Docs overhaul - Introduction+Quickstart (#221)

* Consolidating

* WIP

* typo

* Fixing Cohere

* Added Introduction

* WIP

* Consolidating

* WIP

* typo

* Fixing Cohere

* Added Introduction

* WIP

* Adding syntax highlighting

* Added to env var

* Wrapping up

* Done

docs/mint.json

@@ -31,12 +31,7 @@
   "navigation": [
     {
       "group": "Getting Started",
-      "pages": [
-        "v1/introduction",
-        "v1/installation",
-        "v1/quickstart",
-        "v1/examples"
-      ]
+      "pages": ["v1/introduction", "v1/quickstart", "v1/examples"]
     },
     {
       "group": "Details",

docs/v1/details/environment-variables.mdx

@@ -1,19 +1,24 @@
 ---
 title: "Environment Variables"
 description: "Environment Variables that AgentOps will look for"
+mode: "wide"
 ---
 
 ### Sample .env file
 
-```.env
+```python .env
 # API KEY for using AgentOps. Find this in your settings page
 AGENTOPS_API_KEY=<YOUR API KEY>
-# Secondary API Key for simultaneously recording to a second project
+
+##########################################################
+# Only copy the following if you're going to override them
+
+# (OPTIONAL) Secondary API Key for simultaneously recording to a second project
 AGENTOPS_PARENT_KEY=<YOUR PARENT KEY>
-# The AgentOps API endpoint. Defaults to https://api.agentops.ai
+# (OPTIONAL) The AgentOps API endpoint. Defaults to https://api.agentops.ai
 AGENTOPS_API_ENDPOINT=https://api.agentops.ai
-# Logging level. <DEBUG, INFO, CRITICAL>
+# (OPTIONAL) Logging level. <DEBUG, INFO, CRITICAL>
 AGENTOPS_LOGGING_LEVEL=INFO
-# Whether to opt out of recording environment data. <FALSE, TRUE>
+# (OPTIONAL) Whether to opt out of recording environment data. <FALSE, TRUE>
 AGENTOPS_ENV_DATA_OPT_OUT=FALSE
 ```

docs/v1/details/tracking-agents.mdx

@@ -5,11 +5,9 @@ description: "Use the `@track_agent()` decorator to tag your agents"
 
 All Events are automatically associated with the agent that originated them. Agents are given a name which is what you will see in the dashboard.
 
-<div
-  style={{ backgroundColor: "#f0f0f0", padding: "10px", borderRadius: "10px" }}
->
+<Frame type="glass" caption="This event is labeled with the name of the Agent that originated it">
   <img height="200" src="/images/agent-name.png" />
-</div>
+</Frame>
 
 The example below tags the agent MyAgent with name `SomeCustomName`.
 

docs/v1/details/tracking-llm-calls.mdx

@@ -22,7 +22,7 @@ Still not working? Please let us know! You can find us on [Discord](https://disc
 To get started, just follow the quick start guide.
 
 <CardGroup cols={2}>
-  <Card title="Quick Start" icon="code" href="/v0/quickstart">
+  <Card title="Quick Start" icon="code" href="/v1/quickstart">
     Get started with AgentOps in under 5 minutes
   </Card>
 </CardGroup>

docs/v1/examples.mdx

@@ -1,19 +1,20 @@
 ---
 title: 'Examples'
 description: 'Sample code that you can run!'
+mode: "wide"
 ---
 
 ## Explore our example code to see AgentOps in action!
 
 <CardGroup cols={2}>
     <Card title="Basic Example" icon="square-code" href="https://github.com/AgentOps-AI/agentops-py/blob/main/examples/openai-gpt.ipynb">
-        Juypter Notebook with sample code that you can run! Start here!
+        Jupyter Notebook with sample code that you can run! Start here!
     </Card>
     <Card title="Multi-Agent Code Example" icon="object-group" href="https://github.com/AgentOps-AI/agentops-py/blob/main/examples/multi_agent_example.ipynb">
-        Juypter Notebook with a simple multi-agent design
+        Jupyter Notebook with a simple multi-agent design
     </Card>
     <Card title="LangChain Example" icon="link" href="https://github.com/AgentOps-AI/agentops-py/blob/main/examples/langchain_examples.ipynb">
-        Juypter Notebook with a sample LangChain integration
+        Jupyter Notebook with a sample LangChain integration
     </Card>
     <Card title="Crew.ai Example" icon="ship" href="/v1/integrations/crewai">
         CrewAI multi-agent framework with AgentOps support
@@ -28,7 +29,7 @@ description: 'Sample code that you can run!'
       ![thumbnail](https://cdn.loom.com/sessions/thumbnails/0e0d2986f3d644a58d1e186dc81cd8b1-with-play.gif)
     </Card>
     <Card title="Multi-Agent" icon="link" href="https://www.loom.com/share/2a62fd70efe8423fb495946b09c2d8ac">
-        Juypter Notebook with a sample LangChain integration
