added maxim observability

docs.json

@@ -469,6 +469,7 @@
                   "observability/langwatch",
                   "observability/langsmith",
                   "observability/langtrace",
+                  "observability/maxim",
                   "observability/weave"
                 ]
               },

observability/maxim.mdx

@@ -0,0 +1,162 @@
+---
+title: Maxim
+description: Integrate Agno with Maxim to send traces and gain insights into your agent's performance.
+---
+
+## Integrating Agno with Maxim
+
+Maxim AI provides comprehensive agent monitoring, evaluation, and observability for your Agno applications. With Maxim's one-line integration, you can easily trace and analyse agent interactions, performance metrics, and more.
+
+## Prerequisites
+
+1. **Install Dependencies**
+
+   Ensure you have the necessary packages installed:
+
+   ```bash
+   pip install agno openai maxim-py
+   ```
+
+2. **Setup Maxim Account**
+
+   - Sign up for an account at [Maxim](https://getmaxim.ai/).
+   - Generate your API key from the Maxim dashboard.
+   - Create a repository to store your traces.
+
+3. **Set Environment Variables**
+
+   Configure your environment with the Maxim API key:
+
+   ```bash
+   export MAXIM_API_KEY=<your-api-key>
+   export MAXIM_LOG_REPO_ID=<your-repo-id>
+   export OPENAI_API_KEY=<your-openai-api-key>
+   ```
+
+## Sending Traces to Maxim
+
+### Example: Basic Maxim Integration
+
+This example demonstrates how to instrument your Agno agent with Maxim and send traces for monitoring and evaluation.
+
+```python
+import os
+from dotenv import load_dotenv
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from agno.tools.yfinance import YFinanceTools
+from maxim import Maxim
+from maxim.logger.agno import instrument_agno
+
+# Load environment variables
+load_dotenv()
+
+# Instrument Agno with Maxim
+instrument_agno(Maxim().logger())
+
+# Create and configure the agent
+agent = Agent(
+    name="Stock Price Agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[YFinanceTools()],
+    instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
+    show_tool_calls=True,
+    markdown=True,
+)
+
+# Use the agent
+response = agent.run("What is the current price of Tesla?")
+print(response.content)
+```
+
+### Example: Multi-Agent System with Maxim
+
+This example demonstrates how to set up a multi-agent system with comprehensive Maxim tracing.
+
+```python
+import os
+from dotenv import load_dotenv
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from agno.tools.googlesearch import GoogleSearchTools
+from agno.tools.yfinance import YFinanceTools
+from maxim import Maxim
+from maxim.logger.agno import instrument_agno
+
+# Load environment variables
+load_dotenv()
+
+# Instrument Agno with Maxim
+instrument_agno(Maxim().logger())
+
+# Create individual agents
+web_search_agent = Agent(
+    name="Web Agent",
+    role="Search the web for information",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[GoogleSearchTools()],
+    instructions="Always include sources",
+    show_tool_calls=True,
+    markdown=True,
+)
+
+finance_agent = Agent(
+    name="Finance Agent",
+    role="Get financial data",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
+    instructions="Use tables to display data",
+    markdown=True,
+)
+
+# Create multi-agent system
+multi_ai_agent = Agent(
+    team=[web_search_agent, finance_agent],
+    model=OpenAIChat(id="gpt-4o"),
+    instructions="You are a helpful financial assistant. Answer user questions about stocks, companies, and financial data.",
+    show_tool_calls=True,
+    markdown=True
+)
+
+# Use the multi-agent system
+response = multi_ai_agent.run("What's the current stock price of Apple and what are the latest analyst recommendations?")
+print(response.content)
+```
+
+![agno.gif](/images/maxim.gif)
+
+## Features
+
+### Observability & Tracing
+
+Maxim provides comprehensive observability for your Agno agents:
+
+- **Agent Tracing**: Track your agent's complete lifecycle, including tool calls, agent trajectories, and decision flows
+- **Token Usage**: Monitor prompt and completion token consumption
+- **Model Information**: Track which models are being used and their performance
+- **Tool Calls**: Detailed logging of all tool executions and their results
+- **Performance Metrics**: Latency, cost, and error rate tracking
+
+### Evaluation & Analytics
+
+- **Auto Evaluations**: Automatically evaluate captured logs based on filters and sampling
+- **Human Evaluations**: Use human evaluation or rating to assess log quality
+- **Node Level Evaluations**: Evaluate any component of your trace for detailed insights
+- **Dashboards**: Visualize traces over time, usage metrics, latency & error rates
+
+### Alerting
+
+Set thresholds on error rates, cost, token usage, user feedback, and latency to get real-time alerts via Slack or PagerDuty.
+
+## Notes
+
+- **Environment Variables**: Ensure your environment variables are correctly set for the API key and repository ID.
+- **Instrumentation Order**: Call `instrument_agno()` **before** creating or executing any agents to ensure proper tracing.
+- **Debug Mode**: Enable debug mode to see detailed logging information:
+
+  ```python
+  instrument_agno(Maxim().logger(), {"debug" : True})
+  ```
+- **Maxim Docs**: For more information on Maxim's features and capabilities, refer to the [Maxim documentation](https://getmaxim.ai/docs).
+
+By following these steps, you can effectively integrate Agno with Maxim, enabling comprehensive observability, evaluation, and monitoring of your AI agents.