Examples

These are complete workflows, not isolated commands. Each starts from a built CGraph (see Installation) and shows how the pieces fit together in practice.

Workflow 1 — Index a repository and ask the first questions

You've just cloned a service you don't know well and want an agent to help.

1. Build the graph.

cgraph --root . --out cgraph-out

2. Start the daemon so queries stay warm.

graphd --root .

3. Ask what a symbol is and where it's used.

cgraph-client query   '{"q":"PaymentProcessor"}'
cgraph-client explain '{"q":"PaymentProcessor"}'

Instead of grepping the string PaymentProcessor across the tree, you get the node and its role in the graph.

Workflow 2 — Check a refactor before you make it

You're about to change a function's signature and want to know the blast radius.

cgraph-client impact '{"q":"parseConfig"}'

impact returns the transitive set of nodes reachable from parseConfig — the things that could break. You review that set before editing, not after the tests fail.

cgraph-client path '{"from":"HttpServer","to":"Database"}'

Workflow 3 — Give an agent budgeted context

An agent is working on a task and needs the relevant neighborhood around a node, fit into its context window.

cgraph-client context '{"q":"PaymentProcessor","budget":5000}'

With a query present, context uses adaptive gather — the full 2-hop core plus a query-relevant third hop — and packs it to the 5000-token budget. The response's reach counters show what was included and what was gated, so the selection is auditable. See Context Packing for the mechanics.

Workflow 4 — Keep the graph current during a session

While you work, the daemon folds edits in automatically. After a big change — switching branches, say — it collapses into a single rescan. To force a refresh or check state:

cgraph-client update '{"path":"."}'
cgraph-client status

status reports node and edge counts, cache hit rate, and enrichment state — a quick way to confirm the graph is current before relying on it.

The same, from an agent

Every command above has an MCP tool equivalent (graph_query, graph_explain, graph_impact, graph_path, graph_context, graph_update, graph_status). Once CGraph is registered (see AI Agent Integration), the agent runs these workflows itself — you just describe the goal.