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Code Graph RAG vs Paritok: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Code Graph RAG and Paritok — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

C

Code Graph RAG

vitali87

Freemium

Multi-language monorepo RAG: Tree-sitter parses your codebase into a Memgraph knowledge graph so you can query, edit, and refactor in plain English.

Key features

  • Multi-Language Graph Ingest: Tree-sitter parses Python, TypeScript, TSX, JavaScript, Rust, Go, Java, C, C++, C#, PHP, Lua, and Dart into a single language-agnostic Memgraph schema.
  • Natural-Language Cypher: The interactive CLI turns plain-English questions into Cypher queries and answers grounded in the real code structure, not vector-only guesses.
  • AST-Based Surgical Editing: The agent edits code through structural patches with a diff preview before any change is applied.
  • Structural Search & Replace: ast-grep is exposed as an agent tool, so you match and rewrite by AST pattern across the whole codebase instead of regex.
  • Pluggable ast-grep Tier: Add a new language from a single YAML pattern file — Ruby was added this way with Module/Function/Class nodes plus import edges.
  • Data-Flow Tracing: FLOWS_TO taint edges follow values through assignments, function calls, and I/O sinks across C, Java, C#, and Go.
  • Dead-Code Detection: Walk call and reference edges from entry points to find functions and modules nothing reaches.
  • Shared Graph Across Projects: Index many repos into one shared graph and query across them; a `clean` subcommand resets from scratch with confirmation.

Best for

  • Monorepo Q&A: Ask 'where is refund logic in this monorepo?' and get grounded answers from a graph of the real code, not stale docs.
  • AI-Assisted Refactoring: Rename or restructure APIs across languages with AST patches and a diff preview before commit.
  • Cross-Language Data-Flow Audits: Trace a value through assignments and function calls to see where sensitive data ends up.
  • Dead-Code Cleanup: Find unreachable functions and modules by walking call edges from entry points.
  • Codebase Onboarding: Give a new engineer or agent a queryable graph they can explore in natural language.
  • Structural Migrations: Use ast-grep to rewrite deprecated patterns (imports, error handling, config lookups) across a polyglot codebase.
View Code Graph RAG details
P

Paritok

Paritok

Freemium

Non-destructive compression gateway that drops between coding agents and LLMs to cut input tokens by up to 85% without changing the agent.

Key features

  • Drop-In Gateway: One environment variable (ANTHROPIC_BASE_URL) reroutes your agent through Paritok — no agent, prompt, or tool changes.
  • Tool Schema Compression: 46-schema tool blocks (~29K tokens) drop to ~8K per turn by keeping relevant tools and stubbing the rest, frozen per conversation for cache stability.
  • Code-Native 4B Model: A 4B compression model trained on 45K real agent trajectories keeps identifiers, paths, and errors while shrinking file reads and outputs to ~26% of original.
  • read_original Recall: Every compressed segment is tagged; the agent asks read_original(ref) and gets the exact bytes locally without spending an extra turn.
  • Stale History Summarization: Turns beyond a configurable recent window get summarized once when your context budget fills, so recent turns stay pristine and overflows drop to zero.
  • Multi-Agent Compatibility: Works today with Claude Code, Cursor, Codex, OpenHands, and any OpenAI-compatible upstream — Anthropic and OpenAI both supported.
  • Compounding Savings: Saved share grows across a session — 25% at 1 turn, 54% at 10 turns, 63% at 20 turns — against a 96,500-token baseline.
  • Open Weights and Benchmark: SWE-bench Lite floor of 86.5% quality retained at 25.7% compression rate, with weights and training pipeline published.

Best for

  • MCP-Heavy Workflows: Cut input bills for agents that ship 70+ MCP tool schemas on every turn.
  • Long Coding Sessions: Run 3× longer coding-agent sessions before context saturation forces a hard compact.
  • Bill Reduction: Estimate 54% off input tokens on a 5-developer team at 20-turn Claude Sonnet sessions (~$6,550/year).
  • Self-Hosted Privacy: Route agent traffic through your own hardware with no data leaving your network — 8GB GPU is enough.
  • Enterprise Cost Governance: Add compression at the gateway layer so all coding agents on the team benefit without engineering per-agent.
  • Cursor/Codex/Claude Code Fleet: Standardize compression across a mixed toolchain of coding agents behind one gateway.
View Paritok details