+        Jupyter Notebook with a sample LangChain integration
         ![thumbnail](https://cdn.loom.com/sessions/thumbnails/2a62fd70efe8423fb495946b09c2d8ac-with-play.gif)
     </Card>
     <Card title="Crew.ai" icon="ship" href="https://www.loom.com/share/cfcaaef8d4a14cc7a974843bda1076bf">

docs/v1/integrations/cohere.mdx

@@ -1,6 +1,6 @@
 ---
 title: Cohere
-description: "AgentOps's support for Cohere(>=5.4.0) is first class like Jack Harlow. This is a living integration, should you need any added functionality please message us on Discord!"
+description: "AgentOps's support for Cohere(>=5.4.0) is first class. This is a living integration, should you need any added functionality please message us on Discord!"
 ---
 
 ## Install the SDK
@@ -16,7 +16,7 @@ poetry add agentops
 
 ## Get an API key
 
-Retrieve an API Key from your [settings](https://app.agentops.ai/settings/projects) page.
+Retrieve an API Key from the Settings > [Projects & API Keys](https://app.agentops.ai/settings/projects) page.
 
 API keys are tied to individual projects.<br></br>
 A Default Project has been created for you, so just click <span style={{ padding: "5px 10px", backgroundColor: "black", color: "white", borderRadius: "5px"}}>Copy API Key</span>
@@ -32,9 +32,7 @@ A Default Project has been created for you, so just click <span style={{ padding
 
 ## Using AgentOps with Cohere
 
-### Requires cohere>=5.4.0
-
-#### Session replays in 3 lines of code
+<div style={{ color: "#880000" }}>Requires cohere>=5.4.0</div>
 
 Initialize the AgentOps client and automatically get analytics on every LLM call.
 

docs/v1/introduction.mdx

@@ -1,23 +1,60 @@
 ---
-title: Introduction
-description: "Build your next agent with evals, observability, and replay."
+title: "Introduction"
+description: "Build your next agent with evals, observability, and replays"
+mode: "wide"
 ---
 
-## Get Started
+<Check>[Give us a star](https://github.com/AgentOps-AI/agentops) on GitHub if you find AgentOps helpful (you may be our 1,000th 😊)</Check>
 
-<CardGroup cols={2}>
-  <Card title="Installing AgentOps" icon="wrench" href="./installation">
-    Prepare your system for AgentOps
-  </Card>
-  <Card title="Quick Start" icon="rocket" href="./quickstart">
-    Start using AgentOps with just 3 lines of code
-  </Card>
-</CardGroup>
+# AgentOps is your new terminal
 
-## Integrate with your agent
+<Card title="Terminals suck" icon="trash" iconType="solid" color="#CC0000">
+	- • Lose track of what your agents did in between executions
+	- • Parsing through terminal output searching for LLM completions
+	- • Printing "tool called"
+</Card>
+<Card title="AgentOps doesn't" icon="face-smile-halo" iconType="duotone" color="#00CC00">
+	- • Visual dashboard so you can see what your agents did in human-readable format
+	- • LLM calls are magically recorded - prompt, completion, timestamps for each - with one line of code
+	- • Agents and their events (including tool calls) are recorded with one more line of code
+	- • Errors are magically associated to its causal event
+	- • Record any other events to your session with two more lines of code
+	- • Tons of other useful data if you're developing with supported agent frameworks: SDK version
+</Card>
 
-<CardGroup cols={2}>
-  <Card title="Example Code" icon="square-code" href="./examples">
-    Sample code that you can run!
-  </Card>
-</CardGroup>
+## The AgentOps Dashboard
+
+With just two lines of code, you can free yourself from the chains of the terminal and instead visualize your agents' behavior
+in your AgentOps Dashboard. After setting up AgentOps, each execution of your program is recorded as a session and the above
+data is automatically recorded for you.
+
+The examples below were captured with two lines of code.
+
+### Session Drilldown
+Here you will find a list of all of your previously recorded sessions and useful data about each such as total execution time.
+You also get helpful debugging info such as any SDK versions you were on if you're building on a supported agent framework like Crew or AutoGen.
+LLM calls are presented as a familiar chat history view, and charts give you a breakdown of the types of events that were called and how long they took.
+
+<Frame type="glass" caption="Session Summary">
+  <img height="200" src="/images/session-summary.gif" />
