ByteBell compiles your repos into a reversible IR. Feed it to your copilots, agents, and SDKs over MCP. 70% cheaper, 70% faster, +15% more accurate.
Most tools re-read your files every session and forget what they meant. ByteBell grows one living structure your whole codebase connects through, then serves it to any tool over MCP.
Claude, GPT, Gemini, DeepSeek, Llama, Qwen. ByteBell builds the same IR from any model and locks you to none. ~$13 indexes 1,000 files, once; it re-derives only what changes on each commit.
Intent, dependencies and business logic compiled into a single graph. code → IR → code round-trips, so every query returns precise meaning, not a dump of files that rots the window.
Your IDE, copilots, agents and review tools all read the exact same trunk through one MCP endpoint. Add it in seconds. No re-indexing per tool, no per-session warmup.
Ask in plain English and get the exact files, across every repo, in milliseconds, by meaning, not by grep. ByteBell understands purpose and relationships, not just symbols.
ByteBell maps how repos depend on each other. Before a merge, your agent knows exactly what breaks org-wide, across 50 or 500 repositories, instead of finding out in prod.
Instead of dumping thousands of files into the window, agents pull a small slice of structured meaning from the graph. Context stays clean all session. 70% cheaper, 70% faster, no compaction death-spiral.
Because the IR round-trips, meaning becomes programmable: generate code from a spec, recover the spec back out of code, and round-trip every AI change against the IR to catch hallucinations before they merge.
ByteBell speaks the Model Context Protocol, so the same IR powers your editor, your agents and your review bots. No custom adapters, no per-tool re-indexing.
ASTs map call edges. Context files store stale prose. The questions developers actually ask are about intent, and that lives in the trunk, not the symbols.
| The question you ask | AST | LLM reads files | CLAUDE.md | ByteBell IR |
|---|---|---|---|---|
| “What calls validateCard()?” | Precise | ~ Sometimes | ~ If documented | Precise |
| “Which code handles payment?” | Blind | ~ If it fits | ~ If hand-written | By meaning |
| “What breaks across 50 repos?” | Single-repo | Too big | Doesn’t scale | Cross-repo graph |
Once code ⇄ IR ⇄ code round-trips, a lot becomes possible that a parser simply can’t do.
Change the intent once; regenerate code across 100 repos in lockstep.
Generate code from a PRD, and recover the PRD back out of the code.
Round-trip every AI change against the IR to catch hallucinations early.
Meaning preserved: Java→Go, REST→gRPC, monolith→services.
New engineers ask the codebase questions and get answers with file refs.
“Where’s the rate limiter?” across every repo, in milliseconds.
Index once, then point any MCP-compatible tool at ByteBell. Self-host the open source, or use the hosted IR.
Open source on GitHub ↗ByteBell runs the entire indexing and serving pipeline inside your own infrastructure. You own the graph. No third-party server ever sees your source.
Deploy via Docker in your own cloud or datacenter. Admin panel on your domain, your control.
Run fully offline against local or self-hosted models. Nothing egresses your perimeter.
The graph and metadata are yours. Portable, inspectable, and never a vendor hostage.
A reversible IR turns code into a language humans and agents share.
Open-source first. No per-seat lock-in. On-premise, hybrid, or air-gapped.
Index once. Serve exact context to every tool you already use, over MCP, for 70% less. Get started free in minutes.