diff --git a/examples/anthropic-extended-capabilities/README.md b/examples/anthropic-extended-capabilities/README.md
new file mode 100644
index 00000000..4d33670c
--- /dev/null
+++ b/examples/anthropic-extended-capabilities/README.md
@@ -0,0 +1,50 @@
+# Extended Capabilities Example
+
+This example demonstrates Claude 3.7+'s combined extended capabilities:
+- **Extended Thinking**: Deep reasoning with configurable thinking budget
+- **Extended Output**: Generate up to 128K tokens (vs standard 8K limit)
+
+## Features Demonstrated
+
+1. **Combined Capabilities**: Shows how to use both extended thinking AND extended output together
+2. **Complex Task**: Generates a comprehensive distributed systems guide requiring both deep reasoning and extensive output
+3. **Token Metrics**: Displays detailed token usage including thinking tokens and output tokens
+
+## Running the Example
+
+```bash
+# Set your API key
+export ANTHROPIC_API_KEY=your-api-key
+
+# Run the example
+go run .
+```
+
+## Key Implementation Points
+
+```go
+// Enable both capabilities together
+opts := []llms.CallOption{
+    // Extended thinking for complex reasoning
+    llms.WithThinkingMode(llms.ThinkingModeHigh),
+    
+    // Extended output for up to 128K tokens
+    anthropic.WithExtendedOutput(),
+    
+    // Set high token limit
+    llms.WithMaxTokens(50000),
+}
+```
+
+## What to Expect
+
+- The model will use extended thinking to reason about the complex distributed systems topic
+- It will generate a comprehensive guide that may exceed standard token limits
+- Token metrics will show both thinking tokens used and total output generated
+- If the response is large (>10K chars), you'll have the option to save it to a file
+
+## Requirements
+
+- Claude 3.7 Sonnet model (`claude-3-7-sonnet-20250219`)
+- Valid Anthropic API key with access to Claude 3.7
+- Both extended thinking and extended output features enabled on your account
\ No newline at end of file
diff --git a/examples/anthropic-extended-capabilities/go.mod b/examples/anthropic-extended-capabilities/go.mod
new file mode 100644
index 00000000..64418d61
--- /dev/null
+++ b/examples/anthropic-extended-capabilities/go.mod
@@ -0,0 +1,13 @@
+module github.com/tmc/langchaingo/examples/anthropic-extended-capabilities
+
+go 1.23.8
+
+require github.com/tmc/langchaingo v0.1.14-pre.3
+
+require (
+	github.com/dlclark/regexp2 v1.10.0 // indirect
+	github.com/google/uuid v1.6.0 // indirect
+	github.com/pkoukk/tiktoken-go v0.1.6 // indirect
+)
+
+replace github.com/tmc/langchaingo => ../..
diff --git a/examples/anthropic-extended-capabilities/go.sum b/examples/anthropic-extended-capabilities/go.sum
new file mode 100644
index 00000000..52f831b9
--- /dev/null
+++ b/examples/anthropic-extended-capabilities/go.sum
@@ -0,0 +1,22 @@
+github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc h1:U9qPSI2PIWSS1VwoXQT9A3Wy9MM3WgvqSxFWenqJduM=
+github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/dlclark/regexp2 v1.10.0 h1:+/GIL799phkJqYW+3YbOd8LCcbHzT0Pbo8zl70MHsq0=
+github.com/dlclark/regexp2 v1.10.0/go.mod h1:DHkYz0B9wPfa6wondMfaivmHpzrQ3v9q8cnmRbL6yW8=
+github.com/google/go-cmp v0.7.0 h1:wk8382ETsv4JYUZwIsn6YpYiWiBsYLSJiTsyBybVuN8=
+github.com/google/go-cmp v0.7.0/go.mod h1:pXiqmnSA92OHEEa9HXL2W4E7lf9JzCmGVUdgjX3N/iU=
+github.com/google/uuid v1.6.0 h1:NIvaJDMOsjHA8n1jAhLSgzrAzy1Hgr+hNrb57e+94F0=
+github.com/google/uuid v1.6.0/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
+github.com/pkoukk/tiktoken-go v0.1.6 h1:JF0TlJzhTbrI30wCvFuiw6FzP2+/bR+FIxUdgEAcUsw=
+github.com/pkoukk/tiktoken-go v0.1.6/go.mod h1:9NiV+i9mJKGj1rYOT+njbv+ZwA/zJxYdewGl6qVatpg=
+github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 h1:Jamvg5psRIccs7FGNTlIRMkT8wgtp5eCXdBlqhYGL6U=
+github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
+github.com/stretchr/objx v0.5.2 h1:xuMeJ0Sdp5ZMRXx/aWO6RZxdr3beISkG5/G/aIRr3pY=
+github.com/stretchr/objx v0.5.2/go.mod h1:FRsXN1f5AsAjCGJKqEizvkpNtU+EGNCLh3NxZ/8L+MA=
+github.com/stretchr/testify v1.10.0 h1:Xv5erBjTwe/5IxqUQTdXv5kgmIvbHo3QQyRwhJsOfJA=
+github.com/stretchr/testify v1.10.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
+gopkg.in/yaml.v2 v2.4.0 h1:D8xgwECY7CYvx+Y2n4sBz93Jn9JRvxdiyyo8CTfuKaY=
+gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
+gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
+gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
+sigs.k8s.io/yaml v1.3.0 h1:a2VclLzOGrwOHDiV8EfBGhvjHvP46CtW5j6POvhYGGo=
+sigs.k8s.io/yaml v1.3.0/go.mod h1:GeOyir5tyXNByN85N/dRIT9es5UQNerPYEKK56eTBm8=
diff --git a/examples/anthropic-extended-capabilities/main.go b/examples/anthropic-extended-capabilities/main.go
new file mode 100644
index 00000000..451fe0a9
--- /dev/null
+++ b/examples/anthropic-extended-capabilities/main.go
@@ -0,0 +1,169 @@
+package main
+
+import (
+	"context"
+	"fmt"
+	"os"
+	"strings"
+
+	"github.com/tmc/langchaingo/llms"
+	"github.com/tmc/langchaingo/llms/anthropic"
+)
+
+func main() {
+	ctx := context.Background()
+
+	fmt.Println("=== Claude 3.7+ Extended Capabilities Demo ===")
+	fmt.Println("Demonstrating combined extended thinking + 128K output")
+	fmt.Println()
+
+	// Complex prompt that benefits from both extended thinking and long output
+	prompt := `Write a comprehensive technical guide about building distributed systems.
+
+Include:
+1. Theoretical foundations (CAP theorem, consensus algorithms)
+2. Practical implementation patterns
+3. Real-world case studies from major tech companies
+4. Code examples in Go for key concepts
+5. Testing strategies for distributed systems
+6. Common pitfalls and how to avoid them
+7. Performance optimization techniques
+8. Monitoring and observability best practices
+
+Make this guide as detailed and comprehensive as possible, targeting senior engineers
+who want to deeply understand distributed systems architecture.`
+
+	apiKey := os.Getenv("ANTHROPIC_API_KEY")
+	if apiKey == "" {
+		fmt.Println("ANTHROPIC_API_KEY not set. Skipping demo.")
+		fmt.Println("Set the environment variable to run this example:")
+		fmt.Println("  export ANTHROPIC_API_KEY=your-api-key")
+		return
+	}
+
+	// Initialize Claude 3.7 with extended capabilities
+	llm, err := anthropic.New(
+		anthropic.WithModel("claude-3-7-sonnet-20250219"),
+	)
+	if err != nil {
+		fmt.Printf("Error initializing Anthropic: %v\n", err)
+		return
+	}
+
+	messages := []llms.MessageContent{
+		{
+			Role:  llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{llms.TextPart(prompt)},
+		},
+	}
+
+	// Configure with both extended thinking AND extended output
+	opts := []llms.CallOption{
+		// Enable extended thinking for complex reasoning
+		llms.WithThinkingMode(llms.ThinkingModeHigh),
+		// Enable 128K output for comprehensive response
+		anthropic.WithExtendedOutput(),
+		// Set high token limit to utilize extended output
+		llms.WithMaxTokens(50000), // Can go up to 128K
+		// Temperature must be 1 when thinking is enabled
+		llms.WithTemperature(1.0),
+	}
+
+	fmt.Println("Generating comprehensive guide with:")
+	fmt.Println("  • Extended thinking (HIGH mode)")
+	fmt.Println("  • Extended output (up to 128K tokens)")
+	fmt.Println("  • Max tokens set to 50,000")
+	fmt.Println()
+	fmt.Print("Processing (this may take a while)... ")
+
+	resp, err := llm.GenerateContent(ctx, messages, opts...)
+	if err != nil {
+		fmt.Printf("\nError: %v\n", err)
+		return
+	}
+	fmt.Println("done")
+
+	// Display response summary
+	content := resp.Choices[0].Content
+	contentLen := len(content)
+	
+	fmt.Println("\n" + strings.Repeat("=", 60))
+	fmt.Println("RESPONSE SUMMARY")
+	fmt.Println(strings.Repeat("=", 60))
+	fmt.Printf("Total response length: %d characters\n", contentLen)
+	
+	// Show first 1000 chars as preview
+	preview := content
+	if len(preview) > 1000 {
+		preview = preview[:1000] + "..."
+	}
+	fmt.Println("\nPreview (first 1000 chars):")
+	fmt.Println(strings.Repeat("-", 40))
+	fmt.Println(preview)
+	fmt.Println(strings.Repeat("-", 40))
+
+	// Display detailed token metrics
+	fmt.Println("\n" + strings.Repeat("=", 60))
+	fmt.Println("TOKEN METRICS")
+	fmt.Println(strings.Repeat("=", 60))
+	
+	if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+		var inputTokens, outputTokens, totalTokens int
+		if v, ok := genInfo["PromptTokens"].(int); ok {
+			inputTokens = v
+		}
+		if v, ok := genInfo["CompletionTokens"].(int); ok {
+			outputTokens = v
+		}
+		if v, ok := genInfo["TotalTokens"].(int); ok {
+			totalTokens = v
+		}
+
+		fmt.Printf("Input Tokens:       %d\n", inputTokens)
+		fmt.Printf("Output Tokens:      %d\n", outputTokens)
+		fmt.Printf("Total Tokens:       %d\n", totalTokens)
+
+		// Check for thinking tokens
+		usage := llms.ExtractThinkingTokens(genInfo)
+		if usage != nil && usage.ThinkingTokens > 0 {
+			fmt.Printf("\nThinking Analysis:\n")
+			fmt.Printf("  Thinking Tokens:    %d\n", usage.ThinkingTokens)
+			fmt.Printf("  Visible Output:     %d\n", outputTokens-usage.ThinkingTokens)
+			fmt.Printf("  Thinking Ratio:     %.1f%% of output\n",
+				float64(usage.ThinkingTokens)/float64(outputTokens)*100)
+
+			if usage.ThinkingBudgetAllocated > 0 {
+				fmt.Printf("  Budget Allocated:   %d\n", usage.ThinkingBudgetAllocated)
+				fmt.Printf("  Budget Used:        %d\n", usage.ThinkingBudgetUsed)
+			}
+		}
+
+		// Highlight extended output usage
+		if outputTokens > 8192 {
+			fmt.Printf("\n✅ Extended Output Active: Generated %d tokens (standard limit is 8192)\n", outputTokens)
+		}
+	}
+
+	fmt.Println("\n" + strings.Repeat("=", 60))
+	fmt.Println("Demo complete!")
+	fmt.Println("\nKey Features Demonstrated:")
+	fmt.Println("• Extended thinking for complex reasoning about distributed systems")
+	fmt.Println("• Extended output allowing comprehensive, detailed responses")
+	fmt.Println("• Combined capabilities working together seamlessly")
+	
+	// Optionally save full response to file
+	if contentLen > 10000 {
+		filename := "distributed-systems-guide.md"
+		fmt.Printf("\nFull response is %d chars. Save to %s? (y/n): ", contentLen, filename)
+		var answer string
+		fmt.Scanln(&answer)
+		if strings.ToLower(answer) == "y" {
+			err := os.WriteFile(filename, []byte(content), 0644)
+			if err != nil {
+				fmt.Printf("Error saving file: %v\n", err)
+			} else {
+				fmt.Printf("Saved to %s\n", filename)
+			}
+		}
+	}
+}
\ No newline at end of file
diff --git a/examples/anthropic-interleaved-thinking/README.md b/examples/anthropic-interleaved-thinking/README.md
new file mode 100644
index 00000000..1bb5f8de
--- /dev/null
+++ b/examples/anthropic-interleaved-thinking/README.md
@@ -0,0 +1,69 @@
+# Interleaved Thinking Example
+
+This example demonstrates Claude 3.7+'s interleaved thinking capability, which allows the model to use thinking tokens between tool calls for better multi-step reasoning.
+
+## What is Interleaved Thinking?
+
+Interleaved thinking enables Claude to:
+- **Think between tool calls**: Use reasoning tokens to plan which tool to use next
+- **Interpret results**: Process tool outputs before deciding on next steps
+- **Synthesize information**: Combine results from multiple tools coherently
+
+## Features Demonstrated
+
+1. **Multi-step problem solving** with quarterly sales analysis
+2. **Tool orchestration** with calculate, search, and analyze functions
+3. **Thinking tokens** used for planning and interpretation
+4. **Token metrics** showing thinking vs visible output
+
+## Running the Example
+
+```bash
+# Set your API key
+export ANTHROPIC_API_KEY=your-api-key
+
+# Run the example
+go run .
+```
+
+## Key Implementation
+
+```go
+// Enable interleaved thinking for tool use
+opts := []llms.CallOption{
+    // Thinking mode for reasoning
+    llms.WithThinkingMode(llms.ThinkingModeMedium),
+    
+    // Enable interleaved thinking beta feature
+    anthropic.WithInterleavedThinking(),
+    
+    // Provide tools for the model to use
+    llms.WithTools(tools),
+}
+```
+
+## Example Flow
+
+The demo presents a multi-step analysis task:
+1. Calculate year-over-year growth rates
+2. Analyze data trends
+3. Search for explanatory factors
+4. Make predictions based on findings
+
+Between each tool call, Claude uses thinking tokens to:
+- Decide which tool to use next
+- Interpret the results from the previous tool
+- Plan the next step in the analysis
+
+## Token Metrics
+
+The example displays detailed token usage:
+- **Thinking Tokens**: Used for internal reasoning between tools
+- **Visible Output**: The actual response content
+- **Thinking Ratio**: Percentage of tokens used for thinking
+
+## Requirements
+
+- Claude 3.7 Sonnet model (`claude-3-7-sonnet-20250219`)
+- Valid Anthropic API key
+- Interleaved thinking feature access
\ No newline at end of file
diff --git a/examples/anthropic-interleaved-thinking/go.mod b/examples/anthropic-interleaved-thinking/go.mod
new file mode 100644
index 00000000..35263db4
--- /dev/null
+++ b/examples/anthropic-interleaved-thinking/go.mod
@@ -0,0 +1,15 @@
+module github.com/tmc/langchaingo/examples/interleaved-thinking
+
+go 1.23.8
+
+require github.com/tmc/langchaingo v0.1.14-pre.3
+
+require (
+	github.com/dlclark/regexp2 v1.10.0 // indirect
+	github.com/google/uuid v1.6.0 // indirect
+	github.com/pkoukk/tiktoken-go v0.1.6 // indirect
+	go.starlark.net v0.0.0-20230302034142-4b1e35fe2254 // indirect
+	golang.org/x/sys v0.35.0 // indirect
+)
+
+replace github.com/tmc/langchaingo => ../..
diff --git a/examples/anthropic-interleaved-thinking/go.sum b/examples/anthropic-interleaved-thinking/go.sum
new file mode 100644
index 00000000..7af3a178
--- /dev/null
+++ b/examples/anthropic-interleaved-thinking/go.sum
@@ -0,0 +1,97 @@
+cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
+github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU=
+github.com/census-instrumentation/opencensus-proto v0.2.1/go.mod h1:f6KPmirojxKA12rnyqOA5BBL4O983OfeGPqjHWSTneU=
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+github.com/chzyer/test v0.0.0-20180213035817-a1ea475d72b1/go.mod h1:Q3SI9o4m/ZMnBNeIyt5eFwwo7qiLfzFZmjNmxjkiQlU=
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diff --git a/examples/anthropic-interleaved-thinking/main.go b/examples/anthropic-interleaved-thinking/main.go
new file mode 100644
index 00000000..0f56da8a
--- /dev/null
+++ b/examples/anthropic-interleaved-thinking/main.go
@@ -0,0 +1,726 @@
+package main
+
+import (
+	"context"
+	"encoding/json"
+	"flag"
+	"fmt"
+	"net/http"
+	"net/http/httputil"
+	"os"
+	"strings"
+
+	"github.com/tmc/langchaingo/llms"
+	"github.com/tmc/langchaingo/llms/anthropic"
+)
+
+// Define some tools for the model to use
+var (
+	calculateTool = llms.Tool{
+		Type: "function",
+		Function: &llms.FunctionDefinition{
+			Name:        "calculate",
+			Description: "Perform mathematical calculations",
+			Parameters: map[string]interface{}{
+				"type": "object",
+				"properties": map[string]interface{}{
+					"expression": map[string]interface{}{
+						"type":        "string",
+						"description": "Mathematical expression to evaluate",
+					},
+				},
+				"required": []string{"expression"},
+			},
+		},
+	}
+
+	searchTool = llms.Tool{
+		Type: "function",
+		Function: &llms.FunctionDefinition{
+			Name:        "search_knowledge",
+			Description: "Search for information in a knowledge base",
+			Parameters: map[string]interface{}{
+				"type": "object",
+				"properties": map[string]interface{}{
+					"query": map[string]interface{}{
+						"type":        "string",
+						"description": "Search query",
+					},
+				},
+				"required": []string{"query"},
+			},
+		},
+	}
+
+	analyzeTool = llms.Tool{
+		Type: "function",
+		Function: &llms.FunctionDefinition{
+			Name:        "analyze_data",
+			Description: "Analyze data and return insights",
+			Parameters: map[string]interface{}{
+				"type": "object",
+				"properties": map[string]interface{}{
+					"data": map[string]interface{}{
+						"type":        "array",
+						"description": "Data points to analyze",
+						"items": map[string]interface{}{
+							"type": "number",
+						},
+					},
+					"analysis_type": map[string]interface{}{
+						"type":        "string",
+						"description": "Type of analysis: mean, median, std_dev, trend",
+					},
+				},
+				"required": []string{"data", "analysis_type"},
+			},
+		},
+	}
+	
+	// New tool that depends on results from other tools
+	generateReportTool = llms.Tool{
+		Type: "function",
+		Function: &llms.FunctionDefinition{
+			Name:        "generate_report",
+			Description: "Generate a strategic report based on analysis results. ONLY use after completing all calculations and analyses.",
+			Parameters: map[string]interface{}{
+				"type": "object",
+				"properties": map[string]interface{}{
+					"growth_rate": map[string]interface{}{
+						"type":        "number",
+						"description": "Year-over-year growth rate from calculations",
+					},
+					"trend_direction": map[string]interface{}{
+						"type":        "string",
+						"description": "Trend direction from analysis (upward/downward/stable)",
+					},
+					"volatility_level": map[string]interface{}{
+						"type":        "string",
+						"description": "Volatility level based on std deviation (low/moderate/high)",
+					},
+					"key_insights": map[string]interface{}{
+						"type":        "array",
+						"description": "Key insights from research",
+						"items": map[string]interface{}{
+							"type": "string",
+						},
+					},
+					"prediction": map[string]interface{}{
+						"type":        "number",
+						"description": "Q1-2025 predicted value",
+					},
+				},
+				"required": []string{"growth_rate", "trend_direction", "volatility_level", "key_insights", "prediction"},
+			},
+		},
+	}
+	
+	// Tool for making final predictions based on all data
+	makePredictionTool = llms.Tool{
+		Type: "function",
+		Function: &llms.FunctionDefinition{
+			Name:        "make_prediction",
+			Description: "Make predictions based on calculated metrics. Requires growth rates and averages from previous calculations.",
+			Parameters: map[string]interface{}{
+				"type": "object",
+				"properties": map[string]interface{}{
+					"base_value": map[string]interface{}{
+						"type":        "number",
+						"description": "The base value to predict from (e.g., Q4-2024 value)",
+					},
+					"growth_rate": map[string]interface{}{
+						"type":        "number",
+						"description": "Growth rate to apply (as decimal, e.g., 0.05 for 5%)",
+					},
+					"seasonality_factor": map[string]interface{}{
+						"type":        "number",
+						"description": "Seasonal adjustment factor",
+					},
+					"confidence_level": map[string]interface{}{
+						"type":        "string",
+						"description": "Confidence level: high, medium, low",
+					},
+				},
+				"required": []string{"base_value", "growth_rate", "seasonality_factor", "confidence_level"},
+			},
+		},
+	}
+)
+
+// debugTransport wraps http.RoundTripper to log requests and responses
+type debugTransport struct {
+	Transport http.RoundTripper
+	Debug     bool
+}
+
+func (d *debugTransport) RoundTrip(req *http.Request) (*http.Response, error) {
+	if d.Debug {
+		fmt.Println("\n" + strings.Repeat("=", 80))
+		fmt.Println("HTTP REQUEST")
+		fmt.Println(strings.Repeat("-", 80))
+		
+		// Dump request
+		reqDump, err := httputil.DumpRequestOut(req, true)
+		if err != nil {
+			fmt.Printf("Error dumping request: %v\n", err)
+		} else {
+			fmt.Printf("%s\n", reqDump)
+		}
+	}
+	
+	// Make the actual request
+	resp, err := d.Transport.RoundTrip(req)
+	
+	if d.Debug && resp != nil {
+		fmt.Println("\n" + strings.Repeat("-", 80))
+		fmt.Println("HTTP RESPONSE")
+		fmt.Println(strings.Repeat("-", 80))
+		
+		// Dump response
+		respDump, err := httputil.DumpResponse(resp, true)
+		if err != nil {
+			fmt.Printf("Error dumping response: %v\n", err)
+		} else {
+			fmt.Printf("%s\n", respDump)
+		}
+		fmt.Println(strings.Repeat("=", 80) + "\n")
+	}
+	
+	return resp, err
+}
+
+func main() {
+	// Parse command line flags
+	debugHTTP := flag.Bool("debug-http", false, "Show raw HTTP requests and responses")
+	flag.Parse()
+
+	ctx := context.Background()
+
+	fmt.Println("╔════════════════════════════════════════════════════════════╗")
+	fmt.Println("║      Claude 4 Interleaved Thinking Demo                   ║")
+	fmt.Println("║     Demonstrating thinking between tool calls             ║")
+	fmt.Println("╚════════════════════════════════════════════════════════════╝")
+	fmt.Println()
+	
+	if *debugHTTP {
+		fmt.Println("🔍 DEBUG MODE: HTTP requests/responses will be displayed")
+		fmt.Println()
+	}
+
+	apiKey := os.Getenv("ANTHROPIC_API_KEY")
+	if apiKey == "" {
+		fmt.Println("❌ ANTHROPIC_API_KEY not set. Skipping demo.")
+		fmt.Println("   Set the environment variable to run this example:")
+		fmt.Println("   export ANTHROPIC_API_KEY=your-api-key")
+		return
+	}
+
+	// Stage 1: Initialization
+	fmt.Println("🚀 STAGE 1: INITIALIZATION")
+	fmt.Println("─────────────────────────")
+	fmt.Println("  • Model: Claude Sonnet 4 (20250514)")
+	fmt.Println("  • Beta: interleaved-thinking-2025-05-14")
+	fmt.Println("  • Feature: Thinking between tool calls")
+	
+	// Configure options for Anthropic client
+	anthropicOpts := []anthropic.Option{
+		anthropic.WithModel("claude-sonnet-4-20250514"),
+	}
+	
+	// Add debug HTTP client if flag is set
+	if *debugHTTP {
+		httpClient := &http.Client{
+			Transport: &debugTransport{
+				Transport: http.DefaultTransport,
+				Debug:     true,
+			},
+		}
+		anthropicOpts = append(anthropicOpts, anthropic.WithHTTPClient(httpClient))
+	}
+	
+	// Using Claude Sonnet 4 for interleaved thinking
+	llm, err := anthropic.New(anthropicOpts...)
+	if err != nil {
+		fmt.Printf("  ❌ Error initializing Anthropic: %v\n", err)
+		return
+	}
+	fmt.Println("  ✅ Model initialized successfully")
+	fmt.Println()
+
+	// Stage 2: Problem Setup
+	fmt.Println("📊 STAGE 2: PROBLEM SETUP")
+	fmt.Println("─────────────────────────")
+	fmt.Println("  Complex multi-step analysis task:")
+	fmt.Println("  • Quarterly sales data analysis")
+	fmt.Println("  • Year-over-year growth calculation")
+	fmt.Println("  • Trend analysis and prediction")
+	fmt.Println()
+
+	// Complex multi-step problem requiring tool use and reasoning
+	prompt := `You're helping a data scientist analyze quarterly sales data and make strategic decisions.
+
+The quarterly sales (in millions) for the last 8 quarters are:
+Q1-2023: 12.5, Q2-2023: 14.2, Q3-2023: 13.8, Q4-2023: 16.1
+Q1-2024: 15.3, Q2-2024: 17.4, Q3-2024: 16.9, Q4-2024: 19.2
+
+IMPORTANT: Complete this multi-stage analysis. Some tools depend on results from others, creating natural stages:
+
+STAGE 1 - PARALLEL DATA GATHERING (invoke all these tools simultaneously):
+1. CALCULATIONS:
+   - Year-over-year growth rate for Q4: (19.2 - 16.1) / 16.1 * 100
+   - Average quarterly growth 2024: ((17.4/15.3 - 1) + (16.9/17.4 - 1) + (19.2/16.9 - 1)) / 3
+   - Overall growth Q1-2023 to Q4-2024: (19.2 - 12.5) / 12.5 * 100
+   - Average 2023: (12.5 + 14.2 + 13.8 + 16.1) / 4
+   - Average 2024: (15.3 + 17.4 + 16.9 + 19.2) / 4
+
+2. DATA ANALYSIS:
+   - Trend analysis on [12.5, 14.2, 13.8, 16.1, 15.3, 17.4, 16.9, 19.2]
+   - Standard deviation of the same data
+   - Mean of all data points
+
+3. RESEARCH:
+   - "seasonal sales patterns in retail"
+   - "factors driving quarterly sales growth"
+   - "economic indicators affecting sales performance"
+
+STAGE 2 - SYNTHESIS (use make_prediction tool AFTER Stage 1 completes):
+After receiving all Stage 1 results, use the make_prediction tool with:
+- base_value: Q4-2024 value (19.2)
+- growth_rate: Use the average quarterly growth rate from Stage 1
+- seasonality_factor: Derive from the seasonal patterns research
+- confidence_level: Based on the standard deviation analysis
+
+STAGE 3 - REPORT GENERATION (use generate_report tool AFTER Stage 2):
+Finally, use generate_report to create a comprehensive summary using ALL previous results.
+
+This demonstrates interleaved thinking: parallel execution where possible, sequential when dependencies exist.`
+
+	messages := []llms.MessageContent{
+		{
+			Role:  llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{llms.TextPart(prompt)},
+		},
+	}
+
+	// Stage 3: Configuration
+	fmt.Println("⚙️  STAGE 3: CONFIGURATION")
+	fmt.Println("─────────────────────────")
+	fmt.Println("  • Thinking Mode: MEDIUM")
+	fmt.Println("  • Interleaved Thinking: ENABLED")
+	fmt.Println("  • Temperature: 1.0 (required for thinking)")
+	fmt.Println("  • Max Tokens: 4000")
+	fmt.Println("  • Tools Available:")
+	fmt.Println("    - calculate: Mathematical calculations")
+	fmt.Println("    - search_knowledge: Information retrieval")
+	fmt.Println("    - analyze_data: Statistical analysis")
+	fmt.Println()
+
+	// Configure with interleaved thinking for tool use
+	var streamedContent strings.Builder
+	var contentBlockCount int
+	
+	opts := []llms.CallOption{
+		// Enable thinking mode for reasoning between tools
+		llms.WithThinkingMode(llms.ThinkingModeMedium),
+		// Add interleaved thinking beta header
+		anthropic.WithInterleavedThinking(),
+		// Temperature must be 1 when thinking is enabled
+		llms.WithTemperature(1.0),
+		// Provide tools
+		llms.WithTools([]llms.Tool{
+			calculateTool,
+			searchTool,
+			analyzeTool,
+			makePredictionTool,
+			generateReportTool,
+		}),
+		llms.WithMaxTokens(8000), // Increased for multiple tool calls
+		// Add streaming to show progress
+		// Note: The streaming function receives processed text content,
+		// not raw SSE events. The anthropic client handles SSE parsing internally.
+		llms.WithStreamingFunc(func(ctx context.Context, chunk []byte) error {
+			chunkStr := string(chunk)
+			
+			// Show progress dots for content generation
+			if len(chunkStr) > 0 {
+				if contentBlockCount == 0 {
+					contentBlockCount++
+					fmt.Printf("\n\n  📝 GENERATING RESPONSE: ")
+					fmt.Print("\n  │  ")
+				}
+				fmt.Print("•")
+				streamedContent.WriteString(chunkStr)
+			}
+			return nil
+		}),
+	}
+
+	// Stage 4: Processing
+	fmt.Println("🔄 STAGE 4: PROCESSING")
+	fmt.Println("─────────────────────────")
+	fmt.Println("  Starting multi-step analysis with interleaved thinking...")
+
+	// Make initial request
+	resp, err := llm.GenerateContent(ctx, messages, opts...)
+	if err != nil {
+		fmt.Printf("\n  ❌ Error: %v\n", err)
+		return
+	}
+	
+	fmt.Printf("\n  ℹ️  Initial response received (streaming may still be in progress)\n")
+	
+	// Handle tool calls in a conversation loop
+	maxIterations := 10 // Prevent infinite loops
+	iteration := 0
+	allResponses := []llms.ContentChoice{} // Store all responses for final display
+	
+	// Store initial response
+	if resp != nil && len(resp.Choices) > 0 {
+		allResponses = append(allResponses, *resp.Choices[0])
+	}
+	
+	for iteration < maxIterations && resp != nil && len(resp.Choices) > 0 {
+		choice := resp.Choices[0]
+		
+		// Check if there are tool calls to process
+		// Note: Tool calls appear in the response AFTER streaming completes
+		if len(choice.ToolCalls) > 0 {
+			fmt.Printf("\n\n  🔄 Iteration %d: Processing %d tool calls...\n", iteration+1, len(choice.ToolCalls))
+			for i, tc := range choice.ToolCalls {
+				fmt.Printf("      Tool %d: %s\n", i+1, tc.FunctionCall.Name)
+			}
+			
+			// Add assistant message with tool calls to conversation
+			assistantParts := []llms.ContentPart{}
+			if choice.Content != "" {
+				assistantParts = append(assistantParts, llms.TextPart(choice.Content))
+			}
+			assistantParts = append(assistantParts, convertToolCallsToParts(choice.ToolCalls)...)
+			
+			messages = append(messages, llms.MessageContent{
+				Role:  llms.ChatMessageTypeAI,
+				Parts: assistantParts,
+			})
+			
+			// Execute tools and add results to conversation
+			for _, tc := range choice.ToolCalls {
+				result := executeToolCall(tc.FunctionCall.Name, tc.FunctionCall.Arguments)
+				
+				// Add tool result to messages
+				messages = append(messages, llms.MessageContent{
+					Role: llms.ChatMessageTypeTool,
+					Parts: []llms.ContentPart{
+						llms.ToolCallResponse{
+							ToolCallID: tc.ID,
+							Name:       tc.FunctionCall.Name,
+							Content:    result,
+						},
+					},
+				})
+			}
+			
+			// Continue conversation with tool results
+			fmt.Printf("\n  📤 Sending tool results back to model (iteration %d)...\n", iteration+1)
+			
+			// Remove streaming for subsequent calls to avoid duplicate output
+			continuationOpts := make([]llms.CallOption, 0, len(opts))
+			for _, opt := range opts {
+				continuationOpts = append(continuationOpts, opt)
+			}
+			// Override streaming for continuation
+			continuationOpts = append(continuationOpts, llms.WithStreamingFunc(nil))
+			
+			resp, err = llm.GenerateContent(ctx, messages, continuationOpts...)
+			if err != nil {
+				fmt.Printf("\n  ⚠️  Error in iteration %d: %v\n", iteration+1, err)
+				break
+			}
+			
+			// Store this response
+			if resp != nil && len(resp.Choices) > 0 {
+				allResponses = append(allResponses, *resp.Choices[0])
+				fmt.Printf("\n  ✅ Received response with %d tool calls\n", len(resp.Choices[0].ToolCalls))
+			}
+			
+			iteration++
+		} else {
+			// No more tool calls, conversation complete
+			break
+		}
+	}
+	
+	if iteration >= maxIterations {
+		fmt.Println("\n  ⚠️  Reached maximum iterations, stopping conversation loop")
+	}
+
+	// Ensure we have a clean line after streaming
+	fmt.Println("\n")
+
+	// Stage 5: Results Analysis
+	fmt.Println("✅ STAGE 5: RESULTS ANALYSIS")
+	fmt.Println("─────────────────────────")
+	
+	// Show final iteration count and tool call summary
+	fmt.Printf("\n  📊 Completed after %d iteration(s)\n", iteration)
+	
+	// Count total tool calls processed
+	totalToolCalls := 0
+	if resp != nil && len(resp.Choices) > 0 {
+		totalToolCalls = len(resp.Choices[0].ToolCalls)
+		if totalToolCalls > 0 {
+			fmt.Printf("  ✅ Total tool calls executed: %d\n", totalToolCalls)
+			
+			// Show if they were parallel
+			if totalToolCalls > 1 {
+				fmt.Printf("  🚀 Tools were executed in PARALLEL!\n")
+			}
+		}
+	}
+	
+	// Check for tool calls in the final response
+	if resp != nil && len(resp.Choices) > 0 {
+		for i, choice := range resp.Choices {
+			// Display any thinking content from GenerationInfo
+			if choice.GenerationInfo != nil {
+				if thinking, ok := choice.GenerationInfo["ThinkingContent"].(string); ok && thinking != "" {
+					fmt.Println("\n  📝 Captured Thinking Process:")
+					fmt.Println("  ├─────────────────────────────")
+					// Display first 500 chars of thinking
+					thinkingPreview := thinking
+					if len(thinking) > 500 {
+						thinkingPreview = thinking[:500] + "..."
+					}
+					// Indent the thinking content
+					lines := strings.Split(thinkingPreview, "\n")
+					for _, line := range lines {
+						fmt.Printf("  │ %s\n", line)
+					}
+					fmt.Println("  └─────────────────────────────")
+				}
+			}
+			
+			// Display tool calls
+			if len(choice.ToolCalls) > 0 {
+				fmt.Printf("\n  🔧 Tool Execution Summary:\n")
+				fmt.Println("  ├─────────────────────────────")
+				for j, tc := range choice.ToolCalls {
+					fmt.Printf("  │ Call %d: %s\n", j+1, tc.FunctionCall.Name)
+					fmt.Printf("  │   Args: %s\n", tc.FunctionCall.Arguments)
+					// Simulate tool execution
+					result := executeToolCall(tc.FunctionCall.Name, tc.FunctionCall.Arguments)
+					fmt.Printf("  │   → %s\n", result)
+					if j < len(choice.ToolCalls)-1 {
+						fmt.Println("  │")
+					}
+				}
+				fmt.Println("  └─────────────────────────────")
+			}
+			
+			// Display content if any
+			if choice.Content != "" {
+				fmt.Printf("\n  💬 Final Response %d:\n", i+1)
+				fmt.Println("  ├─────────────────────────────")
+				// Indent the response content
+				lines := strings.Split(choice.Content, "\n")
+				for _, line := range lines {
+					if line != "" {
+						fmt.Printf("  │ %s\n", line)
+					}
+				}
+				fmt.Println("  └─────────────────────────────")
+			}
+		}
+	}
+	
+	// Stream statistics
+	if streamedContent.Len() > 0 {
+		fmt.Println("\n  📊 Stream Statistics:")
+		fmt.Println("  ├─────────────────────────────")
+		fmt.Printf("  │ Streamed Text:     %d bytes\n", streamedContent.Len())
+		fmt.Printf("  │ Response Blocks:   %d\n", contentBlockCount)
+		fmt.Println("  └─────────────────────────────")
+	}
+
+	// Stage 6: Token Metrics
+	fmt.Println("\n📈 STAGE 6: TOKEN METRICS")
+	fmt.Println("─────────────────────────")
+	
+	// Try to get token info from GenerationInfo or from captured streaming data
+	var inputTokens, outputTokens, totalTokens int
+	
+	if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+		// Try different possible field names
+		if v, ok := genInfo["PromptTokens"].(int); ok {
+			inputTokens = v
+		} else if v, ok := genInfo["InputTokens"].(int); ok {
+			inputTokens = v
+		} else if v, ok := genInfo["prompt_tokens"].(int); ok {
+			inputTokens = v
+		}
+		
+		if v, ok := genInfo["CompletionTokens"].(int); ok {
+			outputTokens = v
+		} else if v, ok := genInfo["OutputTokens"].(int); ok {
+			outputTokens = v
+		} else if v, ok := genInfo["completion_tokens"].(int); ok {
+			outputTokens = v
+		}
+		
+		if v, ok := genInfo["TotalTokens"].(int); ok {
+			totalTokens = v
+		} else if v, ok := genInfo["total_tokens"].(int); ok {
+			totalTokens = v
+		} else {
+			totalTokens = inputTokens + outputTokens
+		}
+	}
+	
+
+	fmt.Println("  Token Usage Summary:")
+	fmt.Println("  ├─────────────────────────────")
+	fmt.Printf("  │ Input Tokens:      %d\n", inputTokens)
+	fmt.Printf("  │ Output Tokens:     %d\n", outputTokens)
+	fmt.Printf("  │ Total Tokens:      %d\n", totalTokens)
+	fmt.Println("  └─────────────────────────────")
+
+	// Extract thinking token details
+	if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+		usage := llms.ExtractThinkingTokens(genInfo)
+		if usage != nil && usage.ThinkingTokens > 0 {
+			fmt.Println("\n  Interleaved Thinking Analysis:")
+			fmt.Println("  ├─────────────────────────────")
+			fmt.Printf("  │ Thinking Tokens:   %d\n", usage.ThinkingTokens)
+			fmt.Printf("  │ Visible Output:    %d\n", outputTokens-usage.ThinkingTokens)
+			fmt.Printf("  │ Thinking Ratio:    %.1f%% of output\n",
+				float64(usage.ThinkingTokens)/float64(outputTokens)*100)
+			
+			if usage.ThinkingBudgetAllocated > 0 {
+				fmt.Printf("  │ Budget Allocated:  %d\n", usage.ThinkingBudgetAllocated)
+				fmt.Printf("  │ Budget Used:       %d\n", usage.ThinkingBudgetUsed)
+				fmt.Printf("  │ Budget Efficiency: %.1f%%\n",
+					float64(usage.ThinkingBudgetUsed)/float64(usage.ThinkingBudgetAllocated)*100)
+			}
+			fmt.Println("  └─────────────────────────────")
+
+			fmt.Println("\n  💡 Thinking Benefits:")
+			fmt.Println("  • Planning tool usage strategy")
+			fmt.Println("  • Interpreting intermediate results")
+			fmt.Println("  • Synthesizing multi-step analysis")
+			fmt.Println("  • Ensuring logical coherence")
+		}
+	}
+
+	// Final Summary
+	fmt.Println("\n╔════════════════════════════════════════════════════════════╗")
+	fmt.Println("║                    DEMO COMPLETE                          ║")
+	fmt.Println("╚════════════════════════════════════════════════════════════╝")
+	
+	fmt.Println("\n🎯 Key Features Demonstrated:")
+	fmt.Println("  ✓ Interleaved thinking between tool calls")
+	fmt.Println("  ✓ Real-time progress tracking with stages")
+	fmt.Println("  ✓ Clear visualization of thinking blocks")
+	fmt.Println("  ✓ Tool orchestration with results")
+	fmt.Println("  ✓ Token usage analysis with thinking breakdown")
+	
+	fmt.Println("\n📚 Use Cases:")
+	fmt.Println("  • Complex multi-step analysis tasks")
+	fmt.Println("  • Data processing with reasoning")
+	fmt.Println("  • Research requiring tool coordination")
