Recipe — Tiny (all-local, zero-infra)

Ladder position: this recipe is rung (a) — Zero-infra dev of the supported deployment configurations guide, which lists every default this tier relies on.

For a laptop, a dev box, or an edge node. No databases, no external services. The knowledge graph runs entirely on this machine (the Rust engine as a local daemon plus embedded LadybugDB); the only thing you need is a model provider (a hosted API key, or a local vLLM/Ollama endpoint).

What runs

Component	How
agent-utilities	pip/uv install, in-process
Knowledge graph	GRAPH_BACKEND=tiered → epistemic_graph (L1) + embedded LadybugDB (L2)
OWL/RDF + reasoning	on by default — local OWL-RL inference (epistemic-graph) over the LPG, no external triplestore
SPARQL	local endpoint at GET/POST {gateway}/api/sparql (rdflib materialization + engine GetTriples fast path) — zero external deps
graph-os MCP	optional, uv run graph-os (stdio)
External services	none

OWL/RDF is a core, always-on layer here — not an enterprise add-on. The tiny profile consumes the bundled ontologies, infers new relationships, and serves SPARQL locally with no Fuseki/Stardog. (Fuseki/Stardog are an optional enterprise scale-out, configured only in the enterprise recipe.)

Steps

git clone https://github.com/Knuckles-Team/agent-utilities && cd agent-utilities
./scripts/bootstrap.sh

bootstrap.sh does it all: creates a venv, installs .[all], writes a .env, and runs a smoke test. Or do it manually:

uv sync                         # or: pip install -e ".[agent]"
cp .env.example .env            # then edit the model provider line

.env (generalized)

# --- Knowledge graph: zero-infra default (this is already the default) ---
GRAPH_BACKEND=tiered

# --- Engine: auto-spawn the local Rust engine on first connect ---
# Off by default; without it the `epistemic-graph-server` daemon (ships with
# the epistemic-graph wheel) must already be running. Autostart only ever
# applies to the local endpoint.
EPISTEMIC_GRAPH_AUTOSTART=1

# --- Model provider: pick ONE ---
OPENAI_API_KEY=sk-REDACTED
# ...or a local OpenAI-compatible endpoint (vLLM/Ollama):
# OPENAI_BASE_URL=http://localhost:8000/v1

# Optional identity label for MCP/harness client processes
AGENT_ID=local-developer

Verify

from agent_utilities import create_agent
agent, _ = create_agent(name="assistant", skill_types=["universal", "graphs"])
print(agent.run_sync("Add a node 'hello' of type Greeting, then count nodes.").output)

Or via MCP — register graph-os in your IDE's mcp_config.json:

{ "mcpServers": { "graph-os": { "command": "uv", "args": ["run", "graph-os"] } } }

When to graduate

The moment you want the KG to survive restarts on a server, or to share it across processes/containers, move to Single-node prod — it's a one-line GRAPH_DB_URI change. The full progression (auth, durable state, multi-host scale-out, autonomy) is the deployment configurations ladder.