🌐 layer0

Fast. Minimal. No bloat. Fully self-hostable.

Hybrid RAG (vector + knowledge graph) and long-term memory for AI agents — written in Rust, runs offline, no vendor lock-in.

One binary. One SQLite file per database. layer0 serve and you're done.

Get started

For humans

Download a prebuilt binary from GitHub Releases (Linux/macOS/Windows) — or build from source:

cargo build --release

Then:

layer0 init # write config, generate MCP client files
layer0 serve # auto-downloads models, starts sidecar, serves on :8080

layer0 store "layer0 indexes chunks with sqlite-vec."
layer0 ask "What does layer0 use for vector search?"# Named databases — each gets its own isolated SQLite file
layer0 db create-database myproject
layer0 store "project context" --database myproject
layer0 search "context" --database myproject
layer0 db delete-database myproject

Set ANTHROPIC_API_KEY before serve to use Claude for chat/graph extraction instead of the local gemma fallback.

For AI agents

Install the skills so your agent can set up and use layer0 without manual steps:

npx skills add amajorai/layer0

This installs two skills into your agent:

• layer0-setup — install, configure, and start layer0
• layer0-memory — store, search, and recall memories across sessions

To connect via MCP, add to .claude/mcp.json (or .cursor/mcp.json):

{
"mcpServers": {
"layer0": { "command": "layer0", "args": ["mcp"] }
}
}

layer0 init writes this file automatically.

MCP tools: store_memory, search_memory, rag_query, get_document, delete_memory, graph_query, memory_stats, list_databases, create_database, delete_database, list_collections, create_collection, delete_collection.

How it works

Ingest

flowchart LR
D([Document]) --> C[Chunk\n512 tok · 64 overlap]
C --> E[Embed\nnomic]
E --> V[(vec_chunks\nsqlite-vec)]
D --> F[(documents_fts\nBM25)]
D --> X[Graph extract\nLLM]
X --> G[(graph_nodes\ngraph_edges)]

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Retrieval

flowchart LR
Q([Query]) --> V[Vector ANN\nsqlite-vec]
Q --> B[BM25\nFTS5]
V --> R[RRF fusion]
B --> R
R --> G[Graph\ntraversal]
G --> Re[Rerank]
Re --> A([Answer])

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Features

• Hybrid RAG — vector ANN + knowledge graph fused with Reciprocal Rank Fusion, then reranked. Or run vector-only or graph-only mode.
• Chunked retrieval — documents split into overlapping chunks, embedded per chunk for tight context.
• sqlite-vec ANN index — cosine KNN over a vec0 virtual table.
• Knowledge graph, auto-built at ingest — entities + relationships extracted by the LLM at store time.
• Local-first, zero-config — serve installs llama.cpp and downloads models automatically on first run.
• No vendor lock-in — fully offline with local gemma, or swap in any OpenAI-compatible backend via config.
• OpenAI-compatible API, MCP server, and CLI (including a layer0 config TUI).
• Optional API-key auth, multi-database / multi-collection scoping.
• Per-database isolation — each named database gets its own SQLite file; default stays backward-compatible.
• Self-update — layer0 update pulls the latest release from GitHub.
• No Docker, no external services.

Configuration

Global config: ~/.layer0/config.toml. Edit with layer0 config (TUI) or by hand. Full commented template at config/default.toml.

Environment overrides: LAYER0__ prefix with double underscores, e.g. LAYER0__SERVER__PORT=9000. ANTHROPIC_API_KEY is picked up automatically.

Example

[server]
host = "127.0.0.1"port = 8080cors_origins = ["*"]
# api_key = "change-me"
[llm]
base_url = "http://127.0.0.1:8081"embedding_model = "nomic-embed-text-v1.5"timeout_secs = 120context_length = 2048
[chat]
provider = "anthropic"base_url = "https://api.anthropic.com"model = "claude-haiku-4-5"timeout_secs = 120
[embeddings]
dimensions = 768# must match the embedding modelbatch_size = 16search_limit = 1000
[chunking]
chunk_size = 512chunk_overlap = 64
[rag]
mode = "hybrid"# hybrid | vector | graphrerank = trueextract_graph = true
[installer]
llama_server_port = 8081embedding_repo = "nomic-ai/nomic-embed-text-v1.5-GGUF"embedding_file = "nomic-embed-text-v1.5.Q4_K_M.gguf"chat_repo = "bartowski/google_gemma-4-E4B-it-GGUF"chat_file = "google_gemma-4-E4B-it-Q4_K_M.gguf"chat_server_port = 8082auto_start = true
[update]
repo = "amajorai/layer0"auto_check = trueauto_update = false

Parameters

[server]

Key	Default	Description
host	"127.0.0.1"	Bind address. "0.0.0.0" to expose on the network.
port	8080	HTTP port.
cors_origins	["*"]	Allowed CORS origins.
api_key	(unset)	Require X-API-Key or Authorization: Bearer on all requests except /health.

[database]

Key	Default	Description
max_connections	5	SQLite connection pool size.

[llm] — local embeddings sidecar (OpenAI wire format)

Key	Default	Description
base_url	"http://127.0.0.1:8081"	Embeddings endpoint. Point at any OpenAI-compatible server to skip the sidecar.
embedding_model	"nomic-embed-text-v1.5"	Model name in embedding requests.
rerank_model	(unset)	Optional dedicated reranking model.
api_key	(unset)	API key for the embeddings endpoint.
timeout_secs	120	Per-request timeout.
context_length	2048	Model context window (tokens).

