Agenta-AI · mmabrouk · Jul 25, 2026 · Jul 30, 2026 · Jul 30, 2026 · Jul 31, 2026
diff --git a/docs/blog/entries/annotate-your-llm-response-preview.mdx b/docs/blog/entries/annotate-your-llm-response-preview.mdx
@@ -20,7 +20,7 @@ This is useful to:
 - Run custom evaluation workflows
 - Measure application performance in real-time
 
-Check out the how to [annotate traces from API](/observability/trace-with-python-sdk/annotate-traces) for more details. Or try our new tutorial (available as [jupyter notebook](https://github.com/Agenta-AI/agenta/blob/main/examples/jupyter/capture_user_feedback.ipynb)) [here](/tutorials/cookbooks/capture-user-feedback).
+Check out the how to [annotate traces from API](/1.0/observability/trace-with-python-sdk/annotate-traces) for more details. Or try our new tutorial (available as [jupyter notebook](https://github.com/Agenta-AI/agenta/blob/main/examples/jupyter/capture_user_feedback.ipynb)) [here](/1.0/tutorials/cookbooks/capture-user-feedback).
 
 <Image
   style={{
@@ -50,7 +50,7 @@ This is useful to:
 - Run custom evaluation workflows
 - Measure application performance in real-time
 
-Check out the how to [annotate traces from API](/observability/trace-with-python-sdk/annotate-traces) for more details. Or try our new tutorial (available as [jupyter notebook](https://github.com/Agenta-AI/agenta/blob/main/examples/jupyter/observability/capture_user_feedback.ipynb)) [here](/tutorials/cookbooks/capture-user-feedback).
+Check out the how to [annotate traces from API](/1.0/observability/trace-with-python-sdk/annotate-traces) for more details. Or try our new tutorial (available as [jupyter notebook](https://github.com/Agenta-AI/agenta/blob/main/examples/jupyter/observability/capture_user_feedback.ipynb)) [here](/1.0/tutorials/cookbooks/capture-user-feedback).
 
 <Image
   style={{

diff --git a/docs/blog/entries/chat-sessions-observability.mdx b/docs/blog/entries/chat-sessions-observability.mdx
@@ -148,9 +148,9 @@ Identify latency issues across entire conversations, not just single requests. S
 
 Learn more in our documentation:
 
-- [Track Chat Sessions (Python SDK)](/observability/trace-with-python-sdk/track-chat-sessions)
-- [Session Tracking (OpenTelemetry)](/observability/trace-with-opentelemetry/session-tracking)
-- [Observability Overview](/observability/overview)
+- [Track Chat Sessions (Python SDK)](/1.0/observability/trace-with-python-sdk/track-chat-sessions)
+- [Session Tracking (OpenTelemetry)](/1.0/observability/trace-with-opentelemetry/session-tracking)
+- [Observability Overview](/1.0/observability/overview)
 
 ## What's Next
 

diff --git a/docs/blog/entries/customize-llm-as-a-judge-output-schemas.mdx b/docs/blog/entries/customize-llm-as-a-judge-output-schemas.mdx
@@ -27,7 +27,7 @@ The LLM-as-a-Judge evaluator now supports custom output schemas. Create multiple
 
 You can configure output types (binary, multiclass), include reasoning to improve prediction quality, or provide a raw JSON schema with any structure you define. Use these custom schemas in your evaluations to capture exactly the feedback you need.
 
-Learn more in the [LLM-as-a-Judge documentation](/evaluation/configure-evaluators/llm-as-a-judge).
+Learn more in the [LLM-as-a-Judge documentation](/1.0/evaluation/configure-evaluators/llm-as-a-judge).
 
 </Summary>
 
@@ -93,4 +93,4 @@ To use custom output schemas with LLM-as-a-Judge:
 4. For advanced use, provide your JSON schema
 5. Run your evaluation
 
-Learn more in the [LLM-as-a-Judge documentation](/evaluation/configure-evaluators/llm-as-a-judge).
+Learn more in the [LLM-as-a-Judge documentation](/1.0/evaluation/configure-evaluators/llm-as-a-judge).
diff --git a/docs/blog/entries/deployment-webhooks-and-github-automations.mdx b/docs/blog/entries/deployment-webhooks-and-github-automations.mdx
@@ -12,7 +12,7 @@ You can now trigger automations when a prompt deployment happens in Agenta. Send
 
 This makes it easier to connect prompt deployments to CI, repository sync jobs, and pull request workflows. If your GitHub workflow needs the latest prompt content, fetch it from Agenta during the run and commit the result back to your repo.
 
