rename filter_chain/output_filter_chain to input_filters/output_filters, scope output filters to chat completions only

cli/planoai/config_generator.py

@@ -426,13 +426,13 @@ def validate_and_render_schema():
                     "Please provide model_providers either under listeners or at root level, not both. Currently we don't support multiple listeners with model_providers"
                 )
 
-    # Validate filter_chain IDs on listeners reference valid agent/filter IDs
+    # Validate input_filters IDs on listeners reference valid agent/filter IDs
     for listener in listeners:
-        listener_filter_chain = listener.get("filter_chain", [])
+        listener_input_filters = listener.get("input_filters", [])
-        for fc_id in listener_filter_chain:
+        for fc_id in listener_input_filters:
             if fc_id not in agent_id_keys:
                 raise Exception(
-                    f"Listener '{listener.get('name', 'unknown')}' references filter_chain id '{fc_id}' "
+                    f"Listener '{listener.get('name', 'unknown')}' references input_filters id '{fc_id}' "
                     f"which is not defined in agents or filters. Available ids: {', '.join(sorted(agent_id_keys))}"
                 )
 

config/plano_config_schema.yaml

@@ -93,11 +93,11 @@ properties:
                 required:
                   - id
                   - description
-            filter_chain:
+            input_filters:
               type: array
               items:
                 type: string
-            output_filter_chain:
+            output_filters:
               type: array
               items:
                 type: string

crates/brightstaff/src/handlers/agent_selector.rs

@@ -195,8 +195,8 @@ mod tests {
             listener_type: ListenerType::Agent,
             name: name.to_string(),
             agents: Some(agents),
-            filter_chain: None,
+            input_filters: None,
-            output_filter_chain: None,
+            output_filters: None,
             port: 8080,
             router: None,
         }

crates/brightstaff/src/handlers/integration_tests.rs

@@ -75,8 +75,8 @@ mod tests {
             listener_type: ListenerType::Agent,
             name: "test-listener".to_string(),
             agents: Some(vec![agent_pipeline.clone()]),
-            filter_chain: None,
+            input_filters: None,
-            output_filter_chain: None,
+            output_filters: None,
             port: 8080,
             router: None,
         };

crates/brightstaff/src/handlers/llm.rs

@@ -46,9 +46,9 @@ pub async fn llm_chat(
     llm_providers: Arc<RwLock<LlmProviders>>,
     span_attributes: Arc<Option<SpanAttributes>>,
     state_storage: Option<Arc<dyn StateStorage>>,
-    filter_chain: Arc<Option<Vec<String>>>,
+    input_filters: Arc<Option<Vec<String>>>,
-    filter_agents: Arc<HashMap<String, Agent>>,
+    input_filter_agents: Arc<HashMap<String, Agent>>,
-    output_filter_chain: Arc<Option<Vec<String>>>,
+    output_filters: Arc<Option<Vec<String>>>,
     output_filter_agents: Arc<HashMap<String, Agent>>,
 ) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
     let request_path = request.uri().path().to_string();
@@ -89,9 +89,9 @@ pub async fn llm_chat(
         request_id,
         request_path,
         request_headers,
-        filter_chain,
+        input_filters,
-        filter_agents,
+        input_filter_agents,
-        output_filter_chain,
+        output_filters,
         output_filter_agents,
     )
     .instrument(request_span)
@@ -110,9 +110,9 @@ async fn llm_chat_inner(
     request_id: String,
     request_path: String,
     mut request_headers: hyper::HeaderMap,
-    filter_chain: Arc<Option<Vec<String>>>,
+    input_filters: Arc<Option<Vec<String>>>,
-    filter_agents: Arc<HashMap<String, Agent>>,
+    input_filter_agents: Arc<HashMap<String, Agent>>,
-    output_filter_chain: Arc<Option<Vec<String>>>,
+    output_filters: Arc<Option<Vec<String>>>,
     output_filter_agents: Arc<HashMap<String, Agent>>,
 ) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
     // Set service name for LLM operations
@@ -267,11 +267,11 @@ async fn llm_chat_inner(
         debug!("removed plano_preference_config from metadata");
     }
 
-    // === Filter chain processing for model listener ===
+    // === Input filters processing for model listener ===
     {
-        if let Some(ref fc) = *filter_chain {
+        if let Some(ref fc) = *input_filters {
             if !fc.is_empty() {
-                debug!(filter_chain = ?fc, "processing model listener filter chain");
+                debug!(input_filters = ?fc, "processing model listener input filters");
 
                 // Create a temporary AgentFilterChain to reuse PipelineProcessor
                 let temp_filter_chain = AgentFilterChain {
@@ -287,7 +287,7 @@ async fn llm_chat_inner(
                     .process_filter_chain(
                         &messages,
                         &temp_filter_chain,
-                        &filter_agents,
+                        &input_filter_agents,
                         &request_headers,
                     )
                     .await
@@ -508,14 +508,23 @@ async fn llm_chat_inner(
         propagator.inject_context(&cx, &mut HeaderInjector(&mut request_headers));
     });
 
