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zesdex/apps/infrastructure/src/workflow/hive_mind/synthesis.rs
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//! Consensus synthesis — reconciles multiple node outputs into one assessment.
//!
//! Flow: load settings → resolve LLM credentials → ask the model to distill the
//! node outputs into a single consensus (conflicts, agreements, key findings)
//! → return the synthesized text. If the LLM call fails for any reason, we
//! degrade gracefully to a concatenation-based summary so consensus synthesis
//! never breaks the surrounding hive-mind cycle (mirrors the isolated-errors
//! philosophy used for the nodes themselves).
use anyhow::Result;
use tracing::{info, warn};
use zesdex_domain::cms::{AppConfigRepository, SettingsRepository};
use zesdex_domain::core::message::ChatMessage;
use zesdex_domain::core::Store;
use zesdex_domain::workflow::NodeOutput;
use crate::persistence::{JsonAppConfigRepository, JsonSettingsRepository};
use crate::tools::ToolCtx;
/// Maximum characters of node output to feed into the synthesis prompt per node.
/// Keeps the prompt bounded so a huge/talkative node cannot blow up the request.
const MAX_NODE_OUTPUT_CHARS: usize = 4000;
/// Synthesize a consensus from all node outputs using the LLM.
///
/// Flow: combine node outputs → ask the model to reconcile them into a single
/// consensus → return the synthesized text. Falls back to a plain
/// concatenation summary if the LLM is unreachable or the call fails.
pub async fn synthesize_consensus(nodes: &[NodeOutput], _tool_ctx: &ToolCtx) -> Result<String> {
info!("Synthesizing consensus from {} nodes", nodes.len());
let combined = build_combined_body(nodes);
// 1. Resolve LLM credentials (same source of truth as execute_cycle).
let store = Store::new();
let settings = JsonSettingsRepository::new()
.load(&store.base_dir)
.unwrap_or_default();
let app_config = JsonAppConfigRepository::new()
.load(&store.base_dir)
.unwrap_or_default();
let (provider, model) =
crate::subagent::provider::resolve_subagent_provider(&settings, &app_config);
let base_url = app_config
.providers
.get(&provider)
.map(|p| p.api_base.clone())
.unwrap_or_else(|| zesdex_domain::agent::defaults::DEFAULT_API_BASE.to_string());
let api_key = crate::llm::provider::resolve_api_key(&settings, &app_config);
let client = crate::llm::provider::LlmClient::new(api_key, model, Some(base_url));
// 2. Build the synthesis prompt.
let system_msg = ChatMessage::system(
"You are a consensus synthesizer for a multi-agent hive mind. \
Several independent nodes analysed a problem and produced the outputs \
below. Distill them into ONE coherent consensus report with these \
sections:\n\
- AGREEMENTS: points multiple nodes converge on.\n\
- CONFLICTS: contradictory conclusions, with which node(s) support each side.\n\
- KEY FINDINGS: the most important, actionable takeaways.\n\
- RECOMMENDATION: a single recommended next action, or 'no clear consensus' \
if the outputs are too divergent.\n\
Be concise and factual. If a node errored, note it and ignore its content.\n\
Do not invent facts not present in the node outputs.",
);
let user_msg = ChatMessage::user(format!(
"Consolidate these {} node outputs into a single consensus:\n\n{}",
nodes.len(),
combined
));
// 3. Call the model and gracefully degrade on failure.
match call_consensus(&client, &[system_msg, user_msg]).await {
Ok(text) => {
let trimmed = text.trim();
if trimmed.is_empty() {
warn!("consensus LLM returned empty output; falling back to concat summary");
Ok(concat_summary(nodes))
} else {
Ok(format!(
"# Consensus Synthesis\n\n\
Nodes synthesized: {}\n\n{}",
nodes.len(),
trimmed
))
}
}
Err(e) => {
warn!(error = %e, "consensus LLM call failed; falling back to concat summary");
Ok(concat_summary(nodes))
}
}
}
/// Run the LLM consensus call and return the assistant text.
async fn call_consensus(
client: &crate::llm::provider::LlmClient,
messages: &[ChatMessage],
) -> Result<String> {
use zesdex_application::ports::ProviderService;
let (msg, _) = client.chat(messages, None, Some(1024), Some(0.3)).await?;
Ok(msg.content.unwrap_or_default())
}
/// Build the concatenated node-output body for the prompt.
fn build_combined_body(nodes: &[NodeOutput]) -> String {
let mut combined = String::new();
for node in nodes {
let output = crate::utils::truncate_chars(&node.output, MAX_NODE_OUTPUT_CHARS);
combined.push_str(&format!(
"\n## {}{}\n{}\n",
node.id, node.directive, output
));
}
combined
}
/// Fallback: a plain concatenation summary (the behaviour of the original stub).
fn concat_summary(nodes: &[NodeOutput]) -> String {
let combined = build_combined_body(nodes);
format!(
"# Consensus Synthesis\n\n\
Nodes synthesized: {}\n\n\
## Summary\n\
The following node outputs were collected:\n\
{}\n\n\
## Key Findings\n\
Review the individual node outputs above for detailed findings.",
nodes.len(),
combined
)
}
#[cfg(test)]
mod tests {
use super::*;
use zesdex_domain::workflow::NodeOutput;
fn node(id: &str, output: &str) -> NodeOutput {
NodeOutput {
id: id.to_string(),
directive: "directive".to_string(),
output: output.to_string(),
}
}
#[test]
fn build_combined_body_truncates_oversized_output() {
let long = "é".repeat(MAX_NODE_OUTPUT_CHARS + 500);
let body = build_combined_body(&[node("n1", &long)]);
// Must contain the header and a char-truncated (<= cap) payload without
// panicking on a multi-byte boundary.
assert!(body.contains("## n1"));
// Header "## n1 — directive\n" ~= 20 chars, so char count stays just above cap.
let char_count = body.chars().count();
assert!(
char_count <= MAX_NODE_OUTPUT_CHARS + 50,
"expected body near {MAX_NODE_OUTPUT_CHARS} chars, got {char_count}"
);
assert!(
char_count > 1000,
"expected a many-node output, got small: {char_count}"
);
}
#[test]
fn concat_summary_includes_all_node_ids() {
let nodes = vec![node("node-a", "a out"), node("node-b", "b out")];
let summary = concat_summary(&nodes);
assert!(summary.contains("node-a"));
assert!(summary.contains("node-b"));
assert!(summary.contains("Nodes synthesized: 2"));
}
}