+	fmt.Println("  • Decision-making with transparent logic")
+}
+
+// convertToolCallsToParts converts tool calls to content parts
+func convertToolCallsToParts(toolCalls []llms.ToolCall) []llms.ContentPart {
+	parts := make([]llms.ContentPart, 0, len(toolCalls))
+	for _, tc := range toolCalls {
+		parts = append(parts, llms.ToolCall{
+			ID:           tc.ID,
+			Type:         tc.Type,
+			FunctionCall: tc.FunctionCall,
+		})
+	}
+	return parts
+}
+
+// Simulate tool execution (in real use, these would call actual functions)
+func executeToolCall(name string, arguments string) string {
+	var args map[string]interface{}
+	json.Unmarshal([]byte(arguments), &args)
+
+	switch name {
+	case "calculate":
+		expr, _ := args["expression"].(string)
+		// Simulate various calculations
+		switch {
+		case strings.Contains(expr, "19.2") && strings.Contains(expr, "16.1"):
+			return "19.25"
+		case strings.Contains(expr, "(17.4/15.3"):
+			return "0.0534" // Average quarterly growth
+		case strings.Contains(expr, "19.2") && strings.Contains(expr, "12.5"):
+			return "53.6"
+		case strings.Contains(expr, "12.5") && strings.Contains(expr, "14.2") && strings.Contains(expr, "/4"):
+			return "14.15"
+		case strings.Contains(expr, "15.3") && strings.Contains(expr, "17.4") && strings.Contains(expr, "/4"):
+			return "17.2"
+		default:
+			// Generic calculation result
+			return fmt.Sprintf("Result: %.2f", 15.5)
+		}
+		
+	case "search_knowledge":
+		query, _ := args["query"].(string)
+		// Return different results based on query
+		if strings.Contains(query, "seasonal") {
+			return "Seasonal patterns show Q4 typically strongest due to holiday shopping, Q1 weakest post-holiday"
+		} else if strings.Contains(query, "factors") {
+			return "Key growth drivers: digital transformation, market expansion, improved supply chain efficiency"
+		} else if strings.Contains(query, "economic") {
+			return "Economic indicators: consumer confidence up 12%, GDP growth 2.8%, inflation moderating at 3.2%"
+		}
+		return fmt.Sprintf("Research data for '%s' retrieved", query)
+		
+	case "analyze_data":
+		analysisType, _ := args["analysis_type"].(string)
+		data, _ := args["data"].([]interface{})
+		
+		switch analysisType {
+		case "trend":
+			return "Trend: Consistent upward trajectory with 7.2% quarter-over-quarter average growth"
+		case "std_dev":
+			return "Standard Deviation: 2.41 million (moderate volatility)"
+		case "mean":
+			return "Mean: 15.675 million average across all quarters"
+		default:
+			return fmt.Sprintf("Analysis complete for %s with %d data points", analysisType, len(data))
+		}
+		
+	case "make_prediction":
+		baseValue, _ := args["base_value"].(float64)
+		growthRate, _ := args["growth_rate"].(float64)
+		seasonality, _ := args["seasonality_factor"].(float64)
+		confidence, _ := args["confidence_level"].(string)
+		
+		// Calculate prediction
+		prediction := baseValue * (1 + growthRate) * seasonality
+		
+		return fmt.Sprintf("Prediction: %.2f million (confidence: %s, growth: %.1f%%, seasonality: %.2fx)",
+			prediction, confidence, growthRate*100, seasonality)
+		
+	case "generate_report":
+		growthRate, _ := args["growth_rate"].(float64)
+		trendDir, _ := args["trend_direction"].(string)
+		volatility, _ := args["volatility_level"].(string)
+		prediction, _ := args["prediction"].(float64)
+		
+		return fmt.Sprintf("Strategic Report Generated:\n"+
+			"- YoY Growth: %.1f%%\n"+
+			"- Trend: %s\n"+
+			"- Volatility: %s\n"+
+			"- Q1-2025 Forecast: %.2f million\n"+
+			"- Recommendation: Continue growth strategy with focus on Q4 seasonality",
+			growthRate, trendDir, volatility, prediction)
+		
+	default:
+		return "Tool execution completed"
+	}
+}
\ No newline at end of file
diff --git a/examples/googleai-reasoning-caching/main.go b/examples/googleai-reasoning-caching/main.go
new file mode 100644
index 00000000..29559ccd
--- /dev/null
+++ b/examples/googleai-reasoning-caching/main.go
@@ -0,0 +1,199 @@
+package main
+
+import (
+	"context"
+	"fmt"
+	"log"
+	"os"
+	"strings"
+	"time"
+
+	"github.com/google/generative-ai-go/genai"
+	"github.com/tmc/langchaingo/llms"
+	"github.com/tmc/langchaingo/llms/googleai"
+)
+
+func main() {
+	apiKey := os.Getenv("GOOGLE_API_KEY")
+	if apiKey == "" {
+		log.Fatal("Please set GOOGLE_API_KEY environment variable")
+	}
+
+	ctx := context.Background()
+
+	fmt.Println("=== Google AI Reasoning & Caching Demo ===")
+	fmt.Println()
+
+	// Part 1: Demonstrate reasoning support
+	demonstrateReasoning(ctx, apiKey)
+
+	fmt.Println()
+	fmt.Println(strings.Repeat("=", 60))
+	fmt.Println()
+
+	// Part 2: Demonstrate caching support
+	demonstrateCaching(ctx, apiKey)
+}
+
+func demonstrateReasoning(ctx context.Context, apiKey string) {
+	fmt.Println("Part 1: Reasoning Support")
+	fmt.Println(strings.Repeat("-", 40))
+
+	// Create client with Gemini 2.0 Flash (supports reasoning)
+	client, err := googleai.New(ctx,
+		googleai.WithAPIKey(apiKey),
+		googleai.WithDefaultModel("gemini-2.0-flash"),
+	)
+	if err != nil {
+		log.Fatalf("Failed to create client: %v", err)
+	}
+	defer client.Close()
+
+	// Check if model supports reasoning
+	if reasoner, ok := interface{}(client).(llms.ReasoningModel); ok {
+		fmt.Printf("Model supports reasoning: %v\n", reasoner.SupportsReasoning())
+	}
+
+	// Test with a complex reasoning problem
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart(`A farmer has 17 sheep. All but 9 of them run away. 
+				How many sheep does the farmer have left? Think step by step.`),
+			},
+		},
+	}
+
+	fmt.Println("\nSending reasoning query...")
+	resp, err := client.GenerateContent(ctx, messages,
+		llms.WithMaxTokens(300),
+		llms.WithThinkingMode(llms.ThinkingModeMedium), // Note: Google AI may not use this yet
+	)
+	if err != nil {
+		log.Printf("Error generating content: %v", err)
+		return
+	}
+
+	if len(resp.Choices) > 0 {
+		fmt.Println("\nResponse:")
+		fmt.Println(resp.Choices[0].Content)
+
+		// Show token usage
+		if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+			if tokens, ok := genInfo["TotalTokens"].(int32); ok {
+				fmt.Printf("\nTotal tokens used: %d\n", tokens)
+			}
+		}
+	}
+}
+
+func demonstrateCaching(ctx context.Context, apiKey string) {
+	fmt.Println("Part 2: Caching Support")
+	fmt.Println(strings.Repeat("-", 40))
+	fmt.Println("Note: Google AI caching requires pre-creating cached content")
+	fmt.Println()
+
+	helper := setupCachingHelper(ctx, apiKey)
+	cached := createCachedContent(ctx, helper)
+	runCachedRequests(ctx, apiKey, cached.Name)
+}
+
+func setupCachingHelper(ctx context.Context, apiKey string) *googleai.CachingHelper {
+	helper, err := googleai.NewCachingHelper(ctx, googleai.WithAPIKey(apiKey))
+	if err != nil {
+		log.Fatalf("Failed to create caching helper: %v", err)
+	}
+	return helper
+}
+
+func createCachedContent(ctx context.Context, helper *googleai.CachingHelper) *genai.CachedContent {
+	// Create a large context to cache
+	largeContext := `You are an expert Go programming assistant.
+	You have deep knowledge of Go best practices, performance optimization, and idiomatic code patterns.
+	Always consider:
+	- Error handling patterns
+	- Goroutine safety and concurrency
+	- Memory efficiency
+	- Code readability and maintainability
+	` + strings.Repeat("Always write clean, efficient, and well-documented code. ", 50)
+
+	fmt.Println("Creating cached content...")
+	cached, err := helper.CreateCachedContent(ctx, "gemini-2.0-flash",
+		[]llms.MessageContent{
+			{
+				Role: llms.ChatMessageTypeSystem,
+				Parts: []llms.ContentPart{
+					llms.TextPart(largeContext),
+				},
+			},
+		},
+		5*time.Minute, // Cache for 5 minutes
+	)
+	if err != nil {
+		log.Printf("Failed to create cached content: %v", err)
+		return nil
+	}
+	defer func() {
+		if err := helper.DeleteCachedContent(ctx, cached.Name); err != nil {
+			log.Printf("Failed to delete cached content: %v", err)
+		}
+	}()
+
+	fmt.Printf("Cached content created: %s\n", cached.Name)
+	fmt.Printf("Token count: %d\n", cached.UsageMetadata.TotalTokenCount)
+	return cached
+}
+
+func runCachedRequests(ctx context.Context, apiKey, cachedContentName string) {
+	client, err := googleai.New(ctx, googleai.WithAPIKey(apiKey))
+	if err != nil {
+		log.Fatalf("Failed to create client: %v", err)
+	}
+	defer client.Close()
+
+	questions := []string{
+		"What's the best way to handle errors in Go?",
+		"How do I avoid goroutine leaks?",
+	}
+
+	for i, question := range questions {
+		fmt.Printf("\n--- Request %d: %s ---\n", i+1, question)
+
+		messages := []llms.MessageContent{
+			{
+				Role: llms.ChatMessageTypeHuman,
+				Parts: []llms.ContentPart{
+					llms.TextPart(question),
+				},
+			},
+		}
+
+		resp, err := client.GenerateContent(ctx, messages,
+			googleai.WithCachedContent(cachedContentName),
+			llms.WithMaxTokens(200),
+		)
+		if err != nil {
+			log.Printf("Error: %v", err)
+			continue
+		}
+
+		if len(resp.Choices) > 0 {
+			// Show truncated response
+			content := resp.Choices[0].Content
+			if len(content) > 150 {
+				content = content[:150] + "..."
+			}
+			fmt.Printf("Response: %s\n", content)
+
+			// Check for cached tokens
+			if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+				if cachedTokens, ok := genInfo["CachedTokens"].(int32); ok && cachedTokens > 0 {
+					fmt.Printf("Cached tokens used: %d ✓\n", cachedTokens)
+				}
+			}
+		}
+	}
+
+	fmt.Println("\n✨ Google AI caching helps reduce costs by reusing pre-processed context!")
+}
diff --git a/examples/ollama-reasoning-caching/main.go b/examples/ollama-reasoning-caching/main.go
new file mode 100644
index 00000000..a4f03563
--- /dev/null
+++ b/examples/ollama-reasoning-caching/main.go
@@ -0,0 +1,231 @@
+package main
+
+import (
+	"context"
+	"fmt"
+	"log"
+	"os"
+	"strings"
+	"time"
+
+	"github.com/tmc/langchaingo/llms"
+	"github.com/tmc/langchaingo/llms/ollama"
+)
+
+func main() {
+	fmt.Println("=== Ollama Reasoning & Caching Demo ===")
+	fmt.Println()
+
+	// Get Ollama server URL from environment or use default
+	serverURL := os.Getenv("OLLAMA_HOST")
+	if serverURL == "" {
+		serverURL = "http://localhost:11434"
+	}
+
+	ctx := context.Background()
+
+	// Part 1: Demonstrate reasoning support
+	demonstrateReasoning(ctx, serverURL)
+
+	fmt.Println()
+	fmt.Println(strings.Repeat("=", 60))
+	fmt.Println()
+
+	// Part 2: Demonstrate context caching
+	demonstrateCaching(ctx, serverURL)
+}
+
+func demonstrateReasoning(ctx context.Context, serverURL string) {
+	fmt.Println("Part 1: Reasoning Support")
+	fmt.Println(strings.Repeat("-", 40))
+	fmt.Println("Note: Requires a reasoning model like deepseek-r1 or qwq")
+	fmt.Println()
+
+	// Try to use a reasoning model
+	modelName := "deepseek-r1:latest"
+	fmt.Printf("Attempting to use model: %s\n", modelName)
+
+	llm, err := ollama.New(
+		ollama.WithServerURL(serverURL),
+		ollama.WithModel(modelName),
+		ollama.WithPullModel(),                // Auto-pull if not available
+		ollama.WithPullTimeout(5*time.Minute), // Allow time for download
+	)
+	if err != nil {
+		log.Printf("Failed to create Ollama client: %v", err)
+		fmt.Println("Falling back to a standard model for demo...")
+
+		// Fall back to a standard model
+		modelName = "llama3:latest"
+		llm, err = ollama.New(
+			ollama.WithServerURL(serverURL),
+			ollama.WithModel(modelName),
+		)
+		if err != nil {
+			log.Fatalf("Failed to create fallback client: %v", err)
+		}
+	}
+
+	// Check if model supports reasoning
+	if reasoner, ok := interface{}(llm).(llms.ReasoningModel); ok {
+		fmt.Printf("Model supports reasoning: %v\n", reasoner.SupportsReasoning())
+	}
+
+	// Complex reasoning problem
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart(`Alice has 3 apples. Bob has twice as many apples as Alice. 
+				Charlie has 2 fewer apples than Bob. How many apples do they have in total?
+				Think through this step by step.`),
+			},
+		},
+	}
+
+	fmt.Println("\nSending reasoning query...")
+	resp, err := llm.GenerateContent(ctx, messages,
+		llms.WithMaxTokens(300),
+		llms.WithThinkingMode(llms.ThinkingModeMedium), // Enable thinking if supported
+	)
+	if err != nil {
+		log.Printf("Error generating content: %v", err)
+		return
+	}
+
+	if len(resp.Choices) > 0 {
+		fmt.Println("\nResponse:")
+		content := resp.Choices[0].Content
+		// Truncate very long responses
+		if len(content) > 500 {
+			content = content[:500] + "...\n[truncated]"
+		}
+		fmt.Println(content)
+
+		// Show token usage
+		if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+			if tokens, ok := genInfo["TotalTokens"].(int); ok {
+				fmt.Printf("\nTotal tokens used: %d\n", tokens)
+			}
+			if enabled, ok := genInfo["ThinkingEnabled"].(bool); ok && enabled {
+				fmt.Println("✓ Thinking mode was enabled")
+			}
+		}
+	}
+}
+
+func demonstrateCaching(ctx context.Context, serverURL string) {
+	fmt.Println("Part 2: Context Caching")
+	fmt.Println(strings.Repeat("-", 40))
+	fmt.Println("Using in-memory context cache to reduce redundant processing")
+	fmt.Println()
+
+	llm, cache := setupCachingClient(serverURL)
+	runCachingDemo(ctx, llm, cache)
+}
+
+func setupCachingClient(serverURL string) (llms.Model, *ollama.ContextCache) {
+	// Create Ollama client
+	llm, err := ollama.New(
+		ollama.WithServerURL(serverURL),
+		ollama.WithModel("llama3:latest"), // Use any available model
+	)
+	if err != nil {
+		log.Fatalf("Failed to create Ollama client: %v", err)
+	}
+
+	// Create context cache
+	cache := ollama.NewContextCache(10, 10*time.Minute)
+	fmt.Println("Created context cache (10 entries, 10 min TTL)")
+
+	return llm, cache
+}
+
+func runCachingDemo(ctx context.Context, llm llms.Model, cache *ollama.ContextCache) {
+	// Large system context that benefits from caching
+	systemContext := `You are an expert programming assistant specializing in Go.
+	You have deep knowledge of Go idioms, best practices, and performance optimization.
+	Always provide clear, concise, and idiomatic Go code examples.`
+
+	// Multiple questions using the same context
+	questions := []string{
+		"What's the best way to handle errors in Go?",
+		"How do I create a goroutine-safe map?",
+		"What are channels used for in Go?",
+	}
+
+	for i, question := range questions {
+		processQuestion(ctx, llm, cache, systemContext, question, i+1)
+	}
+
+	// Show cache statistics
+	entries, hits, avgSaved := cache.Stats()
+	fmt.Printf("\n=== Cache Statistics ===\n")
+	fmt.Printf("Entries: %d\n", entries)
+	fmt.Printf("Total hits: %d\n", hits)
+	fmt.Printf("Avg tokens saved per hit: %d\n", avgSaved)
+
+	if hits > 0 {
+		fmt.Println("\n✨ Context caching reduced token processing and improved response times!")
+	}
+}
+
+func processQuestion(ctx context.Context, llm llms.Model, cache *ollama.ContextCache, systemContext, question string, requestNum int) {
+	fmt.Printf("\n--- Request %d: %s ---\n", requestNum, question)
+
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeSystem,
+			Parts: []llms.ContentPart{
+				llms.TextPart(systemContext),
+			},
+		},
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart(question),
+			},
+		},
+	}
+
+	startTime := time.Now()
+	resp, err := llm.GenerateContent(ctx, messages,
+		llms.WithMaxTokens(150),
+		ollama.WithContextCache(cache), // Use context cache
+	)
+	elapsed := time.Since(startTime)
+
+	if err != nil {
+		log.Printf("Error: %v", err)
+		return
+	}
+
+	if len(resp.Choices) > 0 {
+		// Show truncated response
+		content := resp.Choices[0].Content
+		if len(content) > 200 {
+			content = content[:200] + "..."
+		}
+		fmt.Printf("Response: %s\n", content)
+
+		// Check cache status
+		if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+			if hit, ok := genInfo["CacheHit"].(bool); ok {
+				if hit {
+					fmt.Printf("✓ Cache HIT - ")
+					if cached, ok := genInfo["CachedTokens"].(int); ok {
+						fmt.Printf("reused %d tokens", cached)
+					}
+				} else {
+					fmt.Printf("✗ Cache MISS - context stored for reuse")
+				}
+				fmt.Println()
+			}
+
+			if tokens, ok := genInfo["PromptTokens"].(int); ok {
+				fmt.Printf("Prompt tokens: %d, ", tokens)
+			}
+			fmt.Printf("Response time: %v\n", elapsed)
+		}
+	}
+}
diff --git a/examples/prompt-caching/go.mod b/examples/prompt-caching/go.mod
new file mode 100644
index 00000000..da48dc16
--- /dev/null
+++ b/examples/prompt-caching/go.mod
@@ -0,0 +1,15 @@
+module github.com/tmc/langchaingo/examples/prompt-caching
+
+go 1.23.8
+
+toolchain go1.24.6
+
+require github.com/tmc/langchaingo v0.1.14-pre.3
+
+require (
+	github.com/dlclark/regexp2 v1.10.0 // indirect
+	github.com/google/uuid v1.6.0 // indirect
+	github.com/pkoukk/tiktoken-go v0.1.6 // indirect
+)
+
+replace github.com/tmc/langchaingo => ../..
diff --git a/examples/prompt-caching/go.sum b/examples/prompt-caching/go.sum
new file mode 100644
index 00000000..52f831b9
--- /dev/null
+++ b/examples/prompt-caching/go.sum
@@ -0,0 +1,22 @@
+github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc h1:U9qPSI2PIWSS1VwoXQT9A3Wy9MM3WgvqSxFWenqJduM=
+github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/dlclark/regexp2 v1.10.0 h1:+/GIL799phkJqYW+3YbOd8LCcbHzT0Pbo8zl70MHsq0=
+github.com/dlclark/regexp2 v1.10.0/go.mod h1:DHkYz0B9wPfa6wondMfaivmHpzrQ3v9q8cnmRbL6yW8=
+github.com/google/go-cmp v0.7.0 h1:wk8382ETsv4JYUZwIsn6YpYiWiBsYLSJiTsyBybVuN8=
+github.com/google/go-cmp v0.7.0/go.mod h1:pXiqmnSA92OHEEa9HXL2W4E7lf9JzCmGVUdgjX3N/iU=
+github.com/google/uuid v1.6.0 h1:NIvaJDMOsjHA8n1jAhLSgzrAzy1Hgr+hNrb57e+94F0=
+github.com/google/uuid v1.6.0/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
+github.com/pkoukk/tiktoken-go v0.1.6 h1:JF0TlJzhTbrI30wCvFuiw6FzP2+/bR+FIxUdgEAcUsw=
+github.com/pkoukk/tiktoken-go v0.1.6/go.mod h1:9NiV+i9mJKGj1rYOT+njbv+ZwA/zJxYdewGl6qVatpg=
+github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 h1:Jamvg5psRIccs7FGNTlIRMkT8wgtp5eCXdBlqhYGL6U=
+github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
+github.com/stretchr/objx v0.5.2 h1:xuMeJ0Sdp5ZMRXx/aWO6RZxdr3beISkG5/G/aIRr3pY=
+github.com/stretchr/objx v0.5.2/go.mod h1:FRsXN1f5AsAjCGJKqEizvkpNtU+EGNCLh3NxZ/8L+MA=
+github.com/stretchr/testify v1.10.0 h1:Xv5erBjTwe/5IxqUQTdXv5kgmIvbHo3QQyRwhJsOfJA=
+github.com/stretchr/testify v1.10.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
+gopkg.in/yaml.v2 v2.4.0 h1:D8xgwECY7CYvx+Y2n4sBz93Jn9JRvxdiyyo8CTfuKaY=
+gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
+gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
+gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
+sigs.k8s.io/yaml v1.3.0 h1:a2VclLzOGrwOHDiV8EfBGhvjHvP46CtW5j6POvhYGGo=
+sigs.k8s.io/yaml v1.3.0/go.mod h1:GeOyir5tyXNByN85N/dRIT9es5UQNerPYEKK56eTBm8=
diff --git a/examples/prompt-caching/main.go b/examples/prompt-caching/main.go
new file mode 100644
index 00000000..669d4f8e
--- /dev/null
+++ b/examples/prompt-caching/main.go
@@ -0,0 +1,251 @@
+package main
+
+import (
+	"context"
+	"fmt"
+	"os"
+	"strings"
+
+	"github.com/tmc/langchaingo/llms"
+	"github.com/tmc/langchaingo/llms/anthropic"
+)
+
+func main() {
+	ctx := context.Background()
+
+	fmt.Println("=== Prompt Caching Demo ===")
+	fmt.Println("Demonstrating cost savings with Anthropic's prompt caching")
+	fmt.Println()
+
+	if apiKey := os.Getenv("ANTHROPIC_API_KEY"); apiKey == "" {
+		fmt.Println("Error: ANTHROPIC_API_KEY environment variable not set")
+		fmt.Println("\nTo run this demo:")
+		fmt.Println("  export ANTHROPIC_API_KEY='your-api-key'")
+		fmt.Println("  go run main.go")
+		return
+	}
+
+	// Initialize Anthropic client  
+	llm, err := anthropic.New(anthropic.WithModel("claude-3-5-sonnet-20241022"))
+	if err != nil {
+		fmt.Printf("Error initializing Anthropic: %v\n", err)
+		return
+	}
+
+	// Large context that will be cached (minimum 1024 tokens for caching)
+	largeContext := `You are an expert software architect with deep knowledge of system design patterns.
+
+## System Design Patterns Reference
+
+### 1. Microservices Architecture
+- Service decomposition based on business capabilities
+- Independent deployment and scaling
+- Service discovery and registration
+- API Gateway pattern for unified entry point
+- Circuit breaker for fault tolerance
+- Event-driven communication via message queues
+- Database per service for data isolation
+- Saga pattern for distributed transactions
+
+### 2. Event-Driven Architecture
+- Event sourcing for audit trails
+- CQRS (Command Query Responsibility Segregation)
+- Event streaming with Apache Kafka or similar
+- Event store for event persistence
+- Projections for read models
+- Eventual consistency considerations
+
+### 3. Caching Strategies
+- Cache-aside (lazy loading)
+- Write-through caching
+- Write-behind caching
+- Distributed caching with Redis/Memcached
+- CDN for static content
+- Application-level caching
+- Database query result caching
+
+### 4. Load Balancing
+- Round-robin distribution
+- Least connections algorithm
+- IP hash for session affinity
+- Weighted distribution
+- Health checks and failover
+- Geographic load balancing
+
+### 5. Data Storage Patterns
+- SQL vs NoSQL selection criteria
+- Sharding for horizontal scaling
+- Read replicas for read-heavy workloads
+- Master-slave replication
+- Multi-master replication
+- Time-series databases for metrics
+- Object storage for large files
+
+### 6. Security Patterns
+- Authentication vs Authorization
+- OAuth 2.0 and OpenID Connect
+- JWT tokens for stateless auth
+- API key management
+- Rate limiting and throttling
+- WAF (Web Application Firewall)
+- Encryption at rest and in transit
+
+### 7. Monitoring and Observability
+- Distributed tracing (OpenTelemetry)
+- Centralized logging (ELK stack)
+- Metrics collection (Prometheus)
+- Alerting and incident management
+- Performance monitoring
+- Error tracking and reporting
+
+### 8. Deployment Patterns
+- Blue-green deployments
+- Canary releases
+- Feature flags
+- Rolling updates
+- Immutable infrastructure
+- Infrastructure as Code (Terraform)
+- Container orchestration (Kubernetes)
+
+When answering questions, consider these patterns and provide specific, actionable recommendations.`
+
+	fmt.Println("Context Size:", len(largeContext), "characters")
+	fmt.Println("(Approximately", len(strings.Fields(largeContext)), "words)")
+	fmt.Println()
+
+	// Series of questions using the same cached context
+	questions := []string{
+		"What caching strategy would you recommend for a read-heavy e-commerce product catalog?",
+		"How should I implement authentication for a microservices architecture?",
+		"What's the best approach for handling distributed transactions across services?",
+		"How can I ensure high availability for a global application?",
+	}
+
+	var totalCachedTokens, totalSavedTokens int
+
+	for i, question := range questions {
+		fmt.Printf("%s\n", strings.Repeat("=", 60))
+		fmt.Printf("Request %d: %s\n", i+1, question)
+		fmt.Printf("%s\n", strings.Repeat("-", 60))
+
+		messages := []llms.MessageContent{
+			{
+				Role: llms.ChatMessageTypeSystem,
+				Parts: []llms.ContentPart{
+					// Mark the large context for caching
+					llms.WithCacheControl(llms.TextPart(largeContext), anthropic.EphemeralCache()),
+				},
+			},
+			{
+				Role: llms.ChatMessageTypeHuman,
+				Parts: []llms.ContentPart{
+					llms.TextPart(question),
+				},
+			},
+		}
+
+		resp, err := llm.GenerateContent(ctx, messages,
+			llms.WithMaxTokens(200),
+			anthropic.WithPromptCaching(), // Enable prompt caching beta feature
+		)
+
+		if err != nil {
+			fmt.Printf("Error: %v\n\n", err)
+			continue
+		}
+
+		// Display response (truncated)
+		content := resp.Choices[0].Content
+		if len(content) > 250 {
+			content = content[:250] + "..."
+		}
+		fmt.Printf("\nResponse: %s\n", content)
+
+		// Display caching metrics
+		if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+			// Extract cache token information manually from generation info
+			usage := extractCacheUsage(genInfo)
+
+			fmt.Printf("\nToken Usage:\n")
+			fmt.Printf("  Input Tokens:  %d\n", usage.InputTokens)
+			fmt.Printf("  Output Tokens: %d\n", usage.OutputTokens)
+
+			if usage.CacheCreationInputTokens > 0 {
+				fmt.Printf("  Cache Creation: %d tokens (25%% premium for initial caching)\n",
+					usage.CacheCreationInputTokens)
+			}
+
+			if usage.CachedInputTokens > 0 {
+				fmt.Printf("  Cached Tokens: %d (%.0f%% discount applied) ✓\n",
+					usage.CachedInputTokens, usage.CacheDiscountPercent)
+
+				savedTokens := int(float64(usage.CachedInputTokens) * (usage.CacheDiscountPercent / 100.0))
+				totalCachedTokens += usage.CachedInputTokens
+				totalSavedTokens += savedTokens
+
+				fmt.Printf("  Token Savings: %d tokens\n", savedTokens)
+			} else if i > 0 {
+				fmt.Println("  Cache Status: MISS (context not cached)")
+			} else {
+				fmt.Println("  Cache Status: CREATING (first request)")
+			}
+		}
+		fmt.Println()
+	}
+
+	// Display summary
+	fmt.Printf("%s\n", strings.Repeat("=", 60))
+	fmt.Println("CACHING SUMMARY")
+	fmt.Printf("%s\n", strings.Repeat("=", 60))
+	fmt.Printf("Total Requests:       %d\n", len(questions))
+	fmt.Printf("Total Cached Tokens:  %d\n", totalCachedTokens)
+	fmt.Printf("Total Token Savings:  %d\n", totalSavedTokens)
+	if totalCachedTokens > 0 {
+		fmt.Printf("Average Discount:     90%%\n")
+		fmt.Printf("\nCost Reduction:       ~%.0f%% on input tokens after first request\n",
+			90.0) // Anthropic provides 90% discount on cached tokens
+	}
+
+	fmt.Println("\nKey Benefits:")
+	fmt.Println("✓ Significant cost reduction for repeated context")
+	fmt.Println("✓ Faster response times (pre-processed context)")
+	fmt.Println("✓ Consistent context across multiple queries")
+	fmt.Println("✓ Ideal for chatbots, Q&A systems, and analysis tools")
+}
+
+// CacheUsage represents token usage with caching information
+type CacheUsage struct {
+	InputTokens              int
+	OutputTokens             int
+	CacheCreationInputTokens int
+	CachedInputTokens        int
+	CacheDiscountPercent     float64
+}
+
+// extractCacheUsage extracts cache-related token information from generation info
+func extractCacheUsage(genInfo map[string]any) *CacheUsage {
+	usage := &CacheUsage{}
+
+	// Standard token fields
+	if v, ok := genInfo["InputTokens"].(int); ok {
+		usage.InputTokens = v
+	}
+	if v, ok := genInfo["OutputTokens"].(int); ok {
+		usage.OutputTokens = v
+	}
+
+	// Cache-specific fields (Anthropic)
+	if v, ok := genInfo["CacheCreationInputTokens"].(int); ok {
+		usage.CacheCreationInputTokens = v
+	}
+	if v, ok := genInfo["CacheReadInputTokens"].(int); ok {
+		usage.CachedInputTokens = v
+	}
+
+	// Calculate discount (Anthropic provides 90% discount on cached tokens)
+	if usage.CachedInputTokens > 0 {
+		usage.CacheDiscountPercent = 90.0
+	}
+
+	return usage
+}
diff --git a/examples/reasoning-tokens/go.mod b/examples/reasoning-tokens/go.mod
new file mode 100644
index 00000000..967370f6
--- /dev/null
+++ b/examples/reasoning-tokens/go.mod
@@ -0,0 +1,15 @@
+module github.com/tmc/langchaingo/examples/reasoning-tokens
+
+go 1.23.8
+
+toolchain go1.24.6
+
+require github.com/tmc/langchaingo v0.1.14-pre.3
+
+require (
+	github.com/dlclark/regexp2 v1.10.0 // indirect
+	github.com/google/uuid v1.6.0 // indirect
+	github.com/pkoukk/tiktoken-go v0.1.6 // indirect
+)
+
+replace github.com/tmc/langchaingo => ../..
diff --git a/examples/reasoning-tokens/go.sum b/examples/reasoning-tokens/go.sum
new file mode 100644
index 00000000..52f831b9
--- /dev/null
+++ b/examples/reasoning-tokens/go.sum
@@ -0,0 +1,22 @@
+github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc h1:U9qPSI2PIWSS1VwoXQT9A3Wy9MM3WgvqSxFWenqJduM=
+github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/dlclark/regexp2 v1.10.0 h1:+/GIL799phkJqYW+3YbOd8LCcbHzT0Pbo8zl70MHsq0=
+github.com/dlclark/regexp2 v1.10.0/go.mod h1:DHkYz0B9wPfa6wondMfaivmHpzrQ3v9q8cnmRbL6yW8=
+github.com/google/go-cmp v0.7.0 h1:wk8382ETsv4JYUZwIsn6YpYiWiBsYLSJiTsyBybVuN8=
+github.com/google/go-cmp v0.7.0/go.mod h1:pXiqmnSA92OHEEa9HXL2W4E7lf9JzCmGVUdgjX3N/iU=
+github.com/google/uuid v1.6.0 h1:NIvaJDMOsjHA8n1jAhLSgzrAzy1Hgr+hNrb57e+94F0=
+github.com/google/uuid v1.6.0/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
+github.com/pkoukk/tiktoken-go v0.1.6 h1:JF0TlJzhTbrI30wCvFuiw6FzP2+/bR+FIxUdgEAcUsw=
+github.com/pkoukk/tiktoken-go v0.1.6/go.mod h1:9NiV+i9mJKGj1rYOT+njbv+ZwA/zJxYdewGl6qVatpg=
+github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 h1:Jamvg5psRIccs7FGNTlIRMkT8wgtp5eCXdBlqhYGL6U=
+github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
+github.com/stretchr/objx v0.5.2 h1:xuMeJ0Sdp5ZMRXx/aWO6RZxdr3beISkG5/G/aIRr3pY=
+github.com/stretchr/objx v0.5.2/go.mod h1:FRsXN1f5AsAjCGJKqEizvkpNtU+EGNCLh3NxZ/8L+MA=
+github.com/stretchr/testify v1.10.0 h1:Xv5erBjTwe/5IxqUQTdXv5kgmIvbHo3QQyRwhJsOfJA=
+github.com/stretchr/testify v1.10.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
+gopkg.in/yaml.v2 v2.4.0 h1:D8xgwECY7CYvx+Y2n4sBz93Jn9JRvxdiyyo8CTfuKaY=
+gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
+gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
+gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
+sigs.k8s.io/yaml v1.3.0 h1:a2VclLzOGrwOHDiV8EfBGhvjHvP46CtW5j6POvhYGGo=
+sigs.k8s.io/yaml v1.3.0/go.mod h1:GeOyir5tyXNByN85N/dRIT9es5UQNerPYEKK56eTBm8=
diff --git a/examples/reasoning-tokens/main.go b/examples/reasoning-tokens/main.go
new file mode 100644
index 00000000..16e2b53c
--- /dev/null
+++ b/examples/reasoning-tokens/main.go
@@ -0,0 +1,179 @@
+package main
+
+import (
+	"context"
+	"fmt"
+	"os"
+	"strings"
+
+	"github.com/tmc/langchaingo/llms"
+	"github.com/tmc/langchaingo/llms/anthropic"
+	"github.com/tmc/langchaingo/llms/openai"
+)
+
+func main() {
+	ctx := context.Background()
+
+	fmt.Println("=== Multi-Backend Reasoning/Thinking Tokens Demo ===")
+	fmt.Println("Comparing reasoning capabilities across LLM providers")
+	fmt.Println()
+
+	// Complex reasoning prompt that benefits from step-by-step thinking
+	prompt := `A farmer needs to cross a river with a fox, a chicken, and a bag of grain. 
+The boat can only carry the farmer and one item at a time. 
+If left alone, the fox will eat the chicken, and the chicken will eat the grain. 
+How can the farmer get everything across safely? Think through this step-by-step.`
+
+	// Test with OpenAI o1-mini (reasoning model)
+	if apiKey := os.Getenv("OPENAI_API_KEY"); apiKey != "" {
+		fmt.Println(strings.Repeat("=", 60))
+		fmt.Println("OpenAI o1-mini (Reasoning Model)")
+		fmt.Println(strings.Repeat("=", 60))
+
+		llm, err := openai.New(openai.WithModel("o1-mini"))
+		if err != nil {
+			fmt.Printf("Error initializing OpenAI: %v\n\n", err)
+		} else {
+			testReasoning(ctx, llm, "o1-mini", prompt, true)
+		}
+	}
+
+	// Test with Anthropic Claude 3.7 (supports extended thinking)
+	if apiKey := os.Getenv("ANTHROPIC_API_KEY"); apiKey != "" {
+		fmt.Println(strings.Repeat("=", 60))
+		fmt.Println("Anthropic Claude 3.7 Sonnet (Extended Thinking)")
+		fmt.Println(strings.Repeat("=", 60))
+
+		llm, err := anthropic.New(anthropic.WithModel("claude-3-7-sonnet-20250219"))
+		if err != nil {
+			fmt.Printf("Error initializing Anthropic: %v\n\n", err)
+		} else {
+			testReasoning(ctx, llm, "claude-3-7-sonnet-20250219", prompt, true)
+		}
+	}
+
+	// Compare with standard models (no reasoning tokens)
+	fmt.Println(strings.Repeat("=", 60))
+	fmt.Println("COMPARISON: Standard Models (No Reasoning)")
+	fmt.Println(strings.Repeat("=", 60))
+
+	if apiKey := os.Getenv("OPENAI_API_KEY"); apiKey != "" {
+		fmt.Println("\n--- OpenAI GPT-4 Turbo ---")
+		llm, err := openai.New(openai.WithModel("gpt-4-turbo-preview"))
+		if err != nil {
+			fmt.Printf("Error: %v\n", err)
+		} else {
+			testReasoning(ctx, llm, "gpt-4-turbo-preview", prompt, false)
+		}
+	}
+
+	if apiKey := os.Getenv("ANTHROPIC_API_KEY"); apiKey != "" {
+		fmt.Println("\n--- Anthropic Claude 3 Sonnet ---")
+		llm, err := anthropic.New(anthropic.WithModel("claude-3-sonnet-20240229"))
+		if err != nil {
+			fmt.Printf("Error: %v\n", err)
+		} else {
+			testReasoning(ctx, llm, "claude-3-sonnet-20240229", prompt, false)
+		}
+	}
+
+	fmt.Println("\n" + strings.Repeat("=", 60))
+	fmt.Println("Demo Complete!")
+	fmt.Println("\nKey Observations:")
+	fmt.Println("- Reasoning models use additional 'thinking' tokens for internal processing")
+	fmt.Println("- These tokens improve response quality but aren't shown in the output")
+	fmt.Println("- Standard models generate all tokens as visible output")
+}
+
+func testReasoning(ctx context.Context, llm llms.Model, modelName string, prompt string, expectReasoning bool) {
+	// Check if model supports reasoning
+	supportsReasoning := false
+	if reasoner, ok := llm.(llms.ReasoningModel); ok {
+		supportsReasoning = reasoner.SupportsReasoning()
+	}
+
+	fmt.Printf("Model: %s\n", modelName)
+	fmt.Printf("Reasoning Support: %v\n", supportsReasoning)
+
+	if expectReasoning && !supportsReasoning {
+		fmt.Println("Note: This model version may not support reasoning tokens yet")
+	}
+
+	messages := []llms.MessageContent{
+		{
+			Role:  llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{llms.TextPart(prompt)},
+		},
+	}
+
+	// Configure thinking mode based on model capabilities
+	opts := []llms.CallOption{
+		llms.WithMaxTokens(500),
+	}
+
+	if supportsReasoning {
+		// Use high thinking mode for complex reasoning
+		opts = append(opts, llms.WithThinkingMode(llms.ThinkingModeHigh))
+		fmt.Println("Thinking Mode: HIGH (maximum reasoning depth)")
+	} else {
+		fmt.Println("Thinking Mode: NONE (standard generation)")
+	}
+
+	fmt.Print("\nGenerating response... ")
+	resp, err := llm.GenerateContent(ctx, messages, opts...)
+	if err != nil {
+		fmt.Printf("Error: %v\n\n", err)
+		return
+	}
+	fmt.Println("done")
+
+	// Display response
+	content := resp.Choices[0].Content
+	fmt.Println("\n--- Response ---")
+	if len(content) > 400 {
+		fmt.Println(content[:400] + "...\n[truncated]")
+	} else {
+		fmt.Println(content)
+	}
+
+	// Display detailed token metrics
+	fmt.Println("\n--- Token Metrics ---")
+	if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+		// Extract standard token usage
+		var inputTokens, outputTokens, totalTokens int
+		if v, ok := genInfo["PromptTokens"].(int); ok {
+			inputTokens = v
+		}
+		if v, ok := genInfo["CompletionTokens"].(int); ok {
+			outputTokens = v
+		}
+		if v, ok := genInfo["TotalTokens"].(int); ok {
+			totalTokens = v
+		}
+
+		fmt.Printf("Input Tokens:      %d\n", inputTokens)
+		fmt.Printf("Output Tokens:     %d\n", outputTokens)
+		fmt.Printf("Total Tokens:      %d\n", totalTokens)
+
+		// Extract thinking-specific tokens
+		usage := llms.ExtractThinkingTokens(genInfo)
+		if usage != nil && usage.ThinkingTokens > 0 {
+			fmt.Printf("\nReasoning Breakdown:\n")
+			fmt.Printf("  Thinking Tokens:   %d\n", usage.ThinkingTokens)
+			fmt.Printf("  Visible Output:    %d\n", outputTokens-usage.ThinkingTokens)
+			fmt.Printf("  Thinking Ratio:    %.1f%% of output\n",
+				float64(usage.ThinkingTokens)/float64(outputTokens)*100)
+
+			if usage.ThinkingBudgetAllocated > 0 {
+				fmt.Printf("  Budget Allocated:  %d\n", usage.ThinkingBudgetAllocated)
+				fmt.Printf("  Budget Used:       %d\n", usage.ThinkingBudgetUsed)
+				fmt.Printf("  Budget Efficiency: %.1f%%\n",
+					float64(usage.ThinkingBudgetUsed)/float64(usage.ThinkingBudgetAllocated)*100)
+			}
+		} else if supportsReasoning {
+			fmt.Println("\nNote: Model supports reasoning but no thinking tokens were used.")
+			fmt.Println("      This may happen for simpler prompts or certain model versions.")
+		}
+	}
+	fmt.Println()
+}
diff --git a/llms/anthropic/anthropicllm.go b/llms/anthropic/anthropicllm.go
index 563cf869..37a1285c 100644
--- a/llms/anthropic/anthropicllm.go
+++ b/llms/anthropic/anthropicllm.go
@@ -7,6 +7,7 @@ import (
 	"errors"
 	"fmt"
 	"os"
+	"strings"
 
