From f02e0060fade95df35606ff65050be43261729b6 Mon Sep 17 00:00:00 2001 From: siddharth Sambharia Date: Tue, 3 Jun 2025 17:45:28 +0530 Subject: [PATCH 1/2] feat/portkey-ai-docs-udpated (#2936) --- docs/observability/portkey.mdx | 912 +++++++++++++++++++++++++++------ 1 file changed, 767 insertions(+), 145 deletions(-) diff --git a/docs/observability/portkey.mdx b/docs/observability/portkey.mdx index c8d3e7a87..a2f66f566 100644 --- a/docs/observability/portkey.mdx +++ b/docs/observability/portkey.mdx @@ -7,196 +7,818 @@ icon: key Portkey CrewAI Header Image -[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast. -Portkey adds 4 core production capabilities to any CrewAI agent: -1. Routing to **200+ LLMs** -2. Making each LLM call more robust -3. Full-stack tracing & cost, performance analytics -4. Real-time guardrails to enforce behavior +## Introduction -## Getting Started +Portkey enhances CrewAI with production-readiness features, turning your experimental agent crews into robust systems by providing: + +- **Complete observability** of every agent step, tool use, and interaction +- **Built-in reliability** with fallbacks, retries, and load balancing +- **Cost tracking and optimization** to manage your AI spend +- **Access to 200+ LLMs** through a single integration +- **Guardrails** to keep agent behavior safe and compliant +- **Version-controlled prompts** for consistent agent performance + + +### Installation & Setup - - ```bash - pip install -qU crewai portkey-ai - ``` - - - To build CrewAI Agents with Portkey, you'll need two keys: - - **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key - - **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault + +```bash +pip install -U crewai portkey-ai +``` + - ```python - from crewai import LLM - from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + +Create a Portkey API key with optional budget/rate limits from the [Portkey dashboard](https://app.portkey.ai/). You can also attach configurations for reliability, caching, and more to this key. More on this later. + - gpt_llm = LLM( - model="gpt-4", - base_url=PORTKEY_GATEWAY_URL, - api_key="dummy", # We are using Virtual key - extra_headers=createHeaders( - api_key="YOUR_PORTKEY_API_KEY", - virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey - ) - ) - ``` - - - ```python - from crewai import Agent, Task, Crew - - # Define your agents with roles and goals - coder = Agent( - role='Software developer', - goal='Write clear, concise code on demand', - backstory='An expert coder with a keen eye for software trends.', - llm=gpt_llm - ) - - # Create tasks for your agents - task1 = Task( - description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!", - expected_output="A clear and concise HTML code", - agent=coder - ) - - # Instantiate your crew - crew = Crew( - agents=[coder], - tasks=[task1], - ) - - result = crew.kickoff() - print(result) - ``` - - - -## Key Features - -| Feature | Description | -|:--------|:------------| -| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface | -| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks | -| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata | -| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs | -| 🚧 Security Controls | Set budget limits and implement role-based access control | -| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement | -| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching | - - -## Production Features with Portkey Configs - -All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference. - - - - - - -### 1. Use 250+ LLMs -Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api) - - -Easily switch between different LLM providers: + +The integration is simple - you just need to update the LLM configuration in your CrewAI setup: ```python -# Anthropic Configuration -anthropic_llm = LLM( - model="claude-3-5-sonnet-latest", +from crewai import LLM +from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + +# Create an LLM instance with Portkey integration +gpt_llm = LLM( + model="gpt-4o", base_url=PORTKEY_GATEWAY_URL, - api_key="dummy", + api_key="dummy", # We are using a Virtual key, so this is a placeholder extra_headers=createHeaders( api_key="YOUR_PORTKEY_API_KEY", - virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys - trace_id="anthropic_agent" + virtual_key="YOUR_LLM_VIRTUAL_KEY", + trace_id="unique-trace-id", # Optional, for request tracing ) ) -# Azure OpenAI Configuration -azure_llm = LLM( - model="gpt-4", +#Use them in your Crew Agents like this: + + @agent + def lead_market_analyst(self) -> Agent: + return Agent( + config=self.agents_config['lead_market_analyst'], + verbose=True, + memory=False, + llm=gpt_llm + ) + +``` + + +**What are Virtual Keys?