sverklo
/* local-first code intel ◦ the audit gap ◦ 34× fewer tokens than grep ◦ MIT */

Stop Claude Code from burning tokens on grep.

1 tool call instead of 7-12. Claude Code, Cursor, and Windsurf hunt your codebase with grep loops that burn input tokens and still hallucinate function names. Sverklo answers in one MCP call against a real symbol graph — running locally on your machine. 180-task bench across 6 codebases: 35× fewer input tokens than grep, F1 0.58 — overall leader (smart-grep next at 0.34, P4 file-deps win 0.84 vs 0.40).

Local-first, MIT-licensed, no API keys, no cloud upload. The code-intel MCP that ships its bench — the slice where sverklo loses to grep is on the same page as the wins.

view the bench → see where sverklo loses →
Already using one of these? One-click install: Install in Cursor Install in VS Code Install in VS Code Insiders Claude Code setup
  • 6→36MCP tools (default → full)
  • 24languages
  • 7AI agents
  • 0API keys
  • MITlicensed
~/your-project claude code
# install sverklo and set it up in your project $ npm install -g sverklo $ cd your-project && sverklo init .mcp.json ← project-scoped MCP server .claude/settings.local.json ← auto-allow sverklo tools AGENTS.md / CLAUDE.md ← prefer-sverklo instructions (auto-detected) # ask Claude anything — sverklo is preferred automatically > how does the auth middleware work? › sverklo_search query="authentication middleware" ─ src/middleware/auth.ts:42 validateToken() score: 0.89 ─ src/routes/auth.ts:18 requireAuth() score: 0.84 ─ src/lib/jwt.ts:12 verifyJWT() score: 0.81 ✓ 3 results in 23ms — 847 files searched, 2,431 tokens used
# install sverklo $ npm install -g sverklo # add to .cursor/mcp.json (or Cursor Settings → MCP) $ cat .cursor/mcp.json { "mcpServers": { "sverklo": { "command": "sverklo", "args": ["mcp", "."] } } } # Cursor agent picks up sverklo on next session > where do we handle stripe webhooks? › sverklo_context task="stripe webhook handling" ─ src/api/webhooks/stripe.ts:14 handleStripeEvent() score: 0.92 ─ src/billing/events.ts:31 processCharge() score: 0.78 ✓ bundle: 4 files, 2 memories — 1,840 tokens
# install sverklo and run init — Antigravity is auto-detected $ npm install -g sverklo $ cd your-project && sverklo init ~/.gemini/antigravity/mcp_config.json ← global MCP config added sverklo (project: ~/your-project) Restart Antigravity to pick up the new MCP server. # Gemini in Antigravity uses sverklo via MCP > review my branch before I merge to main › sverklo_review_diff ref="main..HEAD" ─ ◆ risk 52 src/api/billing.ts 2 removed symbols still referenced; 8 importers › sverklo_test_map ref="main..HEAD" ─ 5 covered · 1 uncovered (risk 78) verdict: BLOCKED — fix dangling refs in 3 files
# one server, all your repos — no per-project config needed $ npm install -g sverklo $ cd ~/api && sverklo init # auto-registers $ cd ~/frontend && sverklo init # auto-registers $ sverklo setup --global # one-time global MCP config # now every AI editor has access to all your repos $ sverklo list api ~/api 37 tools fresh frontend ~/frontend 37 tools fresh

/* 90-second walkthrough — terminal then live Claude Code MCP integration */

/* what happened when we published the bench */

A public benchmark, two competitors, fixes shipped on both sides in 36 hours.

  1. Apr 28 Published 180-task bench across 6 codebases with two competing local-first MCP code-intel servers (jcodemunch-mcp, GitNexus) and the slices where sverklo lost — including FastAPI P5 dead-code (0.00 vs grep's 1.00).
  2. May 2 → 3 jcodemunch maintainer @jgravelle shipped v1.80.7 / 1.80.8 / 1.80.9 against specific bench findings. P5 recall 0.00 → 1.00; lodash P1 0/10 → 9/10.
  3. May 4 Adding lodash to the bench exposed the symmetric blind spot in sverklo's parser. sverklo v0.20.2 ships the fix. Sverklo P1 0.30 → 0.73; overall F1 0.45 → 0.56 (now leader).

"the bigger opportunity here is the potential genesis of an 'MCP Server Arena' on par with what the leading AI/LLM/Chatbot arenas provide…"

— Jake Gravelle, jcodemunch-mcp maintainer, on r/mcp
26ms
search p95
react · 4 368 files indexed
<1.2ms
impact analysis
indexed graph join, no scan
+29%
F1 vs Claude alone
on real merge requests
2-4×
fewer tokens
per agent review pass
/* honest about when this helps */

Sverklo doesn't replace grep. It complements it.

