How does RavMem's codebase indexing work?

You run one index pass. RavMem builds a symbol graph of your entire codebase using Tree-sitter. Agents pull only the delta they need per task: not the full repo every time.

The Context Enginefor

The bestfor your agents.

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Why RavMem

AI agents forget everything between sessions. RavMem gives them persistent memory — shared, structured, and always up to date.

Index your codebase once. Every agent on your team queries the same knowledge graph — no rescanning, no drift, no duplicated context.

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00 — Foundation

INDEX ONCE SHARE.

The codebase is indexed into a shared RavMem instance. One pipeline run. One persistent knowledge graph. Available to every agent on your team — no per-machine setup.

01 — Efficiency

ZERO RESCAN EVER.

When code changes, only changed files are re-indexed. Agents pull only the delta — not the full codebase. Token usage for context sync drops to near zero.

02 — Multi-Agent

ONE GRAPH ALL AGENTS.

10 AI agents across 10 machines. One shared RavMem instance. Each agent gets consistent, up-to-date codebase context without rescanning. Built for collaborative AI.

03 — Safety

KNOW WHAT BREAKS.

Before you refactor, know exactly what breaks. RavMem traverses the call graph to surface every transitive dependency with configurable depth. Ship with confidence.

04 — Search

FIND ANYTHING FAST.

BM25 keyword precision combined with semantic vector similarity via Reciprocal Rank Fusion. Find any symbol whether you know its name or just its intent.

05 — Deploy

SELF HOST OR NOT.

Run RavMem on your own infrastructure with full control, or let us manage it. Identical APIs, MCP endpoints, and encryption either way. Always secure.

000%

01

INDEX ONCE

// Built for scale

One index.
Zero re-scans.
Complete context.

A team of 10 AI agents rescanning a 50,000-symbol codebase 5 times a day equals millions of tokens wasted weekly. RavMem reduces this to a single shared index and per-agent delta pulls.

12+Languages

8MCP Tools

0Re-scan Tokens

100%Context Preserved

✦ Persistent Memory Engine

Your agents
RememberEverything.

Index once. Share across every agent. Query forever.

FEATURES

THEFEATURES

F—01

Knowledge
Graph

Maintain a queryable knowledge graph across hours or days. Every function, class, method, and interface with typed edges for calls, imports, and inheritance.

PersistentStructural

F—02

Shared
Context

10 agents across 10 machines. One shared RavMem instance. No context drift. No duplication. Every agent thinks with the same structural understanding.

Multi-AgentSync

F—03

Hybrid
Search

BM25 + semantic search combined via Reciprocal Rank Fusion. Keyword precision meets vector similarity. Find any symbol across your entire codebase instantly.

BM25Semantic

F—04

Change
Impact

Before you refactor, know exactly what breaks. Traverse every transitive dependency. Blast radius analysis at the symbol level — not the file level.

RefactorSafety

F—05

Git-Aware
Index

Only re-index what changed. Git-diff aware with MD5 fallback. CI stays fast on monorepos. Incremental re-indexing means zero wasted compute on every push.

IncrementalFast

F—06

MCP
Native

8 MCP tools expose your knowledge graph to Claude, Cursor, Copilot — any MCP-aware agent. Push context. Pull context. Resume exactly where any agent left off.

MCPProtocol

PERSISTENT MEMORYNEVER FORGET

SHARED CONTEXTONE SOURCE

AGENT TEAMSWORK TOGETHER

QUERY ANYTHINGINSTANT ACCESS

BUILD FASTERWITH RAVMEM

PERSISTENT MEMORY ✦ KNOWLEDGE GRAPH ✦ MULTI-AGENT SYNC ✦ INDEX ONCE ✦ QUERY FOREVER ✦ ZERO CONTEXT DRIFT ✦ MCP NATIVE ✦ AGENT TEAMS ✦ PERSISTENT MEMORY ✦ KNOWLEDGE GRAPH ✦ MULTI-AGENT SYNC ✦ INDEX ONCE ✦ QUERY FOREVER ✦ ZERO CONTEXT DRIFT ✦ MCP NATIVE ✦ AGENT TEAMS ✦ PERSISTENT MEMORY ✦ KNOWLEDGE GRAPH ✦ MULTI-AGENT SYNC ✦ INDEX ONCE ✦ QUERY FOREVER ✦ ZERO CONTEXT DRIFT ✦ MCP NATIVE ✦ AGENT TEAMS ✦

Knowledge Graph

Every function, class, method and interface — indexed into a queryable knowledge graph with typed edges for calls, imports, and inheritance.

Persist structural understanding across sessions. Query any symbol, trace any dependency, in milliseconds.

Tree-sitter parsing
KuzuDB graph store
Typed edges
Incremental updates

Agent Memory

10 agents. 10 machines. One shared memory. No context drift. No duplication. Every agent thinks with the same structural understanding.

Push context before a session ends. Pull it back instantly on the next one.

Shared context store
Zero drift
Session resume
Multi-agent sync

MCP Native

8 MCP tools expose your knowledge graph to Claude, Cursor, Copilot — any MCP-aware agent. One protocol, every tool.

Push context. Pull context. Resume exactly where any agent left off.

8 MCP tools
Claude / Cursor
Context push/pull
Standard protocol

MULTI-AGENT SYSTEMS

10 agents. One shared memory. Zero drift across your entire team.

DELTA SYNC

Only changed files get re-indexed. Monorepos stay fast on every push.

BLAST RADIUS ANALYSIS

Know exactly what breaks before you refactor a single line.

HYBRID SEARCH

BM25 precision combined with semantic similarity via Reciprocal Rank Fusion.

MCP NATIVE TOOLS

8 tools. Works with Claude, Cursor, Copilot — plug in and query instantly.

SELF-HOSTED OR MANAGED

Your infrastructure or ours. Same API, same encryption, full control.

12+ LANGUAGES

TypeScript, Python, Go, Rust, Java, C++, Ruby, Swift, Kotlin and more.

ZERO RESCAN

Build the index once. Agents pull deltas. Millions of tokens saved weekly.

// What's next

Coming Soon

// Get in touch

Let's build together.

FAQ's

RavMem is a persistent context engine for AI agent teams. It indexes your codebase once and serves every agent from a single shared memory — no redundant rescans.

You run one index pass. RavMem builds a symbol graph of your entire codebase. Agents pull only the delta they need per task — not the full repo every time.

12+ languages out of the box, including TypeScript, Python, Go, Rust, Java, C++, Ruby, Swift, Kotlin, PHP, C#, and Elixir.

Vector stores return semantically similar chunks — they don't understand call graphs, imports, or symbol relationships. RavMem gives agents structural context, not just text similarity.

We're in early access. Drop us a message and we'll add you to the list.

One index. Every agent. Zero rescans.

Early Access →

// Early access

Context for every agent.

RavMem is rolling out to select teams. Join the waitlist and be first to build on persistent AI memory.

The Context EngineforAI Agent Teams