feat: scaffolded together the initial llm node type and its executor

src/graph/executor.rs

@@ -7,10 +7,11 @@
 //! template as the graph's return value.
 
 use super::agent::AgentNodeExecutor;
+use super::llm::{self, LlmNodeExecutor};
 use super::parser::GraphParser;
 use super::script::ScriptExecutor;
 use super::state::StateManager;
-use super::types::{EndNode, Graph, Node, NodeType};
+use super::types::{EndNode, Graph, LlmNode, Node, NodeType};
 use super::user_interaction::{ApprovalNodeExecutor, InputNodeExecutor};
 use super::validator::GraphValidator;
 use crate::config::RequestContext;
@@ -169,6 +170,7 @@ fn node_type_label(node: &Node) -> &'static str {
         NodeType::Script(_) => "script",
         NodeType::Approval(_) => "approval",
         NodeType::Input(_) => "input",
+        NodeType::Llm(_) => "llm",
         NodeType::End(_) => "end",
     }
 }
@@ -217,10 +219,43 @@ async fn step(
                 InputNodeExecutor::execute(input_node, node.next.as_deref(), state, ctx).await?;
             Ok(StepResult::Continue(next))
         }
+        NodeType::Llm(llm_node) => {
+            let result = LlmNodeExecutor::execute(llm_node, state, ctx).await;
+            let (output, failed) = match result {
+                Ok(out) => (out, false),
+                Err(e) => {
+                    warn!("[graph:{}] llm node '{}' failed: {e}", graph_name, current);
+                    (format!("LLM node failed: {e}"), true)
+                }
+            };
+            apply_state_updates_with_llm_output(llm_node, state, &output);
+            let next = next_for_llm_node(node, failed, llm_node.fallback.as_deref())?;
+            Ok(StepResult::Continue(next))
+        }
         NodeType::End(end_node) => Ok(StepResult::End(resolve_end_output(end_node, state))),
     }
 }
 
+fn next_for_llm_node(node: &Node, failed: bool, fallback: Option<&str>) -> Result<String> {
+    if failed && let Some(fb) = fallback {
+        return Ok(fb.to_string());
+    }
+    node.next.clone().ok_or_else(|| {
+        anyhow!(
+            "llm node '{}' has no `next` set; llm nodes need static routing",
+            node.id
+        )
+    })
+}
+
+fn apply_state_updates_with_llm_output(
+    node: &super::types::LlmNode,
+    state: &mut StateManager,
+    output: &str,
+) {
+    crate::graph::llm::apply_state_updates_with_output(node, state, output);
+}
+
 /// Apply the end node's `state_updates`, then interpolate its `output`
 /// template against the resulting state. Both use lenient interpolation
 /// so the graph still produces a result even when some keys are absent.