-Learn more: [Webhooks](/prompt-engineering/integrating-prompts/webhooks) | [GitHub Automations](/prompt-engineering/integrating-prompts/github)
+Learn more: [Webhooks](/1.0/prompt-engineering/integrating-prompts/webhooks) | [GitHub Automations](/1.0/prompt-engineering/integrating-prompts/github)
 
 </Summary>
 
@@ -56,6 +56,6 @@ For GitHub automations, Agenta calls the GitHub API directly. `repository_dispat
 
 Start with these guides:
 
-- [Webhooks](/prompt-engineering/integrating-prompts/webhooks)
-- [GitHub](/prompt-engineering/integrating-prompts/github)
-- [Fetch Prompts via SDK/API](/prompt-engineering/integrating-prompts/fetch-prompt-programatically)
+- [Webhooks](/1.0/prompt-engineering/integrating-prompts/webhooks)
+- [GitHub](/1.0/prompt-engineering/integrating-prompts/github)
+- [Fetch Prompts via SDK/API](/1.0/prompt-engineering/integrating-prompts/fetch-prompt-programatically)
diff --git a/docs/blog/entries/documentation-architecture-overhaul.mdx b/docs/blog/entries/documentation-architecture-overhaul.mdx
@@ -15,8 +15,8 @@ We've completely rewritten and restructured our documentation with a new archite
 
 Key improvements include:
 - **[Diataxis Framework](https://diataxis.fr/)**: Organized content into Tutorials, How-to Guides, Reference, and Explanation sections for better discoverability
-- **[Expanded Observability Docs](/observability/overview)**: Added missing documentation for tracing, annotations, and observability features
-- **[JavaScript/TypeScript Support](/observability/quick-start-opentelemetry)**: Added code examples and documentation for JavaScript developers alongside Python
+- **[Expanded Observability Docs](/1.0/observability/overview)**: Added missing documentation for tracing, annotations, and observability features
+- **[JavaScript/TypeScript Support](/1.0/observability/quick-start-opentelemetry)**: Added code examples and documentation for JavaScript developers alongside Python
 - **Ask AI Feature**: Ask questions directly to the documentation for instant answers
 
 </Summary>
@@ -33,10 +33,10 @@ We've reorganized all documentation using the [Diataxis framework](https://diata
 
 One of the biggest gaps in our previous documentation was observability. We've added comprehensive documentation covering:
 
-- [Tracing with OpenTelemetry](/observability/trace-with-opentelemetry/getting-started)
-- [Tracing LLM applications with JS/TS](/observability/quick-start-opentelemetry)
-- [Using the Metrics API to fetch metrics](/observability/query-data/analytics-data)
-- [Using the Query API to fetch traces](/observability/query-data/query-api)
+- [Tracing with OpenTelemetry](/1.0/observability/trace-with-opentelemetry/getting-started)
+- [Tracing LLM applications with JS/TS](/1.0/observability/quick-start-opentelemetry)
+- [Using the Metrics API to fetch metrics](/1.0/observability/query-data/analytics-data)
+- [Using the Query API to fetch traces](/1.0/observability/query-data/query-api)
 
 ### JavaScript/TypeScript Support
 

diff --git a/docs/blog/entries/documentation-overhaul-new-models-and-platform-improvements.mdx b/docs/blog/entries/documentation-overhaul-new-models-and-platform-improvements.mdx
@@ -13,7 +13,7 @@ We've made significant improvements across Agenta with a major documentation ove
 
 We've completely rewritten our prompt management and prompt engineering documentation.
 
-Start exploring the new documentation in our updated [Quick Start Guide](/prompt-engineering/quick-start).
+Start exploring the new documentation in our updated [Quick Start Guide](/1.0/prompt-engineering/quick-start).
 
 **New Model Support**:
 
@@ -55,7 +55,7 @@ We've made significant improvements across Agenta with a major documentation ove
 
 We've completely rewritten our prompt management and prompt engineering documentation.
 
-Start exploring the new documentation in our updated [Quick Start Guide](/prompt-engineering/quick-start).
+Start exploring the new documentation in our updated [Quick Start Guide](/1.0/prompt-engineering/quick-start).
 