** Virtual keys in Portkey securely store your LLM provider API keys (OpenAI, Anthropic, etc.) in an encrypted vault. They allow for easier key rotation and budget management. [Learn more about virtual keys here](https://portkey.ai/docs/product/ai-gateway/virtual-keys). + + + + +## Production Features + +### 1. Enhanced Observability + +Portkey provides comprehensive observability for your CrewAI agents, helping you understand exactly what's happening during each execution. + + + + + + + +Traces provide a hierarchical view of your crew's execution, showing the sequence of LLM calls, tool invocations, and state transitions. + +```python +# Add trace_id to enable hierarchical tracing in Portkey +portkey_llm = LLM( + model="gpt-4o", base_url=PORTKEY_GATEWAY_URL, api_key="dummy", extra_headers=createHeaders( api_key="YOUR_PORTKEY_API_KEY", - virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys - trace_id="azure_agent" + virtual_key="YOUR_OPENAI_VIRTUAL_KEY", + trace_id="unique-session-id" # Add unique trace ID + ) +) +``` + + + + + + + +Portkey logs every interaction with LLMs, including: + +- Complete request and response payloads +- Latency and token usage metrics +- Cost calculations +- Tool calls and function executions + +All logs can be filtered by metadata, trace IDs, models, and more, making it easy to debug specific crew runs. + + + + + + + +Portkey provides built-in dashboards that help you: + +- Track cost and token usage across all crew runs +- Analyze performance metrics like latency and success rates +- Identify bottlenecks in your agent workflows +- Compare different crew configurations and LLMs + +You can filter and segment all metrics by custom metadata to analyze specific crew types, user groups, or use cases. + + + + + Analytics with metadata filters + + +Add custom metadata to your CrewAI LLM configuration to enable powerful filtering and segmentation: + +```python +portkey_llm = LLM( + model="gpt-4o", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_OPENAI_VIRTUAL_KEY", + metadata={ + "crew_type": "research_crew", + "environment": "production", + "_user": "user_123", # Special _user field for user analytics + "request_source": "mobile_app" + } ) ) ``` +This metadata can be used to filter logs, traces, and metrics on the Portkey dashboard, allowing you to analyze specific crew runs, users, or environments. + + -### 2. Caching -Improve response times and reduce costs with two powerful caching modes: -- **Simple Cache**: Perfect for exact matches -- **Semantic Cache**: Matches responses for requests that are semantically similar -[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic) +### 2. Reliability - Keep Your Crews Running Smoothly -```py -config = { - "cache": { - "mode": "semantic", # or "simple" for exact matching +When running crews in production, things can go wrong - API rate limits, network issues, or provider outages. Portkey's reliability features ensure your agents keep running smoothly even when problems occur. + +It's simple to enable fallback in your CrewAI setup by using a Portkey Config: + +```python +from crewai import LLM +from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + +# Create LLM with fallback configuration +portkey_llm = LLM( + model="gpt-4o", + max_tokens=1000, + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + config={ + "strategy": { + "mode": "fallback" + }, + "targets": [ + { + "provider": "openai", + "api_key": "YOUR_OPENAI_API_KEY", + "override_params": {"model": "gpt-4o"} + }, + { + "provider": "anthropic", + "api_key": "YOUR_ANTHROPIC_API_KEY", + "override_params": {"model": "claude-3-opus-20240229"} + } + ] + } + ) +) + +# Use this LLM configuration with your agents +``` + +This configuration will automatically try Claude if the GPT-4o request fails, ensuring your crew can continue operating. + + + + Handles temporary failures automatically. If an LLM call fails, Portkey will retry the same request for the specified number of times - perfect for rate limits or network blips. + + + Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes. + + + Send different requests to different providers. Route complex reasoning to GPT-4, creative tasks to Claude, and quick responses to Gemini based on your needs. + + + Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability. + + + Spread requests across multiple API keys or providers. Great for high-volume crew operations and staying within rate limits. + + + +### 3. Prompting in CrewAI + +Portkey's Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your CrewAI agents. Instead of hardcoding prompts or instructions, use Portkey's prompt rendering API to dynamically fetch and apply your versioned prompts. + + +![Prompt Playground Interface](https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Portkey%20Docs.webp) + + + + +Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It's where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to: + +1. Iteratively develop prompts before using them in your agents +2. Test prompts with different variables and models +3. Compare outputs between different prompt versions +4. Collaborate with team members on prompt development + +This visual environment makes it easier to craft