[chat] — remote chat backend (resolution: remote key present → remote; no key → local gemma)

Key	Default	Description
provider	"anthropic"	Provider label.
base_url	"https://api.anthropic.com"	Any OpenAI-compatible endpoint works.
model	"claude-haiku-4-5"	Model identifier.
api_key	(unset)	Falls back to ANTHROPIC_API_KEY. Absent → local gemma fallback.
timeout_secs	120	Per-request timeout.

[embeddings]

Key	Default	Description
dimensions	768	Vector size. Must match the model (nomic = 768). Changing requires re-embedding.
batch_size	16	Chunks per embedding request.
search_limit	1000	Max candidates from the vector index before reranking.

[chunking]

Key	Default	Description
chunk_size	512	Target chunk size in tokens.
chunk_overlap	64	Overlap between chunks in tokens.

[rag]

Key	Default	Description
mode	"hybrid"	hybrid — vector + graph + RRF + rerank. vector — semantic only. graph — graph-led, vector-seeded.
rerank	true	Reranking pass on final results.
extract_graph	true	Build knowledge graph at ingest. Auto-skipped when mode = "vector".

[installer] — llama.cpp + model management (paths default under ~/.layer0/)

Key	Default	Description
llama_server_port	8081	Embedding sidecar port. Must match [llm].base_url.
embedding_repo	"nomic-ai/nomic-embed-text-v1.5-GGUF"	HuggingFace repo for the embedding model.
embedding_file	"nomic-embed-text-v1.5.Q4_K_M.gguf"	GGUF file to download.
chat_repo	"bartowski/google_gemma-4-E4B-it-GGUF"	HuggingFace repo for local chat fallback. Try google_gemma-4-E2B-it-GGUF for lighter hardware.
chat_file	"google_gemma-4-E4B-it-Q4_K_M.gguf"	GGUF file to download.
chat_server_port	8082	Chat sidecar port.
hf_token	(unset)	HuggingFace token for gated models.
auto_start	true	Install llama.cpp, download models, and start sidecars on layer0 serve.

[update]

Key	Default	Description
repo	"amajorai/layer0"	GitHub repo to pull releases from.
auto_check	true	Log when a newer release exists at startup.
auto_update	false	Auto-apply updates at startup (takes effect on next restart).

CLI — Databases & Collections

# Database management
layer0 db databases # list all databases
layer0 db create-database <name># create (also creates ~/.layer0/databases/<name>.db)
layer0 db delete-database <name># delete database and its .db file# Collection management
layer0 db collections <database># list collections in a database
layer0 db create-collection <database><name>
layer0 db delete-collection <database><name># Scoped operations
layer0 store "text" --database mydb --collection notes
layer0 search "query" --database mydb --collection notes
layer0 ask "question" --database mydb
# Database stats and document list
layer0 db stats
layer0 db list --database mydb --collection notes

HTTP API

Base: http://localhost:8080

POST /v1/documents store (auto-chunked + embedded)
POST /v1/search hybrid search (vector + BM25 [+ graph] [+ rerank])
POST /v1/rag answer grounded in memory
GET/DELETE /v1/documents[/:id] list / fetch / delete
/v1/graph/... nodes, edges, BFS query
POST /v1/embeddings OpenAI-compatible
POST /v1/chat/completions OpenAI-compatible (routes to the chat backend)
GET /v1/stats counts
GET /health liveness (no auth)
# Database & collection management
GET /v1/db list databases
POST /v1/db create database {"name":"mydb"}
GET /v1/db/:database get database
DELETE /v1/db/:database delete database + its .db file
GET /v1/db/:database/collections list collections
POST /v1/db/:database/collections create collection {"name":"notes"}
GET /v1/db/:database/:collection get collection
DELETE /v1/db/:database/:collection delete collection
# Scoped data routes (each opens the database's own .db file)
/v1/db/:database/:collection/documents store / list
/v1/db/:database/:collection/search search
/v1/db/:database/:collection/rag RAG query
/v1/db/:database/:collection/graph/... nodes, edges, BFS
/v1/db/:database/:collection/stats scoped counts

Architecture

graph LR
CLI[layer0 CLI] --> Core
Server[HTTP Server\nOpenAI-compatible] --> Core
MCP[MCP Server\nstdio JSON-RPC] --> Core
Core[layer0-core\nRAG · graph · embeddings · DB] --> DB[(SQLite\nlayer0.db\ndefault)]
Core --> DBN[(SQLite\ndatabases/\nname.db)]
Core --> Sidecar[llama.cpp sidecar\nembeddings · chat fallback]
Core --> Remote[Remote API\nClaude · any OpenAI-compat]

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Chat resolution: ACP (planned) → remote backend (when API key present) → local gemma sidecar. Embeddings are always local unless [llm].base_url points at a remote endpoint.

Database schema

Each database is a self-contained SQLite file. The default database lives at ~/.layer0/layer0.db; every other named database lives at ~/.layer0/databases/<name>.db.

Table	Contents
documents	Source documents + metadata (FTS5 mirror in documents_fts)
chunks	Per-document chunks (the retrieval unit)
vec_chunks	sqlite-vec vec0 cosine index over chunk embeddings
graph_nodes / graph_edges	Knowledge graph
collections	Named sub-scopes within the database
databases	Registry of all named databases (in layer0.db only)
models	Model registry (in layer0.db only)

Database names may contain letters, digits, _, -, . — max 64 characters. The names NUL, CON, PRN, AUX, COM*, LPT* are reserved on Windows and rejected.

License

MIT

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