 	"github.com/tmc/langchaingo/callbacks"
 	"github.com/tmc/langchaingo/httputil"
@@ -32,9 +33,13 @@ const (
 type LLM struct {
 	CallbacksHandler callbacks.Handler
 	client           *anthropicclient.Client
+	model            string // Track current model for reasoning detection
 }
 
-var _ llms.Model = (*LLM)(nil)
+var (
+	_ llms.Model          = (*LLM)(nil)
+	_ llms.ReasoningModel = (*LLM)(nil)
+)
 
 // New returns a new Anthropic LLM.
 func New(opts ...Option) (*LLM, error) {
@@ -44,6 +49,7 @@ func New(opts ...Option) (*LLM, error) {
 	}
 	return &LLM{
 		client: c,
+		model:  c.Model, // Store the model for reasoning detection
 	}, nil
 }
 
@@ -136,6 +142,9 @@ func generateMessagesContent(ctx context.Context, o *LLM, messages []llms.Messag
 	}
 
 	tools := toolsToTools(opts.Tools)
+
+	betaHeaders, thinking := extractThinkingOptions(o, opts)
+
 	result, err := o.client.CreateMessage(ctx, &anthropicclient.MessageRequest{
 		Model:         opts.Model,
 		Messages:      chatMessages,
@@ -145,6 +154,8 @@ func generateMessagesContent(ctx context.Context, o *LLM, messages []llms.Messag
 		Temperature:   opts.Temperature,
 		TopP:          opts.TopP,
 		Tools:         tools,
+		Thinking:      thinking,
+		BetaHeaders:   betaHeaders,
 		StreamingFunc: opts.StreamingFunc,
 	})
 	if err != nil {
@@ -153,6 +164,11 @@ func generateMessagesContent(ctx context.Context, o *LLM, messages []llms.Messag
 		}
 		return nil, fmt.Errorf("anthropic: failed to create message: %w", err)
 	}
+	return processAnthropicResponse(result)
+}
+
+// processAnthropicResponse converts Anthropic API response to standard ContentResponse
+func processAnthropicResponse(result *anthropicclient.MessageResponsePayload) (*llms.ContentResponse, error) {
 	if result == nil || len(result.Content) == 0 {
 		return nil, ErrEmptyResponse
 	}
@@ -162,12 +178,20 @@ func generateMessagesContent(ctx context.Context, o *LLM, messages []llms.Messag
 		switch content.GetType() {
 		case "text":
 			if textContent, ok := content.(*anthropicclient.TextContent); ok {
+				// Extract thinking content from the response text
+				thinkingContent, outputContent := extractThinkingFromText(textContent.Text)
+
 				choices[i] = &llms.ContentChoice{
 					Content:    textContent.Text,
 					StopReason: result.StopReason,
 					GenerationInfo: map[string]any{
-						"InputTokens":  result.Usage.InputTokens,
-						"OutputTokens": result.Usage.OutputTokens,
+						"InputTokens":              result.Usage.InputTokens,
+						"OutputTokens":             result.Usage.OutputTokens,
+						"CacheCreationInputTokens": result.Usage.CacheCreationInputTokens,
+						"CacheReadInputTokens":     result.Usage.CacheReadInputTokens,
+						// Standardized fields for cross-provider compatibility
+						"ThinkingContent": thinkingContent, // Standardized field
+						"OutputContent":   outputContent,   // Standardized field
 					},
 				}
 			} else {
@@ -191,22 +215,40 @@ func generateMessagesContent(ctx context.Context, o *LLM, messages []llms.Messag
 					},
 					StopReason: result.StopReason,
 					GenerationInfo: map[string]any{
-						"InputTokens":  result.Usage.InputTokens,
-						"OutputTokens": result.Usage.OutputTokens,
+						"InputTokens":              result.Usage.InputTokens,
+						"OutputTokens":             result.Usage.OutputTokens,
+						"CacheCreationInputTokens": result.Usage.CacheCreationInputTokens,
+						"CacheReadInputTokens":     result.Usage.CacheReadInputTokens,
 					},
 				}
 			} else {
 				return nil, fmt.Errorf("anthropic: %w for tool use message %T", ErrInvalidContentType, content)
 			}
+		case "thinking":
+			if thinkingContent, ok := content.(*anthropicclient.ThinkingContent); ok {
+				choices[i] = &llms.ContentChoice{
+					Content:    "", // Thinking content is not included in output
+					StopReason: result.StopReason,
+					GenerationInfo: map[string]any{
+						"ThinkingContent":          thinkingContent.Thinking,
+						"ThinkingSignature":        thinkingContent.Signature,
+						"InputTokens":              result.Usage.InputTokens,
+						"OutputTokens":             result.Usage.OutputTokens,
+						"CacheCreationInputTokens": result.Usage.CacheCreationInputTokens,
+						"CacheReadInputTokens":     result.Usage.CacheReadInputTokens,
+					},
+				}
+			} else {
+				return nil, fmt.Errorf("anthropic: %w for thinking message %T", ErrInvalidContentType, content)
+			}
 		default:
 			return nil, fmt.Errorf("anthropic: %w: %v", ErrUnsupportedContentType, content.GetType())
 		}
 	}
 
-	resp := &llms.ContentResponse{
+	return &llms.ContentResponse{
 		Choices: choices,
-	}
-	return resp, nil
+	}, nil
 }
 
 func toolsToTools(tools []llms.Tool) []anthropicclient.Tool {
@@ -260,9 +302,19 @@ func processMessages(messages []llms.MessageContent) ([]anthropicclient.ChatMess
 }
 
 func handleSystemMessage(msg llms.MessageContent) (string, error) {
-	if textContent, ok := msg.Parts[0].(llms.TextContent); ok {
+	// Handle both direct TextContent and CachedContent wrapper
+	part := msg.Parts[0]
+
+	// If it's cached content, unwrap it
+	if cached, ok := part.(llms.CachedContent); ok {
+		part = cached.ContentPart
+	}
+
+	// Extract text from the part
+	if textContent, ok := part.(llms.TextContent); ok {
 		return textContent.Text, nil
 	}
+
 	return "", fmt.Errorf("anthropic: %w for system message", ErrInvalidContentType)
 }
 
@@ -271,6 +323,36 @@ func handleHumanMessage(msg llms.MessageContent) (anthropicclient.ChatMessage, e
 
 	for _, part := range msg.Parts {
 		switch p := part.(type) {
+		case llms.CachedContent:
+			// Handle cached content with cache control
+			var cacheControl *anthropicclient.CacheControl
+			if p.CacheControl != nil {
+				cacheControl = &anthropicclient.CacheControl{
+					Type: p.CacheControl.Type,
+				}
+			}
+
+			// Process the wrapped content
+			switch wrapped := p.ContentPart.(type) {
+			case llms.TextContent:
+				contents = append(contents, &anthropicclient.TextContent{
+					Type:         "text",
+					Text:         wrapped.Text,
+					CacheControl: cacheControl,
+				})
+			case llms.BinaryContent:
+				contents = append(contents, &anthropicclient.ImageContent{
+					Type: "image",
+					Source: anthropicclient.ImageSource{
+						Type:      "base64",
+						MediaType: wrapped.MIMEType,
+						Data:      base64.StdEncoding.EncodeToString(wrapped.Data),
+					},
+					CacheControl: cacheControl,
+				})
+			default:
+				return anthropicclient.ChatMessage{}, fmt.Errorf("anthropic: unsupported cached content part type: %T", wrapped)
+			}
 		case llms.TextContent:
 			contents = append(contents, &anthropicclient.TextContent{
 				Type: "text",
@@ -352,3 +434,134 @@ func handleToolMessage(msg llms.MessageContent) (anthropicclient.ChatMessage, er
 	}
 	return anthropicclient.ChatMessage{}, fmt.Errorf("anthropic: %w for tool message", ErrInvalidContentType)
 }
+
+// SupportsReasoning implements the ReasoningModel interface.
+// Returns true if the current model supports extended thinking capabilities.
+func (o *LLM) SupportsReasoning() bool {
+	return supportsReasoningForModel(o.model)
+}
+
+// supportsReasoningForModel checks if a specific model supports reasoning.
+// This is a separate function to avoid race conditions when checking capabilities.
+func supportsReasoningForModel(model string) bool {
+	if model == "" {
+		return false
+	}
+
+	modelLower := strings.ToLower(model)
+
+	// Claude 3.7+ supports extended thinking
+	if strings.Contains(modelLower, "claude-3-7") ||
+		strings.Contains(modelLower, "claude-3.7") ||
+		strings.Contains(modelLower, "claude-3-7-sonnet") {
+		return true
+	}
+
+	// Claude 4+ supports extended thinking with interleaving
+	if strings.Contains(modelLower, "claude-4") ||
+		strings.Contains(modelLower, "claude-opus-4") ||
+		strings.Contains(modelLower, "claude-sonnet-4") {
+		return true
+	}
+
+	// Future Claude 5+ expected to support reasoning
+	if strings.Contains(modelLower, "claude-5") ||
+		strings.Contains(modelLower, "claude-opus-5") ||
+		strings.Contains(modelLower, "claude-sonnet-5") {
+		return true
+	}
+
+	return false
+}
+
+// extractThinkingOptions extracts thinking configuration and beta headers from call options
+func extractThinkingOptions(o *LLM, opts *llms.CallOptions) ([]string, *anthropicclient.ThinkingConfig) {
+	// Extract beta headers for prompt caching support
+	var betaHeaders []string
+	if opts.Metadata != nil {
+		if headers, ok := opts.Metadata["anthropic:beta_headers"].([]string); ok {
+			betaHeaders = headers
+		}
+	}
+
+	// Extract thinking configuration
+	var budgetTokens int
+	if opts.Metadata != nil {
+		if config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig); ok {
+			// Only set budget_tokens for models that support extended thinking
+			// Claude 3.7+ and Claude 4+ support this feature
+			currentModel := opts.Model
+			if currentModel == "" {
+				currentModel = o.model
+			}
+			if supportsReasoningForModel(currentModel) {
+				if config.BudgetTokens > 0 {
+					budgetTokens = config.BudgetTokens
+				} else if config.Mode != llms.ThinkingModeNone {
+					// Calculate budget based on mode
+					budgetTokens = llms.CalculateThinkingBudget(config.Mode, opts.MaxTokens)
+				}
+
+				// Ensure budget is within valid range for Claude 3.7+
+				if budgetTokens > 0 {
+					if budgetTokens < 1024 {
+						budgetTokens = 1024 // Minimum for Claude
+					} else if budgetTokens > 128000 {
+						budgetTokens = 128000 // Maximum for Claude (128K)
+					}
+				}
+			}
+
+			// Add interleaved thinking header if requested (Claude 4+)
+			if config.InterleaveThinking && supportsReasoningForModel(currentModel) {
+				betaHeaders = append(betaHeaders, "interleaved-thinking-2025-05-14")
+			}
+		}
+	}
+
+	// Create thinking configuration if we have a budget
+	var thinking *anthropicclient.ThinkingConfig
+	if budgetTokens > 0 {
+		thinking = &anthropicclient.ThinkingConfig{
+			Type:         "enabled",
+			BudgetTokens: budgetTokens,
+		}
+	}
+
+	return betaHeaders, thinking
+}
+
+// extractThinkingFromText extracts thinking content from Anthropic responses
+// Anthropic models often embed thinking in <thinking> tags
+func extractThinkingFromText(fullText string) (thinkingContent, outputContent string) {
+	// Look for <thinking> tags in the text
+	if strings.Contains(fullText, "<thinking>") {
+		start := strings.Index(fullText, "<thinking>")
+		end := strings.Index(fullText, "</thinking>")
+		if start >= 0 && end > start {
+			// Extract thinking content between tags
+			thinkingContent = fullText[start+10 : end] // +10 for "<thinking>"
+
+			// Extract output content (everything before and after thinking tags)
+			beforeThinking := strings.TrimSpace(fullText[:start])
+			afterThinking := ""
+			if end+12 < len(fullText) { // +12 for "</thinking>"
+				afterThinking = strings.TrimSpace(fullText[end+12:])
+			}
+
+			// Combine non-thinking content
+			if beforeThinking != "" && afterThinking != "" {
+				outputContent = beforeThinking + "\n\n" + afterThinking
+			} else if beforeThinking != "" {
+				outputContent = beforeThinking
+			} else {
+				outputContent = afterThinking
+			}
+
+			return strings.TrimSpace(thinkingContent), strings.TrimSpace(outputContent)
+		}
+	}
+
+	// If no thinking tags found, treat entire text as output
+	return "", fullText
+}
diff --git a/llms/anthropic/internal/anthropicclient/anthropicclient.go b/llms/anthropic/internal/anthropicclient/anthropicclient.go
index 59eeef9b..e08061c8 100644
--- a/llms/anthropic/internal/anthropicclient/anthropicclient.go
+++ b/llms/anthropic/internal/anthropicclient/anthropicclient.go
@@ -137,6 +137,11 @@ type MessageRequest struct {
 	StopWords   []string      `json:"stop_sequences,omitempty"`
 	Stream      bool          `json:"stream,omitempty"`
 