effective prompts for each step in your CrewAI agents' workflow. + + + +The Prompt Render API retrieves your prompt templates with all parameters configured: + +```python +from crewai import Agent, LLM +from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL, Portkey + +# Initialize Portkey admin client +portkey_admin = Portkey(api_key="YOUR_PORTKEY_API_KEY") + +# Retrieve prompt using the render API +prompt_data = portkey_client.prompts.render( + prompt_id="YOUR_PROMPT_ID", + variables={ + "agent_role": "Senior Research Scientist", } +) + +backstory_agent_prompt=prompt_data.data.messages[0]["content"] + + +# Set up LLM with Portkey integration +portkey_llm = LLM( + model="gpt-4o", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_OPENAI_VIRTUAL_KEY" + ) +) + +# Create agent using the rendered prompt +researcher = Agent( + role="Senior Research Scientist", + goal="Discover groundbreaking insights about the assigned topic", + backstory=backstory_agent, # Use the rendered prompt + verbose=True, + llm=portkey_llm +) +``` + + + +You can: +- Create multiple versions of the same prompt +- Compare performance between versions +- Roll back to previous versions if needed +- Specify which version to use in your code: + +```python +# Use a specific prompt version +prompt_data = portkey_admin.prompts.render( + prompt_id="YOUR_PROMPT_ID@version_number", + variables={ + "agent_role": "Senior Research Scientist", + "agent_goal": "Discover groundbreaking insights" + } +) +``` + + + +Portkey prompts use Mustache-style templating for easy variable substitution: + +``` +You are a {{agent_role}} with expertise in {{domain}}. + +Your mission is to {{agent_goal}} by leveraging your knowledge +and experience in the field. + +Always maintain a {{tone}} tone and focus on providing {{focus_area}}. +``` + +When rendering, simply pass the variables: + +```python +prompt_data = portkey_admin.prompts.render( + prompt_id="YOUR_PROMPT_ID", + variables={ + "agent_role": "Senior Research Scientist", + "domain": "artificial intelligence", + "agent_goal": "discover groundbreaking insights", + "tone": "professional", + "focus_area": "practical applications" + } +) +``` + + + + + Learn more about Portkey's prompt management features + + +### 4. Guardrails for Safe Crews + +Guardrails ensure your CrewAI agents operate safely and respond appropriately in all situations. + +**Why Use Guardrails?** + +CrewAI agents can experience various failure modes: +- Generating harmful or inappropriate content +- Leaking sensitive information like PII +- Hallucinating incorrect information +- Generating outputs in incorrect formats + +Portkey's guardrails add protections for both inputs and outputs. + +**Implementing Guardrails** + +```python +from crewai import Agent, LLM +from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + +# Create LLM with guardrails +portkey_llm = LLM( + model="gpt-4o", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_OPENAI_VIRTUAL_KEY", + config={ + "input_guardrails": ["guardrails-id-xxx", "guardrails-id-yyy"], + "output_guardrails": ["guardrails-id-zzz"] + } + ) +) + +# Create agent with guardrailed LLM +researcher = Agent( + role="Senior Research Scientist", + goal="Discover groundbreaking insights about the assigned topic", + backstory="You are an expert researcher with deep domain knowledge.", + verbose=True, + llm=portkey_llm +) +``` + +Portkey's guardrails can: +- Detect and redact PII in both inputs and outputs +- Filter harmful or inappropriate content +- Validate response formats against schemas +- Check for hallucinations against ground truth +- Apply custom business logic and rules + + + Explore Portkey's guardrail features to enhance agent safety + + +### 5. User Tracking with Metadata + +Track individual users through your CrewAI agents using Portkey's metadata system. + +**What is Metadata in Portkey?** + +Metadata allows you to associate custom data with each request, enabling filtering, segmentation, and analytics. The special `_user` field is specifically designed for user tracking. + +```python +from crewai import Agent, LLM +from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + +# Configure LLM with user tracking +portkey_llm = LLM( + model="gpt-4o", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_OPENAI_VIRTUAL_KEY", + metadata={ + "_user": "user_123", # Special _user field for user analytics + "user_tier": "premium", + "user_company": "Acme Corp", + "session_id": "abc-123" + } + ) +) + +# Create agent with tracked LLM +researcher = Agent( + role="Senior Research Scientist", + goal="Discover groundbreaking insights about the assigned topic", + backstory="You are an expert researcher with deep domain knowledge.", + verbose=True, + llm=portkey_llm +) +``` + +**Filter Analytics by User** + +With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis: + + + + + +This enables: +- Per-user cost tracking and budgeting +- Personalized user analytics +- Team or organization-level metrics +- Environment-specific