Skip to content

comparison.md

Latest commit

 

History

History
237 lines (170 loc) · 8.49 KB

comparison.md

File metadata and controls

237 lines (170 loc) · 8.49 KB
Raw

Comparison — CodeGraph vs Alternatives

This document provides a detailed comparison of CodeGraph with other tools in the code intelligence and AI agent tooling space.

Quick Comparison Table

Tool Category Local-First Semantic Graph MCP Native Multi-Storage Binary Size Best For
CodeGraph Code graph + MCP ✅ ✅ (tree-sitter semgraph) ✅ Built-in ✅ 6 backends ~58 MB Local-first AI agents needing full semantic graph
Aider RepoMap Repo map generator ✅ ❌ (ctags-based) ❌ ❌ N/A Aider users wanting quick repo overview
Sourcegraph Cody Cloud code search ❌ (self-host option) ✅ (CodeQL) Via extension ❌ N/A Enterprise multi-repo search
Bloop Code indexer ✅ ❌ (search only) ❌ ❌ ~30 MB Fast local code search
CodeQL Semantic analysis ✅/Cloud ✅ (QL queries) ❌ ❌ Heavy Security auditing, variant analysis
Kythe Code graph ✅ ✅ ❌ ❌ Complex setup Large-scale build-integrated graphs
LSP servers (rust-analyzer, clangd, etc.) Per-language IDE ✅ Per-lang only ❌ ❌ Per-lang IDE integration per language
ast-grep Structural search ✅ ❌ (pattern match) ❌ ❌ ~10 MB AST pattern matching/replace
context7 Docs MCP ❌ N/A ✅ ❌ N/A API/documentation context

Detailed Breakdown

CodeGraph (This Project)

What it is: A local-first semantic code graph built on tree-sitter that serves AI agents via the Model Context Protocol (MCP).

Strengths:

  • True semantic graph: Symbols have global IDs; call chains capture control flow (LOOP, IF_TRUE, RETURN, etc.)
  • MCP-native: 24 tools exposed directly — no wrapper needed
  • Multi-storage: SQLite (default), LMDB, Redis, Postgres, MySQL, in-memory
  • Single binary: ~58 MB with all backends + embedding runtime bundled
  • 14 languages with full extraction: TypeScript, TSX, JavaScript, Python, Go, Rust, Java, C, C++, C#, Ruby, PHP, Scala, Swift, Lua
  • Optional semantic search: fastembed (BGE-small) for hybrid KNN + keyword search
  • Behavior sandbox: JIT compile functions + run against Rhai mocks
  • Full re-index always: Simpler, no stale state; 139 files in ~190 ms

Trade-offs:

  • No incremental sync (by design — full re-index on change)
  • Requires MCP-compatible agent (Claude Code, Cursor, Codex, opencode, Hermes, Antigravity)
  • Postgres/MySQL schema applied manually (no auto-migrations)

Aider RepoMap

What it is: A repository map generator integrated into Aider (AI pair programmer). Uses ctags + tree-sitter to create a condensed representation of the codebase for LLM context.

Strengths:

  • Tightly integrated with Aider's editing workflow
  • Fast, lightweight
  • Works with 50+ languages via ctags

Weaknesses vs CodeGraph:

  • Not a persistent semantic graph — regenerates per session
  • No global symbol IDs or call chains
  • No MCP server — only works within Aider
  • No storage backends, no semantic search
  • Ctags-based (less precise than tree-sitter extraction)

When to choose: You use Aider exclusively and want zero-setup repo context.

Sourcegraph Cody

What it is: Enterprise code search + AI assistant. Uses CodeQL for semantic analysis, offers cloud and self-hosted options.

Strengths:

  • Cross-repository search at scale
  • CodeQL-powered semantic queries
  • Enterprise features (RBAC, audit logs, compliance)
  • IDE integrations (VS Code, JetBrains)

Weaknesses vs CodeGraph:

  • Cloud-first (self-host is complex)
  • Heavy infrastructure (PostgreSQL, Redis, Kafka, etc.)
  • Not a local-first single binary
  • No native MCP server (uses proprietary protocol)
  • Expensive for teams

When to choose: Enterprise needing multi-repo search, compliance, and can invest in infrastructure.

