> ## Documentation Index
> Fetch the complete documentation index at: https://docs.adenhq.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Graph

> How nodes, edges, shared memory, and HITL form Hive runtime behavior

## Why a Graph

Business workflows are rarely linear. Hive models execution as a directed graph so agents can branch, retry, loop, and escalate.

## Nodes

Nodes are units of work that read from shared memory, perform logic, and write outputs.

Core node types:

* `event_loop`: multi-turn LLM + tools loop (primary autonomous workhorse)
* `function`: deterministic Python logic
* `router`: rule-based or model-assisted path selection
* `human_input`: pause for supervised input
* `llm_tool_use` and `llm_generate`: simpler one-shot LLM nodes

### Self-Correction in `event_loop`

Each iteration can end with:

* Accept
* Retry
* Escalate

This reflexive loop improves within-session reliability without restarting the full run.

## Edges

Edges define control flow:

* On success
* On failure
* Conditional
* LLM-decided

Edges can also map outputs to downstream inputs and enable parallel branches.

## Shared Memory

Shared memory is session-scoped state used for cross-node communication.

* Nodes declare read/write contracts
* Execution remains traceable and structured
* Final state represents run output and side effects

## Human-in-the-Loop

HITL nodes pause execution and preserve session state until a human responds.

Use HITL for high-impact actions (financial operations, outbound communications, approvals).

## Graph Shape Example

```text theme={null}
intake -> research -> draft -> [human review] -> send -> done
              ^                                     |
              +------------ on failure -------------+
```

## Related Concepts

* [Goals and Outcomes](/building-agent/concepts/goals-outcomes)
* [Worker Agent](/building-agent/concepts/worker-agent)
* [Evolution](/building-agent/concepts/evolution)