diff --git a/run_agent.py b/run_agent.py
index 078f8f0f4..711330625 100644
--- a/run_agent.py
+++ b/run_agent.py
@@ -20,6 +20,7 @@ Usage:
     response = agent.run_conversation("Tell me about the latest Python updates")
 """
 
+import copy
 import json
 import logging
 import os
@@ -457,6 +458,94 @@ Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix."""
         return compressed
 
 
+# =============================================================================
+# Anthropic Prompt Caching (system_and_3 strategy)
+# =============================================================================
+# Reduces input token costs by ~75% on multi-turn conversations by caching
+# the conversation prefix. Uses 4 cache_control breakpoints (Anthropic max):
+#   1. System prompt (stable across all turns)
+#   2-4. Last 3 non-system messages (rolling window)
+#
+# Cached tokens are read at 0.1x input price. Cache writes cost 1.25x (5m TTL)
+# or 2x (1h TTL). Only applied to Claude models via OpenRouter.
+
+def _apply_cache_marker(msg: dict, cache_marker: dict) -> None:
+    """
+    Add cache_control to a single message, handling all format variations.
+
+    - tool messages: cache_control at message level (Anthropic API quirk)
+    - string content: converted to multipart content array
+    - list content: marker added to last item
+    - None content (assistant with tool_calls): message level
+    """
+    role = msg.get("role", "")
+    content = msg.get("content")
+
+    if role == "tool":
+        msg["cache_control"] = cache_marker
+        return
+
+    if content is None:
+        msg["cache_control"] = cache_marker
+        return
+
+    if isinstance(content, str):
+        msg["content"] = [{"type": "text", "text": content, "cache_control": cache_marker}]
+        return
+
+    if isinstance(content, list) and content:
+        last = content[-1]
+        if isinstance(last, dict):
+            last["cache_control"] = cache_marker
+
+
+def apply_anthropic_cache_control(
+    api_messages: List[Dict[str, Any]],
+    cache_ttl: str = "5m",
+) -> List[Dict[str, Any]]:
+    """
+    Apply system_and_3 caching strategy to messages for Anthropic models.
+
+    Places up to 4 cache_control breakpoints:
+      1. System prompt (index 0, stable across all turns)
+      2-4. Last 3 non-system messages (rolling cache frontier)
+
+    Each breakpoint tells Anthropic "cache everything from the start up to here."
+    Multiple breakpoints create a ladder of cached prefixes at different depths,
+    which provides robust cache hits even when the most recent cache entry hasn't
+    propagated yet.
+
+    Args:
+        api_messages: Fully assembled message list (system prompt first).
+        cache_ttl: "5m" (default, 1.25x write cost) or "1h" (2x write cost).
+
+    Returns:
+        Deep copy of messages with cache_control breakpoints injected.
+    """
+    messages = copy.deepcopy(api_messages)
+    if not messages:
+        return messages
+
+    marker = {"type": "ephemeral"}
+    if cache_ttl == "1h":
+        marker["ttl"] = "1h"
+
+    breakpoints_used = 0
+
+    # Breakpoint 1: System prompt (always stable, gives a guaranteed minimum hit)
+    if messages[0].get("role") == "system":
+        _apply_cache_marker(messages[0], marker)
+        breakpoints_used += 1
+
+    # Breakpoints 2-4: Last 3 non-system messages (rolling window)
+    remaining = 4 - breakpoints_used
+    non_sys = [i for i in range(len(messages)) if messages[i].get("role") != "system"]
+    for idx in non_sys[-remaining:]:
+        _apply_cache_marker(messages[idx], marker)
+
+    return messages
+
+
 # =============================================================================
 # Default System Prompt Components
 # =============================================================================
@@ -645,7 +734,9 @@ class AIAgent:
         self.ephemeral_system_prompt = ephemeral_system_prompt
         self.log_prefix_chars = log_prefix_chars
         self.log_prefix = f"{log_prefix} " if log_prefix else ""
-        self.base_url = base_url or ""  # Store for OpenRouter detection
+        # Store effective base URL for feature detection (prompt caching, reasoning, etc.)
+        # When no base_url is provided, the client defaults to OpenRouter, so reflect that here.
+        self.base_url = base_url or "https://openrouter.ai/api/v1"
         self.tool_progress_callback = tool_progress_callback
         self._last_reported_tool = None  # Track for "new tool" mode
         
@@ -668,6 +759,14 @@ class AIAgent:
         self.reasoning_config = reasoning_config  # None = use default (xhigh for OpenRouter)
         self.prefill_messages = prefill_messages or []  # Prefilled conversation turns
         
+        # Anthropic prompt caching: auto-enabled for Claude models via OpenRouter.
+        # Reduces input costs by ~75% on multi-turn conversations by caching the
+        # conversation prefix. Uses system_and_3 strategy (4 breakpoints).
+        is_openrouter = "openrouter" in self.base_url.lower()
+        is_claude = "claude" in self.model.lower()
+        self._use_prompt_caching = is_openrouter and is_claude
+        self._cache_ttl = "5m"  # Default 5-minute TTL (1.25x write cost)
+        
         # Configure logging
         if self.verbose_logging:
             logging.basicConfig(
@@ -773,6 +872,10 @@ class AIAgent:
             prompt_preview = self.ephemeral_system_prompt[:60] + "..." if len(self.ephemeral_system_prompt) > 60 else self.ephemeral_system_prompt
             print(f"🔒 Ephemeral system prompt: '{prompt_preview}' (not saved to trajectories)")
         
+        # Show prompt caching status
+        if self._use_prompt_caching and not self.quiet_mode:
+            print(f"💾 Prompt caching: ENABLED (Claude via OpenRouter, {self._cache_ttl} TTL)")
+        
         # Session logging setup - auto-save conversation trajectories for debugging
         self.session_start = datetime.now()
         if session_id:
@@ -1582,6 +1685,13 @@ class AIAgent:
                 # Insert system message at the beginning
                 api_messages = [{"role": "system", "content": active_system_prompt}] + api_messages
             
+            # Apply Anthropic prompt caching for Claude models via OpenRouter.
+            # Auto-detected: if model name contains "claude" and base_url is OpenRouter,
+            # inject cache_control breakpoints (system + last 3 messages) to reduce
+            # input token costs by ~75% on multi-turn conversations.
+            if self._use_prompt_caching:
+                api_messages = apply_anthropic_cache_control(api_messages, cache_ttl=self._cache_ttl)
+            
             # Calculate approximate request size for logging
             total_chars = sum(len(str(msg)) for msg in api_messages)
             approx_tokens = total_chars // 4  # Rough estimate: 4 chars per token
@@ -1811,6 +1921,16 @@ class AIAgent:
                         
                         if self.verbose_logging:
                             logging.debug(f"Token usage: prompt={usage_dict['prompt_tokens']:,}, completion={usage_dict['completion_tokens']:,}, total={usage_dict['total_tokens']:,}")
+                        
+                        # Log cache hit stats when prompt caching is active
+                        if self._use_prompt_caching:
+                            details = getattr(response.usage, 'prompt_tokens_details', None)
+                            cached = getattr(details, 'cached_tokens', 0) or 0 if details else 0
+                            written = getattr(details, 'cache_write_tokens', 0) or 0 if details else 0
+                            prompt = usage_dict["prompt_tokens"]
+                            hit_pct = (cached / prompt * 100) if prompt > 0 else 0
+                            if not self.quiet_mode:
+                                print(f"{self.log_prefix}   💾 Cache: {cached:,}/{prompt:,} tokens ({hit_pct:.0f}% hit, {written:,} written)")
                     
                     break  # Success, exit retry loop