monitoring (staging vs. production) + + + Explore how to use custom metadata to enhance your analytics + + +### 6. Caching for Efficient Crews + +Implement caching to make your CrewAI agents more efficient and cost-effective: + + + +```python +from crewai import Agent, LLM +from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + +# Configure LLM with simple caching +portkey_llm = LLM( + model="gpt-4o", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_OPENAI_VIRTUAL_KEY", + config={ + "cache": { + "mode": "simple" + } + } + ) +) + +# Create agent with cached LLM +researcher = Agent( + role="Senior Research Scientist", + goal="Discover groundbreaking insights about the assigned topic", + backstory="You are an expert researcher with deep domain knowledge.", + verbose=True, + llm=portkey_llm +) +``` + +Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions. + + + +```python +from crewai import Agent, LLM +from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + +# Configure LLM with semantic caching +portkey_llm = LLM( + model="gpt-4o", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_OPENAI_VIRTUAL_KEY", + config={ + "cache": { + "mode": "semantic" + } + } + ) +) + +# Create agent with semantically cached LLM +researcher = Agent( + role="Senior Research Scientist", + goal="Discover groundbreaking insights about the assigned topic", + backstory="You are an expert researcher with deep domain knowledge.", + verbose=True, + llm=portkey_llm +) +``` + +Semantic caching considers the contextual similarity between input requests, caching responses for semantically similar inputs. + + + +### 7. Model Interoperability + +CrewAI supports multiple LLM providers, and Portkey extends this capability by providing access to over 200 LLMs through a unified interface. You can easily switch between different models without changing your core agent logic: + +```python +from crewai import Agent, LLM +from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + +# Set up LLMs with different providers +openai_llm = LLM( + model="gpt-4o", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_OPENAI_VIRTUAL_KEY" + ) +) + +anthropic_llm = LLM( + model="claude-3-5-sonnet-latest", + max_tokens=1000, + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY" + ) +) + +# Choose which LLM to use for each agent based on your needs +researcher = Agent( + role="Senior Research Scientist", + goal="Discover groundbreaking insights about the assigned topic", + backstory="You are an expert researcher with deep domain knowledge.", + verbose=True, + llm=openai_llm # Use anthropic_llm for Anthropic +) +``` + +Portkey provides access to LLMs from providers including: + +- OpenAI (GPT-4o, GPT-4 Turbo, etc.) +- Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, etc.) +- Mistral AI (Mistral Large, Mistral Medium, etc.) +- Google Vertex AI (Gemini 1.5 Pro, etc.) +- Cohere (Command, Command-R, etc.) +- AWS Bedrock (Claude, Titan, etc.) +- Local/Private Models + + + See the full list of LLM providers supported by Portkey + + +## Set Up Enterprise Governance for CrewAI + +**Why Enterprise Governance?** +If you are using CrewAI inside your organization, you need to consider several governance aspects: +- **Cost Management**: Controlling and tracking AI spending across teams +- **Access Control**: Managing which teams can use specific models +- **Usage Analytics**: Understanding how AI is being used across the organization +- **Security & Compliance**: Maintaining enterprise security standards +- **Reliability**: Ensuring consistent service across all users + +Portkey adds a comprehensive governance layer to address these enterprise needs. Let's implement these controls step by step. + + + +Virtual Keys are Portkey's secure way to manage your LLM provider API keys. They provide essential controls like: +- Budget limits for API usage +- Rate limiting capabilities +- Secure API key storage + +To create a virtual key: +Go to [Virtual Keys](https://app.portkey.ai/virtual-keys) in the Portkey App. Save and copy the virtual key ID + + + + + + +Save your virtual key ID - you'll need it for the next step. + + + + +Configs in Portkey define how your requests are routed, with features like advanced routing, fallbacks, and retries. + +To create your config: +1. Go to [Configs](https://app.portkey.ai/configs) in Portkey dashboard +2. Create new config with: + ```json + { + "virtual_key": "YOUR_VIRTUAL_KEY_FROM_STEP1", + "override_params": { + "model": "gpt-4o" // Your preferred model name + } + } + ``` +3. Save and note the Config name for the next step + + + + + + + + +Now create a Portkey API key and attach the config you created in Step 2: + +1. Go to [API Keys](https://app.portkey.ai/api-keys) in Portkey and Create new API key +2. Select your config from `Step 2` +3. Generate and save your API key + + + + + + + + +After setting up your Portkey API key with the attached config, connect it to your CrewAI agents: + +```python +from crewai import