Code Graph RAG vs Kaily: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Code Graph RAG and Kaily — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
C
Code Graph RAG
vitali87
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.
Kaily
Kaily (formerly Copilot.live)
An AI teammate for helpdesk, website chat, voice calls and collaborative document Q&A that automates support and team workflows.
Key features
- Multi-Channel Support: Combines website chatbots, helpdesk automation and AI-driven voice calls to handle customer interactions, qualify leads, and escalate to humans when needed.
- Team-Shared Document Chat: Allows multiple team members to participate in a single PDF or document conversation concurrently, enabling group Q&A and collaborative review in real time.
- Multi-Source Ingestion: Consolidates information from PDFs, Google Docs, Notion pages, and website links to produce answers that span multiple sources.
- Citations & Traceability: Every generated answer includes exact page and section citations (e.g., page numbers and chapter pointers) so users can verify source material quickly.
- Integrations & Embeds: Connects with Slack, Chrome, Google Drive and supports embeddable website chat to fit into existing workflows and touchpoints.
- Workflow Automation: Automates repetitive support and sales tasks, issue resolution steps, and can be configured to trigger downstream actions based on conversations.
- Enterprise Customization: Offers team and enterprise-focused options with customizable pricing, onboarding, security settings, and integrations to meet organizational requirements.
- Team-shared document chats allowing multiple users to participate simultaneously in a single PDF or document conversation
- Supports ingestion of PDFs, Google Docs, Notion pages, and website links (multi-source data integration)
- Per-answer citations with exact page numbers and sections for traceability and verification
- Integrations: Slack connector, Chrome extension, Google Drive integration (embeds into existing workflows)
- Web-based interface optimized for collaborative workflows and document-centric Q&A
- Configurable for team/enterprise usage — pricing and plans customized by team size
- Can be embedded or used as an AI chatbot (used as a portfolio chatbot example)
- Focused on enterprise/team scenarios such as contract review, HR policy lookups, and research report analysis
Best for
- Contract review for legal teams: Multiple lawyers collaboratively query PDFs, get pinpointed answers with page citations, and discuss findings in a shared document chat.
- HR policy lookup: HR staff search across handbooks and policy documents in one place to answer employee questions and cite exact sections.
- Research and reports analysis: Research teams aggregate PDFs, Google Docs and web sources to extract insights, annotate pages, and hold shared Q&A sessions.
- Customer support automation: Deploy website chatbot and AI voice calls to answer common customer questions, create tickets, and escalate complex issues to agents.
- Sales qualification on websites: Use embedded chat to engage visitors, automate qualification flows, and route leads to sales reps with context and transcripts.
- Knowledge base consolidation: Unify scattered documentation (Docs, Notion, websites) into a searchable, source-cited assistant for internal teams.
- Legal teams collaboratively reviewing and querying contract PDFs with page/section citations
- HR teams searching and discussing internal policy manuals across documents
- Research teams analyzing reports and consolidating answers from multiple document sources
- Customer support or operations teams automating resolution workflows and knowledge lookup
- Embedding an AI chatbot on websites/portfolios to provide contextual information about content