Bloop

What it is: Fast Rust-based code indexer and search tool. Focuses on regex/keyword search with some semantic awareness.

Strengths:

  • Very fast indexing and search
  • Rust-based, single binary (~30 MB)
  • Local-first

Weaknesses vs CodeGraph:

  • No semantic graph — no global IDs, no call chains
  • No MCP server
  • No semantic search (embeddings)
  • Limited language extraction depth

When to choose: You only need fast code search, not semantic graph for AI agents.

CodeQL

What it is: Semantic code analysis engine from GitHub. Uses a query language (QL) to find vulnerabilities and patterns.

Strengths:

  • Deep semantic analysis via QL
  • Industry standard for security research
  • Variant analysis (find similar bugs)
  • GitHub Advanced Security integration

Weaknesses vs CodeGraph:

  • Query-based, not graph-native — you write QL, don't traverse a graph
  • Heavy (Java-based, large download)
  • No MCP server
  • No persistent graph storage for agent queries
  • Steep learning curve (QL language)

When to choose: Security auditing, variant analysis, compliance — not for AI agent context.

Kythe

What it is: Google's language-agnostic code graph platform. Extracts facts from builds, stores in a graph.

Strengths:

  • Language-agnostic (supports 15+ languages)
  • Build-system integrated (Bazel, Gradle, etc.)
  • Scales to massive codebases (Google-scale)

Weaknesses vs CodeGraph:

  • Complex setup (requires build integration)
  • No MCP server
  • No single binary — distributed services
  • Not designed for local AI agent use
  • Steep operational overhead

When to choose: Large org with build infrastructure wanting cross-language code graph.

LSP Servers (rust-analyzer, clangd, pyright, etc.)

What it is: Language Server Protocol implementations per language. Provide IDE-grade semantic analysis.

Strengths:

  • Best-in-class per-language semantics
  • IDE integration (completion, goto definition, refactor)
  • Local-first

Weaknesses vs CodeGraph:

  • Per-language only — no cross-language graph
  • No unified symbol IDs across languages
  • No MCP server (though some bridges exist)
  • No persistent graph storage
  • Not designed for AI agent consumption

When to choose: IDE development — not for AI agent context.

ast-grep

What it is: Structural search and replace using tree-sitter patterns. Like grep but AST-aware.

Strengths:

  • 30+ languages via tree-sitter
  • Pattern matching on AST nodes
  • Fast, single binary (~10 MB)
  • Local-first

Weaknesses vs CodeGraph:

  • No persistent graph — ephemeral pattern matching
  • No global symbol IDs or call chains
  • No MCP server
  • No semantic search (embeddings)
  • Not a queryable index

When to choose: One-off structural search/replace, codemods — not for persistent AI context.

context7 (Upstash)

What it is: MCP server for documentation and API context. Not a code graph.

Strengths:

  • MCP-native
  • Good for API/docs lookup

Weaknesses vs CodeGraph:

  • No code analysis whatsoever
  • Cloud-only
  • Different use case entirely

When to choose: Agents need API documentation context, not codebase understanding.

Decision Matrix

Your Need Recommended Tool
Local AI agent + semantic graph + MCP CodeGraph
Aider user, quick repo map Aider RepoMap
Enterprise multi-repo search + compliance Sourcegraph Cody
Fast local code search only Bloop
Security auditing, variant analysis CodeQL
Massive monorepo with build integration Kythe
IDE development (completion, refactor) LSP servers
Structural search/replace, codemods ast-grep
API documentation for agents context7

Methodology

This comparison is based on:

  • Public documentation and GitHub repos (as of 2026)
  • Feature matrices from project READMEs
  • Architecture descriptions (local vs cloud, graph vs search vs pattern-match)
  • No hands-on benchmarking — performance claims are from respective projects

Missing from this table: Greptile (cloud PR review), Continue.dev (IDE extension), Cursor indexing (IDE-tied), Glean (enterprise search), semgrep (linting), bito/DeepSource (cloud review). These serve different primary use cases.

See Also