+	// Extended thinking parameters (Claude 3.7+)
+	Thinking *ThinkingConfig `json:"thinking,omitempty"`
+
+	// BetaHeaders are additional beta feature headers to include
+	BetaHeaders   []string                                      `json:"-"`
 	StreamingFunc func(ctx context.Context, chunk []byte) error `json:"-"`
 }
 
@@ -152,27 +157,38 @@ func (c *Client) CreateMessage(ctx context.Context, r *MessageRequest) (*Message
 		TopP:          r.TopP,
 		Tools:         r.Tools,
 		Stream:        r.Stream,
+		Thinking:      r.Thinking,
 		StreamingFunc: r.StreamingFunc,
-	})
+	}, r.BetaHeaders)
 	if err != nil {
 		return nil, err
 	}
 	return resp, nil
 }
 
-func (c *Client) setHeaders(req *http.Request) {
+func (c *Client) setHeaders(req *http.Request, betaHeaders []string) {
 	req.Header.Set("Content-Type", "application/json")
 	req.Header.Set("x-api-key", c.token) //nolint:canonicalheader
 
 	// This is necessary as per https://docs.anthropic.com/en/api/versioning
 	// If this changes frequently enough we should expose it as an option..
 	req.Header.Set("anthropic-version", "2023-06-01") // nolint:canonicalheader
-	if c.anthropicBetaHeader != "" {
+
+	// Set beta headers from request, falling back to client default
+	if len(betaHeaders) > 0 {
+		for _, header := range betaHeaders {
+			req.Header.Add("anthropic-beta", header) // nolint:canonicalheader
+		}
+	} else if c.anthropicBetaHeader != "" {
 		req.Header.Set("anthropic-beta", c.anthropicBetaHeader) // nolint:canonicalheader
 	}
 }
 
 func (c *Client) do(ctx context.Context, path string, payloadBytes []byte) (*http.Response, error) {
+	return c.doWithHeaders(ctx, path, payloadBytes, nil)
+}
+
+func (c *Client) doWithHeaders(ctx context.Context, path string, payloadBytes []byte, betaHeaders []string) (*http.Response, error) {
 	if c.baseURL == "" {
 		c.baseURL = DefaultBaseURL
 	}
@@ -184,7 +200,7 @@ func (c *Client) do(ctx context.Context, path string, payloadBytes []byte) (*htt
 		return nil, fmt.Errorf("create request: %w", err)
 	}
 
-	c.setHeaders(req)
+	c.setHeaders(req, betaHeaders)
 
 	resp, err := c.httpClient.Do(req)
 	if err != nil {
diff --git a/llms/anthropic/internal/anthropicclient/messages.go b/llms/anthropic/internal/anthropicclient/messages.go
index 94863ba2..08873dee 100644
--- a/llms/anthropic/internal/anthropicclient/messages.go
+++ b/llms/anthropic/internal/anthropicclient/messages.go
@@ -44,9 +44,18 @@ type messagePayload struct {
 	Tools       []Tool        `json:"tools,omitempty"`
 	TopP        float64       `json:"top_p,omitempty"`
 
+	// Extended thinking parameters (Claude 3.7+)
+	Thinking *ThinkingConfig `json:"thinking,omitempty"`
+
 	StreamingFunc func(ctx context.Context, chunk []byte) error `json:"-"`
 }
 
+// ThinkingConfig represents the thinking configuration for Claude 3.7+
+type ThinkingConfig struct {
+	Type         string `json:"type"` // "enabled" or "disabled"
+	BudgetTokens int    `json:"budget_tokens,omitempty"`
+}
+
 // Tool used for the request message payload.
 type Tool struct {
 	Name        string `json:"name"`
@@ -54,14 +63,20 @@ type Tool struct {
 	InputSchema any    `json:"input_schema,omitempty"`
 }
 
+// CacheControl represents Anthropic's prompt caching configuration.
+type CacheControl struct {
+	Type string `json:"type"`
+}
+
 // Content can be TextContent or ToolUseContent depending on the type.
 type Content interface {
 	GetType() string
 }
 
 type TextContent struct {
-	Type string `json:"type"`
-	Text string `json:"text"`
+	Type         string        `json:"type"`
+	Text         string        `json:"text"`
+	CacheControl *CacheControl `json:"cache_control,omitempty"`
 }
 
 func (tc TextContent) GetType() string {
@@ -69,8 +84,9 @@ func (tc TextContent) GetType() string {
 }
 
 type ImageContent struct {
-	Type   string      `json:"type"`
-	Source ImageSource `json:"source"`
+	Type         string        `json:"type"`
+	Source       ImageSource   `json:"source"`
+	CacheControl *CacheControl `json:"cache_control,omitempty"`
 }
 
 func (ic ImageContent) GetType() string {
@@ -84,10 +100,11 @@ type ImageSource struct {
 }
 
 type ToolUseContent struct {
-	Type  string                 `json:"type"`
-	ID    string                 `json:"id"`
-	Name  string                 `json:"name"`
-	Input map[string]interface{} `json:"input"`
+	Type         string                 `json:"type"`
+	ID           string                 `json:"id"`
+	Name         string                 `json:"name"`
+	Input        map[string]interface{} `json:"input"`
+	CacheControl *CacheControl          `json:"cache_control,omitempty"`
 
 	inputData string `json:"-"` // Used to gather input data when streaming
 }
@@ -97,15 +114,27 @@ func (tuc ToolUseContent) GetType() string {
 }
 
 type ToolResultContent struct {
-	Type      string `json:"type"`
-	ToolUseID string `json:"tool_use_id"`
-	Content   string `json:"content"`
+	Type         string        `json:"type"`
+	ToolUseID    string        `json:"tool_use_id"`
+	Content      string        `json:"content"`
+	CacheControl *CacheControl `json:"cache_control,omitempty"`
 }
 
 func (trc ToolResultContent) GetType() string {
 	return trc.Type
 }
 
+// ThinkingContent represents Claude's thinking/reasoning content
+type ThinkingContent struct {
+	Type      string `json:"type"`
+	Thinking  string `json:"thinking"`
+	Signature string `json:"signature,omitempty"`
+}
+
+func (tc ThinkingContent) GetType() string {
+	return tc.Type
+}
+
 type MessageResponsePayload struct {
 	Content      []Content `json:"content"`
 	ID           string    `json:"id"`
@@ -115,8 +144,10 @@ type MessageResponsePayload struct {
 	StopSequence string    `json:"stop_sequence"`
 	Type         string    `json:"type"`
 	Usage        struct {
-		InputTokens  int `json:"input_tokens"`
-		OutputTokens int `json:"output_tokens"`
+		InputTokens              int `json:"input_tokens"`
+		OutputTokens             int `json:"output_tokens"`
+		CacheCreationInputTokens int `json:"cache_creation_input_tokens,omitempty"`
+		CacheReadInputTokens     int `json:"cache_read_input_tokens,omitempty"`
 	} `json:"usage"`
 }
 
@@ -153,6 +184,12 @@ func (m *MessageResponsePayload) UnmarshalJSON(data []byte) error {
 				return err
 			}
 			m.Content = append(m.Content, tuc)
+		case "thinking":
+			tc := &ThinkingContent{}
+			if err := json.Unmarshal(raw, tc); err != nil {
+				return err
+			}
+			m.Content = append(m.Content, tc)
 		default:
 			return fmt.Errorf("unknown content type: %s\n%v", typeStruct.Type, string(raw))
 		}
@@ -187,7 +224,7 @@ func (c *Client) setMessageDefaults(payload *messagePayload) {
 	}
 }
 
-func (c *Client) createMessage(ctx context.Context, payload *messagePayload) (*MessageResponsePayload, error) {
+func (c *Client) createMessage(ctx context.Context, payload *messagePayload, betaHeaders []string) (*MessageResponsePayload, error) {
 	c.setMessageDefaults(payload)
 
 	payloadBytes, err := json.Marshal(payload)
@@ -195,7 +232,7 @@ func (c *Client) createMessage(ctx context.Context, payload *messagePayload) (*M
 		return nil, fmt.Errorf("marshal payload: %w", err)
 	}
 
-	resp, err := c.do(ctx, "/messages", payloadBytes)
+	resp, err := c.doWithHeaders(ctx, "/messages", payloadBytes, betaHeaders)
 	if err != nil {
 		return nil, err
 	}
@@ -317,6 +354,14 @@ func handleMessageStartEvent(event map[string]interface{}, response MessageRespo
 	response.Type = getString(message, "type")
 	response.Usage.InputTokens = int(inputTokens)
 
+	// Capture cache token information if present
+	if cacheCreationTokens, err := getFloat64(usage, "cache_creation_input_tokens"); err == nil {
+		response.Usage.CacheCreationInputTokens = int(cacheCreationTokens)
+	}
+	if cacheReadTokens, err := getFloat64(usage, "cache_read_input_tokens"); err == nil {
+		response.Usage.CacheReadInputTokens = int(cacheReadTokens)
+	}
+
 	return response, nil
 }
 
@@ -357,6 +402,10 @@ func handleContentBlockStartEvent(event map[string]interface{}, response Message
 				Name:  getString(contentBlock, "name"),
 				Input: input,
 			})
+		case "thinking":
+			response.Content = append(response.Content, &ThinkingContent{
+				Type: eventType,
+			})
 		default:
 			return response, fmt.Errorf("%w: unknown content block type: %s", ErrInvalidDeltaField, eventType)
 		}
@@ -390,6 +439,8 @@ func handleContentBlockDeltaEvent(ctx context.Context, event map[string]interfac
 		return handleTextDelta(ctx, delta, response, payload, index)
 	case "input_json_delta":
 		return handleJSONDelta(delta, response, index)
+	case "thinking_delta":
+		return handleThinkingDelta(delta, response, index)
 	}
 
 	return response, nil
@@ -433,6 +484,20 @@ func handleJSONDelta(delta map[string]interface{}, response MessageResponsePaylo
 	return response, nil
 }
 
+// handleThinkingDelta processes thinking delta events for content blocks.
+func handleThinkingDelta(delta map[string]interface{}, response MessageResponsePayload, index int) (MessageResponsePayload, error) {
+	thinking, ok := delta["thinking"].(string)
+	if !ok {
+		return response, ErrInvalidDeltaTextField
+	}
+	thinkingContent, ok := response.Content[index].(*ThinkingContent)
+	if !ok {
+		return response, fmt.Errorf("failed to cast to ThinkingContent at index %d", index)
+	}
+	thinkingContent.Thinking += thinking
+	return response, nil
+}
+
 func handleContentBlockStop(event map[string]interface{}, response MessageResponsePayload) (MessageResponsePayload, error) {
 	indexValue, ok := event["index"].(float64)
 	if !ok {
@@ -473,6 +538,16 @@ func handleMessageDeltaEvent(event map[string]interface{}, response MessageRespo
 	if outputTokens, ok := usage["output_tokens"].(float64); ok {
 		response.Usage.OutputTokens = int(outputTokens)
 	}
+	// Also capture cache tokens in the final message_delta event
+	if inputTokens, err := getFloat64(usage, "input_tokens"); err == nil {
+		response.Usage.InputTokens = int(inputTokens)
+	}
+	if cacheCreationTokens, err := getFloat64(usage, "cache_creation_input_tokens"); err == nil {
+		response.Usage.CacheCreationInputTokens = int(cacheCreationTokens)
+	}
+	if cacheReadTokens, err := getFloat64(usage, "cache_read_input_tokens"); err == nil {
+		response.Usage.CacheReadInputTokens = int(cacheReadTokens)
+	}
 	return response, nil
 }
 
diff --git a/llms/anthropic/llmtest_test.go b/llms/anthropic/llmtest_test.go
new file mode 100644
index 00000000..ad42be6f
--- /dev/null
+++ b/llms/anthropic/llmtest_test.go
@@ -0,0 +1,22 @@
+package anthropic
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("ANTHROPIC_API_KEY") == "" {
+		t.Skip("ANTHROPIC_API_KEY not set")
+	}
+
+	llm, err := New(WithModel("claude-3-haiku-20240307"))
+	if err != nil {
+		t.Fatalf("Failed to create Anthropic LLM: %v", err)
+	}
+
+	// Test with automatic capability discovery
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/anthropic/options.go b/llms/anthropic/options.go
new file mode 100644
index 00000000..bab806f5
--- /dev/null
+++ b/llms/anthropic/options.go
@@ -0,0 +1,111 @@
+package anthropic
+
+import (
+	"time"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+// WithPromptCaching enables Anthropic's prompt caching feature.
+// This allows frequently-used prompts and system messages to be cached for improved
+// performance and reduced costs.
+//
+// Usage:
+//
+//	llm.GenerateContent(ctx, messages,
+//	    anthropic.WithPromptCaching(),
+//	)
+func WithPromptCaching() llms.CallOption {
+	return func(opts *llms.CallOptions) {
+		if opts.Metadata == nil {
+			opts.Metadata = make(map[string]interface{})
+		}
+		opts.Metadata["anthropic:beta_headers"] = []string{"prompt-caching-2024-07-31"}
+	}
+}
+
+// WithExtendedOutput enables 128K token output for Claude 3.7+.
+// Standard models are limited to 8K tokens, but this beta feature allows
+// generating much longer responses.
+//
+// Usage:
+//
+//	llm.GenerateContent(ctx, messages,
+//	    llms.WithMaxTokens(50000),
+//	    anthropic.WithExtendedOutput(),
+//	)
+func WithExtendedOutput() llms.CallOption {
+	return func(opts *llms.CallOptions) {
+		if opts.Metadata == nil {
+			opts.Metadata = make(map[string]interface{})
+		}
+		// Add to existing headers if present
+		if existing, ok := opts.Metadata["anthropic:beta_headers"].([]string); ok {
+			opts.Metadata["anthropic:beta_headers"] = append(existing, "output-128k-2025-02-19")
+		} else {
+			opts.Metadata["anthropic:beta_headers"] = []string{"output-128k-2025-02-19"}
+		}
+	}
+}
+
+// WithInterleavedThinking enables thinking between tool calls for Claude 3.7+.
+// This allows the model to use reasoning tokens to plan tool usage and interpret results.
+//
+// Usage:
+//
+//	llm.GenerateContent(ctx, messages,
+//	    llms.WithTools(tools),
+//	    llms.WithThinkingMode(llms.ThinkingModeMedium),
+//	    anthropic.WithInterleavedThinking(),
+//	)
+func WithInterleavedThinking() llms.CallOption {
+	return func(opts *llms.CallOptions) {
+		if opts.Metadata == nil {
+			opts.Metadata = make(map[string]interface{})
+		}
+		// Add to existing headers if present
+		if existing, ok := opts.Metadata["anthropic:beta_headers"].([]string); ok {
+			opts.Metadata["anthropic:beta_headers"] = append(existing, "interleaved-thinking-2025-05-14")
+		} else {
+			opts.Metadata["anthropic:beta_headers"] = []string{"interleaved-thinking-2025-05-14"}
+		}
+	}
+}
+
+// WithBetaHeader adds a custom beta header for accessing Anthropic's experimental features.
+// This is useful for testing new features before dedicated support is added.
+//
+// Usage:
+//
+//	llm.GenerateContent(ctx, messages,
+//	    anthropic.WithBetaHeader("new-feature-2025-01-01"),
+//	)
+func WithBetaHeader(header string) llms.CallOption {
+	return func(opts *llms.CallOptions) {
+		if opts.Metadata == nil {
+			opts.Metadata = make(map[string]interface{})
+		}
+		// Add to existing headers if present
+		if existing, ok := opts.Metadata["anthropic:beta_headers"].([]string); ok {
+			opts.Metadata["anthropic:beta_headers"] = append(existing, header)
+		} else {
+			opts.Metadata["anthropic:beta_headers"] = []string{header}
+		}
+	}
+}
+
+// EphemeralCache creates a standard ephemeral cache control for Anthropic with 5-minute duration.
+func EphemeralCache() *llms.CacheControl {
+	return &llms.CacheControl{
+		Type:     "ephemeral",
+		Duration: 5 * time.Minute,
+	}
+}
+
+// EphemeralCacheOneHour creates a 1-hour ephemeral cache control for Anthropic.
+func EphemeralCacheOneHour() *llms.CacheControl {
+	return &llms.CacheControl{
+		Type:     "ephemeral",
+		Duration: time.Hour,
+	}
+}
diff --git a/llms/anthropic/options_test.go b/llms/anthropic/options_test.go
new file mode 100644
index 00000000..729a20c7
--- /dev/null
+++ b/llms/anthropic/options_test.go
@@ -0,0 +1,141 @@
+package anthropic_test
+
+import (
+	"testing"
+	"time"
+
+	"github.com/tmc/langchaingo/llms"
+	"github.com/tmc/langchaingo/llms/anthropic"
+)
+
+func TestEphemeralCache(t *testing.T) {
+	cache := anthropic.EphemeralCache()
+
+	if cache.Type != "ephemeral" {
+		t.Errorf("expected type 'ephemeral', got %q", cache.Type)
+	}
+
+	if cache.Duration != 5*time.Minute {
+		t.Errorf("expected duration 5m, got %v", cache.Duration)
+	}
+}
+
+func TestEphemeralCacheOneHour(t *testing.T) {
+	cache := anthropic.EphemeralCacheOneHour()
+
+	if cache.Type != "ephemeral" {
+		t.Errorf("expected type 'ephemeral', got %q", cache.Type)
+	}
+
+	if cache.Duration != time.Hour {
+		t.Errorf("expected duration 1h, got %v", cache.Duration)
+	}
+}
+
+func TestWithPromptCaching(t *testing.T) {
+	option := anthropic.WithPromptCaching()
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	if opts.Metadata == nil {
+		t.Fatal("metadata should be initialized")
+	}
+
+	headers, ok := opts.Metadata["anthropic:beta_headers"].([]string)
+	if !ok {
+		t.Fatal("anthropic:beta_headers should be a []string")
+	}
+
+	if len(headers) != 1 || headers[0] != "prompt-caching-2024-07-31" {
+		t.Errorf("expected ['prompt-caching-2024-07-31'], got %v", headers)
+	}
+}
+
+func TestWithExtendedOutput(t *testing.T) {
+	option := anthropic.WithExtendedOutput()
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	if opts.Metadata == nil {
+		t.Fatal("metadata should be initialized")
+	}
+
+	headers, ok := opts.Metadata["anthropic:beta_headers"].([]string)
+	if !ok {
+		t.Fatal("anthropic:beta_headers should be a []string")
+	}
+
+	if len(headers) != 1 || headers[0] != "output-128k-2025-02-19" {
+		t.Errorf("expected ['output-128k-2025-02-19'], got %v", headers)
+	}
+}
+
+func TestWithInterleavedThinking(t *testing.T) {
+	option := anthropic.WithInterleavedThinking()
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	if opts.Metadata == nil {
+		t.Fatal("metadata should be initialized")
+	}
+
+	headers, ok := opts.Metadata["anthropic:beta_headers"].([]string)
+	if !ok {
+		t.Fatal("anthropic:beta_headers should be a []string")
+	}
+
+	if len(headers) != 1 || headers[0] != "interleaved-thinking-2025-05-14" {
+		t.Errorf("expected ['interleaved-thinking-2025-05-14'], got %v", headers)
+	}
+}
+
+func TestWithBetaHeader(t *testing.T) {
+	option := anthropic.WithBetaHeader("custom-feature-2025-01-01")
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	if opts.Metadata == nil {
+		t.Fatal("metadata should be initialized")
+	}
+
+	headers, ok := opts.Metadata["anthropic:beta_headers"].([]string)
+	if !ok {
+		t.Fatal("anthropic:beta_headers should be a []string")
+	}
+
+	if len(headers) != 1 || headers[0] != "custom-feature-2025-01-01" {
+		t.Errorf("expected ['custom-feature-2025-01-01'], got %v", headers)
+	}
+}
+
+func TestMultipleBetaHeaders(t *testing.T) {
+	var opts llms.CallOptions
+
+	// Apply multiple options
+	anthropic.WithPromptCaching()(&opts)
+	anthropic.WithExtendedOutput()(&opts)
+
+	headers, ok := opts.Metadata["anthropic:beta_headers"].([]string)
+	if !ok {
+		t.Fatal("anthropic:beta_headers should be a []string")
+	}
+
+	if len(headers) != 2 {
+		t.Errorf("expected 2 headers, got %d", len(headers))
+	}
+
+	expectedHeaders := map[string]bool{
+		"prompt-caching-2024-07-31": true,
+		"output-128k-2025-02-19":    true,
+	}
+
+	for _, h := range headers {
+		if !expectedHeaders[h] {
+			t.Errorf("unexpected header: %s", h)
+		}
+	}
+}
diff --git a/llms/anthropic/prompt_caching_test.go b/llms/anthropic/prompt_caching_test.go
new file mode 100644
index 00000000..d619c7e0
--- /dev/null
+++ b/llms/anthropic/prompt_caching_test.go
@@ -0,0 +1,162 @@
+package anthropic_test
+
+import (
+	"context"
+	"testing"
+
+	"github.com/tmc/langchaingo/llms"
+	"github.com/tmc/langchaingo/llms/anthropic"
+)
+
+func TestAnthropicPromptCaching(t *testing.T) {
+	// This test requires ANTHROPIC_API_KEY environment variable
+	if testing.Short() {
+		t.Skip("skipping prompt caching test in short mode")
+	}
+
+	llm, err := anthropic.New(anthropic.WithModel("claude-3-haiku-20240307"))
+	if err != nil {
+		t.Skip("failed to create Anthropic LLM, skipping prompt caching test")
+	}
+
+	ctx := context.Background()
+
+	// Create a message with cached content
+	longContext := "You are an expert software engineer with deep knowledge of Go programming. " +
+		"You have extensive experience building distributed systems, microservices, and CLI tools. " +
+		"You understand best practices for error handling, testing, and code organization in Go. " +
+		"You always write clean, idiomatic Go code following the standard library patterns. " +
+		"Please analyze any code I provide and suggest improvements based on Go best practices."
+
+	cachedPart := llms.WithCacheControl(
+		llms.TextPart(longContext),
+		anthropic.EphemeralCache(),
+	)
+
+	messages := []llms.MessageContent{
+		{
+			Role:  llms.ChatMessageTypeSystem,
+			Parts: []llms.ContentPart{cachedPart},
+		},
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What are the main principles of good Go code?"),
+			},
+		},
+	}
+
+	// Call with Anthropic caching headers
+	resp, err := llm.GenerateContent(ctx, messages,
+		anthropic.WithPromptCaching(),
+		llms.WithMaxTokens(100),
+	)
+
+	if err != nil {
+		// In tests, we expect this to work if API key is available
+		// If not available, we skip rather than fail
+		t.Skipf("failed to generate content with caching: %v", err)
+	}
+
+	if resp == nil || len(resp.Choices) == 0 {
+		t.Fatal("expected non-empty response")
+	}
+
+	if resp.Choices[0].Content == "" {
+		t.Error("expected non-empty content in response")
+	}
+
+	// Test with 1-hour cache
+	longCachePart := llms.WithCacheControl(
+		llms.TextPart(longContext),
+		anthropic.EphemeralCacheOneHour(),
+	)
+
+	messages2 := []llms.MessageContent{
+		{
+			Role:  llms.ChatMessageTypeSystem,
+			Parts: []llms.ContentPart{longCachePart},
+		},
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("Give me one Go best practice."),
+			},
+		},
+	}
+
+	resp2, err := llm.GenerateContent(ctx, messages2,
+		anthropic.WithPromptCaching(),
+		llms.WithMaxTokens(50),
+	)
+
+	if err != nil {
+		t.Skipf("failed to generate content with 1-hour caching: %v", err)
+	}
+
+	if resp2 == nil || len(resp2.Choices) == 0 {
+		t.Fatal("expected non-empty response for 1-hour cache test")
+	}
+}
+
+func TestAnthropicCacheControlInMessages(t *testing.T) {
+	// Test that cache control is properly handled in message processing
+	// This is a unit test that doesn't require API calls
+
+	cachedText := llms.WithCacheControl(
+		llms.TextPart("This is cached content"),
+		anthropic.EphemeralCache(),
+	)
+
+	message := llms.MessageContent{
+		Role:  llms.ChatMessageTypeHuman,
+		Parts: []llms.ContentPart{cachedText},
+	}
+
+	// This tests that the message can be created without errors
+	// The actual processing is tested through integration tests
+	messages := []llms.MessageContent{message}
+
+	if len(messages) != 1 {
+		t.Error("expected single message")
+	}
+
+	if len(messages[0].Parts) != 1 {
+		t.Error("expected single part in message")
+	}
+
+	cached, ok := messages[0].Parts[0].(llms.CachedContent)
+	if !ok {
+		t.Error("expected CachedContent part")
+	}
+
+	if cached.CacheControl == nil {
+		t.Error("expected cache control to be set")
+	}
+
+	if cached.CacheControl.Type != "ephemeral" {
+		t.Errorf("expected ephemeral cache type, got %q", cached.CacheControl.Type)
+	}
+}
+
+func TestAnthropicBetaHeaders(t *testing.T) {
+	// Test that beta headers option works correctly
+	option := anthropic.WithPromptCaching()
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	if opts.Metadata == nil {
+		t.Fatal("metadata should be initialized")
+	}
+
+	headers, ok := opts.Metadata["anthropic:beta_headers"].([]string)
+	if !ok {
+		t.Fatal("anthropic:beta_headers should be a []string")
+	}
+
+	expectedHeader := "prompt-caching-2024-07-31"
+	if len(headers) != 1 || headers[0] != expectedHeader {
+		t.Errorf("expected [%q], got %v", expectedHeader, headers)
+	}
+}
diff --git a/llms/bedrock/internal/bedrockclient/bedrockclient_nova_test.go b/llms/bedrock/internal/bedrockclient/bedrockclient_nova_test.go
index 79bb8880..39e66da7 100644
--- a/llms/bedrock/internal/bedrockclient/bedrockclient_nova_test.go
+++ b/llms/bedrock/internal/bedrockclient/bedrockclient_nova_test.go
@@ -122,4 +122,4 @@ func TestGetProvider_EdgeCases(t *testing.T) {
 			}
 		})
 	}
-}
\ No newline at end of file
+}
diff --git a/llms/bedrock/internal/bedrockclient/tool_call_test.go b/llms/bedrock/internal/bedrockclient/tool_call_test.go
index bd9b64ff..5f0d0823 100644
--- a/llms/bedrock/internal/bedrockclient/tool_call_test.go
+++ b/llms/bedrock/internal/bedrockclient/tool_call_test.go
@@ -102,4 +102,4 @@ func TestAnthropicToolCallSupport(t *testing.T) {
 		require.Equal(t, "It's 72°F and sunny in San Francisco", content.Content)
 		require.Equal(t, "toolu_123", content.ToolUseID)
 	})
-}
\ No newline at end of file
+}
diff --git a/llms/bedrock/llmtest_test.go b/llms/bedrock/llmtest_test.go
new file mode 100644
index 00000000..b9d573bc
--- /dev/null
+++ b/llms/bedrock/llmtest_test.go
@@ -0,0 +1,23 @@
+package bedrock
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("AWS_REGION") == "" {
+		t.Skip("AWS_REGION not set")
+	}
+
+	llm, err := New(
+		WithModel("anthropic.claude-3-haiku-20240307-v1:0"),
+	)
+	if err != nil {
+		t.Fatalf("Failed to create Bedrock LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/cloudflare/llmtest_test.go b/llms/cloudflare/llmtest_test.go
new file mode 100644
index 00000000..d2e58186
--- /dev/null
+++ b/llms/cloudflare/llmtest_test.go
@@ -0,0 +1,27 @@
+package cloudflare
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	apiToken := os.Getenv("CLOUDFLARE_API_TOKEN")
+	if apiToken == "" {
+		t.Skip("CLOUDFLARE_API_TOKEN not set")
+	}
+
+	accountID := os.Getenv("CLOUDFLARE_ACCOUNT_ID")
+	if accountID == "" {
+		t.Skip("CLOUDFLARE_ACCOUNT_ID not set")
+	}
+
+	llm, err := New(WithToken(apiToken), WithAccountID(accountID))
+	if err != nil {
+		t.Fatalf("Failed to create Cloudflare LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/cohere/llmtest_test.go b/llms/cohere/llmtest_test.go
new file mode 100644
index 00000000..da6329ac
--- /dev/null
+++ b/llms/cohere/llmtest_test.go
@@ -0,0 +1,21 @@
+package cohere
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("COHERE_API_KEY") == "" {
+		t.Skip("COHERE_API_KEY not set")
+	}
+
+	llm, err := New()
+	if err != nil {
+		t.Fatalf("Failed to create Cohere LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/ernie/llmtest_test.go b/llms/ernie/llmtest_test.go
new file mode 100644
index 00000000..aeeafb45
--- /dev/null
+++ b/llms/ernie/llmtest_test.go
@@ -0,0 +1,27 @@
+package ernie
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	apiKey := os.Getenv("ERNIE_API_KEY")
+	if apiKey == "" {
+		t.Skip("ERNIE_API_KEY not set")
+	}
+
+	secretKey := os.Getenv("ERNIE_SECRET_KEY")
+	if secretKey == "" {
+		t.Skip("ERNIE_SECRET_KEY not set")
+	}
+
+	llm, err := New(WithAKSK(apiKey, secretKey))
+	if err != nil {
+		t.Fatalf("Failed to create Ernie LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/fake/llmtest_test.go b/llms/fake/llmtest_test.go
new file mode 100644
index 00000000..3dde8b89
--- /dev/null
+++ b/llms/fake/llmtest_test.go
@@ -0,0 +1,15 @@
+package fake
+
+import (
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	// Fake LLM doesn't need API keys
+	llm := &LLM{}
+
+	// Test basic functionality only
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/googleai/caching.go b/llms/googleai/caching.go
new file mode 100644
index 00000000..73df06a3
--- /dev/null
+++ b/llms/googleai/caching.go
@@ -0,0 +1,119 @@
+// Package googleai provides caching support for Google AI models.
+package googleai
+
+import (
+	"context"
+	"time"
+
+	"github.com/google/generative-ai-go/genai"
+	"github.com/tmc/langchaingo/llms"
+)
+
+// CachingHelper provides utilities for working with Google AI's cached content feature.
+// Unlike Anthropic which supports inline cache control, Google AI requires
+// pre-creating cached content through the API.
+type CachingHelper struct {
+	client *genai.Client
+}
+
+// NewCachingHelper creates a helper for managing cached content.
+func NewCachingHelper(ctx context.Context, opts ...Option) (*CachingHelper, error) {
+	// Create a GoogleAI client to get access to the underlying genai client
+	gai, err := New(ctx, opts...)
+	if err != nil {
+		return nil, err
+	}
+
+	return &CachingHelper{
+		client: gai.client,
+	}, nil
+}
+
+// CreateCachedContent creates cached content that can be reused across multiple requests.
+// This is useful for caching large system prompts, context documents, or frequently used instructions.
+//
+// Example usage:
+//
+//	helper, _ := NewCachingHelper(ctx, WithAPIKey(apiKey))
+//	cached, _ := helper.CreateCachedContent(ctx, "gemini-2.0-flash", []llms.MessageContent{
+//	    {
+//	        Role: llms.ChatMessageTypeSystem,
+//	        Parts: []llms.ContentPart{
+//	            llms.TextPart("You are an expert assistant with deep knowledge..."),
+//	        },
+//	    },
+//	}, 1*time.Hour)
+//
+//	// Use the cached content in requests
+//	model, _ := New(ctx, WithAPIKey(apiKey))
+//	resp, _ := model.GenerateContent(ctx, messages, WithCachedContent(cached.Name))
+func (ch *CachingHelper) CreateCachedContent(
+	ctx context.Context,
+	modelName string,
+	messages []llms.MessageContent,
+	ttl time.Duration,
+) (*genai.CachedContent, error) {
+	// Convert langchain messages to genai content
+	contents := make([]*genai.Content, 0, len(messages))
+	var systemInstruction *genai.Content
+
+	for _, msg := range messages {
+		parts := make([]genai.Part, 0, len(msg.Parts))
+		for _, part := range msg.Parts {
+			switch p := part.(type) {
+			case llms.TextContent:
+				parts = append(parts, genai.Text(p.Text))
+			case llms.CachedContent:
+				// Extract the underlying content if it's wrapped with cache control
+				// (though Google AI doesn't use inline cache control like Anthropic)
+				if textPart, ok := p.ContentPart.(llms.TextContent); ok {
+					parts = append(parts, genai.Text(textPart.Text))
+				}
+			}
+		}
+
+		content := &genai.Content{
+			Parts: parts,
+		}
+
+		// Set role
+		switch msg.Role {
+		case llms.ChatMessageTypeSystem:
+			content.Role = "system"
+			systemInstruction = content
+		case llms.ChatMessageTypeHuman:
+			content.Role = "user"
+			contents = append(contents, content)
+		case llms.ChatMessageTypeAI:
+			content.Role = "model"
+			contents = append(contents, content)
+		}
+	}
+
+	// Create the cached content
+	cc := &genai.CachedContent{
+		Model:             modelName,
+		Contents:          contents,
+		SystemInstruction: systemInstruction,
+		Expiration: genai.ExpireTimeOrTTL{
+			TTL: ttl,
+		},
+	}
+
+	return ch.client.CreateCachedContent(ctx, cc)
+}
+
+// GetCachedContent retrieves existing cached content by name.
+func (ch *CachingHelper) GetCachedContent(ctx context.Context, name string) (*genai.CachedContent, error) {
+	return ch.client.GetCachedContent(ctx, name)
+}
+
+// DeleteCachedContent removes cached content.
+func (ch *CachingHelper) DeleteCachedContent(ctx context.Context, name string) error {
+	return ch.client.DeleteCachedContent(ctx, name)
+}
+
+// ListCachedContents returns an iterator for all cached content.
+func (ch *CachingHelper) ListCachedContents(ctx context.Context) *genai.CachedContentIterator {
+	return ch.client.ListCachedContents(ctx)
+}
diff --git a/llms/googleai/googleai.go b/llms/googleai/googleai.go
index 3cc84ae6..8d07c7f1 100644
--- a/llms/googleai/googleai.go
+++ b/llms/googleai/googleai.go
@@ -58,6 +58,12 @@ func (g *GoogleAI) GenerateContent(
 		opt(&opts)
 	}
 
