an auditable tool, symbol-level answers to structural questions, and an agent that can ask for whatever it actually needs rather than working from a guess made before the conversation started.

Method

Every TokenSave figure on this page was verified against the source at origin/master (v7.12.1), not taken from its own README:

87 tools — asserted by test_tool_definitions_complete in src/mcp/tools/handlers/mod.rs; 86 when the optional ast-grep binary is absent from PATH.

60 languages — counted from the [features] tiers in Cargo.toml: 11 lite, 9 medium, 40 full. The README's "50+" is conservative.

0.24s — wall-clock real time for a complete tokensave status invocation, best of repeated runs on an Apple Silicon Mac. Your hardware and repo size will differ.

Zero runtime dependencies — otool -L on the released binary; system frameworks only.

GrapeRoot figures — PyPI graperoot 3.10.19 (licence field: Proprietary), the Codex-CLI-Compact repository and its README, September 2026.

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