+</Frame>
+
+Find any past sessions from your Session Drawer.
+<Frame type="glass" caption="Session Drawer">
+  <img height="250" src="/images/session-drawer.gif" />
+</Frame>
+
+Most powerful of all is the Session Waterfall. On the left, a time visualization of all your LLM calls, Action events, Tool calls, and Errors.
+On the right, specific details about the event you've selected on the waterfall. For instance the exact prompt and completion for a given LLM call.
+Most of which has been automatically recorded for you.
+
+<Frame type="glass" caption="Session Waterfall">
+  <img height="200" src="/images/session-waterfall.gif" />
+</Frame>
+
+
+### Session Overview
+View a meta-analysis of all of your sessions in a single view.
+<Frame type="glass" caption="Session Overview">
+  <img height="200" src="/images/overview.gif" />
+</Frame>

docs/v1/quickstart.mdx

@@ -1,28 +1,71 @@
 ---
 title: "Quickstart"
-description: "Start using AgentOps with just 3 lines of code"
+description: "Start using AgentOps with just 2 lines of code"
 ---
 
-<CodeGroup>
-```python python
-import openai
-import agentops
-
-# Beginning of program's code (i.e. main.py, __init__.py)
-
-agentops.init(<INSERT YOUR API KEY HERE>)
-
-# Woohoo! You're on your way 🎉
-
-```
-</CodeGroup>
-
-When the `openai`, `litellm`, or `cohere` packages have been imported, instantiating the AgentOps client will automatically
-instrument them, meaning you will be able to see all of your sessions on the AgentOps
-dashboard along with the full LLM chat histories, cost, token counts, etc.
-
-
-## Decorate Functions
+<Steps>
+  <Step title="Install the SDK">
+    <CodeGroup>
+      ```bash pip 
+      pip install agentops
+      ```
+      ```bash poetry
+      poetry add agentops
+      ```
+    </CodeGroup>
+    <Check>[Give us a star](https://github.com/AgentOps-AI/agentops) on GitHub while you're at it (you may be our 1,000th 😊)</Check>
+  </Step>
+  <Step title="Add 2 lines of code">
+    <CodeGroup>
+      ```python python
+      # IMPORTANT: Must be after 'import openai' or 'from crewai import Agent' etc
+      import agentops
+
+      # Beginning of program's code (i.e. main.py, __init__.py)
+      agentops.init(<INSERT YOUR API KEY HERE>)
+      ```
+    </CodeGroup>
+    <Check>Just these two lines and you have magically tracked LLM calls!</Check>
+    <Tip>
+      After the `openai`, `litellm`, or `cohere` packages have been imported, importing + instantiating the AgentOps client
+      will automatically instrument them, meaning you will be able to see all of your sessions on the AgentOps
+      DEBUGashboard along with the full LLM chat histories, cost, token counts, etc.
+    </Tip>
+
+  </Step>
+  <Step title="Get an API key">
+    Retrieve an API Key from the Settings > [Projects & API Keys](https://app.agentops.ai/settings/projects) page.
+    <div/>{/* Intentionally blank div for newline */}
+    Set this API Key in your [environment variables](/v1/details/environment-variables)
+
+    <Frame type="glass" caption="Settings > Projects & API Keys">
+      <img height="200" src="/images/api-keys.png" />
+    </Frame>
+
+    <Info>
+    API keys are tied to individual projects.<br></br>
+    A Default Project has been created for you, so just click Copy API Key
+    </Info>
+    {/* TODO: Install Tailwind for this stuff */}
+  </Step>
+  <Step title="Run your agent">
+    Execute your program and visit [app.agentops.ai/drilldown](https://app.agentops.ai/drilldown) to observe your Agent! 🕵️
+
+    <Tip>After your run, AgentOps prints a clickable url to console linking directly to your session in the Dashboard</Tip> 
+     <div/>{/* Intentionally blank div for newline */}
+
+     <Frame type="glass" caption="Clickable link to session">
+        <img height="200" src="/images/link-to-session.gif" />
+    </Frame>
+  </Step>
+</Steps>
+
+
+## More basic functionality
+
+<CardGroup cols={1}>
+
+<Card icon="code" title="Decorate Functions">
   You can instrument other functions inside your code with the handy `@record_function`
   decorator, which will record an `action_type`, the parameters, and the returns. You
   will see these function calls alongside your LLM calls from instantiating the AgentOps client.
@@ -33,7 +76,8 @@ dashboard along with the full LLM chat histories, cost, token counts, etc.
     ...
 ```
 