src/graph/llm.rs

@@ -0,0 +1,187 @@
+//! Execution of `llm`-type graph nodes — one-shot LLM calls with a
+//! bounded tool-call loop, an opt-in tool whitelist, and per-node
+//! overrides for model/temperature/top_p.
+//!
+//! See `docs/implementation/graph-agents/10.5-llm-nodes.md` for the
+//! design. The current implementation provides the routing and
+//! state-update plumbing; the actual call_chat_completions loop lives
+//! in `run_llm_once` and is the next implementation step. Calling
+//! `LlmNodeExecutor::execute` today produces a controlled error so the
+//! tolerant-fail routing in the executor still flows.
+
+use super::state::StateManager;
+use super::types::LlmNode;
+use crate::config::RequestContext;
+use crate::utils::dimmed_text;
+use anyhow::{Context, Result, bail};
+use serde_json::Value;
+
+const OUTPUT_KEY: &str = "output";
+
+pub struct LlmNodeExecutor;
+
+impl LlmNodeExecutor {
+    /// Interpolate the node's templates, run the LLM call, then return
+    /// the model's final response. State updates are applied by the
+    /// graph executor (which knows whether to use the success path or
+    /// the failure path).
+    pub async fn execute(
+        node: &LlmNode,
+        state_manager: &mut StateManager,
+        _parent_ctx: &mut RequestContext,
+    ) -> Result<String> {
+        let _instructions = state_manager
+            .interpolate(&node.instructions)
+            .context("Failed to interpolate llm node instructions")?;
+        let _prompt = state_manager
+            .interpolate(&node.prompt)
+            .context("Failed to interpolate llm node prompt")?;
+
+        eprintln!(
+            "{}",
+            dimmed_text(&format!(
+                "▸   llm call: model={} tools={}",
+                node.model.as_deref().unwrap_or("<active>"),
+                describe_tools_filter(node.tools.as_deref())
+            ))
+        );
+
+        bail!(
+            "llm node execution body not yet implemented — see \
+             docs/implementation/graph-agents/10.5-llm-nodes.md \
+             (steps 3 & 5 of the implementation order)"
+        );
+    }
+}
+
+/// Expose the LLM call's final output as `{{output}}` for the duration
+/// of `state_updates` evaluation, then restore the prior value (or set
+/// it to `Null` if there wasn't one). Same pattern as
+/// `AgentNodeExecutor`'s `{{output}}` scoping.
+pub fn apply_state_updates_with_output(
+    node: &LlmNode,
+    state_manager: &mut StateManager,
+    output: &str,
+) {
+    let Some(updates) = &node.state_updates else {
+        return;
+    };
+    let prev_output = state_manager.state().get(OUTPUT_KEY).cloned();
+    state_manager
+        .state_mut()
+        .set(OUTPUT_KEY.into(), Value::String(output.to_string()));
+
+    for (key, template) in updates {
+        let value = state_manager.interpolate_lenient(template);
+        state_manager
+            .state_mut()
+            .set(key.clone(), Value::String(value));
+    }
+
+    match prev_output {
+        Some(v) => state_manager.state_mut().set(OUTPUT_KEY.into(), v),
+        None => {
+            state_manager
+                .state_mut()
+                .set(OUTPUT_KEY.into(), Value::Null);
+        }
+    }
+}
+
+fn describe_tools_filter(tools: Option<&[String]>) -> String {
+    match tools {
+        None => "<none>".into(),
+        Some(t) if t.is_empty() => "<none>".into(),
+        Some(t) => t.join(","),
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::super::types::*;
+    use super::*;
+    use serde_json::json;
+    use std::collections::HashMap;
+
+    fn manager_with(pairs: &[(&str, Value)]) -> StateManager {
+        let mut map = HashMap::new();
+        for (k, v) in pairs {
+            map.insert((*k).into(), v.clone());
+        }
+        StateManager::new(map)
+    }
+
+    fn node_with(updates: Option<HashMap<String, String>>) -> LlmNode {
+        LlmNode {
+            instructions: "sys".into(),
+            prompt: "user".into(),
+            tools: None,
+            model: None,
+            temperature: None,
+            top_p: None,
+            fallback: None,
+            max_attempts: 1,
+            max_iterations: 10,
+            state_updates: updates,
+            timeout: None,
+        }
+    }
+
+    #[test]
+    fn state_updates_expose_output_during_evaluation() {
+        let mut u = HashMap::new();
+        u.insert("response".into(), "{{output}}".into());
+        let node = node_with(Some(u));
+        let mut state = manager_with(&[]);
+        apply_state_updates_with_output(&node, &mut state, "the answer");
+        assert_eq!(state.state().get("response"), Some(&json!("the answer")));
+    }
+
+    #[test]
+    fn state_updates_can_mix_existing_keys_with_output() {
+        let mut u = HashMap::new();
+        u.insert("summary".into(), "{{topic}}: {{output}}".into());
+        let node = node_with(Some(u));
+        let mut state = manager_with(&[("topic", json!("LOINC"))]);
+        apply_state_updates_with_output(&node, &mut state, "abc");
+        assert_eq!(state.state().get("summary"), Some(&json!("LOINC: abc")));
+    }
+
+    #[test]
+    fn output_key_is_cleared_after_state_updates() {
+        let mut u = HashMap::new();
+        u.insert("k".into(), "{{output}}".into());
+        let node = node_with(Some(u));
+        let mut state = manager_with(&[]);
+        apply_state_updates_with_output(&node, &mut state, "anything");
+        assert_eq!(state.state().get(OUTPUT_KEY), Some(&json!(null)));
+    }
+
+    #[test]
+    fn pre_existing_output_value_is_restored() {
+        let mut u = HashMap::new();
+        u.insert("greeting".into(), "{{output}}".into());
+        let node = node_with(Some(u));
+        let mut state = manager_with(&[("output", json!("preserved"))]);
+        apply_state_updates_with_output(&node, &mut state, "new");
+        assert_eq!(state.state().get("greeting"), Some(&json!("new")));
+        assert_eq!(state.state().get(OUTPUT_KEY), Some(&json!("preserved")));
+    }
+
+    #[test]
+    fn no_state_updates_is_a_noop() {
+        let node = node_with(None);
+        let mut state = manager_with(&[("k", json!("v"))]);
+        apply_state_updates_with_output(&node, &mut state, "x");
+        assert_eq!(state.state().get("k"), Some(&json!("v")));
+        assert!(state.state().get(OUTPUT_KEY).is_none());
+    }
+
+    #[test]
+    fn describe_tools_filter_renders_each_case() {
+        assert_eq!(describe_tools_filter(None), "<none>");
+        assert_eq!(describe_tools_filter(Some(&[])), "<none>");
+        let tools = vec!["a".to_string(), "b".to_string()];
+        assert_eq!(describe_tools_filter(Some(&tools)), "a,b");
+    }
+}

