fix: cost pricing, fire_manual workspace, heartbeat concurrency cap

Round 3 review fixes:

- Cost tracking passes None for cost_per_token when model override is
  active, letting CostGuard look up pricing by model name instead of
  using the default provider's rates (serrrfirat).

- fire_manual() now uses per-user workspace, matching spawn_fire()
  pattern (serrrfirat).

- Removed MULTI_TENANT env var — multi-tenant mode is auto-detected
  solely from GATEWAY_USER_TOKENS presence (serrrfirat + Copilot).

- Multi-user heartbeat capped at 8 concurrent tasks to avoid flooding
  the LLM provider (serrrfirat + Copilot).

- Fixed inject_model_override doc comment accuracy (Copilot).

- Added comment explaining multi-tenant notification routing priority
  (Copilot).

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
This commit is contained in:
2026-03-24 12:25:25 -07:00
co-authored by Claude Opus 4.6
parent 3c42e68bad
commit 11646d20e6
6 changed files with 86 additions and 54 deletions
+2
View File
@@ -544,6 +544,8 @@ impl Agent {
// In multi-tenant mode, extract the owning user_id from
// the response metadata so notifications reach the
// correct user rather than the agent's owner.
// This intentionally overrides the configured notify_target
// because each user's heartbeat should notify that user.
let effective_user = if is_multi_tenant {
response
.metadata
+12 -6
View File
@@ -397,11 +397,17 @@ impl<'a> LoopDelegate for ChatDelegate<'a> {
};
// Record cost and track token usage (global + per-user).
// Use the override model name if set so cost attribution is accurate.
let model_name = reason_ctx
.model_override
.clone()
.unwrap_or_else(|| self.agent.llm().active_model_name());
// When a model override is active, use the override name for attribution
// and let CostGuard look up pricing via costs::model_cost() instead of
// using the default provider's cost_per_token (which reflects the wrong model).
let (model_name, cost_per_token) = if let Some(ref ovr) = reason_ctx.model_override {
(ovr.clone(), None)
} else {
(
self.agent.llm().active_model_name(),
Some(self.agent.llm().cost_per_token()),
)
};
let read_discount = self.agent.llm().cache_read_discount();
let write_multiplier = self.agent.llm().cache_write_multiplier();
let call_cost = self
@@ -416,7 +422,7 @@ impl<'a> LoopDelegate for ChatDelegate<'a> {
output.usage.cache_creation_input_tokens,
read_discount,
write_multiplier,
Some(self.agent.llm().cost_per_token()),
cost_per_token,
)
.await;
tracing::debug!(
+55 -38
View File
@@ -574,8 +574,9 @@ pub fn spawn_multi_user_heartbeat(
}
};
// Run all user heartbeats concurrently so one slow LLM call
// doesn't block others.
// Run user heartbeats concurrently so one slow LLM call doesn't
// block others. Cap concurrency to avoid flooding the LLM provider.
const MAX_CONCURRENT_HEARTBEATS: usize = 8;
let mut join_set = tokio::task::JoinSet::new();
for user_id in &user_ids {
@@ -604,6 +605,13 @@ pub fn spawn_multi_user_heartbeat(
}
});
// Drain completed tasks to stay within the concurrency cap.
while join_set.len() >= MAX_CONCURRENT_HEARTBEATS {
if let Some(join_result) = join_set.join_next().await {
collect_heartbeat_result(join_result, &mut user_failures, &config);
}
}
let uid = user_id.clone();
let cfg = config.clone();
let hyg = hygiene_config.clone();
@@ -626,48 +634,57 @@ pub fn spawn_multi_user_heartbeat(
});
}
// Collect results and update failure counts
// Collect remaining results and update failure counts
while let Some(join_result) = join_set.join_next().await {
let (uid, result) = match join_result {
Ok(pair) => pair,
Err(e) => {
tracing::error!("Multi-user heartbeat task panicked: {}", e);
continue;
}
};
match result {
HeartbeatResult::Ok => {
tracing::trace!(user_id = uid, "Multi-user heartbeat OK");
user_failures.remove(&uid);
}
HeartbeatResult::NeedsAttention(_) => {