Agent, LLM +from portkey_ai import PORTKEY_GATEWAY_URL + +# Configure LLM with your API key +portkey_llm = LLM( + model="gpt-4o", + base_url=PORTKEY_GATEWAY_URL, + api_key="YOUR_PORTKEY_API_KEY" +) + +# Create agent with Portkey-enabled LLM +researcher = Agent( + role="Senior Research Scientist", + goal="Discover groundbreaking insights about the assigned topic", + backstory="You are an expert researcher with deep domain knowledge.", + verbose=True, + llm=portkey_llm +) +``` + + + + + +### Step 1: Implement Budget Controls & Rate Limits + +Virtual Keys enable granular control over LLM access at the team/department level. This helps you: +- Set up [budget limits](https://portkey.ai/docs/product/ai-gateway/virtual-keys/budget-limits) +- Prevent unexpected usage spikes using Rate limits +- Track departmental spending + +#### Setting Up Department-Specific Controls: +1. Navigate to [Virtual Keys](https://app.portkey.ai/virtual-keys) in Portkey dashboard +2. Create new Virtual Key for each department with budget limits and rate limits +3. Configure department-specific limits + + + + + + + +### Step 2: Define Model Access Rules + +As your AI usage scales, controlling which teams can access specific models becomes crucial. Portkey Configs provide this control layer with features like: + +#### Access Control Features: +- **Model Restrictions**: Limit access to specific models +- **Data Protection**: Implement guardrails for sensitive data +- **Reliability Controls**: Add fallbacks and retry logic + +#### Example Configuration: +Here's a basic configuration to route requests to OpenAI, specifically using GPT-4o: + +```json +{ + "strategy": { + "mode": "single" + }, + "targets": [ + { + "virtual_key": "YOUR_OPENAI_VIRTUAL_KEY", + "override_params": { + "model": "gpt-4o" + } + } + ] } ``` -### 3. Production Reliability -Portkey provides comprehensive reliability features: -- **Automatic Retries**: Handle temporary failures gracefully -- **Request Timeouts**: Prevent hanging operations -- **Conditional Routing**: Route requests based on specific conditions -- **Fallbacks**: Set up automatic provider failovers -- **Load Balancing**: Distribute requests efficiently + Create your config on the [Configs page](https://app.portkey.ai/configs) in your Portkey dashboard. -[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/) + + Configs can be updated anytime to adjust controls without affecting running applications. + + + + ### Step 3: Implement Access Controls + Create User-specific API keys that automatically: + - Track usage per user/team with the help of virtual keys + - Apply appropriate configs to route requests + - Collect relevant metadata to filter logs + - Enforce access permissions -### 4. Metrics + Create API keys through: + - [Portkey App](https://app.portkey.ai/) + - [API Key Management API](/api-reference/admin-api/control-plane/api-keys/create-api-key) -Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need. + Example using Python SDK: + ```python + from portkey_ai import Portkey + portkey = Portkey(api_key="YOUR_ADMIN_API_KEY") -- Cost per agent interaction -- Response times and latency -- Token usage and efficiency -- Success/failure rates -- Cache hit rates + api_key = portkey.api_keys.create( + name="engineering-team", + type="organisation", + workspace_id="YOUR_WORKSPACE_ID", + defaults={ + "config_id": "your-config-id", + "metadata": { + "environment": "production", + "department": "engineering" + } + }, + scopes=["logs.view", "configs.read"] + ) + ``` -Portkey Dashboard + For detailed key management instructions, see our [API Keys documentation](/api-reference/admin-api/control-plane/api-keys/create-api-key). + -### 5. Detailed Logging -Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes. + + ### Step 4: Deploy & Monitor + After distributing API keys to your team members, your enterprise-ready CrewAI setup is ready to go. Each team member can now use their designated API keys with appropriate access levels and budget controls. + Monitor usage in Portkey dashboard: + - Cost tracking by department + - Model usage patterns + - Request volumes + - Error rates + -Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata. + -
- Traces - Portkey Traces -
+ +### Enterprise Features Now Available +**Your CrewAI integration now has:** +- Departmental budget controls +- Model access governance +- Usage tracking & attribution +- Security guardrails +- Reliability features + -
- Logs - Portkey Logs -