-## Track Agents
+</Card>
+<Card icon="robot" title="Track Agents">
   If you use specific named agents within your system, you can tie all downstream [Events](/v1/details/events) to a
   Named Agent with the `@track_agent` decorator.
   ```python python
@@ -43,21 +87,19 @@ dashboard along with the full LLM chat histories, cost, token counts, etc.
     ...
 ```
 
-## Ending Your Session
-
+</Card>
+<Card icon="stop" title="Ending Your Session">
   Finally, you should end your session by calling `.end_session()` with whether your session 
   was successful or not `(Success|Fail)`. We suggest setting session state depending on how 
   your agent exits or whether your agent succeeded or not. You can also specify a end state reason,
   such as user interrupted, ran to completion, or unhandled exception.
   ```python python
   # End of program
   agentops.end_session('Success')
-  # Woohoo! You're done 🎉
   ```
+</Card>
+</CardGroup>
 
-## Check out your dashboard
-  Run your agent and then visit [`app.agentops.ai`](https://app.agentops.ai). 
-  From the "Session Drill-Down" you should be able to see all of your OpenAI calls! 
 
 ## Example Code
 
@@ -82,7 +124,6 @@ class sample_agent(...):
 
 # End of program
 agentops.end_session('Success')
-# Woohoo! You're done 🎉
 ```
 
 <Card
@@ -93,7 +134,9 @@ agentops.end_session('Success')
   Jupyter Notebook with sample code that you can run!
 </Card>
 
-That's all you need to get started! Check out the documentation below to see how you can record other events. AgentOps is a lot more powerful this way!
+<Check>
+  That's all you need to get started! Check out the documentation below to see how you can record other events. AgentOps is a lot more powerful this way!
+</Check>
 
 ## Explore our more advanced functionality!
 
@@ -108,4 +151,4 @@ That's all you need to get started! Check out the documentation below to see how
   <Card title="Tracking Agents" icon="robot" href="/v1/details/tracking-agents">
     Associate Events with specific named agents.
   </Card>
-</CardGroup>
+</CardGroup>