 **New Model Support**:
 

diff --git a/docs/blog/entries/dspy-integration.mdx b/docs/blog/entries/dspy-integration.mdx
@@ -37,4 +37,4 @@ We've added DSPy integration to Agenta. You can now trace and debug your DSPy ap
   loading="lazy"
 />
 
-[**View the full DSPy integration →**](/integrations/frameworks/dspy/observability)
+[**View the full DSPy integration →**](/1.0/integrations/frameworks/dspy/observability)
diff --git a/docs/blog/entries/evaluation-sdk.mdx b/docs/blog/entries/evaluation-sdk.mdx
@@ -24,7 +24,7 @@ You can now run programmatic evaluations of complex AI agents and workflows dire
 
 The SDK lets you create test sets in code or fetch them from Agenta. You can use built-in evaluators like LLM-as-a-Judge, semantic similarity, or regex matching. You can also write custom Python evaluators. The SDK evaluates end-to-end workflows or specific spans in execution traces. Evaluations run on your own infrastructure; results display in the Agenta dashboard.
 
-Check out the [Evaluation SDK documentation](/evaluation/evaluation-from-sdk/quick-start) to get started.
+Check out the [Evaluation SDK documentation](/1.0/evaluation/evaluation-from-sdk/quick-start) to get started.
 
 </Summary>
 
@@ -122,4 +122,4 @@ Every evaluation run gets a shareable dashboard link. The dashboard shows full e
 
 ## Next Steps
 
-Check out the [Quick Start Guide](/evaluation/evaluation-from-sdk/quick-start) to build your first evaluation.
+Check out the [Quick Start Guide](/1.0/evaluation/evaluation-from-sdk/quick-start) to build your first evaluation.
diff --git a/docs/blog/entries/filtering-traces-by-annotation.mdx b/docs/blog/entries/filtering-traces-by-annotation.mdx
@@ -19,7 +19,7 @@ The new annotation filtering lets you find:
 - Spans evaluated by a specific evaluator
 - Spans with user feedback like `success=True`
 
-This enables powerful workflows: [capture user feedback](/tutorials/cookbooks/capture-user-feedback) from your app, filter to find traces with bad feedback, add them to test sets, and improve your prompts based on real user data.
+This enables powerful workflows: [capture user feedback](/1.0/tutorials/cookbooks/capture-user-feedback) from your app, filter to find traces with bad feedback, add them to test sets, and improve your prompts based on real user data.
 
 </Summary>
 
@@ -47,7 +47,7 @@ Filter traces based on evaluations and feedback:
 
 This feature enables powerful workflows:
 
-1. **Capture user feedback** from your application using our API ([see tutorial](/tutorials/cookbooks/capture-user-feedback))
+1. **Capture user feedback** from your application using our API ([see tutorial](/1.0/tutorials/cookbooks/capture-user-feedback))
 2. **Filter traces** to find those with bad feedback or low scores
 3. **Add them to test sets** to track problematic cases
 4. **Improve your prompts** based on real user feedback

diff --git a/docs/blog/entries/jinja2-template-support.mdx b/docs/blog/entries/jinja2-template-support.mdx
@@ -13,7 +13,7 @@ import Image from "@theme/IdealImage";
 
 You can now use Jinja2 templates in your prompts. Jinja2 is available in both the Playground and in prompt management.
 
-Learn more in our [blog post](https://agenta.ai/blog/launch-week-2-day-5-jinja2-prompt-templates) or check the [documentation](/prompt-engineering/playground/using-playground#switching-template-formats).
+Learn more in our [blog post](https://agenta.ai/blog/launch-week-2-day-5-jinja2-prompt-templates) or check the [documentation](/1.0/prompt-engineering/playground/using-playground#switching-template-formats).
 
 </Summary>
 
@@ -23,7 +23,7 @@ We're excited to announce a powerful update to the Agenta playground. You can no
 
 This means you can add sophisticated logic directly into your prompt templates. Use conditional statements, apply filters to variables, and transform data on the fly.
 
-Learn more in our [blog post](https://agenta.ai/blog/launch-week-2-day-5-jinja2-prompt-templates) or check the [documentation](/prompt-engineering/playground/using-playground#switching-template-formats).
+Learn more in our [blog post](https://agenta.ai/blog/launch-week-2-day-5-jinja2-prompt-templates) or check the [documentation](/1.0/prompt-engineering/playground/using-playground#switching-template-formats).
 
