chore(docs): bring AMP doc refresh from release/v1.0.0 into main (#3637)

* WIP: v1 docs (#3626)

(cherry picked from commit d46e20fa09bcd2f5916282f5553ddeb7183bd92c)

* docs: parity for all translations

* docs: full name of acronym AMP

* docs: fix lingering unused code

* docs: expand contextual options in docs.json

* docs: add contextual action to request feature on GitHub

* chore: tidy docs formatting

docs/ko/observability/neatlogs.mdx

@@ -85,10 +85,9 @@ GitHub에 따르면, Neatlogs는:
 ### 🔍 전체 데모 (4 분)
 
 <iframe
-  width="100%"
-  height="315"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/8KDme9T2I7Q?si=b8oHteaBwFNs_Duk"
-  title="YouTube video player"
+  title="NeatLogs 개요"
   frameBorder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
   allowFullScreen
@@ -126,5 +125,5 @@ You can now capture, understand, share, and act on your CrewAI agent runs in sec
 No setup overhead. Full trace transparency. Full team collaboration.
 ```
 
-이제 몇 초 만에 CrewAI agent 실행을 캡처, 이해, 공유하고 바로 조치할 수 있습니다.  
-별도의 설정이 필요하지 않습니다. 완전한 트레이스 투명성. 전체 팀 협업 지원.
+이제 몇 초 만에 CrewAI agent 실행을 캡처, 이해, 공유하고 바로 조치할 수 있습니다.
+별도의 설정이 필요하지 않습니다. 완전한 트레이스 투명성. 전체 팀 협업 지원.

docs/ko/observability/truefoundry.mdx

@@ -0,0 +1,147 @@
+---
+title: TrueFoundry Integration
+icon: chart-line
+mode: "wide"
+---
+
+TrueFoundry provides an enterprise-ready [AI Gateway](https://www.truefoundry.com/ai-gateway) which can integrate with agentic frameworks like CrewAI and provides governance and observability for your AI Applications. TrueFoundry AI Gateway serves as a unified interface for LLM access, providing:
+
+- **Unified API Access**: Connect to 250+ LLMs (OpenAI, Claude, Gemini, Groq, Mistral) through one API
+- **Low Latency**: Sub-3ms internal latency with intelligent routing and load balancing
+- **Enterprise Security**: SOC 2, HIPAA, GDPR compliance with RBAC and audit logging
+- **Quota and cost management**: Token-based quotas, rate limiting, and comprehensive usage tracking
+- **Observability**: Full request/response logging, metrics, and traces with customizable retention
+
+## How TrueFoundry Integrates with CrewAI
+
+
+### Installation & Setup
+
+<Steps>
+<Step title="Install CrewAI">
+```bash
+pip install crewai
+```
+</Step>
+
+<Step title="Get TrueFoundry Access Token">
+1. Sign up for a [TrueFoundry account](https://www.truefoundry.com/register)
+2. Follow the steps here in [Quick start](https://docs.truefoundry.com/gateway/quick-start)
+</Step>
+
+<Step title="Configure CrewAI with TrueFoundry">
+![TrueFoundry Code Configuration](/images/new-code-snippet.png)
+
+```python
+from crewai import LLM
+
+# Create an LLM instance with TrueFoundry AI Gateway
+truefoundry_llm = LLM(
+    model="openai-main/gpt-4o",  # Similarly, you can call any model from any provider
+    base_url="your_truefoundry_gateway_base_url",
+    api_key="your_truefoundry_api_key"
+)
+
+# Use in your CrewAI agents
+from crewai import Agent
+
+@agent
+def researcher(self) -> Agent:
+    return Agent(
+        config=self.agents_config['researcher'],
+        llm=truefoundry_llm,
+        verbose=True
+    )
+```
+</Step>
+</Steps>
+
+### Complete CrewAI Example
+
+```python
+from crewai import Agent, Task, Crew, LLM
+
+# Configure LLM with TrueFoundry
+llm = LLM(
+    model="openai-main/gpt-4o",
+    base_url="your_truefoundry_gateway_base_url",
+    api_key="your_truefoundry_api_key"
+)
+
+# Create agents
+researcher = Agent(
+    role='Research Analyst',
+    goal='Conduct detailed market research',
+    backstory='Expert market analyst with attention to detail',
+    llm=llm,
+    verbose=True
+)
+
+writer = Agent(
+    role='Content Writer',
+    goal='Create comprehensive reports',
+    backstory='Experienced technical writer',
+    llm=llm,
+    verbose=True
+)
+
+# Create tasks
+research_task = Task(
+    description='Research AI market trends for 2024',
+    agent=researcher,
+    expected_output='Comprehensive research summary'
+)
+
+writing_task = Task(
+    description='Create a market research report',
+    agent=writer,
+    expected_output='Well-structured report with insights',
+    context=[research_task]
+)
+
+# Create and execute crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, writing_task],
+    verbose=True
+)
+
+result = crew.kickoff()
+```
+
+### Observability and Governance
+
+Monitor your CrewAI agents through TrueFoundry's metrics tab:
+![TrueFoundry metrics](/images/gateway-metrics.png)
+
+With Truefoundry's AI gateway, you can monitor and analyze:
+
+- **Performance Metrics**: Track key latency metrics like Request Latency, Time to First Token (TTFS), and Inter-Token Latency (ITL) with P99, P90, and P50 percentiles
+- **Cost and Token Usage**: Gain visibility into your application's costs with detailed breakdowns of input/output tokens and the associated expenses for each model
+- **Usage Patterns**: Understand how your application is being used with detailed analytics on user activity, model distribution, and team-based usage
+- **Rate limit and Load balancing**: You can set up rate limiting, load balancing and fallback for your models
+
+## Tracing
+
+For a more detailed understanding on tracing, please see [getting-started-tracing](https://docs.truefoundry.com/docs/tracing/tracing-getting-started).For tracing, you can add the Traceloop SDK:
+For tracing, you can add the Traceloop SDK:
+
+```bash
+pip install traceloop-sdk
+```
+
+```python
+from traceloop.sdk import Traceloop
+
+# Initialize enhanced tracing
+Traceloop.init(
+    api_endpoint="https://your-truefoundry-endpoint/api/tracing",
+    headers={
+        "Authorization": f"Bearer {your_truefoundry_pat_token}",
+        "TFY-Tracing-Project": "your_project_name",
+    },
+)
+```
+
+This provides additional trace correlation across your entire CrewAI workflow.
+![TrueFoundry CrewAI Tracing](/images/tracing_crewai.png)