Semantic Code Search
Built for AI Agents
Give your AI agent deep codebase understanding.
MCP-native. Works with Claude Code, Cursor, Codex and more.
Works with your favorite AI tools
Stop feeding your best model secondhand context
Most AI agents delegate code exploration to a smaller, cheaper model. That model filters and summarizes before the reasoning model ever sees anything — silently dropping context along the way. SHARC puts the query in the reasoning model's hands directly.
The model that needs to understand your code is the one that queries and reads it.
No summarization. No intermediate model. Zero information loss.
Now see it in practice
Even when the reasoning model explores directly, file-by-file traversal burns tokens and misses context. One semantic query replaces dozens of reads.
> How does chat history persistence work in this codebase?
- Search (**/*.ts)
- Found 64 files
- Search (**/*.tsx)
- Found 96 files
- Search (chat.*history|saveChat|database)
- Found 29 files
- Read (lib/db/schema.ts)
- Read 174 lines
- Read (lib/db/queries.ts)
- Read 594 lines
- Read (app/(chat)/api/history/route.ts)
- Read 47 lines
- Read (components/sidebar-history.tsx)
- Read 370 lines
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Chat history uses Drizzle ORM with PostgreSQL. The schema defines User, Chat,
Message_v2, and Vote_v2 tables...> How does chat history persistence work?
- sharc - search_code (query: "chat history persistence", limit: 3) (MCP)
- Found 3 results for query: "chat history persistence"
1. Code snippet (typescript) [ai-chatbot]
Location: lib/db/queries.ts:83-105
Score: 0.9847
... +22 lines (ctrl+o to expand)
2. Code snippet (typescript) [ai-chatbot]
Location: lib/db/queries.ts:157-180
Score: 0.9623
... +18 lines (ctrl+o to expand)
3. Code snippet (typescript) [ai-chatbot]
Location: app/(chat)/api/chat/route.ts:162-173
Score: 0.9418
... +8 lines (ctrl+o to expand)
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Chat persistence uses Drizzle ORM with saveChat() for creation and
getChatsByUserId() for retrieval with cursor-based pagination.MCP Tool for AI Assistants
Drop into any MCP-compatible AI assistant. Zero config, instant code intelligence.
Product Roadmap
Where we've been and where we're headed
Research & Experimentation
Basic embeddings and semantic search exploration
Core Models
SHARC embedding model, reranking & MCP prototype
Public Launch
MCP tool + Inference API goes live
Code Review Agents
AI-powered code review agents built on SHARC-MCP
Questions about SHARC?
We'd love to hear from you. Reach out anytime for docs, onboarding, or integration guidance.