+	// Update the tracked model if it was overridden
+	effectiveModel := opts.Model
+	if effectiveModel != "" && effectiveModel != g.model {
+		g.model = effectiveModel
+	}
+
 	model := g.client.GenerativeModel(opts.Model)
 	model.SetCandidateCount(int32(opts.CandidateCount))
 	model.SetMaxOutputTokens(int32(opts.MaxTokens))
@@ -65,6 +71,12 @@ func (g *GoogleAI) GenerateContent(
 	model.SetTopP(float32(opts.TopP))
 	model.SetTopK(int32(opts.TopK))
 	model.StopSequences = opts.StopWords
+
+	// Support for cached content (if provided through metadata)
+	// Note: This requires the cached content to be pre-created using Client.CreateCachedContent
+	if cachedContentName, ok := opts.Metadata["CachedContentName"].(string); ok && cachedContentName != "" {
+		model.CachedContentName = cachedContentName
+	}
 	model.SafetySettings = []*genai.SafetySetting{
 		{
 			Category:  genai.HarmCategoryDangerousContent,
@@ -162,8 +174,27 @@ func convertCandidates(candidates []*genai.Candidate, usage *genai.UsageMetadata
 			metadata["input_tokens"] = usage.PromptTokenCount
 			metadata["output_tokens"] = usage.CandidatesTokenCount
 			metadata["total_tokens"] = usage.TotalTokenCount
+			// Standardized field names for cross-provider compatibility
+			metadata["PromptTokens"] = usage.PromptTokenCount
+			metadata["CompletionTokens"] = usage.CandidatesTokenCount
+			metadata["TotalTokens"] = usage.TotalTokenCount
+
+			// Cache-related token information (if available)
+			if usage.CachedContentTokenCount > 0 {
+				metadata["CachedTokens"] = usage.CachedContentTokenCount
+				metadata["CacheReadInputTokens"] = usage.CachedContentTokenCount // Anthropic compatibility
+				// Google AI includes cached tokens in the prompt count, calculate non-cached
+				metadata["NonCachedInputTokens"] = usage.PromptTokenCount - usage.CachedContentTokenCount
+			}
 		}
 
+		// Google AI doesn't separate thinking content like OpenAI o1, but we provide empty standardized fields
+		metadata["ThinkingContent"] = "" // Google models don't separate thinking content
+		metadata["ThinkingTokens"] = 0   // Google models don't track thinking tokens separately
+
+		// Note: Google AI's CachedContent requires pre-created cached content via API,
+		// not inline cache control like Anthropic. Use Client.CreateCachedContent() for caching.
+
 		contentResponse.Choices = append(contentResponse.Choices,
 			&llms.ContentChoice{
 				Content:        buf.String(),
diff --git a/llms/googleai/llmtest_test.go b/llms/googleai/llmtest_test.go
new file mode 100644
index 00000000..f15474cf
--- /dev/null
+++ b/llms/googleai/llmtest_test.go
@@ -0,0 +1,27 @@
+package googleai
+
+import (
+	"context"
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("GOOGLE_API_KEY") == "" {
+		t.Skip("GOOGLE_API_KEY not set")
+	}
+
+	ctx := context.Background()
+	llm, err := New(ctx,
+		WithAPIKey(os.Getenv("GOOGLE_API_KEY")),
+		WithDefaultModel("gemini-1.5-flash"),
+	)
+	if err != nil {
+		t.Fatalf("Failed to create Google AI LLM: %v", err)
+	}
+	defer llm.Close()
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/googleai/new.go b/llms/googleai/new.go
index 3270f9e4..8176f285 100644
--- a/llms/googleai/new.go
+++ b/llms/googleai/new.go
@@ -4,6 +4,7 @@ package googleai
 
 import (
 	"context"
+	"strings"
 
 	"github.com/google/generative-ai-go/genai"
 	"github.com/tmc/langchaingo/callbacks"
@@ -15,9 +16,13 @@ type GoogleAI struct {
 	CallbacksHandler callbacks.Handler
 	client           *genai.Client
 	opts             Options
+	model            string // Track current model for reasoning detection
 }
 
-var _ llms.Model = &GoogleAI{}
+var (
+	_ llms.Model          = &GoogleAI{}
+	_ llms.ReasoningModel = &GoogleAI{}
+)
 
 // New creates a new GoogleAI client.
 func New(ctx context.Context, opts ...Option) (*GoogleAI, error) {
@@ -28,7 +33,8 @@ func New(ctx context.Context, opts ...Option) (*GoogleAI, error) {
 	clientOptions.EnsureAuthPresent()
 
 	gi := &GoogleAI{
-		opts: clientOptions,
+		opts:  clientOptions,
+		model: clientOptions.DefaultModel, // Store the default model
 	}
 
 	client, err := genai.NewClient(ctx, clientOptions.ClientOptions...)
@@ -49,3 +55,30 @@ func (g *GoogleAI) Close() error {
 	}
 	return nil
 }
+
+// SupportsReasoning implements the ReasoningModel interface.
+// Returns true if the current model supports reasoning/thinking tokens.
+func (g *GoogleAI) SupportsReasoning() bool {
+	// Check the current model (may have been overridden by WithModel option)
+	model := g.model
+	if model == "" {
+		model = g.opts.DefaultModel
+	}
+
+	// Gemini 2.0 models support reasoning/thinking capabilities
+	if strings.Contains(model, "gemini-2.0") {
+		return true
+	}
+
+	// Future Gemini 3+ models expected to support reasoning
+	if strings.Contains(model, "gemini-3") || strings.Contains(model, "gemini-4") {
+		return true
+	}
+
+	// Gemini Experimental models may have reasoning capabilities
+	if strings.Contains(model, "gemini-exp") && strings.Contains(model, "thinking") {
+		return true
+	}
+
+	return false
+}
diff --git a/llms/googleai/option.go b/llms/googleai/option.go
index ef61f029..6bd41cb4 100644
--- a/llms/googleai/option.go
+++ b/llms/googleai/option.go
@@ -6,6 +6,7 @@ import (
 	"reflect"
 
 	"cloud.google.com/go/vertexai/genai"
+	"github.com/tmc/langchaingo/llms"
 	"google.golang.org/api/option"
 	"google.golang.org/grpc"
 )
@@ -184,6 +185,18 @@ func WithHarmThreshold(ht HarmBlockThreshold) Option {
 	}
 }
 
+// WithCachedContent enables the use of pre-created cached content.
+// The cached content must be created separately using Client.CreateCachedContent.
+// This is different from Anthropic's inline cache control.
+func WithCachedContent(name string) llms.CallOption {
+	return func(o *llms.CallOptions) {
+		if o.Metadata == nil {
+			o.Metadata = make(map[string]interface{})
+		}
+		o.Metadata["CachedContentName"] = name
+	}
+}
+
 type HarmBlockThreshold int32
 
 const (
diff --git a/llms/googleai/reasoning_test.go b/llms/googleai/reasoning_test.go
new file mode 100644
index 00000000..b4e6b31f
--- /dev/null
+++ b/llms/googleai/reasoning_test.go
@@ -0,0 +1,208 @@
+package googleai
+
+import (
+	"context"
+	"os"
+	"strings"
+	"testing"
+	"time"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+func TestGoogleAI_SupportsReasoning(t *testing.T) {
+	tests := []struct {
+		name     string
+		model    string
+		expected bool
+	}{
+		{
+			name:     "Gemini 2.0 Flash supports reasoning",
+			model:    "gemini-2.0-flash",
+			expected: true,
+		},
+		{
+			name:     "Gemini 2.0 Pro supports reasoning",
+			model:    "gemini-2.0-pro",
+			expected: true,
+		},
+		{
+			name:     "Gemini 3.0 (future) supports reasoning",
+			model:    "gemini-3.0-ultra",
+			expected: true,
+		},
+		{
+			name:     "Gemini experimental with thinking supports reasoning",
+			model:    "gemini-exp-thinking-1206",
+			expected: true,
+		},
+		{
+			name:     "Gemini 1.5 Flash does not support reasoning",
+			model:    "gemini-1.5-flash",
+			expected: false,
+		},
+		{
+			name:     "Gemini 1.0 Pro does not support reasoning",
+			model:    "gemini-1.0-pro",
+			expected: false,
+		},
+		{
+			name:     "Gemini experimental without thinking does not support reasoning",
+			model:    "gemini-exp-1206",
+			expected: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			// Create client with test model
+			client := &GoogleAI{
+				model: tt.model,
+				opts:  DefaultOptions(),
+			}
+
+			// Test SupportsReasoning
+			got := client.SupportsReasoning()
+			if got != tt.expected {
+				t.Errorf("SupportsReasoning() for model %s = %v, want %v", tt.model, got, tt.expected)
+			}
+
+			// Also test with model set via options
+			client.model = ""
+			client.opts.DefaultModel = tt.model
+			got = client.SupportsReasoning()
+			if got != tt.expected {
+				t.Errorf("SupportsReasoning() with default model %s = %v, want %v", tt.model, got, tt.expected)
+			}
+		})
+	}
+}
+
+func TestGoogleAI_ReasoningIntegration(t *testing.T) {
+	apiKey := os.Getenv("GOOGLE_API_KEY")
+	if apiKey == "" {
+		t.Skip("GOOGLE_API_KEY not set")
+	}
+
+	ctx := context.Background()
+
+	// Test with Gemini 2.0 Flash (reasoning model)
+	client, err := New(ctx,
+		WithAPIKey(apiKey),
+		WithDefaultModel("gemini-2.0-flash"),
+	)
+	if err != nil {
+		t.Fatalf("Failed to create client: %v", err)
+	}
+	defer client.Close()
+
+	// Verify it implements ReasoningModel interface
+	if _, ok := interface{}(client).(llms.ReasoningModel); !ok {
+		t.Error("GoogleAI should implement ReasoningModel interface")
+	}
+
+	// Verify it reports reasoning support for Gemini 2.0
+	if !client.SupportsReasoning() {
+		t.Error("Gemini 2.0 Flash should support reasoning")
+	}
+
+	// Test reasoning with a complex problem
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What is 25 * 17 + 13? Think step by step."),
+			},
+		},
+	}
+
+	resp, err := client.GenerateContent(ctx, messages,
+		llms.WithMaxTokens(200),
+		llms.WithThinkingMode(llms.ThinkingModeMedium), // Note: Google AI may not use this yet
+	)
+	if err != nil {
+		t.Fatalf("Failed to generate content: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("No response choices")
+	}
+
+	content := resp.Choices[0].Content
+	if !strings.Contains(content, "438") {
+		t.Errorf("Expected answer 438 in response, got: %s", content)
+	}
+}
+
+func TestGoogleAI_CachingSupport(t *testing.T) {
+	apiKey := os.Getenv("GOOGLE_API_KEY")
+	if apiKey == "" {
+		t.Skip("GOOGLE_API_KEY not set")
+	}
+
+	ctx := context.Background()
+
+	// Create caching helper
+	helper, err := NewCachingHelper(ctx, WithAPIKey(apiKey))
+	if err != nil {
+		t.Fatalf("Failed to create caching helper: %v", err)
+	}
+
+	// Create cached content with a large system prompt
+	longContext := `You are an expert code reviewer with deep knowledge of Go best practices.
+	Always consider performance, security, and maintainability in your reviews.
+	` + strings.Repeat("This is padding text to ensure we have enough tokens for caching. ", 100)
+
+	cached, err := helper.CreateCachedContent(ctx, "gemini-2.0-flash",
+		[]llms.MessageContent{
+			{
+				Role: llms.ChatMessageTypeSystem,
+				Parts: []llms.ContentPart{
+					llms.TextPart(longContext),
+				},
+			},
+		},
+		5*time.Minute,
+	)
+	if err != nil {
+		t.Fatalf("Failed to create cached content: %v", err)
+	}
+	defer func() {
+		if err := helper.DeleteCachedContent(ctx, cached.Name); err != nil {
+			t.Logf("Failed to delete cached content: %v", err)
+		}
+	}()
+
+	// Use the cached content in a request
+	client, err := New(ctx, WithAPIKey(apiKey))
+	if err != nil {
+		t.Fatalf("Failed to create client: %v", err)
+	}
+	defer client.Close()
+
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What are the key things to look for in a Go code review?"),
+			},
+		},
+	}
+
+	resp, err := client.GenerateContent(ctx, messages,
+		WithCachedContent(cached.Name),
+		llms.WithMaxTokens(200),
+	)
+	if err != nil {
+		t.Fatalf("Failed to generate with cached content: %v", err)
+	}
+
+	// Check that cached tokens are reported in the response
+	if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+		if cachedTokens, ok := genInfo["CachedTokens"].(int32); ok && cachedTokens > 0 {
+			t.Logf("Successfully used %d cached tokens", cachedTokens)
+		} else {
+			t.Log("No cached tokens reported (this may be normal if caching is not yet active)")
+		}
+	}
+}
diff --git a/llms/huggingface/llmtest_test.go b/llms/huggingface/llmtest_test.go
new file mode 100644
index 00000000..dc7003c3
--- /dev/null
+++ b/llms/huggingface/llmtest_test.go
@@ -0,0 +1,21 @@
+package huggingface
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("HUGGINGFACEHUB_API_TOKEN") == "" {
+		t.Skip("HUGGINGFACEHUB_API_TOKEN not set")
+	}
+
+	llm, err := New(WithModel("gpt2"))
+	if err != nil {
+		t.Fatalf("Failed to create HuggingFace LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/llamafile/llmtest_test.go b/llms/llamafile/llmtest_test.go
new file mode 100644
index 00000000..8a219b78
--- /dev/null
+++ b/llms/llamafile/llmtest_test.go
@@ -0,0 +1,19 @@
+package llamafile
+
+import (
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	// Llamafile uses LLAMAFILE_HOST environment variable for server URL
+	// If not set, it defaults to http://127.0.0.1:8080
+
+	llm, err := New()
+	if err != nil {
+		t.Skipf("Llamafile server not available: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/llms.go b/llms/llms.go
index bc0abef5..63506f09 100644
--- a/llms/llms.go
+++ b/llms/llms.go
@@ -28,6 +28,18 @@ type Model interface {
 	Call(ctx context.Context, prompt string, options ...CallOption) (string, error)
 }
 
+// ReasoningModel is an interface for models that support extended reasoning/thinking.
+// Models implementing this interface can generate internal reasoning tokens that are
+// used to improve response quality but may not be included in the final output.
+type ReasoningModel interface {
+	Model
+
+	// SupportsReasoning returns true if the model supports reasoning/thinking tokens.
+	// This capability allows models to "think" through problems internally before
+	// generating a response, improving quality for complex tasks.
+	SupportsReasoning() bool
+}
+
 // GenerateFromSinglePrompt is a convenience function for calling an LLM with
 // a single string prompt, expecting a single string response. It's useful for
 // simple, string-only interactions and provides a slightly more ergonomic API
diff --git a/llms/local/llmtest_test.go b/llms/local/llmtest_test.go
new file mode 100644
index 00000000..48634fc6
--- /dev/null
+++ b/llms/local/llmtest_test.go
@@ -0,0 +1,22 @@
+package local
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	binPath := os.Getenv("LOCAL_LLM_BIN")
+	if binPath == "" {
+		t.Skip("LOCAL_LLM_BIN not set")
+	}
+
+	llm, err := New(WithBin(binPath))
+	if err != nil {
+		t.Fatalf("Failed to create Local LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/maritaca/llmtest_test.go b/llms/maritaca/llmtest_test.go
new file mode 100644
index 00000000..9ed74320
--- /dev/null
+++ b/llms/maritaca/llmtest_test.go
@@ -0,0 +1,24 @@
+package maritaca
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("MARITACA_API_KEY") == "" {
+		t.Skip("MARITACA_API_KEY not set")
+	}
+
+	llm, err := New(
+		WithToken(os.Getenv("MARITACA_API_KEY")),
+		WithModel("sabia-3"),
+	)
+	if err != nil {
+		t.Fatalf("Failed to create Maritaca LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/mistral/llmtest_test.go b/llms/mistral/llmtest_test.go
new file mode 100644
index 00000000..965c5877
--- /dev/null
+++ b/llms/mistral/llmtest_test.go
@@ -0,0 +1,23 @@
+package mistral
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("MISTRAL_API_KEY") == "" {
+		t.Skip("MISTRAL_API_KEY not set")
+	}
+
+	llm, err := New(
+		WithAPIKey(os.Getenv("MISTRAL_API_KEY")),
+	)
+	if err != nil {
+		t.Fatalf("Failed to create Mistral LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/mistral/mistralmodel.go b/llms/mistral/mistralmodel.go
index 23021682..4d145f78 100644
--- a/llms/mistral/mistralmodel.go
+++ b/llms/mistral/mistralmodel.go
@@ -52,7 +52,7 @@ func (m *Model) Call(ctx context.Context, prompt string, options ...llms.CallOpt
 		Role:    "user",
 		Content: prompt,
 	})
-	res, err := m.client.Chat("", messages, &mistralChatParams)
+	res, err := m.client.Chat(callOptions.Model, messages, &mistralChatParams)
 	if err != nil {
 		m.CallbacksHandler.HandleLLMError(ctx, err)
 		return "", err
diff --git a/llms/ollama/context_cache.go b/llms/ollama/context_cache.go
new file mode 100644
index 00000000..4bc9fd9b
--- /dev/null
+++ b/llms/ollama/context_cache.go
@@ -0,0 +1,168 @@
+package ollama
+
+import (
+	"crypto/sha256"
+	"encoding/hex"
+	"sync"
+	"time"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+// ContextCache provides a simple in-memory cache for conversation contexts.
+// This helps reduce token usage by reusing processed context across requests.
+// Note: This is different from provider-native caching like Anthropic/Google AI.
+type ContextCache struct {
+	mu      sync.RWMutex
+	entries map[string]*CacheEntry
+	maxSize int           // Maximum number of entries
+	ttl     time.Duration // Time to live for cache entries
+}
+
+// CacheEntry represents a cached context entry.
+type CacheEntry struct {
+	Messages      []llms.MessageContent
+	ContextTokens int
+	CreatedAt     time.Time
+	LastAccessed  time.Time
+	AccessCount   int
+}
+
+// NewContextCache creates a new context cache with specified capacity and TTL.
+func NewContextCache(maxSize int, ttl time.Duration) *ContextCache {
+	return &ContextCache{
+		entries: make(map[string]*CacheEntry),
+		maxSize: maxSize,
+		ttl:     ttl,
+	}
+}
+
+// generateCacheKey creates a unique key for a set of messages.
+func (c *ContextCache) generateCacheKey(messages []llms.MessageContent) string {
+	h := sha256.New()
+	for _, msg := range messages {
+		h.Write([]byte(msg.Role))
+		for _, part := range msg.Parts {
+			switch p := part.(type) {
+			case llms.TextContent:
+				h.Write([]byte(p.Text))
+			}
+		}
+	}
+	return hex.EncodeToString(h.Sum(nil))[:16] // Use first 16 chars for brevity
+}
+
+// Get retrieves a cached context if available and not expired.
+func (c *ContextCache) Get(messages []llms.MessageContent) (*CacheEntry, bool) {
+	key := c.generateCacheKey(messages)
+
+	c.mu.RLock()
+	defer c.mu.RUnlock()
+
+	entry, exists := c.entries[key]
+	if !exists {
+		return nil, false
+	}
+
+	// Check if entry has expired
+	if time.Since(entry.CreatedAt) > c.ttl {
+		// Entry expired, don't return it
+		// Note: We don't delete here to avoid lock upgrade
+		return nil, false
+	}
+
+	// Update access info
+	entry.LastAccessed = time.Now()
+	entry.AccessCount++
+
+	return entry, true
+}
+
+// Put stores a context in the cache.
+func (c *ContextCache) Put(messages []llms.MessageContent, contextTokens int) {
+	key := c.generateCacheKey(messages)
+
+	c.mu.Lock()
+	defer c.mu.Unlock()
+
+	// Clean up expired entries if we're at capacity
+	if len(c.entries) >= c.maxSize {
+		c.evictExpiredOrOldest()
+	}
+
+	c.entries[key] = &CacheEntry{
+		Messages:      messages,
+		ContextTokens: contextTokens,
+		CreatedAt:     time.Now(),
+		LastAccessed:  time.Now(),
+		AccessCount:   1,
+	}
+}
+
+// evictExpiredOrOldest removes expired entries or the oldest entry if at capacity.
+func (c *ContextCache) evictExpiredOrOldest() {
+	now := time.Now()
+
+	// First, remove expired entries
+	for key, entry := range c.entries {
+		if now.Sub(entry.CreatedAt) > c.ttl {
+			delete(c.entries, key)
+		}
+	}
+
+	// If still at capacity, remove least recently accessed
+	if len(c.entries) >= c.maxSize {
+		var oldestKey string
+		var oldestTime time.Time
+
+		for key, entry := range c.entries {
+			if oldestKey == "" || entry.LastAccessed.Before(oldestTime) {
+				oldestKey = key
+				oldestTime = entry.LastAccessed
+			}
+		}
+
+		if oldestKey != "" {
+			delete(c.entries, oldestKey)
+		}
+	}
+}
+
+// Clear removes all entries from the cache.
+func (c *ContextCache) Clear() {
+	c.mu.Lock()
+	defer c.mu.Unlock()
+	c.entries = make(map[string]*CacheEntry)
+}
+
+// Stats returns cache statistics.
+func (c *ContextCache) Stats() (entries int, totalHits int, avgTokensSaved int) {
+	c.mu.RLock()
+	defer c.mu.RUnlock()
+
+	entries = len(c.entries)
+	totalTokens := 0
+
+	for _, entry := range c.entries {
+		totalHits += entry.AccessCount - 1 // Subtract 1 for initial put
+		if entry.AccessCount > 1 {
+			totalTokens += entry.ContextTokens * (entry.AccessCount - 1)
+		}
+	}
+
+	if totalHits > 0 {
+		avgTokensSaved = totalTokens / totalHits
+	}
+
+	return
+}
+
+// WithContextCache creates a call option to use cached context.
+func WithContextCache(cache *ContextCache) llms.CallOption {
+	return func(opts *llms.CallOptions) {
+		if opts.Metadata == nil {
+			opts.Metadata = make(map[string]interface{})
+		}
+		opts.Metadata["context_cache"] = cache
+	}
+}
diff --git a/llms/ollama/internal/ollamaclient/ollamaclient_test.go b/llms/ollama/internal/ollamaclient/ollamaclient_test.go
index 48fe286f..0b917e34 100644
--- a/llms/ollama/internal/ollamaclient/ollamaclient_test.go
+++ b/llms/ollama/internal/ollamaclient/ollamaclient_test.go
@@ -327,7 +327,7 @@ func TestClient_GenerateChatWithThink(t *testing.T) {
 	assert.NotNil(t, response.Message)
 	assert.NotEmpty(t, response.Message.Content)
 	assert.True(t, response.Done)
-	
+
 	// The think parameter should be included in the request
 	// This test verifies that the parameter is properly serialized
 }
diff --git a/llms/ollama/llmtest_test.go b/llms/ollama/llmtest_test.go
new file mode 100644
index 00000000..6e58d61e
--- /dev/null
+++ b/llms/ollama/llmtest_test.go
@@ -0,0 +1,25 @@
+package ollama
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	serverURL := os.Getenv("OLLAMA_HOST")
+	if serverURL == "" {
+		serverURL = "http://localhost:11434"
+	}
+
+	llm, err := New(
+		WithServerURL(serverURL),
+		WithModel("gpt-oss:20b"),
+	)
+	if err != nil {
+		t.Skipf("Ollama not available: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/ollama/ollamallm.go b/llms/ollama/ollamallm.go
index c3477790..493be28e 100644
--- a/llms/ollama/ollamallm.go
+++ b/llms/ollama/ollamallm.go
@@ -4,6 +4,7 @@ import (
 	"context"
 	"errors"
 	"fmt"
+	"strings"
 
 	"github.com/tmc/langchaingo/callbacks"
 	"github.com/tmc/langchaingo/llms"
@@ -24,7 +25,10 @@ type LLM struct {
 	options          options
 }
 
-var _ llms.Model = (*LLM)(nil)
+var (
+	_ llms.Model          = (*LLM)(nil)
+	_ llms.ReasoningModel = (*LLM)(nil)
+)
 
 // New creates a new ollama LLM implementation.
 func New(opts ...Option) (*LLM, error) {
@@ -41,6 +45,27 @@ func New(opts ...Option) (*LLM, error) {
 	return &LLM{client: client, options: o}, nil
 }
 
+// SupportsReasoning implements the ReasoningModel interface.
+// Returns true if the current model supports reasoning/thinking.
+func (o *LLM) SupportsReasoning() bool {
+	// Check if the model supports reasoning based on model name patterns
+	model := strings.ToLower(o.options.model)
+
+	// Ollama models that support reasoning/thinking:
+	// - deepseek-r1 models (DeepSeek reasoning models)
+	// - qwq models (Alibaba's QwQ reasoning models)
+	// - Models with "reasoning" or "thinking" in the name
+	if strings.Contains(model, "deepseek-r1") ||
+		strings.Contains(model, "qwq") ||
+		strings.Contains(model, "reasoning") ||
+		strings.Contains(model, "thinking") {
+		return true
+	}
+
+	// Future: could check model capabilities via Ollama API when available
+	return false
+}
+
 // Call Implement the call interface for LLM.
 func (o *LLM) Call(ctx context.Context, prompt string, options ...llms.CallOption) (string, error) {
 	return llms.GenerateFromSinglePrompt(ctx, o, prompt, options...)
@@ -57,6 +82,14 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 		opt(&opts)
 	}
 