+## Frequently Asked Questions -### 6. Enterprise Security Features -- Set budget limit and rate limts per Virtual Key (disposable API keys) -- Implement role-based access control -- Track system changes with audit logs -- Configure data retention policies + + + Portkey adds production-readiness to CrewAI through comprehensive observability (traces, logs, metrics), reliability features (fallbacks, retries, caching), and access to 200+ LLMs through a unified interface. This makes it easier to debug, optimize, and scale your agent applications. + + + Yes! Portkey integrates seamlessly with existing CrewAI applications. You just need to update your LLM configuration code with the Portkey-enabled version. The rest of your agent and crew code remains unchanged. + + + Portkey supports all CrewAI features, including agents, tools, human-in-the-loop workflows, and all task process types (sequential, hierarchical, etc.). It adds observability and reliability without limiting any of the framework's functionality. + -For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs). + + Yes, Portkey allows you to use a consistent `trace_id` across multiple agents in a crew to track the entire workflow. This is especially useful for complex crews where you want to understand the full execution path across multiple agents. + + + + Portkey allows you to add custom metadata to your LLM configuration, which you can then use for filtering. Add fields like `crew_name`, `crew_type`, or `session_id` to easily find and analyze specific crew executions. + + + + Yes! Portkey uses your own API keys for the various LLM providers. It securely stores them as virtual keys, allowing you to easily manage and rotate keys without changing your code. + + + ## Resources -- [📘 Portkey Documentation](https://docs.portkey.ai) -- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) -- [🐦 Twitter](https://twitter.com/portkeyai) -- [💬 Discord Community](https://discord.gg/DD7vgKK299) + + +

Official CrewAI documentation

+ + +

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+ + \ No newline at end of file From 2bd6b72aaee7bb594a55ea32229b35599d335236 Mon Sep 17 00:00:00 2001 From: Lucas Gomide Date: Tue, 3 Jun 2025 11:09:02 -0300 Subject: [PATCH 2/2] Persist available tools from a Tool repository (#2851) * feat: add capability to see and expose public Tool classes * feat: persist available Tools from repository on publish * ci: ignore explictly templates from ruff check Ruff only applies --exclude to files it discovers itself. So we have to skip manually the same files excluded from `ruff.toml` * sytle: fix linter issues * refactor: renaming available_tools_classes by available_exports * feat: provide more context about exportable tools * feat: allow to install a Tool from pypi * test: fix tests * feat: add env_vars attribute to BaseTool * remove TODO: security check since we are handle that on enterprise side --- .github/workflows/linter.yml | 5 +- src/crewai/cli/plus_api.py | 4 +- .../tool/src/{{folder_name}}/__init__.py | 3 + src/crewai/cli/tools/main.py | 24 ++- src/crewai/cli/utils.py | 113 +++++++++++- src/crewai/tools/__init__.py | 8 +- src/crewai/tools/base_tool.py | 9 +- tests/cli/test_plus_api.py | 2 + tests/cli/test_utils.py | 161 ++++++++++++++++++ tests/cli/tools/test_main.py | 40 +++++ 10 files changed, 360 insertions(+), 9 deletions(-) diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml index 3e1571830..421e37c01 100644 --- a/.github/workflows/linter.yml +++ b/.github/workflows/linter.yml @@ -30,4 +30,7 @@ jobs: - name: Run Ruff on Changed Files if: ${{ steps.changed-files.outputs.files != '' }} run: | - echo "${{ steps.changed-files.outputs.files }}" | tr " " "\n" | xargs -I{} ruff check "{}" + echo "${{ steps.changed-files.outputs.files }}" \ + | tr ' ' '\n' \ + | grep -v 'src/crewai/cli/templates/' \ + | xargs -I{} ruff check "{}" diff --git a/src/crewai/cli/plus_api.py b/src/crewai/cli/plus_api.py index 93e5750c8..6961f886e 100644 --- a/src/crewai/cli/plus_api.py +++ b/src/crewai/cli/plus_api.py @@ -1,5 +1,5 @@ from os import getenv -from typing import Optional +from typing import List, Optional from urllib.parse import urljoin import requests @@ -48,6 +48,7 @@ class PlusAPI: version: str, description: Optional[str], encoded_file: str, + available_exports: Optional[List[str]] = None, ): params = { "handle": handle, @@ -55,6 +56,7 @@ class PlusAPI: "version": version, "file": encoded_file, "description": description, + "available_exports": available_exports, } return self._make_request("POST", f"{self.TOOLS_RESOURCE}", json=params) diff --git a/src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py b/src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py index e69de29bb..e51d45087 100644 --- a/src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py +++ b/src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py @@ -0,0 +1,3 @@ +from .tool import {{class_name}} + +__all__ = ["{{class_name}}"] diff --git a/src/crewai/cli/tools/main.py b/src/crewai/cli/tools/main.py index 8fbe1948b..fad69467d 100644 --- a/src/crewai/cli/tools/main.py +++ b/src/crewai/cli/tools/main.py @@ -3,6 +3,7 @@ import os import subprocess import tempfile from pathlib import Path +from typing import Any import click from rich.console import Console @@ -11,6 +12,7 @@ from crewai.cli import git from crewai.cli.command import BaseCommand, PlusAPIMixin from crewai.cli.config import Settings from crewai.cli.utils import ( + extract_available_exports, get_project_description, get_project_name, get_project_version, @@ -82,6 +84,14 @@ class ToolCommand(BaseCommand, PlusAPIMixin): project_description = get_project_description(require=False) encoded_tarball = None + console.print("[bold blue]Discovering tools from your project...