src/graph/mod.rs

@@ -4,6 +4,7 @@
 pub mod agent;
 pub mod dispatch;
 pub mod executor;
+pub mod llm;
 pub mod parser;
 pub mod script;
 pub mod state;
@@ -14,12 +15,13 @@ pub mod validator;
 pub use agent::AgentNodeExecutor;
 pub use dispatch::{active_agent_graph_name, run_active_agent_graph};
 pub use executor::GraphExecutor;
+pub use llm::LlmNodeExecutor;
 pub use parser::{GraphParser, agent_has_graph, load_agent_graph};
 pub use script::ScriptExecutor;
 pub use state::{StateManager, StateRepresentation};
 pub use types::{
-    AgentNode, ApprovalNode, EndNode, Graph, GraphSettings, GraphState, InputNode, Node, NodeType,
+    AgentNode, ApprovalNode, EndNode, Graph, GraphSettings, GraphState, InputNode, LlmNode, Node,
-    ScriptNode,
+    NodeType, ScriptNode,
 };
 pub use user_interaction::{ApprovalNodeExecutor, InputNodeExecutor};
 pub use validator::{GraphValidator, ValidationError, ValidationResult};

src/graph/types.rs

@@ -112,6 +112,7 @@ pub enum NodeType {
     Script(ScriptNode),
     Approval(ApprovalNode),
     Input(InputNode),
+    Llm(LlmNode),
     End(EndNode),
 }
 
@@ -199,6 +200,66 @@ pub struct InputNode {
     pub on_timeout: Option<String>,
 }
 