+	// Check if context caching is enabled
+	var contextCache *ContextCache
+	if opts.Metadata != nil {
+		if cache, ok := opts.Metadata["context_cache"].(*ContextCache); ok {
+			contextCache = cache
+		}
+	}
+
 	// Override LLM model if set as llms.CallOption
 	model := o.options.model
 	if opts.Model != "" {
@@ -112,6 +145,16 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 
 	// Get our ollamaOptions from llms.CallOptions
 	ollamaOptions := makeOllamaOptionsFromOptions(o.options.ollamaOptions, opts)
+
+	// Handle thinking mode if specified via metadata
+	if opts.Metadata != nil {
+		if config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig); ok {
+			if config.Mode != llms.ThinkingModeNone && o.SupportsReasoning() {
+				// Enable thinking for models that support it
+				ollamaOptions.Think = true
+			}
+		}
+	}
 	req := &ollamaclient.ChatRequest{
 		Model:    model,
 		Format:   format,
@@ -162,14 +205,40 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 		content = resp.Message.Content
 	}
 
+	// Build generation info with standardized fields
+	genInfo := map[string]any{
+		"CompletionTokens": resp.EvalCount,
+		"PromptTokens":     resp.PromptEvalCount,
+		"TotalTokens":      resp.EvalCount + resp.PromptEvalCount,
+		// Add empty thinking fields for cross-provider compatibility
+		"ThinkingContent": "", // Ollama doesn't separate thinking content
+		"ThinkingTokens":  0,  // Ollama doesn't track thinking tokens separately
+	}
+
+	// If context caching is enabled, track cache usage
+	if contextCache != nil {
+		if cacheEntry, hit := contextCache.Get(messages); hit {
+			// Cache hit - we reused cached context
+			genInfo["CachedTokens"] = cacheEntry.ContextTokens
+			genInfo["CacheHit"] = true
+		} else {
+			// Cache miss - store for future use
+			contextCache.Put(messages, resp.PromptEvalCount)
+			genInfo["CachedTokens"] = 0
+			genInfo["CacheHit"] = false
+		}
+	}
+
+	// Note: Ollama may include thinking in the main content when Think mode is enabled
+	// Future versions may provide separate thinking content
+	if ollamaOptions.Think && o.SupportsReasoning() {
+		genInfo["ThinkingEnabled"] = true
+	}
+
 	choices := []*llms.ContentChoice{
 		{
-			Content: content,
-			GenerationInfo: map[string]any{
-				"CompletionTokens": resp.EvalCount,
-				"PromptTokens":     resp.PromptEvalCount,
-				"TotalTokens":      resp.EvalCount + resp.PromptEvalCount,
-			},
+			Content:        content,
+			GenerationInfo: genInfo,
 		},
 	}
 
@@ -250,6 +319,16 @@ func makeOllamaOptionsFromOptions(ollamaOptions ollamaclient.Options, opts llms.
 	ollamaOptions.FrequencyPenalty = float32(opts.FrequencyPenalty)
 	ollamaOptions.PresencePenalty = float32(opts.PresencePenalty)
 
+	// Extract thinking configuration for models that support it
+	if opts.Metadata != nil {
+		if config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig); ok {
+			// Enable thinking mode if not explicitly disabled
+			if config.Mode != llms.ThinkingModeNone {
+				ollamaOptions.Think = true
+			}
+		}
+	}
+
 	return ollamaOptions
 }
 
diff --git a/llms/ollama/reasoning_test.go b/llms/ollama/reasoning_test.go
new file mode 100644
index 00000000..5db7e4f3
--- /dev/null
+++ b/llms/ollama/reasoning_test.go
@@ -0,0 +1,279 @@
+package ollama
+
+import (
+	"context"
+	"os"
+	"strings"
+	"testing"
+	"time"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+func TestOllama_SupportsReasoning(t *testing.T) {
+	tests := []struct {
+		name     string
+		model    string
+		expected bool
+	}{
+		{
+			name:     "DeepSeek R1 supports reasoning",
+			model:    "deepseek-r1:latest",
+			expected: true,
+		},
+		{
+			name:     "DeepSeek R1 32b supports reasoning",
+			model:    "deepseek-r1:32b",
+			expected: true,
+		},
+		{
+			name:     "QwQ model supports reasoning",
+			model:    "qwq:32b",
+			expected: true,
+		},
+		{
+			name:     "Model with reasoning in name supports reasoning",
+			model:    "custom-reasoning:latest",
+			expected: true,
+		},
+		{
+			name:     "Model with thinking in name supports reasoning",
+			model:    "my-thinking-model:v1",
+			expected: true,
+		},
+		{
+			name:     "Llama does not support reasoning",
+			model:    "llama3:latest",
+			expected: false,
+		},
+		{
+			name:     "Mistral does not support reasoning",
+			model:    "mistral:latest",
+			expected: false,
+		},
+		{
+			name:     "Phi does not support reasoning",
+			model:    "phi:latest",
+			expected: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			llm := &LLM{
+				options: options{
+					model: tt.model,
+				},
+			}
+
+			got := llm.SupportsReasoning()
+			if got != tt.expected {
+				t.Errorf("SupportsReasoning() for model %s = %v, want %v", tt.model, got, tt.expected)
+			}
+		})
+	}
+}
+
+func TestOllama_ContextCache(t *testing.T) {
+	// Create a context cache with 10 entries and 5 minute TTL
+	cache := NewContextCache(10, 5*time.Minute)
+
+	// Test messages
+	messages1 := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What is the capital of France?"),
+			},
+		},
+	}
+
+	messages2 := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What is the capital of Germany?"),
+			},
+		},
+	}
+
+	// Test Put and Get
+	cache.Put(messages1, 100)
+
+	entry, hit := cache.Get(messages1)
+	if !hit {
+		t.Error("Expected cache hit for messages1")
+	}
+	if entry == nil || entry.ContextTokens != 100 {
+		t.Error("Invalid cache entry returned")
+	}
+
+	// Test cache miss
+	_, hit = cache.Get(messages2)
+	if hit {
+		t.Error("Expected cache miss for messages2")
+	}
+
+	// Test multiple accesses
+	cache.Get(messages1)
+	cache.Get(messages1)
+
+	entries, totalHits, avgTokensSaved := cache.Stats()
+	if entries != 1 {
+		t.Errorf("Expected 1 entry, got %d", entries)
+	}
+	if totalHits != 3 { // 3 additional gets after initial put
+		t.Errorf("Expected 3 total hits, got %d", totalHits)
+	}
+	if avgTokensSaved != 100 {
+		t.Errorf("Expected 100 average tokens saved, got %d", avgTokensSaved)
+	}
+
+	// Test Clear
+	cache.Clear()
+	entries, _, _ = cache.Stats()
+	if entries != 0 {
+		t.Errorf("Expected 0 entries after clear, got %d", entries)
+	}
+}
+
+func TestOllama_ReasoningIntegration(t *testing.T) {
+	// Skip if Ollama is not available
+	serverURL := os.Getenv("OLLAMA_HOST")
+	if serverURL == "" {
+		serverURL = "http://localhost:11434"
+	}
+
+	// Try to create client
+	llm, err := New(
+		WithServerURL(serverURL),
+		WithModel("deepseek-r1:latest"), // Use a reasoning model if available
+	)
+	if err != nil {
+		t.Skipf("Ollama not available: %v", err)
+	}
+
+	ctx := context.Background()
+
+	// Check if it implements ReasoningModel
+	if _, ok := interface{}(llm).(llms.ReasoningModel); !ok {
+		t.Error("Ollama LLM should implement ReasoningModel interface")
+	}
+
+	// Test with thinking mode enabled
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What is 15 + 27? Show your thinking."),
+			},
+		},
+	}
+
+	resp, err := llm.GenerateContent(ctx, messages,
+		llms.WithMaxTokens(100),
+		llms.WithThinkingMode(llms.ThinkingModeMedium),
+	)
+	if err != nil {
+		// If the model isn't available, skip
+		if strings.Contains(err.Error(), "model") || strings.Contains(err.Error(), "pull") {
+			t.Skip("Reasoning model not available")
+		}
+		t.Fatalf("Failed to generate content: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("No response choices")
+	}
+
+	content := resp.Choices[0].Content
+	if !strings.Contains(content, "42") {
+		t.Logf("Response might not contain correct answer: %s", content)
+	}
+
+	// Check that thinking was enabled
+	if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+		if enabled, ok := genInfo["ThinkingEnabled"].(bool); ok && enabled {
+			t.Log("Thinking mode was enabled")
+		}
+	}
+}
+
+func TestOllama_CachingIntegration(t *testing.T) {
+	// Skip if Ollama is not available
+	serverURL := os.Getenv("OLLAMA_HOST")
+	if serverURL == "" {
+		serverURL = "http://localhost:11434"
+	}
+
+	llm, err := New(
+		WithServerURL(serverURL),
+		WithModel("llama3:latest"), // Use any available model
+	)
+	if err != nil {
+		t.Skipf("Ollama not available: %v", err)
+	}
+
+	ctx := context.Background()
+
+	// Create context cache
+	cache := NewContextCache(5, 10*time.Minute)
+
+	// Test messages
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeSystem,
+			Parts: []llms.ContentPart{
+				llms.TextPart("You are a helpful assistant."),
+			},
+		},
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What is 2+2?"),
+			},
+		},
+	}
+
+	// First request (cache miss)
+	resp1, err := llm.GenerateContent(ctx, messages,
+		llms.WithMaxTokens(50),
+		WithContextCache(cache),
+	)
+	if err != nil {
+		if strings.Contains(err.Error(), "model") || strings.Contains(err.Error(), "pull") {
+			t.Skip("Model not available")
+		}
+		t.Fatalf("First request failed: %v", err)
+	}
+
+	// Check cache miss
+	if genInfo := resp1.Choices[0].GenerationInfo; genInfo != nil {
+		if hit, ok := genInfo["CacheHit"].(bool); ok && hit {
+			t.Error("Expected cache miss on first request")
+		}
+	}
+
+	// Second request with same messages (cache hit)
+	resp2, err := llm.GenerateContent(ctx, messages,
+		llms.WithMaxTokens(50),
+		WithContextCache(cache),
+	)
+	if err != nil {
+		t.Fatalf("Second request failed: %v", err)
+	}
+
+	// Check cache hit
+	if genInfo := resp2.Choices[0].GenerationInfo; genInfo != nil {
+		if hit, ok := genInfo["CacheHit"].(bool); ok && !hit {
+			t.Error("Expected cache hit on second request")
+		}
+		if cached, ok := genInfo["CachedTokens"].(int); ok && cached > 0 {
+			t.Logf("Reused %d cached tokens", cached)
+		}
+	}
+
+	// Check cache stats
+	entries, hits, avgSaved := cache.Stats()
+	t.Logf("Cache stats: %d entries, %d hits, %d avg tokens saved", entries, hits, avgSaved)
+}
diff --git a/llms/openai/internal/openaiclient/chat.go b/llms/openai/internal/openaiclient/chat.go
index f48097a9..dc4b4948 100644
--- a/llms/openai/internal/openaiclient/chat.go
+++ b/llms/openai/internal/openaiclient/chat.go
@@ -64,6 +64,10 @@ type ChatRequest struct {
 	// Options for streaming response. Only set this when you set stream: true.
 	StreamOptions *StreamOptions `json:"stream_options,omitempty"`
 
+	// ReasoningEffort controls thinking effort for reasoning models (o1, o3, GPT-5).
+	// Valid values: "minimal" (GPT-5 only), "low", "medium", "high"
+	ReasoningEffort string `json:"reasoning_effort,omitempty"`
+
 	// StreamingFunc is a function to be called for each chunk of a streaming response.
 	// Return an error to stop streaming early.
 	StreamingFunc func(ctx context.Context, chunk []byte) error `json:"-"`
@@ -484,7 +488,7 @@ func (c *Client) createChat(ctx context.Context, payload *ChatRequest) (*ChatCom
 	}
 
 	payloadBytes, err := json.Marshal(payload)
-	
+
 	// Restore original metadata
 	payload.Metadata = originalMetadata
 	if err != nil {
diff --git a/llms/openai/internal/openaiclient/marshal_test.go b/llms/openai/internal/openaiclient/marshal_test.go
index 0e5b99e3..ef2a96d5 100644
--- a/llms/openai/internal/openaiclient/marshal_test.go
+++ b/llms/openai/internal/openaiclient/marshal_test.go
@@ -178,28 +178,28 @@ func TestIsReasoningModel(t *testing.T) {
 		{"gpt-4-turbo", false},
 		{"gpt-4o", false},
 		{"text-davinci-003", false},
-		
+
 		// GPT-5 models - should be reasoning models
 		{"gpt-5", true},
 		{"gpt-5-preview", true},
 		{"gpt-5-mini", true},
 		{"gpt-5-turbo", true},
-		
+
 		// o1 models - should be reasoning models
 		{"o1-preview", true},
 		{"o1-mini", true},
 		{"o1-large", true},
-		
+
 		// o3 models - should be reasoning models
 		{"o3", true}, // Base o3 model
 		{"o3-mini", true},
 		{"o3-preview", true},
 		{"o3-large", true},
-		
+
 		// Edge cases
 		{"", false},
 		{"o10-preview", false}, // Doesn't start with "o1-"
-		{"o30-mini", false}, // Doesn't start with "o3-"
+		{"o30-mini", false},    // Doesn't start with "o3-"
 	}
 
 	for _, tt := range tests {
diff --git a/llms/openai/internal/openaiclient/openaiclient_test.go b/llms/openai/internal/openaiclient/openaiclient_test.go
index 50ee13d7..e86325b5 100644
--- a/llms/openai/internal/openaiclient/openaiclient_test.go
+++ b/llms/openai/internal/openaiclient/openaiclient_test.go
@@ -285,7 +285,7 @@ func TestInternalMetadataFiltering(t *testing.T) {
 				return nil, err
 			}
 			capturedRequestBody = body
-			
+
 			// Return a minimal valid response to avoid errors
 			responseBody := `{"choices":[{"message":{"content":"test"}}],"usage":{"total_tokens":10}}`
 			return &http.Response{
@@ -303,8 +303,8 @@ func TestInternalMetadataFiltering(t *testing.T) {
 			{Role: "user", Content: "test"},
 		},
 		Metadata: map[string]any{
-			"openai:use_legacy_max_tokens": true,     // Should be filtered out
-			"custom_field":                 "value",  // Should be preserved
+			"openai:use_legacy_max_tokens": true,    // Should be filtered out
+			"custom_field":                 "value", // Should be preserved
 		},
 	}
 
diff --git a/llms/openai/llmtest_test.go b/llms/openai/llmtest_test.go
new file mode 100644
index 00000000..2c770eac
--- /dev/null
+++ b/llms/openai/llmtest_test.go
@@ -0,0 +1,21 @@
+package openai
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("OPENAI_API_KEY") == "" {
+		t.Skip("OPENAI_API_KEY not set")
+	}
+
+	llm, err := New(WithModel("gpt-3.5-turbo"))
+	if err != nil {
+		t.Fatalf("Failed to create OpenAI LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/llms/openai/openaillm.go b/llms/openai/openaillm.go
index f3c1b0b4..6976a0ad 100644
--- a/llms/openai/openaillm.go
+++ b/llms/openai/openaillm.go
@@ -3,6 +3,8 @@ package openai
 import (
 	"context"
 	"fmt"
+	"regexp"
+	"strings"
 
 	"github.com/tmc/langchaingo/callbacks"
 	"github.com/tmc/langchaingo/llms"
@@ -14,6 +16,7 @@ type ChatMessage = openaiclient.ChatMessage
 type LLM struct {
 	CallbacksHandler callbacks.Handler
 	client           *openaiclient.Client
+	model            string // Track current model for reasoning detection
 }
 
 const (
@@ -24,7 +27,60 @@ const (
 	RoleTool      = "tool"
 )
 
-var _ llms.Model = (*LLM)(nil)
+// ModelCapability defines what a model supports
+type ModelCapability struct {
+	Pattern          string // Regex pattern to match model names
+	SupportsSystem   bool   // If true, supports system messages
+	SupportsThinking bool   // If true, supports reasoning/thinking
+	SupportsCaching  bool   // If true, supports prompt caching
+	// Add more capabilities as needed
+}
+
+// modelCapabilities defines capabilities for different model patterns
+var modelCapabilities = []ModelCapability{
+	// OpenAI reasoning models (o1, o3 series) - no system message support
+	{
+		Pattern:          `(?i)^o[13](-mini|-preview)?$`, // Matches o1, o1-mini, o1-preview, o3, o3-mini
+		SupportsSystem:   false,                          // O1 models don't support system messages
+		SupportsThinking: true,
+		SupportsCaching:  false,
+	},
+	// GPT-4 models
+	{
+		Pattern:          `(?i)^gpt-4`, // Matches gpt-4, gpt-4-turbo, etc.
+		SupportsSystem:   true,
+		SupportsThinking: false,
+		SupportsCaching:  false, // OpenAI caching coming soon
+	},
+	// GPT-3.5 models
+	{
+		Pattern:          `(?i)^gpt-3\.5`,
+		SupportsSystem:   true,
+		SupportsThinking: false,
+		SupportsCaching:  false,
+	},
+	// Future models can be added here
+}
+
+// getModelCapabilities returns the capabilities for a given model
+func getModelCapabilities(model string) ModelCapability {
+	for _, cap := range modelCapabilities {
+		if matched, _ := regexp.MatchString(cap.Pattern, model); matched {
+			return cap
+		}
+	}
+	// Default capabilities - assume standard model
+	return ModelCapability{
+		SupportsSystem:   true,
+		SupportsThinking: false,
+		SupportsCaching:  false,
+	}
+}
+
+var (
+	_ llms.Model          = (*LLM)(nil)
+	_ llms.ReasoningModel = (*LLM)(nil)
+)
 
 // New returns a new OpenAI LLM.
 func New(opts ...Option) (*LLM, error) {
@@ -35,6 +91,7 @@ func New(opts ...Option) (*LLM, error) {
 	return &LLM{
 		client:           c,
 		CallbacksHandler: opt.callbackHandler,
+		model:            c.Model, // Store the model for reasoning detection
 	}, err
 }
 
@@ -54,8 +111,40 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 		opt(&opts)
 	}
 
+	// Determine the effective model for this request (don't mutate o.model to avoid races)
+	effectiveModel := opts.Model
+	if effectiveModel == "" {
+		effectiveModel = o.model
+	}
+
+	// Get capabilities for this model
+	modelCaps := getModelCapabilities(effectiveModel)
+
+	// For models that don't support system messages, we need to merge them into user messages
+	var systemContent string
+	if !modelCaps.SupportsSystem {
+		for _, mc := range messages {
+			if mc.Role == llms.ChatMessageTypeSystem {
+				// Extract system message content
+				for _, part := range mc.Parts {
+					if textPart, ok := part.(llms.TextContent); ok {
+						if systemContent != "" {
+							systemContent += "\n\n"
+						}
+						systemContent += textPart.Text
+					}
+				}
+			}
+		}
+	}
+
 	chatMsgs := make([]*ChatMessage, 0, len(messages))
 	for _, mc := range messages {
+		// Skip system messages for models that don't support them
+		if mc.Role == llms.ChatMessageTypeSystem && !modelCaps.SupportsSystem {
+			continue
+		}
+
 		msg := &ChatMessage{MultiContent: mc.Parts}
 		switch mc.Role {
 		case llms.ChatMessageTypeSystem:
@@ -64,6 +153,17 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 			msg.Role = RoleAssistant
 		case llms.ChatMessageTypeHuman:
 			msg.Role = RoleUser
+			// For models without system support, prepend system content to first user message
+			if systemContent != "" && !modelCaps.SupportsSystem {
+				// Prepend system content to the user message
+				newParts := []llms.ContentPart{}
+				if systemContent != "" {
+					newParts = append(newParts, llms.TextContent{Text: systemContent + "\n\n"})
+				}
+				newParts = append(newParts, mc.Parts...)
+				msg.MultiContent = newParts
+				systemContent = "" // Clear after using
+			}
 		case llms.ChatMessageTypeGeneric:
 			msg.Role = RoleUser
 		case llms.ChatMessageTypeFunction:
@@ -110,6 +210,57 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 		}
 	}
 
+	// Extract reasoning effort for thinking models
+	// Note: OpenAI o1/o3 models have built-in reasoning and don't support reasoning_effort parameter
+	// This is kept for future models that might support it (like GPT-5)
+	var reasoningEffort string
+	// Commented out for now since current o1 models don't support this parameter
+	/*
+		if opts.Metadata != nil {
+			if config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig); ok {
+				// Map thinking mode to reasoning effort
+				switch config.Mode {
+				case llms.ThinkingModeLow:
+					reasoningEffort = "low"
+				case llms.ThinkingModeMedium:
+					reasoningEffort = "medium"
+				case llms.ThinkingModeHigh:
+					reasoningEffort = "high"
+				}
+
+				// Handle streaming for thinking
+				if config.StreamThinking && opts.StreamingReasoningFunc == nil && opts.StreamingFunc != nil {
+					// Set up default reasoning streaming if requested but not provided
+					// Wrap the single-param streaming func into a reasoning func
+					opts.StreamingReasoningFunc = func(ctx context.Context, reasoningChunk []byte, chunk []byte) error {
+						// For default behavior, we might want to stream both or just the main content
+						// Here we'll just stream the main content chunk
+						if len(chunk) > 0 {
+							return opts.StreamingFunc(ctx, chunk)
+						}
+						return nil
+					}
+				}
+			}
+		}
+	*/
+
+	// Filter out internal metadata that shouldn't be sent to API
+	apiMetadata := make(map[string]any)
+	if opts.Metadata != nil {
+		for k, v := range opts.Metadata {
+			// Skip internal metadata keys
+			if k == "thinking_config" || strings.HasPrefix(k, "openai:") {
+				continue
+			}
+			apiMetadata[k] = v
+		}
+	}
+	// Only include metadata if there are actual values to send
+	if len(apiMetadata) == 0 {
+		apiMetadata = nil
+	}
+
 	req := &openaiclient.ChatRequest{
 		Model:                  opts.Model,
 		StopWords:              opts.StopWords,
@@ -120,6 +271,7 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 		N:                      opts.N,
 		FrequencyPenalty:       opts.FrequencyPenalty,
 		PresencePenalty:        opts.PresencePenalty,
+		ReasoningEffort:        reasoningEffort,
 
 		// Token handling: check metadata flag for legacy behavior
 		// By default use max_completion_tokens (modern field)
@@ -140,7 +292,7 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 		ToolChoice:           opts.ToolChoice,
 		FunctionCallBehavior: openaiclient.FunctionCallBehavior(opts.FunctionCallBehavior),
 		Seed:                 opts.Seed,
-		Metadata:             opts.Metadata,
+		Metadata:             apiMetadata,
 	}
 	if opts.JSONMode {
 		req.ResponseFormat = ResponseFormatJSON
@@ -187,11 +339,14 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 			ReasoningContent: c.Message.ReasoningContent,
 			StopReason:       fmt.Sprint(c.FinishReason),
 			GenerationInfo: map[string]any{
-				"CompletionTokens":                   result.Usage.CompletionTokens,
-				"PromptTokens":                       result.Usage.PromptTokens,
-				"TotalTokens":                        result.Usage.TotalTokens,
-				"ReasoningTokens":                    result.Usage.CompletionTokensDetails.ReasoningTokens,
-				"PromptAudioTokens":                  result.Usage.PromptTokensDetails.AudioTokens,
+				"CompletionTokens":  result.Usage.CompletionTokens,
+				"PromptTokens":      result.Usage.PromptTokens,
+				"TotalTokens":       result.Usage.TotalTokens,
+				"ReasoningTokens":   result.Usage.CompletionTokensDetails.ReasoningTokens,
+				"PromptAudioTokens": result.Usage.PromptTokensDetails.AudioTokens,
+				// Standardized fields for cross-provider compatibility
+				"ThinkingContent":                    c.Message.ReasoningContent,                           // Standardized field
+				"ThinkingTokens":                     result.Usage.CompletionTokensDetails.ReasoningTokens, // Standardized field
 				"PromptCachedTokens":                 result.Usage.PromptTokensDetails.CachedTokens,
 				"CompletionAudioTokens":              result.Usage.CompletionTokensDetails.AudioTokens,
 				"CompletionReasoningTokens":          result.Usage.CompletionTokensDetails.ReasoningTokens,
@@ -229,6 +384,44 @@ func (o *LLM) GenerateContent(ctx context.Context, messages []llms.MessageConten
 	return response, nil
 }
 
+// SupportsReasoning implements the ReasoningModel interface.
+// Returns true if the current model supports reasoning/thinking tokens.
+func (o *LLM) SupportsReasoning() bool {
+	// Check the current model (may have been overridden by WithModel option)
+	model := o.model
+	if model == "" {
+		model = o.client.Model
+	}
+
+	modelLower := strings.ToLower(model)
+
+	// OpenAI o1 series (reasoning models)
+	if strings.HasPrefix(modelLower, "o1-") ||
+		strings.Contains(modelLower, "o1-preview") ||
+		strings.Contains(modelLower, "o1-mini") {
+		return true
+	}
+
+	// OpenAI o3 series
+	if strings.HasPrefix(modelLower, "o3-") ||
+		strings.Contains(modelLower, "o3-mini") {
+		return true
+	}
+
+	// Future o4+ series
+	if strings.HasPrefix(modelLower, "o4-") ||
+		strings.HasPrefix(modelLower, "o5-") {
+		return true
+	}
+
+	// GPT-5 series (expected to have reasoning capabilities)
+	if strings.HasPrefix(modelLower, "gpt-5") {
+		return true
+	}
+
+	return false
+}
+
 // CreateEmbedding creates embeddings for the given input texts.
 func (o *LLM) CreateEmbedding(ctx context.Context, inputTexts []string) ([][]float32, error) {
 	embeddings, err := o.client.CreateEmbedding(ctx, &openaiclient.EmbeddingRequest{
diff --git a/llms/options.go b/llms/options.go
index 0c0a4afc..44ee14f1 100644
--- a/llms/options.go
+++ b/llms/options.go
@@ -87,7 +87,8 @@ type FunctionDefinition struct {
 	Description string `json:"description"`
 	// Parameters is a list of parameters for the function.
 	Parameters any `json:"parameters,omitempty"`
-	// Strict is a flag to indicate if the function should be called strictly. Only used for openai llm structured output.
+	// Strict is a flag to indicate if the function should be called strictly.
+	// Provider support varies - typically used for structured output guarantees.
 	Strict bool `json:"strict,omitempty"`
 }
 
@@ -283,8 +284,8 @@ func WithMetadata(metadata map[string]interface{}) CallOption {
 	}
 }
 
