docs: add Camel AI integration documentation

README.md

@@ -58,7 +58,7 @@ AgentOps helps developers build, evaluate, and monitor AI agents. From prototype
 | 💸 **LLM Cost Management**            | Track spend with LLM foundation model providers               |
 | 🧪 **Agent Benchmarking**             | Test your agents against 1,000+ evals                         |
 | 🔐 **Compliance and Security**        | Detect common prompt injection and data exfiltration exploits |
-| 🤝 **Framework Integrations**         | Native Integrations with CrewAI, AutoGen, & LangChain         |
+| 🤝 **Framework Integrations**         | Native Integrations with AutoGen, Camel AI, CrewAI, & LangChain         |
 
 ## Quick Start ⌨️
 
@@ -177,6 +177,65 @@ With only two lines of code, add full observability and monitoring to Autogen ag
 - [Autogen Observability Example](https://microsoft.github.io/autogen/docs/notebooks/agentchat_agentops)
 - [Autogen - AgentOps Documentation](https://microsoft.github.io/autogen/docs/ecosystem/agentops)
 
+### Camel AI 🐪
+
+Track and analyze CAMEL agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.
+
+- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication framework
+- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)
+- [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)
+
+<details>
+  <summary>Installation</summary>
+
+```bash
+pip install "camel-ai[all]==0.2.11"
+pip install agentops
+```
+
+```python
+import os
+import agentops
+from camel.agents import ChatAgent
+from camel.messages import BaseMessage
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType, ModelType
+
+# Initialize AgentOps
+agentops.init(os.getenv("AGENTOPS_API_KEY"), default_tags=["CAMEL Example"])
+
+# Import toolkits after AgentOps init for tracking
+from camel.toolkits import SearchToolkit
+
+# Set up the agent with search tools
+sys_msg = BaseMessage.make_assistant_message(
+    role_name='Tools calling operator',
+    content='You are a helpful assistant'
+)
+
+# Configure tools and model
+tools = [*SearchToolkit().get_tools()]
+model = ModelFactory.create(
+    model_platform=ModelPlatformType.OPENAI,
+    model_type=ModelType.GPT_4O_MINI,
+)
+
+# Create and run the agent
+camel_agent = ChatAgent(
+    system_message=sys_msg,
+    model=model,
+    tools=tools,
+)
+
+response = camel_agent.step("What is AgentOps?")
+print(response)
+
+agentops.end_session("Success")
+```
+
+Check out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel) for more examples including multi-agent scenarios.
+</details>
+
 ### Langchain 🦜🔗
 
 AgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:

docs/mint.json

@@ -17,7 +17,7 @@
     "anchors": {
       "from": "#D1E5F7",
       "to": "#a9bed4"
-      
+
     }
   },
   "topbarCtaButton": {
@@ -90,6 +90,7 @@
         "v1/integrations/ai21",
         "v1/integrations/anthropic",
         "v1/integrations/autogen",
+        "v1/integrations/camel",
         "v1/integrations/cohere",
         "v1/integrations/crewai",
         "v1/integrations/groq",

docs/v1/integrations/camel.mdx

@@ -0,0 +1,148 @@
+---
+title: 'Camel AI'
+description: 'Track and analyze CAMEL agents including LLMs and Tools usage with AgentOps'
+---
+
+import CodeTooltip from '/snippets/add-code-tooltip.mdx'
+import EnvTooltip from '/snippets/add-env-tooltip.mdx'
+
+[CAMEL](https://www.camel-ai.org/) is the first large language model (LLM) multi-agent framework and an open-source community dedicated to finding the scaling law of agents. CAMEL has comprehensive [documentation](https://docs.camel-ai.org/) available as well as a great [quickstart](https://docs.camel-ai.org/getting_started/installation.html) guide.
+
+## Adding AgentOps to CAMEL agents
+
+<Steps>
+	<Step title="Install the AgentOps SDK">
+		<CodeGroup>
+			```bash pip
+			pip install agentops
+			```
+			```bash poetry
+			poetry add agentops
+			```
+		</CodeGroup>
+	</Step>
+	<Step title="Install CAMEL">
+		<CodeGroup>
+			```bash pip
+			pip install "camel-ai[all]==0.2.11"
+			```
+			```bash poetry
+			poetry add "camel-ai[all]==0.2.11"
+			```
+		</CodeGroup>
+	</Step>
+	<Step title="Add 3 lines of code">
+		<CodeTooltip/>
+			<CodeGroup>
+				```python python
+				import agentops
+				agentops.init(<INSERT YOUR API KEY HERE>)
+				...
+				# MUST END SESSION at end of program
+				agentops.end_session("Success") # Success|Fail|Indeterminate
+				```
+			</CodeGroup>
+		<EnvTooltip />
+      <CodeGroup>
+        ```python .env
+        AGENTOPS_API_KEY=<YOUR API KEY>
+        ```
+      </CodeGroup>
+      Read more about environment variables in [Advanced Configuration](/v1/usage/advanced-configuration)
+	</Step>
+	<Step title="Run your agent">
+		Execute your program and visit [app.agentops.ai/drilldown](https://app.agentops.ai/drilldown) to observe your CAMEL 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="https://github.com/AgentOps-AI/agentops/blob/main/docs/images/link-to-session.gif?raw=true" />
+		</Frame>
+	</Step>
+</Steps>
+
+## Single Agent Example with Tools
+
+Here's a simple example of tracking a CAMEL single agent with tools using AgentOps:
+
+```python
+import agentops
+import os
+from camel.agents import ChatAgent
+from camel.messages import BaseMessage
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType, ModelType
+
+# Initialize AgentOps
+agentops.init(os.getenv("AGENTOPS_API_KEY"))
+
+# Import toolkits after AgentOps init for tracking
+from camel.toolkits import SearchToolkit
+
+# Set up the agent with search tools
+sys_msg = BaseMessage.make_assistant_message(
+    role_name='Tools calling operator',
+    content='You are a helpful assistant'
+)
+
+# Configure tools and model
+tools = [*SearchToolkit().get_tools()]
+model = ModelFactory.create(
+    model_platform=ModelPlatformType.OPENAI,
+    model_type=ModelType.GPT_4O_MINI,
+)
+
+# Create the agent
+camel_agent = ChatAgent(
+    system_message=sys_msg,
+    model=model,
+    tools=tools,
+)
+
+# Run the agent
+user_msg = 'What is CAMEL-AI.org?'
+response = camel_agent.step(user_msg)
+print(response)
+
+# End the session
+agentops.end_session("Success")
+```
+
+## Multi-Agent Example
+
+For more complex scenarios, CAMEL supports multi-agent interactions. Here's how to track multiple agents:
+
+```python
+import agentops
+from typing import List
+from camel.agents.chat_agent import FunctionCallingRecord
+from camel.societies import RolePlaying
+from camel.types import ModelPlatformType, ModelType
+
+# Initialize AgentOps with multi-agent tag
+agentops.start_session(tags=["CAMEL X AgentOps Multi-agent"])
+
+# Import toolkits after AgentOps init
+from camel.toolkits import SearchToolkit, MathToolkit
+
+# Set up your task
+task_prompt = (
+    "Assume now is 2024 in the Gregorian calendar, "
+    "estimate the current age of University of Oxford "
+    "and then add 10 more years to this age, "
+    "and get the current weather of the city where "
+    "the University is located."
+)
+
+# The rest of your multi-agent implementation...
+# See our example notebook for the complete implementation
+```
+
+For complete examples including multi-agent setups and advanced configurations, check out our [example notebooks](https://github.com/AgentOps-AI/agentops/tree/main/examples/camel_examples).
+
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