SVERKLO WINS
  • Exploratory questions ("how does the auth flow work?")
  • Refactor blast radius (sverklo_impact)
  • Large interconnected codebases
  • Memory across sessions, tied to git SHAs
  • Project audits — god nodes, dead code
GREP WINS
  • Focused diff review
  • Exact string matching
  • Reading file contents
  • Build & test verification
  • Anything where you already know the symbol

Sverklo is the right tool when you don't know exactly what to search for. When you do know, grep is fine.

/* diff-aware mr review */

your agent
reviews like
a senior dev.

~/your-project review main..HEAD
> /sverklo:pre-merge › sverklo_review_diff ref="main..HEAD" ─ 7 files changed (12 added, 3 removed, 4 modified) ## ⚠️ Highest-risk files ─ ◆ risk 78 (critical) src/auth/session.ts no matching tests; security-sensitive; 14 importers ─ ◆ risk 52 (high) src/api/billing.ts 2 removed symbols still referenced; 8 importers › sverklo_test_map ref="main..HEAD" ─ 5 covered · 2 uncovered ─ ◆ risk 78 src/auth/session.ts ← block merge › sverklo_impact symbol="oldValidateToken" ─ 4 dangling references in 3 files — must fix verdict: BLOCKED — 1 critical, 4 dangling refs
per-file risk · main..HEAD low med high
src/auth/session.ts 78
no tests · 14 importers · sec-sensitive
src/api/billing.ts 52
2 dangling refs · 8 importers
src/lib/jwt.ts 34
covered · 6 importers
src/utils/clock.ts 12
covered · 1 importer
score = importance × untested-ness × churn × dangling-refs

v0.17 → the GitHub Action posts inline review comments on every PR alongside the sticky summary, anchored to the heuristic-flagged lines.
01

risk score

Every changed file gets a 0–100 score combining untested status, security-sensitive paths, importer fan-in, caller count, dangling references, and churn. Reasons are explicit — no black box.

02

missing-test detection

sverklo_test_map walks the diff, the import graph, and filename conventions to flag changed code without matching tests — ranked by risk.

03

dangling-ref check

Every removed symbol is checked against the symbol-reference graph. If a caller still exists, the merge is blocked with file:line evidence.

04

mcp prompts

Five workflows: /sverklo:review-changes, /sverklo:pre-merge, /sverklo:onboard, /sverklo:architecture-map, /sverklo:debug-issue.

/* review-quality benchmark — published, not claimed */
METHOD
Real merge requests across 5 OSS repos (Express, FastAPI, etc). Same Claude model, same prompt, with and without sverklo's diff-aware tools. F1 vs human-written review. Harness in benchmark/ — reproducible.
RESULTS
+29% F1 after parser improvements on P1/P2 categories. 2–4× fewer tokens per review on most repos. Mixed results on small focused diffs (built-in tools win there). Honest receipts beat marketing math.
WHY WE PUBLISH MIXED
Competitors claim "5–10× fewer tokens" with no methodology. We'd rather be the tool you trust than the one you screenshot. The benchmark harness is in the repo — clone it, run it on your codebase, decide for yourself.
/* performance benchmark — three real codebases, one laptop */
SEARCH p95
26 ms
On facebook/react (4,368 files, 20k chunks). On smaller repos (gin, nestjs) it drops to 11–14 ms. Sverklo lives in the agent's hot loop — the latency budget allows it.
IMPACT ANALYSIS
<1.2 ms
sverklo_impact render on React's most-called symbol. Sub-millisecond on every repo we tested — it's an indexed SQL join, not a 200-grep-match scan.
COLD INDEX (REACT)
152 s
2.5 minutes for a 4k-file monorepo, ~7 ms/chunk. Pay it once per project; incremental refresh after that only re-processes changed files. The honest trade-off — listed first because we think you'd rather know.
Run on Apple Silicon. Steady-state RAM ~200 MB; peak during indexing 400–700 MB while the embedder batches. Full methodology, all metrics, and the reproducer harness in BENCHMARKS.md. See the MCP benchmark comparison.
/* how it works */

indexed.
ranked.
remembered.

01 · query
"how does the auth middleware work?"
02 · retrieve
BM25 + ONNX vectors
+ filename / path channels
03 · rank
PageRank + RRF fusion
channel weights, path × 1.5
04 · output
3 chunks · 23 ms · 2.4k tokens
01

hybrid search

BM25 for precision, semantic embeddings for recall, PageRank for structural importance — fused via RRF. Faster and more accurate than grep.