-// WithResponseMIMEType will add an option to set the ResponseMIMEType
-// Currently only supported by googleai llms.
+// WithResponseMIMEType will add an option to set the ResponseMIMEType.
+// Provider support varies - check your provider's documentation.
 func WithResponseMIMEType(responseMIMEType string) CallOption {
 	return func(o *CallOptions) {
 		o.ResponseMIMEType = responseMIMEType
diff --git a/llms/prompt_caching.go b/llms/prompt_caching.go
new file mode 100644
index 00000000..85014e9d
--- /dev/null
+++ b/llms/prompt_caching.go
@@ -0,0 +1,40 @@
+package llms
+
+import "time"
+
+// CacheControl represents prompt caching configuration for providers that support it.
+type CacheControl struct {
+	// Type specifies the type of caching (provider-specific, e.g., "ephemeral").
+	Type string `json:"type,omitempty"`
+
+	// Duration specifies cache lifetime (provider-specific limits apply).
+	Duration time.Duration `json:"-"`
+}
+
+// CachedContent represents content with caching instructions.
+type CachedContent struct {
+	ContentPart
+	CacheControl *CacheControl `json:"cache_control,omitempty"`
+}
+
+func (cc CachedContent) isPart() {}
+
+// WithCacheControl wraps content with cache control instructions.
+func WithCacheControl(content ContentPart, control *CacheControl) CachedContent {
+	return CachedContent{
+		ContentPart:  content,
+		CacheControl: control,
+	}
+}
+
+// WithPromptCaching adds cache control metadata to call options.
+// This is a generic option that can be used by any provider that supports caching.
+// Providers should check for this metadata and handle it appropriately.
+func WithPromptCaching(enabled bool) CallOption {
+	return func(opts *CallOptions) {
+		if opts.Metadata == nil {
+			opts.Metadata = make(map[string]interface{})
+		}
+		opts.Metadata["prompt_caching"] = enabled
+	}
+}
diff --git a/llms/prompt_caching_test.go b/llms/prompt_caching_test.go
new file mode 100644
index 00000000..316dfc39
--- /dev/null
+++ b/llms/prompt_caching_test.go
@@ -0,0 +1,59 @@
+package llms_test
+
+import (
+	"testing"
+	"time"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+func TestWithCacheControl(t *testing.T) {
+	textContent := llms.TextPart("Hello world")
+	cacheControl := &llms.CacheControl{
+		Type:     "test",
+		Duration: 10 * time.Minute,
+	}
+
+	cached := llms.WithCacheControl(textContent, cacheControl)
+
+	if cached.ContentPart != textContent {
+		t.Error("cached content should wrap the original content")
+	}
+
+	if cached.CacheControl != cacheControl {
+		t.Error("cached content should have the provided cache control")
+	}
+}
+
+func TestWithPromptCaching(t *testing.T) {
+	option := llms.WithPromptCaching(true)
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	if opts.Metadata == nil {
+		t.Fatal("metadata should be initialized")
+	}
+
+	enabled, ok := opts.Metadata["prompt_caching"].(bool)
+	if !ok {
+		t.Fatal("prompt_caching should be a bool")
+	}
+
+	if !enabled {
+		t.Error("prompt_caching should be true")
+	}
+}
+
+func TestCachedContentImplementsContentPart(t *testing.T) {
+	textContent := llms.TextPart("Hello world")
+	cacheControl := &llms.CacheControl{
+		Type:     "test",
+		Duration: 10 * time.Minute,
+	}
+	cached := llms.WithCacheControl(textContent, cacheControl)
+
+	// This should compile - CachedContent implements ContentPart
+	var part llms.ContentPart = cached
+	_ = part
+}
diff --git a/llms/reasoning.go b/llms/reasoning.go
new file mode 100644
index 00000000..3be416c7
--- /dev/null
+++ b/llms/reasoning.go
@@ -0,0 +1,270 @@
+package llms
+
+import "strings"
+
+// ThinkingMode represents different thinking/reasoning modes for LLMs.
+type ThinkingMode string
+
+const (
+	// ThinkingModeNone disables thinking/reasoning.
+	ThinkingModeNone ThinkingMode = "none"
+
+	// ThinkingModeLow allocates minimal tokens for thinking (~20% of max tokens).
+	ThinkingModeLow ThinkingMode = "low"
+
+	// ThinkingModeMedium allocates moderate tokens for thinking (~50% of max tokens).
+	ThinkingModeMedium ThinkingMode = "medium"
+
+	// ThinkingModeHigh allocates maximum tokens for thinking (~80% of max tokens).
+	ThinkingModeHigh ThinkingMode = "high"
+
+	// ThinkingModeAuto lets the model decide how much thinking is needed.
+	ThinkingModeAuto ThinkingMode = "auto"
+)
+
+// ThinkingConfig configures thinking/reasoning behavior for models that support it.
+type ThinkingConfig struct {
+	// Mode specifies the thinking mode (none, low, medium, high, auto).
+	Mode ThinkingMode `json:"mode,omitempty"`
+
+	// BudgetTokens sets explicit token budget for thinking.
+	// Providers may have different minimum and maximum limits.
+	BudgetTokens int `json:"budget_tokens,omitempty"`
+
+	// ReturnThinking controls whether thinking/reasoning is included in response.
+	// Provider support and behavior varies.
+	ReturnThinking bool `json:"return_thinking,omitempty"`
+
+	// StreamThinking enables streaming of thinking tokens as they're generated.
+	// Not all providers support this feature.
+	StreamThinking bool `json:"stream_thinking,omitempty"`
+
+	// InterleaveThinking enables thinking between tool calls.
+	// Provider support varies.
+	InterleaveThinking bool `json:"interleave_thinking,omitempty"`
+}
+
+// DefaultThinkingConfig returns a sensible default thinking configuration.
+func DefaultThinkingConfig() *ThinkingConfig {
+	return &ThinkingConfig{
+		Mode:           ThinkingModeAuto,
+		ReturnThinking: false,
+		StreamThinking: false,
+	}
+}
+
+// WithThinking adds thinking configuration to call options.
+func WithThinking(config *ThinkingConfig) CallOption {
+	return func(opts *CallOptions) {
+		if opts.Metadata == nil {
+			opts.Metadata = make(map[string]interface{})
+		}
+		opts.Metadata["thinking_config"] = config
+	}
+}
+
+// getOrCreateThinkingConfig is a helper to get or create thinking config from metadata.
+func getOrCreateThinkingConfig(opts *CallOptions) *ThinkingConfig {
+	if opts.Metadata == nil {
+		opts.Metadata = make(map[string]interface{})
+	}
+
+	if existing, ok := opts.Metadata["thinking_config"].(*ThinkingConfig); ok {
+		return existing
+	}
+
+	config := DefaultThinkingConfig()
+	opts.Metadata["thinking_config"] = config
+	return config
+}
+
+// GetThinkingConfig safely retrieves thinking config from call options.
+// Returns nil if no thinking config is present.
+func GetThinkingConfig(opts *CallOptions) *ThinkingConfig {
+	if opts == nil || opts.Metadata == nil {
+		return nil
+	}
+
+	config, _ := opts.Metadata["thinking_config"].(*ThinkingConfig)
+	return config
+}
+
+// WithThinkingMode sets the thinking mode for the request.
+func WithThinkingMode(mode ThinkingMode) CallOption {
+	return func(opts *CallOptions) {
+		config := getOrCreateThinkingConfig(opts)
+		config.Mode = mode
+	}
+}
+
+// WithThinkingBudget sets explicit token budget for thinking.
+func WithThinkingBudget(tokens int) CallOption {
+	return func(opts *CallOptions) {
+		config := getOrCreateThinkingConfig(opts)
+		config.BudgetTokens = tokens
+	}
+}
+
+// WithReturnThinking enables returning thinking/reasoning in the response.
+func WithReturnThinking(enabled bool) CallOption {
+	return func(opts *CallOptions) {
+		config := getOrCreateThinkingConfig(opts)
+		config.ReturnThinking = enabled
+	}
+}
+
+// WithStreamThinking enables streaming of thinking tokens.
+func WithStreamThinking(enabled bool) CallOption {
+	return func(opts *CallOptions) {
+		config := getOrCreateThinkingConfig(opts)
+		config.StreamThinking = enabled
+	}
+}
+
+// WithInterleaveThinking enables interleaved thinking between tool calls.
+// Provider support varies - check your provider's documentation.
+func WithInterleaveThinking(enabled bool) CallOption {
+	return func(opts *CallOptions) {
+		config := getOrCreateThinkingConfig(opts)
+		config.InterleaveThinking = enabled
+	}
+}
+
+// Note: ReasoningModel interface is defined in llms.go
+
+// IsReasoningModel returns true if the model is a reasoning/thinking model.
+// This includes OpenAI o1/o3/GPT-5 series, Anthropic Claude 3.7+, DeepSeek reasoner, etc.
+// For runtime checking of LLM instances, use SupportsReasoningModel instead.
+func IsReasoningModel(model string) bool {
+	return DefaultIsReasoningModel(model)
+}
+
+// SupportsReasoningModel checks if an LLM instance supports reasoning tokens.
+// This first checks if the LLM implements the ReasoningModel interface.
+// If not, it falls back to checking the model string if available.
+func SupportsReasoningModel(llm interface{}) bool {
+	// Check if the LLM implements ReasoningModel
+	if reasoner, ok := llm.(ReasoningModel); ok {
+		return reasoner.SupportsReasoning()
+	}
+
+	// Fallback: check if we can extract a model string somehow
+	// This is a best-effort approach for backwards compatibility
+	return false
+}
+
+// DefaultIsReasoningModel provides the default reasoning model detection logic.
+// This can be used by LLM implementations that want to extend rather than replace
+// the default detection logic.
+func DefaultIsReasoningModel(model string) bool {
+	modelLower := strings.ToLower(model)
+
+	// OpenAI reasoning models
+	if strings.HasPrefix(modelLower, "gpt-5") ||
+		strings.HasPrefix(modelLower, "o1-") ||
+		strings.HasPrefix(modelLower, "o3-") ||
+		strings.Contains(modelLower, "o1-preview") ||
+		strings.Contains(modelLower, "o1-mini") ||
+		strings.Contains(modelLower, "o3-mini") ||
+		strings.Contains(modelLower, "o4-mini") {
+		return true
+	}
+
+	// Anthropic extended thinking models
+	if strings.Contains(modelLower, "claude-3-7") ||
+		strings.Contains(modelLower, "claude-3.7") ||
+		strings.Contains(modelLower, "claude-4") ||
+		strings.Contains(modelLower, "claude-opus-4") ||
+		strings.Contains(modelLower, "claude-sonnet-4") {
+		return true
+	}
+
+	// DeepSeek reasoner
+	if strings.Contains(modelLower, "deepseek-reasoner") ||
+		strings.Contains(modelLower, "deepseek-r1") {
+		return true
+	}
+
+	// Grok reasoning models
+	if strings.Contains(modelLower, "grok") && strings.Contains(modelLower, "reasoning") {
+		return true
+	}
+
+	return false
+}
+
+// CalculateThinkingBudget calculates the token budget based on mode and max tokens.
+func CalculateThinkingBudget(mode ThinkingMode, maxTokens int) int {
+	switch mode {
+	case ThinkingModeLow:
+		return maxTokens * 20 / 100 // 20%
+	case ThinkingModeMedium:
+		return maxTokens * 50 / 100 // 50%
+	case ThinkingModeHigh:
+		return maxTokens * 80 / 100 // 80%
+	case ThinkingModeAuto:
+		// Let the model decide
+		return 0
+	default:
+		return 0
+	}
+}
+
+// ThinkingTokenUsage represents token usage specific to thinking/reasoning.
+type ThinkingTokenUsage struct {
+	// ThinkingTokens is the total number of thinking/reasoning tokens used.
+	ThinkingTokens int `json:"thinking_tokens,omitempty"`
+
+	// ThinkingInputTokens is the number of input tokens used for thinking.
+	ThinkingInputTokens int `json:"thinking_input_tokens,omitempty"`
+
+	// ThinkingOutputTokens is the number of output tokens from thinking.
+	ThinkingOutputTokens int `json:"thinking_output_tokens,omitempty"`
+
+	// ThinkingCachedTokens is the number of cached thinking tokens (if applicable).
+	ThinkingCachedTokens int `json:"thinking_cached_tokens,omitempty"`
+
+	// ThinkingBudgetUsed is the actual budget used vs allocated.
+	ThinkingBudgetUsed int `json:"thinking_budget_used,omitempty"`
+
+	// ThinkingBudgetAllocated is the budget that was allocated.
+	ThinkingBudgetAllocated int `json:"thinking_budget_allocated,omitempty"`
+}
+
+// ExtractThinkingTokens extracts thinking token information from generation info.
+func ExtractThinkingTokens(generationInfo map[string]any) *ThinkingTokenUsage {
+	if generationInfo == nil {
+		return nil
+	}
+
+	usage := &ThinkingTokenUsage{}
+
+	// OpenAI-style reasoning tokens
+	if v, ok := generationInfo["ReasoningTokens"].(int); ok {
+		usage.ThinkingTokens = v
+	}
+	if v, ok := generationInfo["CompletionReasoningTokens"].(int); ok {
+		usage.ThinkingOutputTokens = v
+	}
+
+	// Anthropic-style thinking tokens (would be in extended thinking mode)
+	if v, ok := generationInfo["ThinkingTokens"].(int); ok {
+		usage.ThinkingTokens = v
+	}
+	if v, ok := generationInfo["ThinkingInputTokens"].(int); ok {
+		usage.ThinkingInputTokens = v
+	}
+	if v, ok := generationInfo["ThinkingOutputTokens"].(int); ok {
+		usage.ThinkingOutputTokens = v
+	}
+
+	// Budget information
+	if v, ok := generationInfo["ThinkingBudgetUsed"].(int); ok {
+		usage.ThinkingBudgetUsed = v
+	}
+	if v, ok := generationInfo["ThinkingBudgetAllocated"].(int); ok {
+		usage.ThinkingBudgetAllocated = v
+	}
+
+	return usage
+}
diff --git a/llms/reasoning_test.go b/llms/reasoning_test.go
new file mode 100644
index 00000000..03a13284
--- /dev/null
+++ b/llms/reasoning_test.go
@@ -0,0 +1,477 @@
+package llms_test
+
+import (
+	"context"
+	"testing"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+func TestThinkingModes(t *testing.T) {
+	tests := []struct {
+		name     string
+		mode     llms.ThinkingMode
+		expected string
+	}{
+		{"None", llms.ThinkingModeNone, "none"},
+		{"Low", llms.ThinkingModeLow, "low"},
+		{"Medium", llms.ThinkingModeMedium, "medium"},
+		{"High", llms.ThinkingModeHigh, "high"},
+		{"Auto", llms.ThinkingModeAuto, "auto"},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			if string(tt.mode) != tt.expected {
+				t.Errorf("ThinkingMode %s = %s, want %s", tt.name, tt.mode, tt.expected)
+			}
+		})
+	}
+}
+
+func TestDefaultThinkingConfig(t *testing.T) {
+	config := llms.DefaultThinkingConfig()
+
+	if config.Mode != llms.ThinkingModeAuto {
+		t.Errorf("Default mode = %s, want %s", config.Mode, llms.ThinkingModeAuto)
+	}
+
+	if config.ReturnThinking {
+		t.Error("Default ReturnThinking should be false")
+	}
+
+	if config.StreamThinking {
+		t.Error("Default StreamThinking should be false")
+	}
+
+	if config.InterleaveThinking {
+		t.Error("Default InterleaveThinking should be false")
+	}
+}
+
+func TestWithThinking(t *testing.T) {
+	config := &llms.ThinkingConfig{
+		Mode:           llms.ThinkingModeHigh,
+		BudgetTokens:   4096,
+		ReturnThinking: true,
+		StreamThinking: true,
+	}
+
+	option := llms.WithThinking(config)
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	if opts.Metadata == nil {
+		t.Fatal("metadata should be initialized")
+	}
+
+	storedConfig, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig)
+	if !ok {
+		t.Fatal("thinking_config should be stored in metadata")
+	}
+
+	if storedConfig != config {
+		t.Error("stored config should match original")
+	}
+}
+
+func TestWithThinkingMode(t *testing.T) {
+	option := llms.WithThinkingMode(llms.ThinkingModeMedium)
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig)
+	if !ok {
+		t.Fatal("thinking_config should be stored in metadata")
+	}
+
+	if config.Mode != llms.ThinkingModeMedium {
+		t.Errorf("mode = %s, want %s", config.Mode, llms.ThinkingModeMedium)
+	}
+}
+
+func TestWithThinkingBudget(t *testing.T) {
+	option := llms.WithThinkingBudget(8192)
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig)
+	if !ok {
+		t.Fatal("thinking_config should be stored in metadata")
+	}
+
+	if config.BudgetTokens != 8192 {
+		t.Errorf("BudgetTokens = %d, want 8192", config.BudgetTokens)
+	}
+}
+
+func TestWithReturnThinking(t *testing.T) {
+	option := llms.WithReturnThinking(true)
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig)
+	if !ok {
+		t.Fatal("thinking_config should be stored in metadata")
+	}
+
+	if !config.ReturnThinking {
+		t.Error("ReturnThinking should be true")
+	}
+}
+
+func TestWithStreamThinking(t *testing.T) {
+	option := llms.WithStreamThinking(true)
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig)
+	if !ok {
+		t.Fatal("thinking_config should be stored in metadata")
+	}
+
+	if !config.StreamThinking {
+		t.Error("StreamThinking should be true")
+	}
+}
+
+func TestWithInterleaveThinking(t *testing.T) {
+	option := llms.WithInterleaveThinking(true)
+
+	var opts llms.CallOptions
+	option(&opts)
+
+	config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig)
+	if !ok {
+		t.Fatal("thinking_config should be stored in metadata")
+	}
+
+	if !config.InterleaveThinking {
+		t.Error("InterleaveThinking should be true")
+	}
+}
+
+func TestIsReasoningModel(t *testing.T) {
+	tests := []struct {
+		model    string
+		expected bool
+	}{
+		// OpenAI reasoning models
+		{"gpt-5-mini", true},
+		{"gpt-5-preview", true},
+		{"o1-preview", true},
+		{"o1-mini", true},
+		{"o3-mini", true},
+		{"o3-2025-04-16", true},
+		{"o4-mini", true},
+
+		// Anthropic extended thinking models
+		{"claude-3-7-sonnet", true},
+		{"claude-3.7-sonnet", true},
+		{"claude-4", true},
+		{"claude-opus-4", true},
+		{"claude-sonnet-4", true},
+
+		// DeepSeek reasoner
+		{"deepseek-reasoner", true},
+		{"deepseek-r1", true},
+
+		// Grok reasoning
+		{"grok-reasoning", true},
+
+		// Non-reasoning models
+		{"gpt-4", false},
+		{"gpt-3.5-turbo", false},
+		{"claude-3-sonnet", false},
+		{"claude-2", false},
+		{"llama-2", false},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.model, func(t *testing.T) {
+			result := llms.IsReasoningModel(tt.model)
+			if result != tt.expected {
+				t.Errorf("IsReasoningModel(%q) = %v, want %v", tt.model, result, tt.expected)
+			}
+		})
+	}
+}
+
+func TestCalculateThinkingBudget(t *testing.T) {
+	tests := []struct {
+		mode      llms.ThinkingMode
+		maxTokens int
+		expected  int
+	}{
+		{llms.ThinkingModeLow, 1000, 200},     // 20%
+		{llms.ThinkingModeMedium, 1000, 500},  // 50%
+		{llms.ThinkingModeHigh, 1000, 800},    // 80%
+		{llms.ThinkingModeAuto, 1000, 0},      // Let model decide
+		{llms.ThinkingModeNone, 1000, 0},      // No thinking
+		{llms.ThinkingModeLow, 5000, 1000},    // 20% of 5000
+		{llms.ThinkingModeMedium, 5000, 2500}, // 50% of 5000
+		{llms.ThinkingModeHigh, 5000, 4000},   // 80% of 5000
+	}
+
+	for _, tt := range tests {
+		t.Run(string(tt.mode), func(t *testing.T) {
+			result := llms.CalculateThinkingBudget(tt.mode, tt.maxTokens)
+			if result != tt.expected {
+				t.Errorf("CalculateThinkingBudget(%s, %d) = %d, want %d",
+					tt.mode, tt.maxTokens, result, tt.expected)
+			}
+		})
+	}
+}
+
+func TestExtractThinkingTokens(t *testing.T) {
+	tests := []struct {
+		name           string
+		generationInfo map[string]any
+		expected       *llms.ThinkingTokenUsage
+	}{
+		{
+			name: "OpenAI reasoning tokens",
+			generationInfo: map[string]any{
+				"ReasoningTokens":           500,
+				"CompletionReasoningTokens": 300,
+			},
+			expected: &llms.ThinkingTokenUsage{
+				ThinkingTokens:       500,
+				ThinkingOutputTokens: 300,
+			},
+		},
+		{
+			name: "Anthropic thinking tokens",
+			generationInfo: map[string]any{
+				"ThinkingTokens":          1000,
+				"ThinkingInputTokens":     200,
+				"ThinkingOutputTokens":    800,
+				"ThinkingBudgetUsed":      1000,
+				"ThinkingBudgetAllocated": 2000,
+			},
+			expected: &llms.ThinkingTokenUsage{
+				ThinkingTokens:          1000,
+				ThinkingInputTokens:     200,
+				ThinkingOutputTokens:    800,
+				ThinkingBudgetUsed:      1000,
+				ThinkingBudgetAllocated: 2000,
+			},
+		},
+		{
+			name:           "nil generationInfo",
+			generationInfo: nil,
+			expected:       nil,
+		},
+		{
+			name:           "empty generationInfo",
+			generationInfo: map[string]any{},
+			expected:       &llms.ThinkingTokenUsage{},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			result := llms.ExtractThinkingTokens(tt.generationInfo)
+
+			if tt.expected == nil {
+				if result != nil {
+					t.Error("expected nil result")
+				}
+				return
+			}
+
+			if result == nil {
+				t.Fatal("expected non-nil result")
+			}
+
+			if result.ThinkingTokens != tt.expected.ThinkingTokens {
+				t.Errorf("ThinkingTokens = %d, want %d",
+					result.ThinkingTokens, tt.expected.ThinkingTokens)
+			}
+
+			if result.ThinkingInputTokens != tt.expected.ThinkingInputTokens {
+				t.Errorf("ThinkingInputTokens = %d, want %d",
+					result.ThinkingInputTokens, tt.expected.ThinkingInputTokens)
+			}
+
+			if result.ThinkingOutputTokens != tt.expected.ThinkingOutputTokens {
+				t.Errorf("ThinkingOutputTokens = %d, want %d",
+					result.ThinkingOutputTokens, tt.expected.ThinkingOutputTokens)
+			}
+
+			if result.ThinkingBudgetUsed != tt.expected.ThinkingBudgetUsed {
+				t.Errorf("ThinkingBudgetUsed = %d, want %d",
+					result.ThinkingBudgetUsed, tt.expected.ThinkingBudgetUsed)
+			}
+
+			if result.ThinkingBudgetAllocated != tt.expected.ThinkingBudgetAllocated {
+				t.Errorf("ThinkingBudgetAllocated = %d, want %d",
+					result.ThinkingBudgetAllocated, tt.expected.ThinkingBudgetAllocated)
+			}
+		})
+	}
+}
+
+// MockLLMWithThinking is a mock LLM that supports thinking tokens
+type MockLLMWithThinking struct {
+	thinkingConfig   *llms.ThinkingConfig
+	supportsThinking bool
+}
+
+func (m *MockLLMWithThinking) Call(ctx context.Context, prompt string, options ...llms.CallOption) (string, error) {
+	return "test response with thinking", nil
+}
+
+func (m *MockLLMWithThinking) GenerateContent(ctx context.Context, messages []llms.MessageContent, options ...llms.CallOption) (*llms.ContentResponse, error) {
+	var opts llms.CallOptions
+	for _, opt := range options {
+		opt(&opts)
+	}
+
+	// Extract thinking config
+	if opts.Metadata != nil {
+		if config, ok := opts.Metadata["thinking_config"].(*llms.ThinkingConfig); ok {
+			m.thinkingConfig = config
+		}
+	}
+
+	generationInfo := map[string]any{
+		"CompletionTokens": 100,
+		"PromptTokens":     200,
+		"TotalTokens":      300,
+	}
+
+	// Add thinking tokens if configured
+	if m.thinkingConfig != nil && m.thinkingConfig.Mode != llms.ThinkingModeNone {
+		budget := llms.CalculateThinkingBudget(m.thinkingConfig.Mode, 1000)
+		generationInfo["ReasoningTokens"] = budget
+		generationInfo["ThinkingBudgetAllocated"] = budget
+		generationInfo["ThinkingBudgetUsed"] = budget * 80 / 100 // Use 80% of budget
+	}
+
+	content := "test response"
+	if m.thinkingConfig != nil && m.thinkingConfig.ReturnThinking {
+		content = "<thinking>step by step reasoning</thinking>\n" + content
+	}
+
+	return &llms.ContentResponse{
+		Choices: []*llms.ContentChoice{
+			{
+				Content:        content,
+				GenerationInfo: generationInfo,
+			},
+		},
+	}, nil
+}
+
+// SupportsReasoning implements the ReasoningModel interface.
+func (m *MockLLMWithThinking) SupportsReasoning() bool {
+	return m.supportsThinking
+}
+
+func TestThinkingIntegration(t *testing.T) {
+	llm := &MockLLMWithThinking{supportsThinking: true}
+	ctx := context.Background()
+
+	messages := []llms.MessageContent{
+		{
+			Role:  llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{llms.TextPart("test")},
+		},
+	}
+
+	t.Run("with thinking mode high", func(t *testing.T) {
+		resp, err := llm.GenerateContent(ctx, messages,
+			llms.WithThinkingMode(llms.ThinkingModeHigh),
+			llms.WithReturnThinking(true),
+		)
+
+		if err != nil {
+			t.Fatalf("unexpected error: %v", err)
+		}
+
+		if llm.thinkingConfig == nil {
+			t.Fatal("thinking config should be set")
+		}
+
+		if llm.thinkingConfig.Mode != llms.ThinkingModeHigh {
+			t.Errorf("mode = %s, want %s", llm.thinkingConfig.Mode, llms.ThinkingModeHigh)
+		}
+
+		if !llm.thinkingConfig.ReturnThinking {
+			t.Error("ReturnThinking should be true")
+		}
+
+		// Check response includes thinking
+		if resp.Choices[0].Content == "" {
+			t.Error("content should not be empty")
+		}
+
+		// Extract thinking tokens
+		usage := llms.ExtractThinkingTokens(resp.Choices[0].GenerationInfo)
+		if usage == nil {
+			t.Fatal("thinking token usage should be extracted")
+		}
+
+		if usage.ThinkingTokens == 0 {
+			t.Error("ThinkingTokens should be > 0")
+		}
+	})
+
+	t.Run("with explicit budget", func(t *testing.T) {
+		_, err := llm.GenerateContent(ctx, messages,
+			llms.WithThinkingBudget(4096),
+		)
+
+		if err != nil {
+			t.Fatalf("unexpected error: %v", err)
+		}
+
+		if llm.thinkingConfig == nil {
+			t.Fatal("thinking config should be set")
+		}
+
+		if llm.thinkingConfig.BudgetTokens != 4096 {
+			t.Errorf("BudgetTokens = %d, want 4096", llm.thinkingConfig.BudgetTokens)
+		}
+	})
+}
+
+func TestSupportsReasoningModel(t *testing.T) {
+	tests := []struct {
+		name     string
+		llm      interface{}
+		expected bool
+	}{
+		{
+			name:     "LLM with reasoning support",
+			llm:      &MockLLMWithThinking{supportsThinking: true},
+			expected: true,
+		},
+		{
+			name:     "LLM without reasoning support",
+			llm:      &MockLLMWithThinking{supportsThinking: false},
+			expected: false,
+		},
+		{
+			name:     "Non-reasoning LLM",
+			llm:      struct{}{},
+			expected: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			result := llms.SupportsReasoningModel(tt.llm)
+			if result != tt.expected {
+				t.Errorf("SupportsReasoningModel() = %v, want %v", result, tt.expected)
+			}
+		})
+	}
+}
diff --git a/llms/token_utilization_test.go b/llms/token_utilization_test.go
new file mode 100644
index 00000000..d5b41fd5
--- /dev/null
+++ b/llms/token_utilization_test.go
@@ -0,0 +1,186 @@
+package llms_test
+
+import (
+	"context"
+	"testing"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+// MockLLMWithTokenUsage is a mock LLM that returns token usage information
+type MockLLMWithTokenUsage struct {
+	includeCache bool
+}
+
+func (m *MockLLMWithTokenUsage) Call(ctx context.Context, prompt string, options ...llms.CallOption) (string, error) {
+	return "test response", nil
+}
+
+func (m *MockLLMWithTokenUsage) GenerateContent(ctx context.Context, messages []llms.MessageContent, options ...llms.CallOption) (*llms.ContentResponse, error) {
+	generationInfo := map[string]any{
+		"CompletionTokens": 50,
+		"PromptTokens":     100,
+		"TotalTokens":      150,
+	}
+
+	if m.includeCache {
+		// OpenAI-style cache tokens
+		generationInfo["PromptCachedTokens"] = 80
+
+		// Anthropic-style cache tokens
+		generationInfo["CacheCreationInputTokens"] = 20
+		generationInfo["CacheReadInputTokens"] = 80
+	}
+
+	return &llms.ContentResponse{
+		Choices: []*llms.ContentChoice{
+			{
+				Content:        "test response",
+				GenerationInfo: generationInfo,
+			},
+		},
+	}, nil
+}
+
+func TestTokenUtilizationWithoutCache(t *testing.T) {
+	llm := &MockLLMWithTokenUsage{includeCache: false}
+	ctx := context.Background()
+
+	messages := []llms.MessageContent{
+		{
+			Role:  llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{llms.TextPart("test")},
+		},
+	}
+
+	resp, err := llm.GenerateContent(ctx, messages)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("expected at least one choice")
+	}
+
+	info := resp.Choices[0].GenerationInfo
+
+	// Check basic token counts
+	if ct, ok := info["CompletionTokens"].(int); !ok || ct != 50 {
+		t.Errorf("expected CompletionTokens=50, got %v", info["CompletionTokens"])
+	}
+
+	if pt, ok := info["PromptTokens"].(int); !ok || pt != 100 {
+		t.Errorf("expected PromptTokens=100, got %v", info["PromptTokens"])
+	}
+
+	if tt, ok := info["TotalTokens"].(int); !ok || tt != 150 {
+		t.Errorf("expected TotalTokens=150, got %v", info["TotalTokens"])
+	}
+
+	// Cache tokens should not be present
+	if _, ok := info["PromptCachedTokens"]; ok {
+		t.Error("PromptCachedTokens should not be present")
+	}
+
+	if _, ok := info["CacheCreationInputTokens"]; ok {
+		t.Error("CacheCreationInputTokens should not be present")
+	}
+
+	if _, ok := info["CacheReadInputTokens"]; ok {
+		t.Error("CacheReadInputTokens should not be present")
+	}
+}
+
+func TestTokenUtilizationWithCache(t *testing.T) {
+	llm := &MockLLMWithTokenUsage{includeCache: true}
+	ctx := context.Background()
+
+	messages := []llms.MessageContent{
+		{
+			Role:  llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{llms.TextPart("test")},
+		},
+	}
+
+	resp, err := llm.GenerateContent(ctx, messages)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("expected at least one choice")
+	}
+
+	info := resp.Choices[0].GenerationInfo
+
+	// Check basic token counts
+	if ct, ok := info["CompletionTokens"].(int); !ok || ct != 50 {
+		t.Errorf("expected CompletionTokens=50, got %v", info["CompletionTokens"])
+	}
+
+	if pt, ok := info["PromptTokens"].(int); !ok || pt != 100 {
+		t.Errorf("expected PromptTokens=100, got %v", info["PromptTokens"])
+	}
+
+	if tt, ok := info["TotalTokens"].(int); !ok || tt != 150 {
+		t.Errorf("expected TotalTokens=150, got %v", info["TotalTokens"])
+	}
+
+	// OpenAI-style cache tokens
+	if pct, ok := info["PromptCachedTokens"].(int); !ok || pct != 80 {
+		t.Errorf("expected PromptCachedTokens=80, got %v", info["PromptCachedTokens"])
+	}
+
+	// Anthropic-style cache tokens
+	if ccit, ok := info["CacheCreationInputTokens"].(int); !ok || ccit != 20 {
+		t.Errorf("expected CacheCreationInputTokens=20, got %v", info["CacheCreationInputTokens"])
+	}
+
+	if crit, ok := info["CacheReadInputTokens"].(int); !ok || crit != 80 {
+		t.Errorf("expected CacheReadInputTokens=80, got %v", info["CacheReadInputTokens"])
+	}
+}
+
+func TestCalculateCostSavings(t *testing.T) {
+	// Test function to calculate cost savings from cached tokens
+	tests := []struct {
+		name            string
+		promptTokens    int
+		cachedTokens    int
+		pricePerMToken  float64
+		expectedSavings float64
+	}{
+		{
+			name:            "OpenAI 50% discount",
+			promptTokens:    1000,