-    // Determine if output filter chain is configured
+    // Output filters are only supported for /v1/chat/completions — the SSE content
-    let has_output_filter = output_filter_chain
+    // extraction logic is specific to that API shape (choices[].delta.content).
+    let output_filters_configured = output_filters
         .as_ref()
         .as_ref()
         .map(|fc| !fc.is_empty())
         .unwrap_or(false);
+    let has_output_filter = output_filters_configured
+        && request_path == common::consts::CHAT_COMPLETIONS_PATH;
+    if output_filters_configured && !has_output_filter {
+        warn!(
+            path = %request_path,
+            "output filters are configured but only supported for /v1/chat/completions, skipping"
+        );
+    }
 
-    // Save request headers for output filter chain (before they're consumed by upstream request)
+    // Save request headers for output filters (before they're consumed by upstream request)
     let output_filter_request_headers = if has_output_filter {
         Some(request_headers.clone())
     } else {
@@ -589,7 +598,7 @@ async fn llm_chat_inner(
             request_id,
         );
         if has_output_filter {
-            let ofc = output_filter_chain.as_ref().as_ref().unwrap().clone();
+            let ofc = output_filters.as_ref().as_ref().unwrap().clone();
             let ofa = (*output_filter_agents).clone();
             create_streaming_response_with_output_filter(
                 byte_stream,
@@ -603,7 +612,7 @@ async fn llm_chat_inner(
             create_streaming_response(byte_stream, state_processor, 16)
         }
     } else if has_output_filter {
-        let ofc = output_filter_chain.as_ref().as_ref().unwrap().clone();
+        let ofc = output_filters.as_ref().as_ref().unwrap().clone();
         let ofa = (*output_filter_agents).clone();
         create_streaming_response_with_output_filter(
             byte_stream,

crates/brightstaff/src/handlers/utils.rs

@@ -457,13 +457,13 @@ pub async fn filter_non_streaming_response(
     Bytes::from(serde_json::to_string(&value).unwrap_or_else(|_| response_str.to_string()))
 }
 
-/// Creates a streaming response that processes each chunk through an output filter chain.
+/// Creates a streaming response that processes each chunk through output filters.
 /// The output filter is called asynchronously for each SSE chunk's content.
 pub fn create_streaming_response_with_output_filter<S, P>(
     mut byte_stream: S,
     mut inner_processor: P,
     buffer_size: usize,
-    output_filter_chain: Vec<String>,
+    output_filters: Vec<String>,
     output_filter_agents: HashMap<String, Agent>,
     request_headers: HeaderMap,
 ) -> StreamingResponse
@@ -482,7 +482,7 @@ where
                 id: "output_filter".to_string(),
                 default: None,
                 description: None,
-                filter_chain: Some(output_filter_chain),
+                filter_chain: Some(output_filters),
             };
 
             while let Some(item) = byte_stream.next().await {

crates/brightstaff/src/main.rs

@@ -108,10 +108,10 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
         .listeners
         .iter()
         .find(|l| l.listener_type == ListenerType::Model);
-    let model_filter_chain: Arc<Option<Vec<String>>> =
+    let model_input_filters: Arc<Option<Vec<String>>> =
-        Arc::new(model_listener.and_then(|l| l.filter_chain.clone()));
+        Arc::new(model_listener.and_then(|l| l.input_filters.clone()));
-    let model_filter_agents: Arc<HashMap<String, Agent>> = Arc::new(
+    let model_input_filter_agents: Arc<HashMap<String, Agent>> = Arc::new(
-        model_filter_chain
+        model_input_filters
             .as_ref()
             .as_ref()
             .map(|fc| {
@@ -121,10 +121,10 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
             })
             .unwrap_or_default(),
     );
-    let model_output_filter_chain: Arc<Option<Vec<String>>> =
+    let model_output_filters: Arc<Option<Vec<String>>> =
-        Arc::new(model_listener.and_then(|l| l.output_filter_chain.clone()));
+        Arc::new(model_listener.and_then(|l| l.output_filters.clone()));
     let model_output_filter_agents: Arc<HashMap<String, Agent>> = Arc::new(
-        model_output_filter_chain
+        model_output_filters
             .as_ref()
             .as_ref()
             .map(|fc| {
@@ -228,9 +228,9 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
 
