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docs: 0.114.0 release notes, navigation restructure, new guides, deploy video, and cleanup (#2653)
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- Add v0.114.0 release notes with highlights image and doc links
- Restructure docs navigation (Strategy group, Releases tab, navbar links)
- Update quickstart with deployment video and clearer instructions
- Add/rename guides (Custom Manager Agent, Custom LLM)
- Remove legacy concept/tool docs
- Add new images and tool docs
- Minor formatting and content improvements throughout
This commit is contained in:
Tony Kipkemboi
2025-04-21 16:18:21 -07:00
committed by GitHub
parent 311a078ca6
commit 6d0039b117
9 changed files with 122 additions and 127 deletions
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30
docs/changelog.mdx
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@@ -4,6 +4,36 @@ description: View the latest updates and changes to CrewAI
icon: timeline
---
<Update label="2025-04-07" description="v0.114.0">
## Release Highlights
<Frame>
<img src="/images/v01140.png" />
</Frame>
**New Features & Enhancements**
- Agents as an atomic unit. (`Agent(...).kickoff()`)
- Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
- Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
- Enhanced YAML extraction.
- Multimodal agent validation.
- Added Secure fingerprints for agents and crews.
**Core Improvements & Fixes**
- Improved serialization, agent copying, and Python compatibility.
- Added wildcard support to `emit()`
- Added support for additional router calls and context window adjustments.
- Fixed typing issues, validation, and import statements.
- Improved method performance.
- Enhanced agent task handling, event emissions, and memory management.
- Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
**Documentation & Guides**
- Improved documentation structure, theme, and organization.
- Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
- Updated tool configuration examples, prompts, and observability docs.
- Guide on using singular agents within Flows.
</Update>
<Update label="2025-03-17" description="v0.108.0">
**Features**
- Converted tabs to spaces in `crew.py` template

71
docs/concepts/llamaindex-tools.mdx
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@@ -1,71 +0,0 @@
---
title: Using LlamaIndex Tools
description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
icon: toolbox
---
## Using LlamaIndex Tools
<Info>
CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more.
</Info>
Here are the available built-in tools offered by LlamaIndex.
```python Code
from crewai import Agent
from crewai_tools import LlamaIndexTool
# Example 1: Initialize from FunctionTool
from llama_index.core.tools import FunctionTool
your_python_function = lambda ...: ...
og_tool = FunctionTool.from_defaults(
your_python_function,
name="<name>",
description='<description>'
)
tool = LlamaIndexTool.from_tool(og_tool)
# Example 2: Initialize from LlamaHub Tools
from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
wolfram_tools = wolfram_spec.to_tool_list()
tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
# Example 3: Initialize Tool from a LlamaIndex Query Engine
query_engine = index.as_query_engine()
query_tool = LlamaIndexTool.from_query_engine(
query_engine,
name="Uber 2019 10K Query Tool",
description="Use this tool to lookup the 2019 Uber 10K Annual Report"
)
# Create and assign the tools to an agent
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[tool, *tools, query_tool]
)
# rest of the code ...
```
## Steps to Get Started
To effectively use the LlamaIndexTool, follow these steps:
<Steps>
<Step title="Package Installation">
Make sure that `crewai[tools]` package is installed in your Python environment:
<CodeGroup>
```shell Terminal
pip install 'crewai[tools]'
```
</CodeGroup>
</Step>
<Step title="Install and Use LlamaIndex">
Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
</Step>
</Steps>

