graphify

Product Introduction

1. Definition: Graphify is a deterministic, queryable knowledge graph engine for software codebases. It is a developer tool that statically analyzes source code, documentation, SQL schemas, configuration files, and PDFs to construct a persistent, typed graph representation of a software project's entire knowledge base.
2. Core Value Proposition: It exists to solve the problem of fragmented and hard-to-navigate software knowledge by creating a single, auditable source of truth. Its primary value is enabling evidence-backed code understanding, cross-repository impact analysis, and token-efficient AI assistant integration, all while prioritizing on-device privacy and security.

Main Features

1. Deterministic Knowledge Graph Construction: Graphify works by parsing code locally using 36 tree-sitter grammars to build an Abstract Syntax Tree (AST). It then transforms this AST into a persistent, typed knowledge graph where nodes represent code entities (functions, classes, variables) and edges represent explicit relationships (calls, imports, references). This process is deterministic, requires no model calls for code, and avoids embeddings or vector databases for core functionality.
2. Cross-Repository Blast Radius & Impact Analysis: The feature provides a formal verification capability. When a code change is proposed, the engine traverses the graph's edges to identify every dependent component across all connected repositories. This allows developers to "see what breaks before the merge," providing a verifiable impact report rather than a guess, which is critical for regulated and complex environments.
3. Model Context Protocol (MCP) Integration & Persistent Memory: Graphify serves its knowledge graph via an MCP server, making it queryable by 17+ AI coding assistants like Claude Code, Cursor, and GitHub Copilot. This gives assistants structured, source-linked context, drastically reducing "blind" file searches. The graph acts as persistent, shared memory across coding sessions, leading to significant reductions in LLM token usage (cited as up to 79x fewer tokens).
4. Fully On-Device & Open-Core Deployment: The core parsing engine is open-source (Apache 2.0) and runs entirely on the user's machine. It sends no telemetry, analytics, or code to external servers. For teams, Graphify offers a hosted SaaS option (Cloud) and an early-access Enterprise layer for self-hosting or air-gapped deployments, providing flexibility across security postures.

Problems Solved

1. Pain Point: The high cognitive load and time cost for engineers to understand sprawling, interconnected codebases and documentation, especially during onboarding or when making changes in unfamiliar areas. Traditional grep searches and "go to definition" lack multi-hop reasoning and cross-repo visibility.
2. Target Audience: Software engineers and development teams in complex or regulated industries (finance, healthcare, enterprise SaaS) who need auditable understanding of their systems. It is particularly valuable for platform teams, infrastructure engineers, and developers working in large monorepos or distributed microservice architectures.
3. Use Cases: Essential for performing safe refactoring with guaranteed impact analysis, accelerating code reviews by visualizing dependencies, recovering institutional knowledge lost to employee turnover, and supercharging AI assistants with accurate, provenance-tagged code context to prevent hallucinations.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):

   • Pros: Unmatched capability for deterministic, cross-repo impact analysis. Drastically reduces LLM token costs by providing structured graph context. Superior privacy model with fully local parsing. Provides auditable "paths" as evidence for answers, not just snippets. Open-core model offers deployment flexibility.
   • Cons: The value is maximized for large, complex codebases; may be overkill for trivial projects. Requires initial graph construction. The advanced "formal verification" and enterprise features are part of the commercial offering, not the core Apache 2.0 engine.

2. Key Alternatives & Differentiation:

   • vs. Sourcegraph: Sourcegraph is primarily a powerful hosted code search and intelligence platform. Graphify differentiates by being on-device first, building a traversable graph for multi-hop reasoning (vs. advanced search/navigation), and focusing on AI agent integration via MCP rather than a human-centric web UI.
   • vs. RAG/Vector Search Tools (Greptile): Standard RAG chunks and embeds text for similarity search, which can be imprecise for code. Graphify uses deterministic graph traversal, returning explicit dependency paths with line-number provenance, avoiding the "top-k chunk guesswork" and embedding drift problems.
   • vs. AI-Powered Code Review (CodeRabbit): CodeRabbit focuses on per-PR AI review and comments. Graphify provides a persistent knowledge graph that exists independently of PRs, enabling company-wide impact analysis and serving as a memory layer for all developer tools, not just review bots.

Frequently Asked Questions (FAQ)

1. How is Graphify different from using embeddings and vector search (RAG) for code? Graphify does not rely on semantic similarity via embeddings. Instead, it builds a deterministic graph of actual code structure (calls, imports) and traverses it. This yields answers that are explicit paths through the codebase with direct file:line citations, eliminating the guesswork and potential irrelevance inherent in vector similarity search for logical code relationships.
2. Does my code get sent to the cloud when using Graphify? With the open-source engine, no. Code parsing is done locally using tree-sitter grammars with zero telemetry. You can run the entire system offline by using a local LLM (like Ollama) for document processing. The hosted Cloud product does process code in its secure environment, but the on-device option ensures code never leaves your machine.
3. Can Graphify work with my existing AI coding assistant? Yes, if your assistant supports the Model Context Protocol (MCP). Graphify acts as an MCP server, making its graph available to any MCP-compatible client. It has been tested with 17+ assistants, including Claude Code, Cursor, and GitHub Copilot.
4. What programming languages does Graphify support? It supports 36 languages via the tree-sitter parsing ecosystem, which includes mainstream languages like Python, JavaScript, Java, Go, Rust, C++, and more. The deterministic AST extraction works consistently across these supported grammars.
5. Is Graphify really free to use? Yes, the core engine is free and open-source (Apache 2.0). The hosted service at app.graphify.com offers a Free plan with no credit card required. Advanced team features, formal verification, and enterprise self-hosting are part of the paid Pro, Teams, and Enterprise plans.