+/// `llm`-type node: a one-shot LLM call (with bounded tool-call loop)
+/// against a caller-supplied system prompt + user prompt. Unlike
+/// `agent`-type nodes, this does NOT spawn a sub-agent; it runs in a
+/// fresh isolated context. Tool access is opt-in via the `tools`
+/// whitelist (no tools when unset).
+///
+/// Routing (tolerant-fail):
+///   - success                 → `Node.next`
+///   - failure WITH fallback   → `fallback`
+///   - failure WITHOUT fallback → `Node.next`
+///
+/// `state_updates` are always applied. `{{output}}` resolves to the
+/// LLM's response on success, or to an error description on failure.
+#[derive(Debug, Clone, Deserialize, Serialize)]
+pub struct LlmNode {
+    pub instructions: String,
+
+    pub prompt: String,
+
+    /// Whitelist of tool names. Each entry is either an exact function
+    /// name or the shorthand `mcp:<server>` (expands to the three MCP
+    /// meta-functions for that server). Unset = no tools.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub tools: Option<Vec<String>>,
+
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub model: Option<String>,
+
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub temperature: Option<f64>,
+
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub top_p: Option<f64>,
+
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub fallback: Option<String>,
+
+    /// Number of attempts on transient errors. Default 1 = no retries.
+    #[serde(default = "default_llm_max_attempts")]
+    pub max_attempts: u32,
+
+    /// Hard cap on tool-call-loop turns within a single attempt.
+    #[serde(default = "default_llm_max_iterations")]
+    pub max_iterations: u32,
+
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub state_updates: Option<HashMap<String, String>>,
+
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub timeout: Option<u64>,
+}
+
+fn default_llm_max_attempts() -> u32 {
+    1
+}
+
+fn default_llm_max_iterations() -> u32 {
+    10
+}
+
 /// `end`-type node: terminate execution; `output` (templated) is returned
 /// as the graph's final result.
 #[derive(Debug, Clone, Deserialize, Serialize)]
@@ -548,4 +609,93 @@ routes:
         assert_eq!(state.get("user"), Some(&json!("alice")));
         assert!(state.history().is_empty());
     }
+
+    #[test]
+    fn llm_node_with_all_fields() {
+        let yaml = r#"
+id: classify
+type: llm
+instructions: "You are a classifier."
+prompt: "Classify: {{input_text}}"
+tools:
+  - read_query
+  - "mcp:pubmed-search"
+model: anthropic:claude-3-5-haiku-20241022
+temperature: 0.0
+top_p: 0.5
+fallback: skip_classify
+max_attempts: 3
+max_iterations: 5
+state_updates:
+  category: "{{output}}"
+timeout: 30
+next: review
+"#;
+        let node: Node = serde_yaml::from_str(yaml).unwrap();
+        let llm = match node.node_type {
+            NodeType::Llm(l) => l,
+            _ => panic!("expected Llm variant"),
+        };
+        assert_eq!(llm.instructions, "You are a classifier.");
+        assert_eq!(llm.prompt, "Classify: {{input_text}}");
+        let tools = llm.tools.unwrap();
+        assert_eq!(tools, vec!["read_query", "mcp:pubmed-search"]);
+        assert_eq!(
+            llm.model.as_deref(),
+            Some("anthropic:claude-3-5-haiku-20241022")
+        );
+        assert_eq!(llm.temperature, Some(0.0));
+        assert_eq!(llm.top_p, Some(0.5));
+        assert_eq!(llm.fallback.as_deref(), Some("skip_classify"));
+        assert_eq!(llm.max_attempts, 3);
+        assert_eq!(llm.max_iterations, 5);
+        assert_eq!(llm.timeout, Some(30));
+        assert!(llm.state_updates.is_some());
+        assert_eq!(node.next.as_deref(), Some("review"));
+    }
+
+    #[test]
+    fn llm_node_minimal_fields_use_defaults() {
+        let yaml = r#"
+id: pure_text
+type: llm
+instructions: "System."
+prompt: "User."
+next: done
+"#;
+        let node: Node = serde_yaml::from_str(yaml).unwrap();
+        let llm = match node.node_type {
+            NodeType::Llm(l) => l,
+            _ => panic!("expected Llm variant"),
+        };
+        assert_eq!(llm.instructions, "System.");
+        assert_eq!(llm.prompt, "User.");
+        assert!(llm.tools.is_none());
+        assert!(llm.model.is_none());
+        assert!(llm.fallback.is_none());
+        assert_eq!(llm.max_attempts, 1);
+        assert_eq!(llm.max_iterations, 10);
+    }
+
+    #[test]
+    fn llm_node_missing_instructions_fails() {
+        let yaml = r#"
+id: bad
+type: llm
+prompt: "User only — no system prompt."
+"#;
+        let result: std::result::Result<Node, _> = serde_yaml::from_str(yaml);
+        assert!(result.is_err());
+    }
+
+    #[test]
+    fn llm_node_missing_prompt_fails() {
+        let yaml = r#"
+id: bad
+type: llm
+instructions: "System only — no user prompt."
+"#;
+        let result: std::result::Result<Node, _> = serde_yaml::from_str(yaml);
+        assert!(result.is_err());
+    }
 }

src/graph/validator.rs

@@ -262,6 +262,11 @@ fn declared_targets(node: &Node) -> Vec<(String, &'static str)> {
                 out.push((t.clone(), "'on_timeout'"));
             }
         }
+        NodeType::Llm(l) => {
+            if let Some(t) = &l.fallback {
+                out.push((t.clone(), "llm 'fallback'"));
+            }
+        }
         NodeType::Agent(_) | NodeType::End(_) => {}
     }
     out