[/bold blue]") + available_exports = extract_available_exports() + + if available_exports: + console.print( + f"[green]Found these tools to publish: {', '.join(available_exports)}[/green]" + ) + with tempfile.TemporaryDirectory() as temp_build_dir: subprocess.run( ["uv", "build", "--sdist", "--out-dir", temp_build_dir], @@ -105,12 +115,14 @@ class ToolCommand(BaseCommand, PlusAPIMixin): encoded_tarball = base64.b64encode(tarball_contents).decode("utf-8") + console.print("[bold blue]Publishing tool to repository...[/bold blue]") publish_response = self.plus_api_client.publish_tool( handle=project_name, is_public=is_public, version=project_version, description=project_description, encoded_file=f"data:application/x-gzip;base64,{encoded_tarball}", + available_exports=available_exports, ) self._validate_response(publish_response) @@ -167,7 +179,8 @@ class ToolCommand(BaseCommand, PlusAPIMixin): "Successfully authenticated to the tool repository.", style="bold green" ) - def _add_package(self, tool_details): + def _add_package(self, tool_details: dict[str, Any]): + is_from_pypi = tool_details.get("source", None) == "pypi" tool_handle = tool_details["handle"] repository_handle = tool_details["repository"]["handle"] repository_url = tool_details["repository"]["url"] @@ -176,10 +189,13 @@ class ToolCommand(BaseCommand, PlusAPIMixin): add_package_command = [ "uv", "add", - "--index", - index, - tool_handle, ] + + if is_from_pypi: + add_package_command.append(tool_handle) + else: + add_package_command.extend(["--index", index, tool_handle]) + add_package_result = subprocess.run( add_package_command, capture_output=False, diff --git a/src/crewai/cli/utils.py b/src/crewai/cli/utils.py index f88213a58..9780b52fa 100644 --- a/src/crewai/cli/utils.py +++ b/src/crewai/cli/utils.py @@ -1,8 +1,10 @@ +import importlib.util import os import shutil import sys from functools import reduce -from inspect import isfunction, ismethod +from inspect import getmro, isclass, isfunction, ismethod +from pathlib import Path from typing import Any, Dict, List, get_type_hints import click @@ -339,3 +341,112 @@ def fetch_crews(module_attr) -> list[Crew]: if crew_instance := get_crew_instance(attr): crew_instances.append(crew_instance) return crew_instances + + +def is_valid_tool(obj): + from crewai.tools.base_tool import Tool + + if isclass(obj): + try: + return any(base.__name__ == "BaseTool" for base in getmro(obj)) + except (TypeError, AttributeError): + return False + + return isinstance(obj, Tool) + + +def extract_available_exports(dir_path: str = "src"): + """ + Extract available tool classes from the project's __init__.py files. + Only includes classes that inherit from BaseTool or functions decorated with @tool. + + Returns: + list: A list of valid tool class names or ["BaseTool"] if none found + """ + try: + init_files = Path(dir_path).glob("**/__init__.py") + available_exports = [] + + for init_file in init_files: + tools = _load_tools_from_init(init_file) + available_exports.extend(tools) + + if not available_exports: + _print_no_tools_warning() + raise SystemExit(1) + + return available_exports + + except Exception as e: + console.print(f"[red]Error: Could not extract tool classes: {str(e)}[/red]") + console.print( + "Please ensure your project contains valid tools (classes inheriting from BaseTool or functions with @tool decorator)." + ) + raise SystemExit(1) + + +def _load_tools_from_init(init_file: Path) -> list[dict[str, Any]]: + """ + Load and validate tools from a given __init__.py file. + """ + spec = importlib.util.spec_from_file_location("temp_module", init_file) + + if not spec or not spec.loader: + return [] + + module = importlib.util.module_from_spec(spec) + sys.modules["temp_module"] = module + + try: + spec.loader.exec_module(module) + + if not hasattr(module, "__all__"): + console.print( + f"[bold yellow]Warning: No __all__ defined in {init_file}[/bold yellow]" + ) + raise SystemExit(1) + + return [ + { + "name": name, + } + for name in module.