merge fix

.github/workflows/docker_test.yml

@@ -35,7 +35,7 @@ jobs:
       - name: Test image
         run: |
           expected_version=$(cat pyproject.toml | grep version | head -n 1 | cut -d '"' -f 2)
-          version=$(docker run --rm --entrypoint bash langflowai/langflow:latest-dev -c 'python -c "from langflow.version.version import get_version; print(get_version())"')
+          version=$(docker run --rm --entrypoint bash langflowai/langflow:latest-dev -c "python -c 'from langflow.utils.version import get_version_info; print(get_version_info()[\"version\"])'")
           if [ "$expected_version" != "$version" ]; then
               echo "Expected version: $expected_version"
               echo "Actual version: $version"
@@ -51,7 +51,7 @@ jobs:
       - name: Test backend image
         run: |
           expected_version=$(cat pyproject.toml | grep version | head -n 1 | cut -d '"' -f 2)
-          version=$(docker run --rm --entrypoint bash langflowai/langflow-backend:latest-dev -c 'python -c "from langflow.version.version import get_version; print(get_version())"')
+          version=$(docker run --rm --entrypoint bash langflowai/langflow-backend:latest-dev -c "python -c 'from langflow.utils.version import get_version_info; print(get_version_info()[\"version\"])'")
           if [ "$expected_version" != "$version" ]; then
               echo "Expected version: $expected_version"
               echo "Actual version: $version"

.github/workflows/python_test.yml

@@ -145,13 +145,16 @@ jobs:
         if: steps.check-version.outputs.skipped == 'false'
         run: |
           make build main=true
-      - name: Install wheel
+      - name: Install wheel and Test CLI
         if: steps.check-version.outputs.skipped == 'false'
         run: |
-          python -m pip install dist/*.whl
+          uv venv new-venv
+          source new-venv/bin/activate
+          uv pip install dist/*.whl
       - name: Test CLI
         if: steps.check-version.outputs.skipped == 'false'
         run: |
+          source new-venv/bin/activate
           python -m langflow run --host 127.0.0.1 --port 7860 --backend-only &
           SERVER_PID=$!
           # Wait for the server to start

Makefile

@@ -181,7 +181,7 @@ fix_codespell: ## run codespell to fix spelling errors
 
 format: ## run code formatters
 	@uv run ruff check . --fix
-	@uv run ruff format .
+	@uv run ruff format . --config pyproject.toml
 	@cd src/frontend && npm run format
 
 unsafe_fix:

docs/docs/Agents/agent-tool-calling-agent-component.md

@@ -148,10 +148,6 @@ Text Analyzer: I can analyze and transform input text.
 Current Date and Time: I can retrieve the current date and time in various time zones.
 ```
 
-4. Click **Check & Save**.
-
-Your component now has a **Tool Mode** button, and can be used by an agent.
-
 ## Make any component a tool
 
 These components support **Tool Mode**:
@@ -181,12 +177,12 @@ inputs = [
 
 ## Add flows as tools
 
-An agent can use flows that are saved in your workspace as tools.
+An agent can use flows that are saved in your workspace as tools with the [Flow as Tool](/components-logic#flow-as-tool) component.
 
-1. To add a **Flow as tool** component, click and drag a **Flow as tool** component to your workspace.
+1. To add a **Flow as Tool** component, click and drag a **Flow as Tool** component to your workspace.
 2. Select the flow you want the agent to use as a tool.
 3. Connect the tool output to the agent's tools input.
 4. Ask the agent, `What tools are you using to answer my questions?`
-Your **Flow as tool** flow should be visible in the response.
+Your **Flow as Tool** flow should be visible in the response.
 
 

docs/docs/Components/components-agents.md

@@ -1,9 +1,11 @@
 ---
-title: Types of agents in Langflow
+title: Agents
 sidebar_position: 12
 slug: /components-agents
 ---
 
+# Agent components in Langflow
+
 Agent components are used to define the behavior and capabilities of AI agents in your flow. Agents can interact with APIs, databases, and other services and use LLMs as a reasoning engine to decide which course to take in your flow.
 
 ## Agent component {#agent-component}

docs/docs/Components/components-logic.md

@@ -1,3 +1,8 @@
+---
+title: Logic
+sidebar_position: 13
+slug: /components-logic
+---
 # Logic components in Langflow
 
 Logic components provide functionalities for routing, conditional processing, and flow management.
@@ -50,7 +55,7 @@ This component is particularly useful in workflows that require conditional rout
 | false_output | Data/List   | Output when the condition is not met.                |
 
 
-## Flow as Tool
+## Flow as Tool {#flow-as-tool}
 
 This component constructs a tool from a function that runs a loaded flow.
 

docs/docs/Guides/guides-chat-memory.md

@@ -6,7 +6,7 @@ slug: /guides-chat-memory
 
 
 
-Langflow allows every chat message to be stored, and a single flow can have multiple memory sessions. This enables you to create separate _memories_ for agents to store and recall information as needed. 
+Langflow allows every chat message to be stored, and a single flow can have multiple memory sessions. This enables you to create separate _memories_ for agents to store and recall information as needed.
 
 
 In any project, as long as there are [**Chat**](/components-io) being used, memories are always being stored by default. These are messages from a user to the AI or vice-versa.
@@ -26,7 +26,7 @@ Memories can be visualized and managed directly from the **Playground**. You can
 
 Modifying these memories will influence the behavior of the chatbot responses, as long as an agent uses them. Here you have the ability to remove or edit previous messages, allowing them to manipulate and explore how these changes affect model responses.
 
-To modify chat memories, click your **Flow Name**, and then click **Logs**.
+To modify chat memories, from the playground, click the **Options** menu of any session, and then select **Message Logs**.
 
 
 ![](/img/logs.png)
@@ -45,7 +45,7 @@ The **Chat Memory** component also retrieves message histories by `Session ID`,
 
 ![](/img/chat-input-controls-pane.png)
 
-By default, if the `Session ID`  value is empty, it is set to match the the same value as the `Flow ID`. 
+By default, if the `Session ID` value is empty, it is set to the same value as `Flow ID`.
 
 You can also display all messages stored across every flow and session by going to **Settings** > **Messages**.
 

docs/docs/Guides/guides-data-message.md

@@ -60,28 +60,38 @@ print(data.title)  # Outputs: "Hello, World!" because "title" key is in the data
 The `Data` object is also convenient for visualization of outputs, since the output preview has visual elements to inspect data as a table and its cells as pop ups for basic types. The idea is to create a unified way to work and visualize complex information in Langflow.
 
 
-To receive `Data` objects in a component input, you can use the `DataInput` input type. 
+To receive `Data` objects in a component input, you can use the `DataInput` input type.
 
 
-## The Message Object {#f4f17cad02a545068f407d515cbc2902}
+## Message object attributes {#f4f17cad02a545068f407d515cbc2902}
 
 
 ---
 
 
 The `Message` object extends the functionality of `Data` and includes additional attributes and methods for chat interactions.
 
-- **Main Attributes:**
-	- `text_key`: Key to retrieve the primary text data.
-	- `text`: The main text content of the message.
-	- `sender`: Identifier for the sender (e.g., "User" or "AI").
-	- `sender_name`: Name of the sender.
-	- `files`: List of files associated with the message.
-	- `session_id`: Identifier for the chat session.
-	- `timestamp`: Timestamp when the message was created.
-	- `flow_id`: Identifier for the flow.
-
-The `Message` object can be used to send, store and manipulate chat messages within Langflow. You can create a `Message` object by directly assigning key-value pairs to it. For example:
+
+- **Core message data:**
+  - `text`: The main text content of the message
+  - `sender`: Identifier for the sender (e.g., "User" or "AI")
+  - `sender_name`: Name of the sender
+  - `session_id`: Identifier for the chat session
+  - `timestamp`: Timestamp when the message was created (UTC)
+  - `flow_id`: Identifier for the flow
+  - `id`: Unique identifier for the message
+
+- **Content and files:**
+  - `files`: List of files or images associated with the message
+  - `content_blocks`: List of structured content blocks
+  - `properties`: Additional properties including visual styling and source information
+
+- **Message state:**
+  - `error`: Boolean indicating if there was an error
+  - `edit`: Boolean indicating if the message was edited
+  - `category`: Message category ("message", "error", "warning", "info")
+
+The `Message` object can be used to send, store, and manipulate chat messages within Langflow. You can create a `Message` object by directly assigning key-value pairs to it. For example:
 
 
 ```python