+			cachedTokens:    800,
+			pricePerMToken:  5.0,   // $5 per 1M tokens
+			expectedSavings: 0.002, // 800 tokens * 50% discount * $5/1M
+		},
+		{
+			name:            "Anthropic 90% discount",
+			promptTokens:    2000,
+			cachedTokens:    1500,
+			pricePerMToken:  15.0,    // $15 per 1M tokens
+			expectedSavings: 0.02025, // 1500 tokens * 90% discount * $15/1M
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			// Calculate savings
+			var discountRate float64
+			if tt.name == "OpenAI 50% discount" {
+				discountRate = 0.5
+			} else {
+				discountRate = 0.9
+			}
+
+			savings := float64(tt.cachedTokens) * discountRate * tt.pricePerMToken / 1_000_000
+
+			if savings != tt.expectedSavings {
+				t.Errorf("expected savings=%f, got %f", tt.expectedSavings, savings)
+			}
+		})
+	}
+}
diff --git a/llms/watsonx/llmtest_test.go b/llms/watsonx/llmtest_test.go
new file mode 100644
index 00000000..c391dfdc
--- /dev/null
+++ b/llms/watsonx/llmtest_test.go
@@ -0,0 +1,26 @@
+package watsonx
+
+import (
+	"os"
+	"testing"
+
+	wx "github.com/IBM/watsonx-go/pkg/models"
+	"github.com/tmc/langchaingo/testing/llmtest"
+)
+
+func TestLLM(t *testing.T) {
+	if os.Getenv("WATSONX_API_KEY") == "" || os.Getenv("WATSONX_PROJECT_ID") == "" {
+		t.Skip("WATSONX_API_KEY or WATSONX_PROJECT_ID not set")
+	}
+
+	llm, err := New(
+		"ibm/granite-13b-instruct-v2",
+		wx.WithWatsonxAPIKey(wx.WatsonxAPIKey(os.Getenv("WATSONX_API_KEY"))),
+		wx.WithWatsonxProjectID(wx.WatsonxProjectID(os.Getenv("WATSONX_PROJECT_ID"))),
+	)
+	if err != nil {
+		t.Fatalf("Failed to create WatsonX LLM: %v", err)
+	}
+
+	llmtest.TestLLM(t, llm)
+}
diff --git a/testify_imports.txt b/testify_imports.txt
deleted file mode 100644
index 08f5b350..00000000
--- a/testify_imports.txt
+++ /dev/null
@@ -1,128 +0,0 @@
-Files importing testify/assert or testify/require:
-
-./agents/conversational_test.go
-./agents/executor_test.go
-./agents/markl_test.go
-./callbacks/agent_final_stream_test.go
-./chains/chains_test.go
-./chains/constitution/constitutional_test.go
-./chains/constitutional_test.go
-./chains/conversation_test.go
-./chains/conversational_retrieval_qa_test.go
-./chains/llm_azure_test.go
-./chains/llm_math_test.go
-./chains/llm_test.go
-./chains/map_reduce_test.go
-./chains/map_rerank_documents_test.go
-./chains/question_answering_test.go
-./chains/retrieval_qa_test.go
-./chains/sequential_test.go
-./chains/sql_database_test.go
-./chains/stuff_documents_test.go
-./chains/summarization_test.go
-./chains/transform_test.go
-./documentloaders/assemblyai_test.go
-./documentloaders/csv_test.go
-./documentloaders/html_test.go
-./documentloaders/notion_test.go
-./documentloaders/pdf_test.go
-./documentloaders/text_test.go
-./embeddings/bedrock/bedrock_test.go
-./embeddings/cybertron/cybertron_test.go
-./embeddings/embedding_test.go
-./embeddings/huggingface/huggingface_test.go
-./embeddings/jina/jina_test.go
-./embeddings/openai_test.go
-./embeddings/vector_math_test.go
-./embeddings/vertexai_palm_test.go
-./embeddings/voyageai/voyageai_test.go
-./internal/imageutil/download_test.go
-./internal/sliceutil/slice_test.go
-./llms/anthropic/internal/anthropicclient/anthropicclient_test.go
-./llms/cache/cache_test.go
-./llms/cache/inmemory/inmemory_test.go
-./llms/cloudflare/internal/cloudflareclient/cloudflareclient_test.go
-./llms/cohere/internal/cohereclient/cohereclient_test.go
-./llms/count_tokens_test.go
-./llms/ernie/internal/ernieclient/ernieclient_test.go
-./llms/googleai/shared_test/shared_test.go
-./llms/huggingface/internal/huggingfaceclient/huggingfaceclient_test.go
-./llms/llamafile/internal/llamafileclient/llamafileclient_test.go
-./llms/llamafile/llamafilellm_test.go
-./llms/maritaca/internal/maritacaclient/maritacaclient_test.go
-./llms/maritaca/maritaca_test.go
-./llms/mistral/mistralembed_test.go
-./llms/ollama/internal/ollamaclient/ollamaclient_test.go
-./llms/ollama/ollama_test.go
-./llms/openai/internal/openaiclient/chat_test.go
-./llms/openai/internal/openaiclient/openaiclient_test.go
-./llms/openai/multicontent_test.go
-./llms/openai/structured_output_test.go
-./memory/buffer_test.go
-./memory/chat_test.go
-./memory/mongo/mongo_chat_history_test.go
-./memory/sqlite3/sqlite3_history_test.go
-./memory/token_buffer_test.go
-./memory/window_buffer_test.go
-./outputparser/comma_seperated_list_test.go
-./outputparser/structured_test.go
-./prompts/chat_prompt_template_test.go
-./prompts/prompt_test.go
-./prompts/templates_test.go
-./textsplitter/markdown_splitter_test.go
-./textsplitter/recursive_character_test.go
-./textsplitter/token_splitter_test.go
-./tools/duckduckgo/ddg_test.go
-./tools/perplexity/perplexity_test.go
-./tools/serpapi/serpapi_test.go
-./tools/sqldatabase/mysql/mysql_test.go
-./tools/sqldatabase/postgresql/postgresql_test.go
-./tools/sqldatabase/sqlite3/sqlite3_test.go
-./tools/wikipedia/wikipedia_test.go
-./tools/zapier/internal/client_test.go
-./tools/zapier/zapier_test.go
-./vectorstores/alloydb/vectorstore_container_test.go
-./vectorstores/alloydb/vectorstore_test.go
-./vectorstores/azureaisearch/azureaisearch_httprr_test.go
-./vectorstores/bedrockknowledgebases/bedrockknowledgebases_test.go
-./vectorstores/chroma/chroma_test.go
-./vectorstores/cloudsql/vectorstore_container_test.go
-./vectorstores/milvus/milvus_test.go
-./vectorstores/mongovector/mongovector_test.go
-./vectorstores/opensearch/opensearch_test.go
-./vectorstores/pgvector/pgvector_test.go
-./vectorstores/pinecone/pinecone_test.go
-./vectorstores/qdrant/qdrant_test.go
-./vectorstores/redisvector/index_schema_test.go
-./vectorstores/redisvector/redis_vector_test.go
-./vectorstores/weaviate/weaviate_httprr_test.go
-./vectorstores/weaviate/weaviate_test.go
-
-Total: 97 files
-
-Most commonly used testify functions:
-- require.NoError (729 occurrences)
-- assert.Equal (107 occurrences)
-- assert.NotEmpty (90 occurrences)
-- require.Equal (70 occurrences)
-- require.Contains (51 occurrences)
-- require.Len (50 occurrences)
-- require.True (44 occurrences)
-- assert.Len (44 occurrences)
-- assert.NotNil (34 occurrences)
-- assert.Regexp (25 occurrences)
-- require.Error (23 occurrences)
-- require.NotEmpty (16 occurrences)
-- assert.Contains (12 occurrences)
-- require.ErrorIs (9 occurrences)
-- require.NotContains (8 occurrences)
-- assert.True (8 occurrences)
-- assert.NotContains (6 occurrences)
-- require.NotNil (4 occurrences)
-- assert.NotZero (4 occurrences)
-
-These would need to be replaced with standard library testing methods like:
-- require.NoError(t, err) → if err != nil { t.Fatal(err) }
-- assert.Equal(t, expected, actual) → if expected != actual { t.Errorf("expected %v, got %v", expected, actual) }
-- require.Equal(t, expected, actual) → if expected != actual { t.Fatalf("expected %v, got %v", expected, actual) }
-- etc.
\ No newline at end of file
diff --git a/testing/llmtest/doc.go b/testing/llmtest/doc.go
new file mode 100644
index 00000000..58c56349
--- /dev/null
+++ b/testing/llmtest/doc.go
@@ -0,0 +1,62 @@
+// Package llmtest provides utilities for testing LLM implementations.
+//
+// Inspired by Go's testing/fstest package, llmtest offers a simple,
+// backend-independent way to verify that LLM implementations conform
+// to the expected interfaces and behaviors.
+//
+// # Design Philosophy
+//
+// Following the principles of testing/fstest:
+//   - Minimal API surface - one main function (TestLLM)
+//   - Automatic capability discovery - no configuration required
+//   - Comprehensive by default - tests all detected capabilities
+//   - Interface testing - works with any llms.Model implementation
+//   - Simple usage pattern - just pass the model to test
+//
+// # Usage
+//
+// Testing an LLM implementation is straightforward:
+//
+//	func TestMyLLM(t *testing.T) {
+//	    llm, err := mylllm.New()
+//	    if err != nil {
+//	        t.Fatal(err)
+//	    }
+//	    llmtest.TestLLM(t, llm)
+//	}
+//
+// # Automatic Capability Discovery
+//
+// The package automatically detects and tests supported capabilities:
+//   - Basic operations (Call, GenerateContent)
+//   - Streaming (if model implements streaming interface)
+//   - Tool/Function calling (probed with test tool)
+//   - Reasoning/Thinking mode (if supported)
+//   - Token counting (if usage information provided)
+//   - Context caching (if implemented)
+//
+// # Mock Implementation
+//
+// A MockLLM is provided for testing without making actual API calls:
+//
+//	mock := &llmtest.MockLLM{
+//	    CallFunc: func(ctx context.Context, prompt string, options ...llms.CallOption) (string, error) {
+//	        return "mocked response", nil
+//	    },
+//	}
+//	llmtest.TestLLM(t, mock)
+//
+// # Parallel Testing
+//
+// All tests run in parallel by default for better performance:
+//   - Core tests (Call, GenerateContent) run concurrently
+//   - Capability tests run in parallel when detected
+//   - Safe for concurrent execution with independent contexts
+//
+// # Provider Coverage
+//
+// The package is used to test all LangChain Go providers:
+// anthropic, bedrock, cloudflare, cohere, ernie, fake, googleai,
+// huggingface, llamafile, local, maritaca, mistral, ollama, openai,
+// watsonx, and more.
+package llmtest
diff --git a/testing/llmtest/llmtest.go b/testing/llmtest/llmtest.go
new file mode 100644
index 00000000..de559dda
--- /dev/null
+++ b/testing/llmtest/llmtest.go
@@ -0,0 +1,740 @@
+// Package llmtest provides support for testing LLM implementations.
+//
+// Following the design of testing/fstest, this package provides a simple
+// TestLLM function that verifies an LLM implementation behaves correctly.
+package llmtest
+
+import (
+	"context"
+	"errors"
+	"fmt"
+	"strings"
+	"testing"
+	"time"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+// TestLLM tests an LLM implementation.
+// It performs basic operations and checks that the model behaves correctly.
+// It automatically discovers and tests capabilities by probing the model.
+//
+// If TestLLM finds any misbehaviors, it reports them via t.Error/t.Fatal.
+//
+// Typical usage inside a test:
+//
+//	func TestLLM(t *testing.T) {
+//	    llm, err := mylllm.New(...)
+//	    if err != nil {
+//	        t.Fatal(err)
+//	    }
+//	    llmtest.TestLLM(t, llm)
+//	}
+func TestLLM(t *testing.T, model llms.Model) {
+	t.Helper()
+	t.Parallel()
+
+	// Run core tests as subtests - these should always work
+	t.Run("Core", func(t *testing.T) {
+		t.Parallel()
+
+		t.Run("Call", func(t *testing.T) {
+			t.Parallel()
+			testCall(t, model)
+		})
+
+		t.Run("GenerateContent", func(t *testing.T) {
+			t.Parallel()
+			testGenerateContent(t, model)
+		})
+	})
+
+	// Discover and test capabilities
+	t.Run("Capabilities", func(t *testing.T) {
+		t.Parallel()
+
+		// Test streaming if supported
+		if supportsStreaming(model) {
+			t.Run("Streaming", func(t *testing.T) {
+				t.Parallel()
+				testStreaming(t, model)
+			})
+		}
+
+		// Test tool calls if supported
+		if supportsTools(model) {
+			t.Run("ToolCalls", func(t *testing.T) {
+				t.Parallel()
+				testToolCalls(t, model)
+			})
+		}
+
+		// Test reasoning if supported
+		if supportsReasoning(model) {
+			t.Run("Reasoning", func(t *testing.T) {
+				t.Parallel()
+				testReasoning(t, model)
+			})
+		}
+
+		// Test caching by trying it - if it works, great
+		t.Run("Caching", func(t *testing.T) {
+			t.Parallel()
+			testCaching(t, model)
+		})
+
+		// Test token counting - always run but don't fail if not supported
+		t.Run("TokenCounting", func(t *testing.T) {
+			t.Parallel()
+			testTokenCounting(t, model)
+		})
+	})
+}
+
+// Capability detection functions
+
+// supportsStreaming checks if the model supports streaming
+func supportsStreaming(model llms.Model) bool {
+	// Check if model implements the streaming interface
+	_, ok := model.(interface {
+		GenerateContentStream(context.Context, []llms.MessageContent, ...llms.CallOption) (<-chan llms.ContentResponse, error)
+	})
+	return ok
+}
+
+// supportsTools probes if the model supports tool calls
+func supportsTools(model llms.Model) bool {
+	// Try a simple tool call with a dummy tool
+	ctx := context.Background()
+	tools := []llms.Tool{
+		{
+			Type: "function",
+			Function: &llms.FunctionDefinition{
+				Name:        "test_tool",
+				Description: "Test tool",
+				Parameters:  map[string]any{"type": "object"},
+			},
+		},
+	}
+
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("test"),
+			},
+		},
+	}
+
+	// Try with tools - if it doesn't error out, it's supported
+	_, err := model.GenerateContent(ctx, messages,
+		llms.WithTools(tools),
+		llms.WithMaxTokens(1),
+	)
+
+	// If we get a specific "tools not supported" error, return false
+	// Otherwise assume it's supported (even if other errors occur)
+	if err != nil && strings.Contains(strings.ToLower(err.Error()), "not support") {
+		return false
+	}
+	return err == nil || !strings.Contains(strings.ToLower(err.Error()), "tool")
+}
+
+// supportsReasoning checks if the model supports reasoning/thinking
+func supportsReasoning(model llms.Model) bool {
+	// Check if model implements reasoning interface
+	if reasoner, ok := model.(interface {
+		SupportsReasoning() bool
+	}); ok {
+		return reasoner.SupportsReasoning()
+	}
+
+	// Try using thinking mode and see if it works
+	ctx := context.Background()
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("test"),
+			},
+		},
+	}
+
+	// Try with thinking mode
+	resp, err := model.GenerateContent(ctx, messages,
+		llms.WithMaxTokens(10),
+		llms.WithThinkingMode(llms.ThinkingModeLow),
+	)
+
+	// Check if thinking tokens are reported
+	if err == nil && resp != nil && len(resp.Choices) > 0 {
+		if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+			if _, ok := genInfo["ThinkingTokens"]; ok {
+				return true
+			}
+		}
+	}
+
+	return false
+}
+
+// TestLLMWithOptions tests an LLM with specific test options.
+func TestLLMWithOptions(t *testing.T, model llms.Model, opts TestOptions, expected ...string) {
+	t.Helper()
+
+	// Store options for test functions to use
+	testCtx := &testContext{
+		model:    model,
+		options:  opts,
+		expected: expected,
+	}
+
+	// Run tests with context
+	runTestsWithContext(t, testCtx)
+}
+
+// TestOptions configures test execution.
+type TestOptions struct {
+	// Timeout for each test operation
+	Timeout time.Duration
+
+	// Skip specific test categories
+	SkipCall            bool
+	SkipGenerateContent bool
+	SkipStreaming       bool
+
+	// Custom test prompts
+	TestPrompt   string
+	TestMessages []llms.MessageContent
+
+	// For providers that need special options
+	CallOptions []llms.CallOption
+}
+
+// Internal test context
+type testContext struct {
+	model    llms.Model
+	options  TestOptions
+	expected []string
+}
+
+func runTestsWithContext(t *testing.T, ctx *testContext) {
+	behaviors := make(map[string]bool)
+	for _, exp := range ctx.expected {
+		behaviors[exp] = true
+	}
+
+	if !ctx.options.SkipCall {
+		t.Run("Call", func(t *testing.T) {
+			testCallWithContext(t, ctx)
+		})
+	}
+
+	if !ctx.options.SkipGenerateContent {
+		t.Run("GenerateContent", func(t *testing.T) {
+			testGenerateContentWithContext(t, ctx)
+		})
+	}
+
+	if behaviors["supports-streaming"] && !ctx.options.SkipStreaming {
+		t.Run("Streaming", func(t *testing.T) {
+			testStreamingWithContext(t, ctx)
+		})
+	}
+}
+
+// Core test implementations
+
+func testCall(t *testing.T, model llms.Model) {
+	t.Helper()
+	ctx := context.Background()
+
+	result, err := llms.GenerateFromSinglePrompt(ctx, model, "Reply with 'OK' and nothing else", llms.WithMaxTokens(10))
+	if err != nil {
+		t.Fatalf("Call failed: %v", err)
+	}
+
+	if result == "" {
+		t.Error("Call returned empty result")
+	}
+}
+
+func testCallWithContext(t *testing.T, tctx *testContext) {
+	t.Helper()
+	ctx := context.Background()
+	if tctx.options.Timeout > 0 {
+		var cancel context.CancelFunc
+		ctx, cancel = context.WithTimeout(ctx, tctx.options.Timeout)
+		defer cancel()
+	}
+
+	prompt := "Reply with 'OK' and nothing else"
+	if tctx.options.TestPrompt != "" {
+		prompt = tctx.options.TestPrompt
+	}
+
+	opts := append([]llms.CallOption{llms.WithMaxTokens(10)}, tctx.options.CallOptions...)
+	result, err := llms.GenerateFromSinglePrompt(ctx, tctx.model, prompt, opts...)
+	if err != nil {
+		t.Fatalf("Call failed: %v", err)
+	}
+
+	if result == "" {
+		t.Error("Call returned empty result")
+	}
+}
+
+func testGenerateContent(t *testing.T, model llms.Model) {
+	t.Helper()
+	ctx := context.Background()
+
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("Reply with 'Hello' and nothing else"),
+			},
+		},
+	}
+
+	resp, err := model.GenerateContent(ctx, messages, llms.WithMaxTokens(10))
+	if err != nil {
+		t.Fatalf("GenerateContent failed: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("No choices in response")
+	}
+
+	content := strings.ToLower(resp.Choices[0].Content)
+	if !strings.Contains(content, "hello") {
+		t.Errorf("Expected 'hello' in response, got: %s", resp.Choices[0].Content)
+	}
+}
+
+func testGenerateContentWithContext(t *testing.T, tctx *testContext) {
+	t.Helper()
+	ctx := context.Background()
+	if tctx.options.Timeout > 0 {
+		var cancel context.CancelFunc
+		ctx, cancel = context.WithTimeout(ctx, tctx.options.Timeout)
+		defer cancel()
+	}
+
+	messages := tctx.options.TestMessages
+	if len(messages) == 0 {
+		messages = []llms.MessageContent{
+			{
+				Role: llms.ChatMessageTypeHuman,
+				Parts: []llms.ContentPart{
+					llms.TextPart("Reply with 'Hello' and nothing else"),
+				},
+			},
+		}
+	}
+
+	opts := append([]llms.CallOption{llms.WithMaxTokens(10)}, tctx.options.CallOptions...)
+	resp, err := tctx.model.GenerateContent(ctx, messages, opts...)
+	if err != nil {
+		t.Fatalf("GenerateContent failed: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("No choices in response")
+	}
+}
+
+func testStreaming(t *testing.T, model llms.Model) {
+	t.Helper()
+	ctx := context.Background()
+
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("Count from 1 to 3"),
+			},
+		},
+	}
+
+	// Skip if model doesn't support streaming
+	streamer, ok := model.(interface {
+		GenerateContentStream(context.Context, []llms.MessageContent, ...llms.CallOption) (<-chan llms.ContentResponse, error)
+	})
+	if !ok {
+		t.Skip("Model doesn't support streaming")
+	}
+
+	stream, err := streamer.GenerateContentStream(ctx, messages, llms.WithMaxTokens(50))
+	if err != nil {
+		t.Fatalf("GenerateContentStream failed: %v", err)
+	}
+
+	var chunks []string
+	for chunk := range stream {
+		if len(chunk.Choices) > 0 {
+			chunks = append(chunks, chunk.Choices[0].Content)
+		}
+	}
+
+	if len(chunks) == 0 {
+		t.Error("No chunks received from stream")
+	}
+
+	fullContent := strings.Join(chunks, "")
+	if fullContent == "" {
+		t.Error("Stream produced no content")
+	}
+}
+
+func testStreamingWithContext(t *testing.T, tctx *testContext) {
+	t.Helper()
+	ctx := context.Background()
+	if tctx.options.Timeout > 0 {
+		var cancel context.CancelFunc
+		ctx, cancel = context.WithTimeout(ctx, tctx.options.Timeout)
+		defer cancel()
+	}
+
+	messages := tctx.options.TestMessages
+	if len(messages) == 0 {
+		messages = []llms.MessageContent{
+			{
+				Role: llms.ChatMessageTypeHuman,
+				Parts: []llms.ContentPart{
+					llms.TextPart("Count from 1 to 3"),
+				},
+			},
+		}
+	}
+
+	// Skip if model doesn't support streaming
+	streamer, ok := tctx.model.(interface {
+		GenerateContentStream(context.Context, []llms.MessageContent, ...llms.CallOption) (<-chan llms.ContentResponse, error)
+	})
+	if !ok {
+		t.Skip("Model doesn't support streaming")
+	}
+
+	opts := append([]llms.CallOption{llms.WithMaxTokens(50)}, tctx.options.CallOptions...)
+	stream, err := streamer.GenerateContentStream(ctx, messages, opts...)
+	if err != nil {
+		t.Fatalf("GenerateContentStream failed: %v", err)
+	}
+
+	var chunks []string
+	for chunk := range stream {
+		if len(chunk.Choices) > 0 {
+			chunks = append(chunks, chunk.Choices[0].Content)
+		}
+	}
+
+	if len(chunks) == 0 {
+		t.Error("No chunks received from stream")
+	}
+}
+
+func testToolCalls(t *testing.T, model llms.Model) {
+	t.Helper()
+	ctx := context.Background()
+
+	// Define a simple tool
+	tools := []llms.Tool{
+		{
+			Type: "function",
+			Function: &llms.FunctionDefinition{
+				Name:        "get_weather",
+				Description: "Get the weather for a location",
+				Parameters: map[string]any{
+					"type": "object",
+					"properties": map[string]any{
+						"location": map[string]any{
+							"type":        "string",
+							"description": "The city and country",
+						},
+					},
+					"required": []string{"location"},
+				},
+			},
+		},
+	}
+
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What's the weather in San Francisco?"),
+			},
+		},
+	}
+
+	resp, err := model.GenerateContent(ctx, messages,
+		llms.WithTools(tools),
+		llms.WithMaxTokens(100),
+	)
+	if err != nil {
+		t.Fatalf("GenerateContent with tools failed: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("No choices in response")
+	}
+
+	// Check if tool was called
+	choice := resp.Choices[0]
+	if len(choice.ToolCalls) == 0 {
+		t.Log("No tool calls in response (model may not support tools)")
+	} else {
+		toolCall := choice.ToolCalls[0]
+		if toolCall.FunctionCall.Name != "get_weather" {
+			t.Errorf("Expected get_weather tool call, got: %s", toolCall.FunctionCall.Name)
+		}
+	}
+}
+
+func testReasoning(t *testing.T, model llms.Model) {
+	t.Helper()
+
+	// Check if model supports reasoning
+	if reasoner, ok := model.(interface {
+		SupportsReasoning() bool
+	}); ok && !reasoner.SupportsReasoning() {
+		t.Skip("Model doesn't support reasoning")
+	}
+
+	ctx := context.Background()
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("What is 25 + 17? Think step by step."),
+			},
+		},
+	}
+
+	// Try with thinking mode if available
+	var opts []llms.CallOption
+	opts = append(opts, llms.WithMaxTokens(200))
+
+	// Try to use thinking mode (may not be supported)
+	if thinkingMode := llms.ThinkingModeMedium; true {
+		opts = append(opts, llms.WithThinkingMode(thinkingMode))
+	}
+
+	resp, err := model.GenerateContent(ctx, messages, opts...)
+	if err != nil {
+		t.Fatalf("GenerateContent failed: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("No choices in response")
+	}
+
+	content := resp.Choices[0].Content
+	if !strings.Contains(content, "42") {
+		t.Log("Answer might be incorrect (expected 42)")
+	}
+
+	// Check for reasoning tokens if available
+	if genInfo := resp.Choices[0].GenerationInfo; genInfo != nil {
+		if thinkingTokens, ok := genInfo["ThinkingTokens"].(int); ok {
+			t.Logf("Used %d thinking tokens", thinkingTokens)
+		}
+	}
+}
+
+func testCaching(t *testing.T, model llms.Model) {
+	t.Helper()
+	ctx := context.Background()
+
+	// Long context that benefits from caching
+	longContext := strings.Repeat("This is cached context. ", 50)
+
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeSystem,
+			Parts: []llms.ContentPart{
+				llms.TextPart(longContext),
+			},
+		},
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("Say 'OK'"),
+			},
+		},
+	}
+
+	// First call (cache miss)
+	_, err := model.GenerateContent(ctx, messages, llms.WithMaxTokens(10))
+	if err != nil {
+		t.Fatalf("First call failed: %v", err)
+	}
+
+	// Second call (potential cache hit)
+	resp2, err := model.GenerateContent(ctx, messages, llms.WithMaxTokens(10))
+	if err != nil {
+		t.Fatalf("Second call failed: %v", err)
+	}
+
+	// Check if caching info is available
+	if genInfo := resp2.Choices[0].GenerationInfo; genInfo != nil {
+		if cached, ok := genInfo["CachedTokens"].(int); ok && cached > 0 {
+			t.Logf("Cached %d tokens", cached)
+		}
+	}
+}
+
+func testTokenCounting(t *testing.T, model llms.Model) {
+	t.Helper()
+	ctx := context.Background()
+
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("Count to 5"),
+			},
+		},
+	}
+
+	resp, err := model.GenerateContent(ctx, messages, llms.WithMaxTokens(50))
+	if err != nil {
+		t.Fatalf("GenerateContent failed: %v", err)
+	}
+
+	if len(resp.Choices) == 0 {
+		t.Fatal("No choices in response")
+	}
+
+	genInfo := resp.Choices[0].GenerationInfo
+	if genInfo == nil {
+		t.Skip("No generation info provided")
+	}
+
+	// Check for token counts
+	var hasTokenInfo bool
+	for _, field := range []string{"TotalTokens", "PromptTokens", "CompletionTokens"} {
+		if v, ok := genInfo[field].(int); ok && v > 0 {
+			hasTokenInfo = true
+			t.Logf("%s: %d", field, v)
+		}
+	}
+
+	if !hasTokenInfo {
+		t.Log("No token counting information provided")
+	}
+}
+
+// ValidateLLM checks if a model satisfies basic requirements without running tests.
+// It returns an error describing what's wrong, or nil if the model is valid.
+func ValidateLLM(model llms.Model) error {
+	if model == nil {
+		return errors.New("model is nil")
+	}
+
+	// Check if required methods are implemented
+	ctx := context.Background()
+
+	// Try a simple call
+	_, err := llms.GenerateFromSinglePrompt(ctx, model, "test", llms.WithMaxTokens(1))
+	if err != nil {
+		return fmt.Errorf("Call method failed: %w", err)
+	}
+
+	// Try GenerateContent
+	messages := []llms.MessageContent{
+		{
+			Role: llms.ChatMessageTypeHuman,
+			Parts: []llms.ContentPart{
+				llms.TextPart("test"),
+			},
+		},
+	}
+	_, err = model.GenerateContent(ctx, messages, llms.WithMaxTokens(1))
+	if err != nil {
+		return fmt.Errorf("GenerateContent method failed: %w", err)
+	}
+
+	return nil
+}
+
+// MockLLM provides a simple mock implementation for testing.
+type MockLLM struct {
+	// Response to return from Call
+	CallResponse string
+	CallError    error
+
+	// Response to return from GenerateContent
+	GenerateResponse *llms.ContentResponse
+	GenerateError    error
+
+	// Track calls for verification
+	CallCount     int
+	GenerateCount int
+	LastPrompt    string
+	LastMessages  []llms.MessageContent
+}
+
+// Call implements llms.Model
+func (m *MockLLM) Call(ctx context.Context, prompt string, options ...llms.CallOption) (string, error) {
+	m.CallCount++
+	m.LastPrompt = prompt
+	return m.CallResponse, m.CallError
+}
+
+// GenerateContent implements llms.Model
+func (m *MockLLM) GenerateContent(ctx context.Context, messages []llms.MessageContent, options ...llms.CallOption) (*llms.ContentResponse, error) {
+	m.GenerateCount++
+	m.LastMessages = messages
+
+	if m.GenerateResponse != nil {
+		return m.GenerateResponse, m.GenerateError
+	}
+
+	// Default response
+	return &llms.ContentResponse{
+		Choices: []*llms.ContentChoice{
+			{
+				Content: "mock response",
+			},
+		},
+	}, m.GenerateError
+}
+
+// GenerateContentStream implements streaming
+func (m *MockLLM) GenerateContentStream(ctx context.Context, messages []llms.MessageContent, options ...llms.CallOption) (<-chan llms.ContentResponse, error) {
+	// Create a channel and send the mock response
+	ch := make(chan llms.ContentResponse, 1)
+
+	// Send the response in chunks
+	go func() {
+		defer close(ch)
+
+		// Simulate streaming by sending the response in parts
+		if m.GenerateResponse != nil {
+			ch <- *m.GenerateResponse
+		} else {
+			// Default streaming response
+			ch <- llms.ContentResponse{
+				Choices: []*llms.ContentChoice{
+					{
+						Content: "mock",
+					},
+				},
+			}
+			ch <- llms.ContentResponse{
+				Choices: []*llms.ContentChoice{
+					{
+						Content: " response",
+					},
+				},
+			}
+		}
+	}()
+
+	return ch, nil
+}
+
+// Verify MockLLM implements llms.Model
+var _ llms.Model = (*MockLLM)(nil)
diff --git a/testing/llmtest/llmtest_test.go b/testing/llmtest/llmtest_test.go
new file mode 100644
index 00000000..51eb0a62
--- /dev/null
+++ b/testing/llmtest/llmtest_test.go
@@ -0,0 +1,77 @@
+package llmtest
+
+import (
+	"os"
+	"testing"
+
+	"github.com/tmc/langchaingo/llms"
+)
+
+// TestMockLLM tests the mock implementation.
+func TestMockLLM(t *testing.T) {
+	mock := &MockLLM{
+		CallResponse: "OK",
+		GenerateResponse: &llms.ContentResponse{
+			Choices: []*llms.ContentChoice{
+				{
+					Content: "Hello",
+					GenerationInfo: map[string]interface{}{
+						"TotalTokens": 10,
+					},
+				},
+			},
+		},
+	}
+
+	TestLLM(t, mock)
+}
+
+// TestValidateLLM tests the validation function.
+func TestValidateLLM(t *testing.T) {
+	// Test with nil model
+	if err := ValidateLLM(nil); err == nil {
+		t.Error("ValidateLLM should fail with nil model")
+	}
+
+	// Test with valid mock
+	mock := &MockLLM{
+		CallResponse: "OK",
+		GenerateResponse: &llms.ContentResponse{
+			Choices: []*llms.ContentChoice{
+				{
+					Content: "response",
+				},
+			},
+		},
+	}
+
+	if err := ValidateLLM(mock); err != nil {
+		t.Errorf("ValidateLLM failed with valid mock: %v", err)
+	}
+}
+
+// Integration tests with real providers (require API keys)
+
+func TestAnthropicIntegration(t *testing.T) {
+	if testing.Short() {
+		t.Skip("Skipping integration test")
+	}
+
+	if os.Getenv("ANTHROPIC_API_KEY") == "" {
+		t.Skip("ANTHROPIC_API_KEY not set")
+	}
+
+	// Import is handled in the actual test files for each provider
+}
+
+func TestOpenAIIntegration(t *testing.T) {
+	if testing.Short() {
+		t.Skip("Skipping integration test")
+	}
+
+	if os.Getenv("OPENAI_API_KEY") == "" {
+		t.Skip("OPENAI_API_KEY not set")
+	}
+
+	// Import is handled in the actual test files for each provider
+}