02

local embeddings

all-MiniLM-L6-v2 via ONNX runtime. 384-dimensional vectors generated on your machine. No API calls, no data leaves your laptop.

03

pagerank ranking

Files that are imported by many others rank higher. Your agent finds the actually-important code first, not just keyword matches.

04

persistent memory

Save decisions, patterns, and preferences with git-state linking. Stale memories flagged automatically when referenced files change.

05

multi-repo + cross impact

One MCP server serves all your repos via a global registry. Trace how a change ripples across dependent repos. No per-project config needed — sverklo init in any repo, done.

06

configurable pagerank

Drop a .sverklo.yaml in your project root to tune PageRank weights, boost or penalize paths, and customize ranking to match your codebase's shape.

07

incremental

File watcher updates the index on every save. Dependency graph and PageRank recompute in real time. Always fresh.

08

eleven languages

TypeScript, JavaScript, Python, Go, Rust, Java, C, C++, Ruby, PHP, Vue.

/* persistent memory */

your agent
finally
remembers.

tuesday → thursday · two conversations, one memory remember + recall
# tuesday — agent saves the decision > we picked Prisma for the ORM because of better TypeScript types › sverklo_remember category="decision" ✓ memory #42 saved · main@a3f29e1 # thursday — new conversation, fresh context window ───────────────────────────────────────────────────── Loaded 3 memories: ORM choice, auth pattern, API conventions > add a new API endpoint for user profiles # Claude already knows: Prisma + JWT (from memories #42, #43) › writes endpoint with Prisma + JWT — no rediscovery
#42 decision
"We chose Prisma over Drizzle for better TypeScript types."
kind
semantic
scope
workspace [ws]
tags
database · orm
git
main@a3f29e1
valid_from
a3f29e1
confidence
1.0
v0.17 → sverklo_remember scope:"workspace" writes once, surfaces across every repo in the workspace.
init on import
sverklo init imports your existing project knowledge:
  • CLAUDE.md (12)
  • .cursorrules (3)
  • docs/adr/001-prisma.md
  • docs/adr/002-auth.md
  • CONTRIBUTING.md (1)
18 memories · categorized, tagged, embedded

v0.17 → sverklo memory export pushes them to markdown / Notion / JSON.
·

bring your own docs

Auto-imports memories from CLAUDE.md, .cursorrules, AGENTS.md, CONTRIBUTING.md, and ADRs on init. Your existing project knowledge becomes semantically searchable instantly.

·

bi-temporal (unique to sverklo)

Every memory has valid_from_sha and valid_until_sha. Memories are never deleted, only superseded — so you can query "what we believed at commit X". Zep does this for conversations; sverklo is the only one doing it for code.

·

staleness detection

If a memory references a file that no longer exists, it's flagged as stale. No more advice based on deleted code.

·

semantic recall

Memories are embedded and searched the same way as code. Ask "what did we decide about auth?" and get the relevant memory.

·

auto-inject on session start

An MCP resource surfaces top memories to Claude before you type anything. Your decisions travel across sessions automatically.

36 MCP tools for search, impact analysis, review, and memory — defaults to a 6-tool 'core' profile that covers 80% of sessions. Full reference on GitHub.
/* vs the competition */

only one tool
has everything.

The code intelligence and memory space is fragmented. Each competitor gives up at least two of these dimensions. Sverklo is the only tool with all of them.
Tool Code-native Local-first MCP drop-in Git-aware memory Symbol graph Bi-temporal Cross-repo
sverklo
mempalace
claude-mem CC only
Mem0 crippled SDK
Zep / Graphiti Neo4j
Augment Code cloud
Greptile cloud 3rd party
Aider repo-map
Zilliz claude-context Milvus
XRAY MCP partial
Different lanes: Most "AI memory" tools (mempalace, claude-mem, Mem0, Zep) are built to remember conversations. Sverklo is built to understand code. We index 24 languages (10 first-class with structural parsing, 14 via regex fallback), build a symbol-level dependency graph, and run hybrid BM25 + vector + PageRank search — then bolt a bi-temporal memory layer on top that's tied to git commits, not wall-clock time.

The wedge: Sverklo is the only tool that knows what the code looked like when you made that decision — and warns you when the code has drifted since. If you want conversation memory, use mempalace. If you want your agent to actually understand a codebase, use sverklo.

Want a deeper dive? See side-by-side comparisons → against Serena, GitNexus, codebase-memory-mcp, Cursor @codebase, and Sourcegraph Cody.
/* install */

three commands.
zero config.

