refactor: migrated the next_node and apply_state_updates logic for LLM nodes into the LlmExecutor
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+3
-32
@@ -7,11 +7,11 @@
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//! template as the graph's return value.
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use super::agent::AgentNodeExecutor;
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use super::llm::{self, LlmNodeExecutor};
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use super::llm::LlmNodeExecutor;
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use super::parser::GraphParser;
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use super::script::ScriptExecutor;
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use super::state::StateManager;
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use super::types::{EndNode, Graph, LlmNode, Node, NodeType};
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use super::types::{EndNode, Graph, Node, NodeType};
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use super::user_interaction::{ApprovalNodeExecutor, InputNodeExecutor};
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use super::validator::GraphValidator;
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use crate::config::RequestContext;
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@@ -220,42 +220,13 @@ async fn step(
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Ok(StepResult::Continue(next))
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}
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NodeType::Llm(llm_node) => {
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let result = LlmNodeExecutor::execute(llm_node, state, ctx).await;
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let (output, failed) = match result {
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Ok(out) => (out, false),
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Err(e) => {
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warn!("[graph:{}] llm node '{}' failed: {e}", graph_name, current);
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(format!("LLM node failed: {e}"), true)
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}
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};
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apply_state_updates_with_llm_output(llm_node, state, &output);
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let next = next_for_llm_node(node, failed, llm_node.fallback.as_deref())?;
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let next = LlmNodeExecutor::execute(llm_node, node.next.as_deref(), state, ctx).await?;
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Ok(StepResult::Continue(next))
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}
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NodeType::End(end_node) => Ok(StepResult::End(resolve_end_output(end_node, state))),
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}
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}
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fn next_for_llm_node(node: &Node, failed: bool, fallback: Option<&str>) -> Result<String> {
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if failed && let Some(fb) = fallback {
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return Ok(fb.to_string());
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}
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node.next.clone().ok_or_else(|| {
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anyhow!(
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"llm node '{}' has no `next` set; llm nodes need static routing",
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node.id
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)
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})
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}
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fn apply_state_updates_with_llm_output(
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node: &super::types::LlmNode,
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state: &mut StateManager,
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output: &str,
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) {
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crate::graph::llm::apply_state_updates_with_output(node, state, output);
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}
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/// Apply the end node's `state_updates`, then interpolate its `output`
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/// template against the resulting state. Both use lenient interpolation
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/// so the graph still produces a result even when some keys are absent.
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+61
-32
@@ -13,7 +13,7 @@ use super::state::StateManager;
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use super::types::LlmNode;
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use crate::config::RequestContext;
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use crate::utils::dimmed_text;
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use anyhow::{Context, Result, bail};
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use anyhow::{bail, Context, Result};
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use serde_json::Value;
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const OUTPUT_KEY: &str = "output";
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@@ -21,48 +21,77 @@ const OUTPUT_KEY: &str = "output";
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pub struct LlmNodeExecutor;
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impl LlmNodeExecutor {
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/// Interpolate the node's templates, run the LLM call, then return
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/// the model's final response. State updates are applied by the
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/// graph executor (which knows whether to use the success path or
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/// the failure path).
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/// Run the LLM call and resolve routing. Returns the next node ID
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/// to visit. Handles the tolerant-fail contract internally:
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/// success → `node_next`; failure with `fallback` → `fallback`;
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/// failure without `fallback` → `node_next`. State updates are
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/// applied in both success and failure paths, with `{{output}}`
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/// resolving to the LLM's response or an error description.
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pub async fn execute(
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node: &LlmNode,
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node_next: Option<&str>,
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state_manager: &mut StateManager,
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_parent_ctx: &mut RequestContext,
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parent_ctx: &mut RequestContext,
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) -> Result<String> {
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let _instructions = state_manager
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.interpolate(&node.instructions)
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.context("Failed to interpolate llm node instructions")?;
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let _prompt = state_manager
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.interpolate(&node.prompt)
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.context("Failed to interpolate llm node prompt")?;
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eprintln!(
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"{}",
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dimmed_text(&format!(
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"▸ llm call: model={} tools={}",
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node.model.as_deref().unwrap_or("<active>"),
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describe_tools_filter(node.tools.as_deref())
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))
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);
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bail!(
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"llm node execution body not yet implemented — see \
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docs/implementation/graph-agents/10.5-llm-nodes.md \
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(steps 3 & 5 of the implementation order)"
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);
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let result = run(node, state_manager, parent_ctx).await;
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let (output, failed) = match result {
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Ok(out) => (out, false),
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Err(e) => {
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warn!("llm node failed: {e}");
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(format!("LLM node failed: {e}"), true)
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}
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};
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apply_state_updates_with_output(node, state_manager, &output);
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next_for_llm_node(node_next, failed, node.fallback.as_deref())
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}
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}
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async fn run(
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node: &LlmNode,
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state_manager: &mut StateManager,
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_parent_ctx: &mut RequestContext,
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) -> Result<String> {
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let _instructions = state_manager
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.interpolate(&node.instructions)
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.context("Failed to interpolate llm node instructions")?;
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let _prompt = state_manager
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.interpolate(&node.prompt)
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.context("Failed to interpolate llm node prompt")?;
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eprintln!(
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"{}",
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dimmed_text(&format!(
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"▸ llm call: model={} tools={}",
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node.model.as_deref().unwrap_or("<active>"),
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describe_tools_filter(node.tools.as_deref())
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))
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);
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bail!(
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"llm node execution body not yet implemented — see \
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docs/implementation/graph-agents/10.5-llm-nodes.md \
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(steps 3 & 5 of the implementation order)"
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);
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}
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fn next_for_llm_node(
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node_next: Option<&str>,
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failed: bool,
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fallback: Option<&str>,
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) -> Result<String> {
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if failed && let Some(fb) = fallback {
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return Ok(fb.to_string());
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}
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node_next
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.map(String::from)
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.ok_or_else(|| anyhow::anyhow!("llm node has no `next` set; llm nodes need static routing"))
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}
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/// Expose the LLM call's final output as `{{output}}` for the duration
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/// of `state_updates` evaluation, then restore the prior value (or set
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/// it to `Null` if there wasn't one). Same pattern as
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/// `AgentNodeExecutor`'s `{{output}}` scoping.
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pub fn apply_state_updates_with_output(
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node: &LlmNode,
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state_manager: &mut StateManager,
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output: &str,
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) {
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fn apply_state_updates_with_output(node: &LlmNode, state_manager: &mut StateManager, output: &str) {
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let Some(updates) = &node.state_updates else {
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return;
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};
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