 ## Example
 

diff --git a/docs/blog/entries/json-multi-field-match.mdx b/docs/blog/entries/json-multi-field-match.mdx
@@ -129,4 +129,4 @@ Existing JSON Field Match configurations continue to work. We recommend migratin
 
 ## Next Steps
 
-Learn more about configuring and using the JSON Multi-Field Match evaluator in the [Classification and Entity Extraction Evaluators](/evaluation/configure-evaluators/classification-entity-extraction#json-multi-field-match) documentation.
+Learn more about configuring and using the JSON Multi-Field Match evaluator in the [Classification and Entity Extraction Evaluators](/1.0/evaluation/configure-evaluators/classification-entity-extraction#json-multi-field-match) documentation.
diff --git a/docs/blog/entries/llamaindex-integration.mdx b/docs/blog/entries/llamaindex-integration.mdx
@@ -54,4 +54,4 @@ The integration is auto-instrumentation - just add one line of code and you'll s
 
 This helps when you need to understand what's happening inside your RAG pipeline, track performance bottlenecks, or debug issues in production.
 
-Check out the [tutorial](/integrations/frameworks/llamaindex/observability) and the [Jupyter notebook](https://github.com/Agenta-AI/agenta/blob/main/examples/jupyter/llamaindex_integration.ipynb) for more details.
+Check out the [tutorial](/1.0/integrations/frameworks/llamaindex/observability) and the [Jupyter notebook](https://github.com/Agenta-AI/agenta/blob/main/examples/jupyter/llamaindex_integration.ipynb) for more details.
diff --git a/docs/blog/entries/multiple-metrics-in-human-evaluation.mdx b/docs/blog/entries/multiple-metrics-in-human-evaluation.mdx
@@ -11,7 +11,7 @@ We rebuilt the human evaluation workflow from scratch. Now you can set multiple
 
 This lets you evaluate the same output on different metrics like **relevance** or **completeness**. You can also create binary, numerical scores, or even use strings for **comments** or **expected answer**. 
 
-Watch the video below and read the [post](/changelog/multiple-metrics-in-human-evaluation) for more details. Or check out the [docs](/evaluation/human-evaluation/quick-start) to learn how to use the new human evaluation workflow.
+Watch the video below and read the [post](/changelog/multiple-metrics-in-human-evaluation) for more details. Or check out the [docs](/1.0/evaluation/human-evaluation/quick-start) to learn how to use the new human evaluation workflow.
 
 <div style={{display: 'flex', justifyContent: 'center', marginTop: "20px", marginBottom: "20px", flexDirection: 'column', alignItems: 'center'}}>
   <iframe
@@ -46,6 +46,6 @@ This unlocks a whole new set of use cases:
 - Use human evaluation to bootstrap automatic evaluation. You can annotate your outputs with the expected answer or a rubic, then use it to set up an automatic evaluation.
 
 
-Watch the video below and read the [post](/changelog/multiple-metrics-in-human-evaluation) for more details. Or check out the [docs](/evaluation/human-evaluation/quick-start) to learn how to use the new human evaluation workflow.
+Watch the video below and read the [post](/changelog/multiple-metrics-in-human-evaluation) for more details. Or check out the [docs](/1.0/evaluation/human-evaluation/quick-start) to learn how to use the new human evaluation workflow.
 
 ---
Original file line number	Diff line number	Diff line change
Expand Up		@@ -129,4 +129,4 @@ Existing JSON Field Match configurations continue to work. We recommend migratin

		## Next Steps

		Learn more about configuring and using the JSON Multi-Field Match evaluator in the [Classification and Entity Extraction Evaluators](/evaluation/configure-evaluators/classification-entity-extraction#json-multi-field-match) documentation.
		Learn more about configuring and using the JSON Multi-Field Match evaluator in the [Classification and Entity Extraction Evaluators](/1.0/evaluation/configure-evaluators/classification-entity-extraction#json-multi-field-match) documentation.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -54,4 +54,4 @@ The integration is auto-instrumentation - just add one line of code and you'll s

		This helps when you need to understand what's happening inside your RAG pipeline, track performance bottlenecks, or debug issues in production.

		Check out the [tutorial](/integrations/frameworks/llamaindex/observability) and the [Jupyter notebook](https://github.com/Agenta-AI/agenta/blob/main/examples/jupyter/llamaindex_integration.ipynb) for more details.
		Check out the [tutorial](/1.0/integrations/frameworks/llamaindex/observability) and the [Jupyter notebook](https://github.com/Agenta-AI/agenta/blob/main/examples/jupyter/llamaindex_integration.ipynb) for more details.