ByteBell indexes your codebase into a knowledge graph so your AI tools actually understand your code across models, copilots, sessions, cache and memory.
Not a weekend project. ByteBell is production infrastructure built by a team that shipped systems handling millions of users.
New session? Zero memory. Switch tools? Everything gone. Context full? Compacted. No history. No business context. No continuity.
Spent an hour debugging yesterday and the AI understood the entire flow? Today it starts from scratch. 20 minutes re-explaining what you were doing.
Window fills up, compaction kicks in, details are gone. Quality collapses mid-session. After 3-4 compactions, you're getting answers from summaries of summaries.
Claude Code for backend, Cursor for frontend, Codex for a quick fix. Each one starts blind. A better model drops? Start over. Your context is trapped in whichever tool you used last.
What business requirement drove this commit? What design decisions shaped this service? Which conversation produced this AI-generated function? Nobody tracks it. Nobody can answer.
Index once. Query forever. The knowledge graph survives everything — sessions, tools, models, team changes.
Every file has an LLM-generated purpose, summary, and business context in Neo4j. Tomorrow's session starts where today's left off. No re-reading.
persistentClaude Code → Cursor → Codex → DeepSeek. The knowledge graph doesn't care which tool or model you use. Same context through MCP, always.
tool-agnosticDeepSeek-V4 and Kimi-2.6 hit frontier accuracy when retrieval is handled. Better input = better output regardless of the model.
model-agnosticEvery file carries a businessContext explaining why it exists. Design decisions evolve with the code. AI-generated code stays traceable to the conversation that produced it.
traceabilitySemantic entities are org-scoped, not repo-scoped. One repo or fifty, the graph connects everything through shared keywords, concepts, and contracts.
scalesEvery AI session without ByteBell re-reads your codebase at full token price. With ByteBell, your AI queries structured metadata. The difference is 80%+.
Measured on production deployments across 500K+ files. Costs based on OpenRouter pricing.
We tried embeddings. Two functions named process() in different services embed to similar vectors but share zero context. Vectors flatten call graphs, inheritance, and import trees into a single point. We went a different way.
Cosine similarity measures token overlap, not structural relationships. Dependency graphs, inheritance chains, import trees — all lost. Retrieval precision on real codebases is consistently poor.
9-channel parallel search across purpose, summary, business context, functions, classes, imports, keywords, paths, and semantic fields in Neo4j. Structural relationships preserved. Fulltext, not cosine.
Bun + Docker. Local daemon. Binds to 127.0.0.1. No cloud, no telemetry.
Point at OpenRouter with any model — Claude, GPT, DeepSeek, Kimi, or a local model. bytebell set openrouter-api-key sk-or-...
bytebell boot — spins up Neo4j, MongoDB, and Redis in Docker. One command.
bytebell index ./ — LLM analysis per file: purpose, summary, business context, classes, functions, imports. SHA-256 diff means re-indexing only processes changed files.
claude mcp add bytebell http://127.0.0.1:8080/mcp — your AI queries the graph instead of re-reading files. 10-15 tool calls per question.
Tested across 500K+ files, 100+ repositories, multiple models.
→ full comparison: Sourcegraph · Augment · GraphRAG · GitNexus
95% of developers use AI tools weekly. They juggle 2-4 tools. They switch models when something better drops. They need accuracy, not "almost right." ByteBell was built around these realities.
No tricks. The open source version is real and complete. Paid plans add capacity and managed hosting.
Five commands. Persistent context. Open source. Any tool. Any model.