WORKS WITH Claude Code Cursor Windsurf Zed VS Code JetBrains Antigravity & any MCP client
01 — install
Global npm install
Requires Node 20+. Works on macOS, Linux, Windows.
02 — setup
Initialize in your project
Creates .mcp.json, .claude/settings.local.json, and AGENTS.md (or CLAUDE.md if that's what your project uses). Auto-detects Antigravity. Idempotent.
03 — start coding
Open your agent
First run downloads the embedding model (~90MB, ~30s). After that, instant.
/* questions we get asked */

questions,
honestly
answered.

How do I stop Claude Code from hallucinating function names that don't exist in my codebase?

Claude Code hallucinates function names because it generates from training-data patterns rather than your actual symbol graph. It will write getUserByEmail() when your code uses findByEmail(), invent imports for packages you don't depend on, and forget yesterday's design decision because context was compacted. Sverklo solves this with a 37-tool MCP retrieval layer the agent calls before writing code: sverklo_lookup resolves a name to its definition with file:line, sverklo_refs proves whether a symbol exists with caller context, sverklo_verify lets the agent re-check that a quoted span is still present at the cited git SHA. Install with npm install -g sverklo and run sverklo init — it auto-writes the .mcp.json so Claude Code starts using these tools immediately. Local-first, MIT-licensed, no API keys.

Why would I install another MCP server?

Most MCP servers are single-purpose wrappers around an API. Sverklo is local-first code intelligence: hybrid search (BM25 + ONNX embeddings + PageRank), symbol-level impact analysis, diff-aware PR review, and bi-temporal memory — all running on your laptop with no API keys. It works with Claude Code, Cursor, Windsurf, Zed, VS Code, JetBrains, and Google Antigravity, so if you already have an MCP client, sverklo just appears as 37 new tools alongside whatever else you run.

How is Sverklo different from Cursor's @codebase or Claude Context?

Cursor's @codebase indexing is cloud-based and tied to the Cursor editor. Claude Context (Zilliz) requires a Milvus database. Sverklo runs entirely on your laptop with SQLite and ONNX embeddings, works across every major AI coding agent via the MCP protocol, and adds symbol-level impact analysis and bi-temporal memory that neither offers.

Does Sverklo work offline?

Yes. The only network call Sverklo ever makes is downloading the ONNX embedding model on first run (~90MB, cached locally). After that you can disconnect from the internet and everything continues to work — indexing, search, embeddings, memory, dashboard. No telemetry. No cloud calls. Your code never leaves your laptop.

Which AI coding agents does Sverklo support?

Sverklo works with any AI coding agent that speaks the Model Context Protocol (MCP): Claude Code, Cursor, Windsurf, Zed, VS Code, JetBrains, and Google Antigravity. The sverklo init command auto-detects which clients you have installed and writes the right config files.

Is Sverklo free and open source?

Yes. MIT licensed and free forever. All 36 tools have no limits and opt-in telemetry that's off by default (see /security). A future Sverklo Pro tier will add smart auto-capture and larger embedding models, and Sverklo Team will add shared team memory — but the rule is "Pro adds new things, never gates current things." Anything in the OSS server today stays in the OSS server forever.

How do I install Sverklo in Claude Code?

Run npm install -g sverklo, then cd to your project and run sverklo init. That writes .mcp.json at your project root, appends sverklo instructions to your CLAUDE.md, and runs sverklo doctor to verify the MCP handshake. Restart Claude Code and the 37 sverklo tools appear in the /mcp list.

What programming languages does Sverklo support?

24 languages total. 10 first-class with structural parsing: TypeScript/TSX, JavaScript, Python, Go, Rust, C# (tree-sitter), plus Vue (SFC), Markdown, and Jupyter notebooks (custom parsers). 14 more via regex fallback: Java, C, C++, Ruby, PHP, Kotlin, Scala, Swift, Dart, Elixir, Lua, Zig, Haskell, Clojure, OCaml. Hybrid search works across all 24; symbol-level impact analysis is sharpest on the first-class 10.

When is Sverklo the wrong tool?

Sverklo is the right tool when you don't know exactly what string to search for. When you do know the literal string, plain grep is faster and more reliable. On a 30-file repo, you don't need Sverklo. It earns its place on large interconnected codebases where AI coding agents waste thousands of tokens reading the wrong files.

/* one command, MIT-licensed, stays on your laptop */

your agent is still guessing.

Give it a structural understanding of your codebase. Local-first, MIT, no API keys, no cloud.

try playground →