107
docs/docs.json
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@@ -11,22 +11,21 @@
"navigation": {
"tabs": [
{
"tab": "Get Started",
"tab": "Documentation",
"groups": [
{
"group": "Get Started",
"pages": [
"introduction",
"installation",
"quickstart",
"changelog"
"quickstart"
]
},
{
"group": "Guides",
"pages": [
{
"group": "Concepts",
"group": "Strategy",
"pages": [
"guides/concepts/evaluating-use-cases"
]
@@ -79,41 +78,6 @@
"concepts/event-listener"
]
},
{
"group": "How to Guides",
"pages": [
"how-to/create-custom-tools",
"how-to/sequential-process",
"how-to/hierarchical-process",
"how-to/custom-manager-agent",
"how-to/llm-connections",
"how-to/customizing-agents",
"how-to/multimodal-agents",
"how-to/coding-agents",
"how-to/force-tool-output-as-result",
"how-to/human-input-on-execution",
"how-to/kickoff-async",
"how-to/kickoff-for-each",
"how-to/replay-tasks-from-latest-crew-kickoff",
"how-to/conditional-tasks",
"how-to/langchain-tools",
"how-to/llamaindex-tools"
]
},
{
"group": "Agent Monitoring & Observability",
"pages": [
"how-to/agentops-observability",
"how-to/arize-phoenix-observability",
"how-to/langfuse-observability",
"how-to/langtrace-observability",
"how-to/mlflow-observability",
"how-to/openlit-observability",
"how-to/opik-observability",
"how-to/portkey-observability",
"how-to/weave-integration"
]
},
{
"group": "Tools",
"pages": [
@@ -141,6 +105,7 @@
"tools/hyperbrowserloadtool",
"tools/linkupsearchtool",
"tools/llamaindextool",
"tools/langchaintool",
"tools/serperdevtool",
"tools/s3readertool",
"tools/s3writertool",
@@ -170,6 +135,40 @@
"tools/youtubevideosearchtool"
]
},
{
"group": "Agent Monitoring & Observability",
"pages": [
"how-to/agentops-observability",
"how-to/arize-phoenix-observability",
"how-to/langfuse-observability",
"how-to/langtrace-observability",
"how-to/mlflow-observability",
"how-to/openlit-observability",
"how-to/opik-observability",
"how-to/portkey-observability",
"how-to/weave-integration"
]
},
{
"group": "Learn",
"pages": [
"how-to/conditional-tasks",
"how-to/coding-agents",
"how-to/create-custom-tools",
"how-to/custom-llm",
"how-to/custom-manager-agent",
"how-to/customizing-agents",
"how-to/force-tool-output-as-result",
"how-to/hierarchical-process",
"how-to/human-input-on-execution",
"how-to/kickoff-async",
"how-to/kickoff-for-each",
"how-to/llm-connections",
"how-to/multimodal-agents",
"how-to/replay-tasks-from-latest-crew-kickoff",
"how-to/sequential-process"
]
},
{
"group": "Telemetry",
"pages": [
@@ -188,19 +187,35 @@
]
}
]
},
{
"tab": "Releases",
"groups": [
{
"group": "Releases",
"pages": [
"changelog"
]
}
]
}
],
"global": {
"anchors": [
{
"anchor": "Community",
"anchor": "Website",
"href": "https://crewai.com",
"icon": "globe"
},
{
"anchor": "Forum",
"href": "https://community.crewai.com",
"icon": "discourse"
},
{
"anchor": "Tutorials",
"href": "https://www.youtube.com/@crewAIInc",
"icon": "youtube"
"anchor": "Get Help",
"href": "mailto:support@crewai.com",
"icon": "headset"
}
]
}
@@ -214,6 +229,12 @@
"strict": false
},
"navbar": {
"links": [
{
"label": "Start Free Trial",
"href": "https://app.crewai.com"
}
],
"primary": {
"type": "github",
"href": "https://github.com/crewAIInc/crewAI"

10
docs/custom_llm.md → docs/how-to/custom-llm.mdx
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@@ -1,9 +1,13 @@
# Custom LLM Implementations
---
title: Custom LLM Implementation
description: Learn how to create custom LLM implementations in CrewAI.
icon: code
---
## Custom LLM Implementations
CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
## Using Custom LLM Implementations
To create a custom LLM implementation, you need to:
1. Inherit from the `BaseLLM` abstract base class

2
docs/how-to/custom-manager-agent.mdx
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@@ -1,5 +1,5 @@
---
title: Create Your Own Manager Agent
title: Custom Manager Agent
description: Learn how to set a custom agent as the manager in CrewAI, providing more control over task management and coordination.
icon: user-shield
---

6
docs/how-to/replay-tasks-from-latest-crew-kickoff.mdx
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@@ -20,10 +20,8 @@ Here's an example of how to replay from a task:
To use the replay feature, follow these steps:
<Steps>
<Step title="Open your terminal or command prompt.">
</Step>
<Step title="Navigate to the directory where your CrewAI project is located.">
</Step>
<Step title="Open your terminal or command prompt."></Step>
<Step title="Navigate to the directory where your CrewAI project is located."></Step>
<Step title="Run the following commands:">
To view the latest kickoff task_ids use:

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docs/quickstart.mdx
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@@ -336,9 +336,22 @@ email_summarizer_task:
- research_task
```
## Deploying Your Project
## Deploying Your Crew
The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com), where you can deploy your crew in a few clicks.
The easiest way to deploy your crew to production is through [CrewAI Enterprise](http://app.crewai.com).
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
<iframe
width="100%"
height="400"
src="https://www.youtube.com/embed/3EqSV-CYDZA"
title="CrewAI Deployment Guide"
frameborder="0"
style={{ borderRadius: '10px' }}
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen
></iframe>
<CardGroup cols={2}>
<Card

6
docs/concepts/langchain-tools.mdx → docs/tools/langchaintool.mdx
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@@ -1,10 +1,10 @@
---
title: Using LangChain Tools
description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
title: LangChain Tool
description: The `LangChainTool` is a wrapper for LangChain tools and query engines.
icon: link
---
## Using LangChain Tools
## `LangChainTool`
<Info>
CrewAI seamlessly integrates with LangChain's comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.