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fix(agent): use config max_tool_iterations, add memory relevance filtering, rebalance search weights

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Three fixes for conversation quality issues:

1. loop_.rs and channels now read max_tool_iterations from AgentConfig
   instead of using a hardcoded constant of 10, making it configurable.

2. Memory recall now filters entries below a configurable
   min_relevance_score threshold (default 0.4), preventing unrelated
   memories from bleeding into conversation context.

3. Default hybrid search weights rebalanced from 70/30 vector/keyword
   to 40/60, reducing cross-topic semantic bleed.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Edvard 2026-02-17 20:09:06 -05:00 • committed by Chummy
parent 21c5f58363
commit 8a1e7cc7ef
6 changed files with 90 additions and 24 deletions
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28
src/channels/mod.rs
View file

@ -72,6 +72,7 @@ struct ChannelRuntimeContext {
temperature: f64,
auto_save_memory: bool,
max_tool_iterations: usize,
min_relevance_score: f64,
}
fn conversation_memory_key(msg: &traits::ChannelMessage) -> String {
@ -87,13 +88,25 @@ fn channel_delivery_instructions(channel_name: &str) -> Option<&'static str> {
}
}
async fn build_memory_context(mem: &dyn Memory, user_msg: &str) -> String {
async fn build_memory_context(
mem: &dyn Memory,
user_msg: &str,
min_relevance_score: f64,
) -> String {
let mut context = String::new();
if let Ok(entries) = mem.recall(user_msg, 5, None).await {
if !entries.is_empty() {
let relevant: Vec<_> = entries
.iter()
.filter(|e| match e.score {
Some(score) => score >= min_relevance_score,
None => true, // keep entries without a score (e.g. non-vector backends)
})
.collect();
if !relevant.is_empty() {
context.push_str("[Memory context]\n");
for entry in &entries {
for entry in &relevant {
let _ = writeln!(context, "- {}: {}", entry.key, entry.content);
}
context.push('\n');
@ -166,7 +179,8 @@ async fn process_channel_message(ctx: Arc<ChannelRuntimeContext>, msg: traits::C
truncate_with_ellipsis(&msg.content, 80)
);
let memory_context = build_memory_context(ctx.memory.as_ref(), &msg.content).await;
let memory_context =
build_memory_context(ctx.memory.as_ref(), &msg.content, ctx.min_relevance_score).await;
if ctx.auto_save_memory {
let autosave_key = conversation_memory_key(&msg);
@ -1279,6 +1293,7 @@ pub async fn start_channels(config: Config) -> Result<()> {
temperature,
auto_save_memory: config.memory.auto_save,
max_tool_iterations: config.agent.max_tool_iterations,
min_relevance_score: config.memory.min_relevance_score,
});
run_message_dispatch_loop(rx, runtime_ctx, max_in_flight_messages).await;
@ -1504,6 +1519,7 @@ mod tests {
temperature: 0.0,
auto_save_memory: false,
max_tool_iterations: 10,
min_relevance_score: 0.0,
});
process_channel_message(
@ -1546,6 +1562,7 @@ mod tests {
temperature: 0.0,
auto_save_memory: false,
max_tool_iterations: 10,
min_relevance_score: 0.0,
});
process_channel_message(
@ -1642,6 +1659,7 @@ mod tests {
temperature: 0.0,
auto_save_memory: false,
max_tool_iterations: 10,
min_relevance_score: 0.0,
});
let (tx, rx) = tokio::sync::mpsc::channel::<traits::ChannelMessage>(4);
@ -2008,7 +2026,7 @@ mod tests {
.await
.unwrap();
let context = build_memory_context(&mem, "age").await;
let context = build_memory_context(&mem, "age", 0.0).await;
assert!(context.contains("[Memory context]"));
assert!(context.contains("Age is 45"));
}