What did Hlido score CodeGraphContext?

CodeGraphContext scored 76/100 (STEADY) in Hlido's independent, hands-on review.

How much does CodeGraphContext cost?

Hlido recorded the pricing model as: Open source. Derived from evidence Hlido holds, not vendor-supplied; verify current prices on the vendor's pricing page.

Does any vendor pay Hlido for placement?

No. Hlido takes no money from the agents it rates — scoring weights stay private and the evidence behind every verdict is public.

CodeGraphContext

Name: CodeGraphContext review
Item: CodeGraphContext
Rating: 76
Author: Hlido Editor

Coding · tested 2026-08-19 · re-test due 2026-11-19 · by the Hlido desk, not the vendor

In short: A CLI-plus-MCP tool that turns a local codebase into a navigable knowledge graph for AI assistants — real adoption behind it, with marketing that oversells a bit past the evidence.

Visit CodeGraphContext → scorecard.json

Quick answer

CodeGraphContext scores 76/100 (STEADY) on Hlido’s independent, hands-on test (reviewed 2026-08-19). STEADY (76) for a real, MIT-licensed code-knowledge-graph tool with meaningful adoption (4k+ stars, ~18k monthly installs), a dual CLI+MCP surface, and a useful pre-indexed bundle registry — discounted from the higher ba Pricing: Open source (free entry point documented).

Hlido Editor · 2026-08-19

CodeGraphContext (v0.5.1, MIT) indexes local code into a graph an AI assistant can query in natural language: callers and callees, class hierarchies, imports, dead code and complexity. It ships as both a CLI toolkit and an MCP server (cgc mcp setup / cgc mcp start) that wires into Cursor, VS Code, Windsurf and Claude, backed by FalkorDB Lite by default or Neo4j via Docker. The traction signals on the captured surface are concrete and in its favour — 4,095 GitHub stars, 824 forks, ~18,243 downloads in the last month, and a public registry of 96 pre-indexed bundles for well-known repos (React, Django, Terraform, pandas) that load instantly. That is a genuinely useful discovery surface. The place Hlido pulls back is the marketing: a comparison table rates unnamed competitors as "HALLUCINATED" and "SLOWS WITH SIZE" while grading itself "EXTREMELY ACCURATE" across the board, and a lone Reddit quote stands in for testimonials. Those are vendor claims, not measured results, and the accuracy assertion is exactly what a surface review cannot confirm. The underlying idea — graph-structured context instead of blind grep — is sound and increasingly common, and the install path is clean (pip install codegraphcontext). Reviewed from the marketing and docs surface only; the indexing quality itself was not exercised.

Why STEADY

STEADY (76) for a real, MIT-licensed code-knowledge-graph tool with meaningful adoption (4k+ stars, ~18k monthly installs), a dual CLI+MCP surface, and a useful pre-indexed bundle registry — discounted from the higher band because the marketing makes unverifiable superiority claims (a self-graded competitor table, "EXTREMELY ACCURATE") and the review is surface-only, so indexing accuracy was not tested. Low-medium confidence.

What we saw

4 screenshots captured by the Hlido engine during the reviewed run (run-70f92117c1e33804-cgc-codes). Our own captures — not vendor marketing material.

CodeGraphContext — run screenshot 1 (home.png) — home.png

CodeGraphContext — run screenshot 2 (page_.png) — page_.png

CodeGraphContext — run screenshot 3 (page__features.png) — page__features.png

CodeGraphContext — run screenshot 4 (page_understand.png) — page_understand.png

What it does well

Solves a real agent problem: structured graph queries (callers, callees, class hierarchy, imports) instead of token-burning blind greps
Dual surface — a CLI toolkit and an MCP server that sets itself up for Cursor / VS Code / Windsurf / Claude with a wizard
Concrete adoption on the captured surface: 4,095 stars, 824 forks, ~18,243 downloads last month, MIT-licensed
A registry of 96 pre-indexed bundles for popular repos that load instantly — a genuinely useful discovery layer
Sensible defaults (FalkorDB Lite) with a Neo4j upgrade path, live file-watching, and a clean pip install

What it fails at

Marketing overreaches: a self-authored comparison table grades unnamed rivals "HALLUCINATED"/"SLOWS WITH SIZE" and itself "EXTREMELY ACCURATE" — claims, not measurements
Social proof is thin — a single Reddit quote — so the 'loved by developers' framing is weakly supported
Surface-only review — Hlido did not index a codebase, so the accuracy and completeness of the graph are unverified
Requires a graph database (bundled Lite or Neo4j) and setup; not a hands-off install for non-developers

Red flags

The competitor-comparison table and 'EXTREMELY ACCURATE' grade are self-authored marketing, not independent benchmarks — weigh them accordingly

Best for

Developers who want their AI assistant to answer precise structural questions about a codebase (call sites, hierarchies, dead code)
Teams on Cursor / VS Code / Windsurf / Claude wanting an MCP-native code-graph backend
Anyone wanting to try graph context quickly against a popular repo via the pre-indexed bundles

Not recommended for

Users who want measured accuracy guarantees before trusting the graph — the strongest claims are vendor-stated
Non-developers uncomfortable running a local graph database
Teams needing enterprise support or an audited SLA

Pricing & access

ModelOpen source
Free entry pointYes — a free tier or open-source edition is documented

Derived from Hlido-held evidence only (engine checklist + editorial text); quotes are verbatim from the scorecard; not vendor-supplied; re-derived daily. Verify current prices on the vendor's pricing page. Last verified 2026-08-18.

Related agents

Agent relevance

CLI MCP Behavioral-testable

Agentic-Commerce Readiness 54/100 · INTEGRABLE

Independent readiness for agent delegation & transaction. How it’s scored · check live

Install via pip; `cgc mcp setup` configures an IDE and `cgc mcp start` runs the MCP server so an AI assistant queries the code graph in natural language. Also usable as a standalone CLI toolkit.

Agent-friendly score: 8/10

Evidence

Solves a real agent problem: structured graph queries (callers, callees, class hierarchy, imports) instead of token-burning blind greps — source (2026-08-19) verified
Dual surface — a CLI toolkit and an MCP server that sets itself up for Cursor / VS Code / Windsurf / Claude with a wizard — source (2026-08-19) verified
Concrete adoption on the captured surface: 4,095 stars, 824 forks, ~18,243 downloads last month, MIT-licensed — source (2026-08-19) verified
A registry of 96 pre-indexed bundles for popular repos that load instantly — a genuinely useful discovery layer — source (2026-08-19) verified
Hands-on runtime behaviour (executing the tool / a live task) — source (2026-08-19)

scorecard.json · transparency passport · registry · methodology

More: compare agents · best of · developer tools · incident registry

Verdict by Hlido Editor, our automated editorial system · Method: public-surface-tier-2+editorial-narrative-v2 · Methodology version 2026.05 · Next review due 2026-11-19

How this page was produced. The scores, claim verdicts and evidence come from automated hands-on testing of the product’s public surface. The written analysis is drafted by an AI system, and pages publish without a person reviewing each one. Hlido publishes this record and answers for it — tell us if anything here is wrong and we will correct it.

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