Docs/release 0.175.0 docs (#3441)

* docs(install): note OpenAI SDK requirement openai>=1.13.3 for 0.175.0

* docs(cli): document device-code login and config reset guidance; renumber sections

* docs(flows): document conditional @start and resumable execution semantics

* docs(tasks): move max_retries to deprecation note under attributes table

* docs: provider-neutral RAG client config; entity memory batching; trigger payload note; tracing batch manager

* docs(cli): fix duplicate numbering (renumber Login/API Keys/Configuration sections)

docs/en/concepts/cli.mdx

@@ -282,7 +282,25 @@ Watch this video tutorial for a step-by-step demonstration of deploying your cre
   allowfullscreen
 ></iframe>
 
-### 11. API Keys
+### 12. Login
+
+Authenticate with CrewAI Enterprise using a secure device code flow (no email entry required).
+
+```shell Terminal
+crewai login
+```
+
+What happens:
+- A verification URL and short code are displayed in your terminal
+- Your browser opens to the verification URL
+- Enter/confirm the code to complete authentication
+
+Notes:
+- The OAuth2 provider and domain are configured via `crewai config` (defaults use `login.crewai.com`)
+- After successful login, the CLI also attempts to authenticate to the Tool Repository automatically
+- If you reset your configuration, run `crewai login` again to re-authenticate
+
+### 13. API Keys
 
 When running ```crewai create crew``` command, the CLI will show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.
 
@@ -310,7 +328,7 @@ See the following link for each provider's key name:
 
 * [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
 
-### 12. Configuration Management
+### 14. Configuration Management
 
 Manage CLI configuration settings for CrewAI.
 
@@ -385,6 +403,10 @@ Reset all configuration to defaults:
 crewai config reset
 ```
 
+<Tip>
+After resetting configuration, re-run `crewai login` to authenticate again.
+</Tip>
+
 <Note>
 Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
 </Note>

docs/en/concepts/flows.mdx

@@ -97,7 +97,13 @@ The state's unique ID and stored data can be useful for tracking flow executions
 
 ### @start()
 
-The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
+The `@start()` decorator marks entry points for a Flow. You can:
+
+- Declare multiple unconditional starts: `@start()`
+- Gate a start on a prior method or router label: `@start("method_or_label")`
+- Provide a callable condition to control when a start should fire
+
+All satisfied `@start()` methods will execute (often in parallel) when the Flow begins or resumes.
 
 ### @listen()
 

docs/en/concepts/knowledge.mdx

@@ -24,6 +24,41 @@ For file-based Knowledge Sources, make sure to place your files in a `knowledge`
 Also, use relative paths from the `knowledge` directory when creating the source.
 </Tip>
 
+### Vector store (RAG) client configuration
+
+CrewAI exposes a provider-neutral RAG client abstraction for vector stores. The default provider is ChromaDB, and Qdrant is supported as well. You can switch providers using configuration utilities.
+
+Supported today:
+- ChromaDB (default)
+- Qdrant
+
+```python Code
+from crewai.rag.config.utils import set_rag_config, get_rag_client, clear_rag_config
+
+# ChromaDB (default)
+from crewai.rag.chromadb.config import ChromaDBConfig
+set_rag_config(ChromaDBConfig())
+chromadb_client = get_rag_client()
+
+# Qdrant
+from crewai.rag.qdrant.config import QdrantConfig
+set_rag_config(QdrantConfig())
+qdrant_client = get_rag_client()
+
+# Example operations (same API for any provider)
+client = qdrant_client  # or chromadb_client
+client.create_collection(collection_name="docs")
+client.add_documents(
+    collection_name="docs",
+    documents=[{"id": "1", "content": "CrewAI enables collaborative AI agents."}],
+)
+results = client.search(collection_name="docs", query="collaborative agents", limit=3)
+
+clear_rag_config()  # optional reset
+```
+
+This RAG client is separate from Knowledge’s built-in storage. Use it when you need direct vector-store control or custom retrieval pipelines.
+
 ### Basic String Knowledge Example
 
 ```python Code

docs/en/concepts/memory.mdx

@@ -738,6 +738,17 @@ print(f"OpenAI: {openai_time:.2f}s")
 print(f"Ollama: {ollama_time:.2f}s")
 ```
 
+### Entity Memory batching behavior
+
+Entity Memory supports batching when saving multiple entities at once. When you pass a list of `EntityMemoryItem`, the system:
+
+- Emits a single MemorySaveStartedEvent with `entity_count`
+- Saves each entity internally, collecting any partial errors
+- Emits MemorySaveCompletedEvent with aggregate metadata (saved count, errors)
+- Raises a partial-save exception if some entities failed (includes counts)
+
+This improves performance and observability when writing many entities in one operation.
+
 ## 2. External Memory 
 External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
 

docs/en/concepts/tasks.mdx

@@ -61,6 +61,11 @@ crew = Crew(
 | **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
 | **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
 
+<Note type="warning" title="Deprecated: max_retries">
+  The task attribute `max_retries` is deprecated and will be removed in v1.0.0.
+  Use `guardrail_max_retries` instead to control retry attempts when a guardrail fails.
+</Note>
+
 ## Creating Tasks
 
 There are two ways to create tasks in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
@@ -432,7 +437,7 @@ When a guardrail returns `(False, error)`:
 2. The agent attempts to fix the issue
 3. The process repeats until:
    - The guardrail returns `(True, result)`
-   - Maximum retries are reached
+   - Maximum retries are reached (`guardrail_max_retries`)
 
 Example with retry handling:
 ```python Code