Eight honest comparisons against the tools working on the same problem. Each page states where the other tool is better, because a comparison that finds no faults in its own product is marketing rather than information. Two of these tools are not competitors at all, and they are filed accordingly.
All TokenSave figures verified against v7.12.1 source · 17 Sep 2026The strongest technical challenge here: exact resolution through language servers and real atomic refactoring. The counter-argument is what that costs per session, and how often it changes an answer.
Closest in philosophy and far larger — roughly 31,500 stars. Multi-repo, execution flows, Leiden communities and published accuracy benchmarks.
Closest in premise: local tree-sitter parsing, no embeddings, a graph the agent traverses. Differs on non-code nodes, token budgets and a hosted commercial tier.
The project TokenSave was originally a Rust rewrite of. It now has a Rust kernel and bundles its own runtime, and deliberately exposes one tool instead of many.
Similar name, different engineering: regex annotators and shell compaction. Scores higher on the shared agent benchmark; uses about five times the tokens to do it.
The one genuine philosophical opponent: it pre-loads context instead of letting the agent ask. Proprietary core, two runtimes, file-level graph.
These two get compared to TokenSave often, but they solve adjacent problems rather than the same one. Running them alongside it is a coherent setup, not a contradiction — so these pages are written as "what each layer is for" rather than as a contest.
Compresses what the agent reads — files, shell output, the request itself. TokenSave goes deep on code structure. Different layers; most people want both.
Portable cross-agent memory and token accounting read from harness transcripts. Remembers what a graph cannot derive.
Three rules. First, every TokenSave number is verified against source at
origin/master rather than copied from a README — which is how the tool count on these pages
became 87 rather than the "80+" the documentation claims, and the language count 60 rather than "50+".
Second, the other tool's advantages get stated without hedging. Serena resolves references exactly where TokenSave's resolver admits to guessing, and refactors properly — the concession is quoted from TokenSave's own design doc. CodeGraph's one-tool design is a direct argument against TokenSave's 87. code-review-graph has fifty times the users. LeanCTX is also a single Rust binary, which retires TokenSave's favourite differentiator entirely. token-savior scores higher on the shared benchmark. Those all appear on their pages, in those words.
Third, competitors' own figures are labelled as theirs. Where a number comes from another project's documentation it is marked as unverified, because running someone else's benchmark badly is worse than not running it.
Corrections are welcome and get applied: open an issue.