tracing::info!(user_id = uid, "Multi-user heartbeat needs attention");
user_failures.remove(&uid);
}
HeartbeatResult::Skipped => {}
HeartbeatResult::Failed(err) => {
let count = user_failures.entry(uid.clone()).or_insert(0);
*count += 1;
tracing::error!(
user_id = uid,
consecutive_failures = *count,
"Multi-user heartbeat failed: {}",
err
);
if *count >= config.max_failures {
tracing::error!(
user_id = uid,
"Multi-user heartbeat disabled for user after {} consecutive failures",
count
);
}
}
}
collect_heartbeat_result(join_result, &mut user_failures, &config);
}
}
})
}
/// Process a single JoinSet result from the multi-user heartbeat loop.
fn collect_heartbeat_result(
join_result: Result<(String, HeartbeatResult), tokio::task::JoinError>,
user_failures: &mut std::collections::HashMap<String, u32>,
config: &HeartbeatConfig,
) {
let (uid, result) = match join_result {
Ok(pair) => pair,
Err(e) => {
tracing::error!("Multi-user heartbeat task panicked: {}", e);
return;
}
};
match result {
HeartbeatResult::Ok => {
tracing::trace!(user_id = uid, "Multi-user heartbeat OK");
user_failures.remove(&uid);
}
HeartbeatResult::NeedsAttention(_) => {
tracing::info!(user_id = uid, "Multi-user heartbeat needs attention");
user_failures.remove(&uid);
}
HeartbeatResult::Skipped => {}
HeartbeatResult::Failed(err) => {
let count = user_failures.entry(uid.clone()).or_insert(0);
*count += 1;
tracing::error!(
user_id = uid,
consecutive_failures = *count,
"Multi-user heartbeat failed: {}",
err
);
if *count >= config.max_failures {
tracing::error!(
user_id = uid,
"Multi-user heartbeat disabled for user after {} consecutive failures",
count
);
}
}
}
}
#[cfg(test)]
mod tests {
use super::*;
+8 -1
View File
@@ -737,12 +737,19 @@ impl RoutineEngine {
});
}
// Per-user workspace (same pattern as spawn_fire).
let routine_workspace = if routine.user_id == self.workspace.user_id() {
self.workspace.clone()
} else {
Arc::new(Workspace::new_with_db(&routine.user_id, self.store.clone()))
};
// Execute inline for manual triggers (caller wants to wait)
let engine = EngineContext {
config: self.config.clone(),
store: self.store.clone(),
llm: self.llm.clone(),
workspace: self.workspace.clone(),
workspace: routine_workspace,
notify_tx: self.notify_tx.clone(),
running_count: self.running_count.clone(),
scheduler: self.scheduler.clone(),
+3 -4
View File
@@ -120,10 +120,9 @@ impl AgentConfig {
"AGENT_MAX_TOKENS_PER_JOB",
settings.agent.max_tokens_per_job,
)?,
multi_tenant: parse_bool_env(
"MULTI_TENANT",
optional_env("GATEWAY_USER_TOKENS")?.is_some(),
)?,
// Auto-detected from GATEWAY_USER_TOKENS presence. Not a separate
// knob — multi-tenant mode is always implied by configuring user tokens.
multi_tenant: optional_env("GATEWAY_USER_TOKENS")?.is_some(),
})
}
}
+6 -5
View File
@@ -599,11 +599,12 @@ fn build_rig_request(
/// Inject a per-request model override into the rig request's `additional_params`.
///
/// Rig-core bakes the model name at construction time. For OpenAI, Anthropic, and
/// Ollama, the `model` field in the request body determines which model serves the
/// request. Rig-core's `#[serde(flatten)]` on `additional_params` emits these fields
/// AFTER the struct's own `model` field. Most API servers (Python, Go) use
/// last-key-wins when deserializing duplicate JSON keys, so the override takes effect.
/// Rig-core bakes the model name at construction time inside each provider's
/// `CompletionModel` implementation. The actual HTTP request body includes a
/// `model` field set by the provider. Rig-core's `#[serde(flatten)]` on
/// `additional_params` emits these fields AFTER the provider's own fields.
/// Most API servers (Python, Go) use last-key-wins when deserializing
/// duplicate JSON keys, so the injected `model` value takes effect.
fn inject_model_override(rig_req: &mut RigRequest, model_override: Option<&str>) {
let Some(model) = model_override else {
return;