replace enable_token_counting bool with token_counting_strategy enum (estimate|auto)

config/plano_config_schema.yaml

@@ -285,9 +285,14 @@ properties:
       agent_orchestration_model:
         type: string
         description: "Model name for the agent orchestrator (e.g., 'Plano-Orchestrator'). Must match a model in model_providers."
-      enable_token_counting:
-        type: boolean
-        description: "Enable tiktoken-based input token counting for metrics and rate limiting. Default is false."
+      token_counting_strategy:
+        type: string
+        enum: [estimate, auto]
+        description: >
+          Strategy for counting input tokens used in metrics and rate limiting.
+          "estimate" (default): fast character-based approximation (~1 token per 4 chars).
+          "auto": uses the best available tokenizer for each provider (e.g., tiktoken for
+          OpenAI models), falling back to estimate for unsupported providers.
   system_prompt:
     type: string
   prompt_targets:

crates/common/src/configuration.rs

@@ -124,6 +124,15 @@ pub struct Configuration {
     pub state_storage: Option<StateStorageConfig>,
 }
 
+#[derive(Debug, Clone, Serialize, Deserialize, Default, PartialEq)]
+pub enum TokenCountingStrategy {
+    #[default]
+    #[serde(rename = "estimate")]
+    Estimate,
+    #[serde(rename = "auto")]
+    Auto,
+}
+
 #[derive(Debug, Clone, Serialize, Deserialize, Default)]
 pub struct Overrides {
     pub prompt_target_intent_matching_threshold: Option<f64>,
@@ -131,7 +140,7 @@ pub struct Overrides {
     pub use_agent_orchestrator: Option<bool>,
     pub llm_routing_model: Option<String>,
     pub agent_orchestration_model: Option<String>,
-    pub enable_token_counting: Option<bool>,
+    pub token_counting_strategy: Option<TokenCountingStrategy>,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize, Default)]

crates/llm_gateway/src/stream_context.rs

@@ -10,7 +10,7 @@ use std::sync::Arc;
 use std::time::{Duration, SystemTime, UNIX_EPOCH};
 
 use crate::metrics::Metrics;
-use common::configuration::{LlmProvider, LlmProviderType, Overrides};
+use common::configuration::{LlmProvider, LlmProviderType, Overrides, TokenCountingStrategy};
 use common::consts::{
     ARCH_IS_STREAMING_HEADER, ARCH_PROVIDER_HINT_HEADER, ARCH_ROUTING_HEADER, HEALTHZ_PATH,
     RATELIMIT_SELECTOR_HEADER_KEY, REQUEST_ID_HEADER, TRACE_PARENT_HEADER,
@@ -269,15 +269,25 @@ impl StreamContext {
         model: &str,
         json_string: &str,
     ) -> Result<(), ratelimit::Error> {
-        let use_tiktoken = (*self.overrides)
+        let strategy = (*self.overrides)
             .as_ref()
-            .and_then(|o| o.enable_token_counting)
-            .unwrap_or(false);
+            .and_then(|o| o.token_counting_strategy.clone())
+            .unwrap_or_default();
 
-        let token_count = if use_tiktoken {
-            tokenizer::token_count(model, json_string).unwrap_or(0)
-        } else {
-            json_string.len() / 4
+        let (token_count, method) = match strategy {
+            TokenCountingStrategy::Auto => {
+                let provider_id = self.get_provider_id();
+                match provider_id {
+                    ProviderId::OpenAI => (
+                        tokenizer::token_count(model, json_string).unwrap_or(json_string.len() / 4),
+                        "tiktoken",
+                    ),
+                    // Future: add provider-specific tokenizers here
+                    // ProviderId::Mistral => (mistral_tokenizer::count(...), "mistral"),
+                    _ => (json_string.len() / 4, "estimate"),
+                }
+            }
+            TokenCountingStrategy::Estimate => (json_string.len() / 4, "estimate"),
         };
 
         debug!(
@@ -285,7 +295,7 @@ impl StreamContext {
             self.request_identifier(),
             model,
             token_count,
-            if use_tiktoken { "tiktoken" } else { "estimate" }
+            method
         );
 
         self.metrics