         let llm_providers = llm_providers.clone();
         let agents_list = combined_agents_filters_list.clone();
-        let model_filter_chain = model_filter_chain.clone();
+        let model_input_filters = model_input_filters.clone();
-        let model_filter_agents = model_filter_agents.clone();
+        let model_input_filter_agents = model_input_filter_agents.clone();
-        let model_output_filter_chain = model_output_filter_chain.clone();
+        let model_output_filters = model_output_filters.clone();
         let model_output_filter_agents = model_output_filter_agents.clone();
         let listeners = listeners.clone();
         let span_attributes = span_attributes.clone();
@@ -243,9 +243,9 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
             let llm_providers = llm_providers.clone();
             let model_aliases = Arc::clone(&model_aliases);
             let agents_list = agents_list.clone();
-            let model_filter_chain = model_filter_chain.clone();
+            let model_input_filters = model_input_filters.clone();
-            let model_filter_agents = model_filter_agents.clone();
+            let model_input_filter_agents = model_input_filter_agents.clone();
-            let model_output_filter_chain = model_output_filter_chain.clone();
+            let model_output_filters = model_output_filters.clone();
             let model_output_filter_agents = model_output_filter_agents.clone();
             let listeners = listeners.clone();
             let span_attributes = span_attributes.clone();
@@ -305,9 +305,9 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
                             llm_providers,
                             span_attributes,
                             state_storage,
-                            model_filter_chain,
+                            model_input_filters,
-                            model_filter_agents,
+                            model_input_filter_agents,
-                            model_output_filter_chain,
+                            model_output_filters,
                             model_output_filter_agents,
                         )
                         .with_context(parent_cx)

crates/common/src/configuration.rs

@@ -51,8 +51,8 @@ pub struct Listener {
     pub name: String,
     pub router: Option<String>,
     pub agents: Option<Vec<AgentFilterChain>>,
-    pub filter_chain: Option<Vec<String>>,
+    pub input_filters: Option<Vec<String>>,
-    pub output_filter_chain: Option<Vec<String>>,
+    pub output_filters: Option<Vec<String>>,
     pub port: u16,
 }
 

demos/filter_chains/model_listener_filter/README.md

@@ -2,7 +2,7 @@
 
 Run content-safety filters on direct LLM requests — no agent layer required.
 
-This demo uses the `filter_chain` feature on a **model-type listener** to intercept
+This demo uses the `input_filters` feature on a **model-type listener** to intercept
 `/v1/chat/completions` requests and block unsafe content before they reach the LLM provider.
 
 ## Architecture
@@ -10,7 +10,7 @@ This demo uses the `filter_chain` feature on a **model-type listener** to interc
 ```
 Client ──► Plano (model listener :12000)
                │
-               ├─ filter_chain: content_guard ──► Block / Allow
+               ├─ input_filters: content_guard ──► Block / Allow
                │
                └─ model_provider: openai/gpt-4o-mini
 ```

demos/filter_chains/model_listener_filter/config.yaml

@@ -14,7 +14,7 @@ listeners:
   - type: model
     name: llm_gateway
     port: 12000
-    filter_chain:
+    input_filters:
       - content_guard
 
 tracing:

demos/filter_chains/pii_anonymizer/README.md

@@ -2,20 +2,20 @@
 
 Automatically redact PII from LLM requests and restore it in responses — inspired by [Uber's GenAI Gateway PII Redactor](https://www.uber.com/blog/genai-gateway/).
 
-This demo uses both `filter_chain` (input) and `output_filter_chain` (output) on a **model-type listener** to anonymize PII before it reaches the LLM provider, then de-anonymize the response before returning it to the client.
+This demo uses both `input_filters` and `output_filters` on a **model-type listener** to anonymize PII before it reaches the LLM provider, then de-anonymize the response before returning it to the client.
 
 ## Architecture
 
 ```
 Client ──► Plano (model listener :12000)
                │
-               ├─ filter_chain: pii_anonymizer
+               ├─ input_filters: pii_anonymizer
                │     └─ Replace PII with [EMAIL_0], [SSN_0], etc.
                │
                ├─ model_provider: openai/gpt-4o-mini
                │     └─ LLM only sees anonymized data
                │
-               └─ output_filter_chain: pii_deanonymizer
+               └─ output_filters: pii_deanonymizer
                      └─ Restore [EMAIL_0] → original email (per-chunk for streaming)
 ```
 
@@ -82,7 +82,7 @@ Check the PII filter service logs in the terminal running `start_agents.sh`. You
 
 ## How Streaming De-anonymization Works
 
-For streaming responses, each SSE chunk is sent through the output filter chain as it arrives from the LLM:
+For streaming responses, each SSE chunk is sent through the output filters as it arrives from the LLM:
 
 1. Plano receives a chunk with content like `"The email [EMAIL_0] belongs to..."`
 2. The chunk content is sent to the `/deanonymize` endpoint

demos/filter_chains/pii_anonymizer/config.yaml

@@ -17,9 +17,9 @@ listeners:
   - type: model
     name: llm_gateway
     port: 12000
-    filter_chain:
+    input_filters:
       - pii_anonymizer
-    output_filter_chain:
+    output_filters:
       - pii_deanonymizer
 
 tracing:

docs/source/resources/includes/plano_config_full_reference.yaml

@@ -66,8 +66,8 @@ listeners:
     name: model_1
     address: 0.0.0.0
     port: 12000
-    # Optional: attach a filter chain for input guardrails on direct LLM requests
+    # Optional: attach input filters for guardrails on direct LLM requests
-    # filter_chain:
+    # input_filters:
     #   - input_guards
 
   # Prompt listener for function calling (for prompt_targets)