__all__ + if hasattr(module, name) and is_valid_tool(getattr(module, name)) + ] + + except Exception as e: + console.print(f"[red]Warning: Could not load {init_file}: {str(e)}[/red]") + raise SystemExit(1) + + finally: + sys.modules.pop("temp_module", None) + + +def _print_no_tools_warning(): + """ + Display warning and usage instructions if no tools were found. + """ + console.print( + "\n[bold yellow]Warning: No valid tools were exposed in your __init__.py file![/bold yellow]" + ) + console.print( + "Your __init__.py file must contain all classes that inherit from [bold]BaseTool[/bold] " + "or functions decorated with [bold]@tool[/bold]." + ) + console.print( + "\nExample:\n[dim]# In your __init__.py file[/dim]\n" + "[green]__all__ = ['YourTool', 'your_tool_function'][/green]\n\n" + "[dim]# In your tool.py file[/dim]\n" + "[green]from crewai.tools import BaseTool, tool\n\n" + "# Tool class example\n" + "class YourTool(BaseTool):\n" + ' name = "your_tool"\n' + ' description = "Your tool description"\n' + " # ... rest of implementation\n\n" + "# Decorated function example\n" + "@tool\n" + "def your_tool_function(text: str) -> str:\n" + ' """Your tool description"""\n' + " # ... implementation\n" + " return result\n" + ) diff --git a/src/crewai/tools/__init__.py b/src/crewai/tools/__init__.py index 41819ccbc..2467fa906 100644 --- a/src/crewai/tools/__init__.py +++ b/src/crewai/tools/__init__.py @@ -1 +1,7 @@ -from .base_tool import BaseTool, tool +from .base_tool import BaseTool, tool, EnvVar + +__all__ = [ + "BaseTool", + "tool", + "EnvVar", +] \ No newline at end of file diff --git a/src/crewai/tools/base_tool.py b/src/crewai/tools/base_tool.py index fb0428ccd..e7d43422b 100644 --- a/src/crewai/tools/base_tool.py +++ b/src/crewai/tools/base_tool.py @@ -1,7 +1,7 @@ import asyncio from abc import ABC, abstractmethod from inspect import signature -from typing import Any, Callable, Type, get_args, get_origin +from typing import Any, Callable, Type, get_args, get_origin, Optional, List from pydantic import ( BaseModel, @@ -14,6 +14,11 @@ from pydantic import BaseModel as PydanticBaseModel from crewai.tools.structured_tool import CrewStructuredTool +class EnvVar(BaseModel): + name: str + description: str + required: bool = True + default: Optional[str] = None class BaseTool(BaseModel, ABC): class _ArgsSchemaPlaceholder(PydanticBaseModel): @@ -25,6 +30,8 @@ class BaseTool(BaseModel, ABC): """The unique name of the tool that clearly communicates its purpose.""" description: str """Used to tell the model how/when/why to use the tool.""" + env_vars: List[EnvVar] = [] + """List of environment variables used by the tool.""" args_schema: Type[PydanticBaseModel] = Field( default_factory=_ArgsSchemaPlaceholder, validate_default=True ) diff --git a/tests/cli/test_plus_api.py b/tests/cli/test_plus_api.py index daefeee42..da26ba35f 100644 --- a/tests/cli/test_plus_api.py +++ b/tests/cli/test_plus_api.py @@ -61,6 +61,7 @@ class TestPlusAPI(unittest.TestCase): "version": version, "file": encoded_file, "description": description, + "available_exports": None, } mock_make_request.assert_called_once_with( "POST", "/crewai_plus/api/v1/tools", json=params @@ -87,6 +88,7 @@ class TestPlusAPI(unittest.TestCase): "version": version, "file": encoded_file, "description": description, + "available_exports": None, } mock_make_request.assert_called_once_with( "POST", "/crewai_plus/api/v1/tools", json=params diff --git a/tests/cli/test_utils.py b/tests/cli/test_utils.py index 0270b12fc..115bb67eb 100644 --- a/tests/cli/test_utils.py +++ b/tests/cli/test_utils.py @@ -1,6 +1,7 @@ import os import shutil import tempfile +from pathlib import Path import pytest @@ -100,3 +101,163 @@ def test_tree_copy_to_existing_directory(temp_tree): assert os.path.isfile(os.path.join(dest_dir, "file1.txt")) finally: shutil.rmtree(dest_dir) + + +@pytest.fixture +def temp_project_dir(): + """Create a temporary directory for testing tool extraction.""" + with tempfile.TemporaryDirectory() as temp_dir: + yield Path(temp_dir) + + +def create_init_file(directory, content): + return create_file(directory / "__init__.py", content) + + +def test_extract_available_exports_empty_project(temp_project_dir, capsys): + with pytest.raises(SystemExit): + utils.extract_available_exports(dir_path=temp_project_dir) + captured = capsys.readouterr() + + assert "No valid tools were exposed in your __init__.py file" in captured.out + + +def test_extract_available_exports_no_init_file(temp_project_dir, capsys): + (temp_project_dir / "some_file.py").write_text("print('hello')") + with pytest.raises(SystemExit): + utils.extract_available_exports(dir_path=temp_project_dir) + captured = capsys.readouterr() + + assert "No valid tools were exposed in your __init__.py file" in captured.out + + +def test_extract_available_exports_empty_init_file(temp_project_dir, capsys): + create_init_file(temp_project_dir, "") + with pytest.raises(SystemExit): + utils.extract_available_exports(dir_path=temp_project_dir) + captured = capsys.readouterr() + + assert "Warning: No __all__ defined in" in captured.out + + +def test_extract_available_exports_no_all_variable(temp_project_dir, capsys): + create_init_file( + temp_project_dir, + "from crewai.tools import BaseTool\n\nclass MyTool(BaseTool):\n pass", + ) + with pytest.raises(SystemExit): + utils.extract_available_exports(dir_path=temp_project_dir) + captured = capsys.readouterr() + + assert "Warning: No __all__ defined in" in captured.out + + +def test_extract_available_exports_valid_base_tool_class(temp_project_dir): + create_init_file( + temp_project_dir, + """from crewai.tools import BaseTool + +class MyTool(BaseTool): + name: str = "my_tool" + description: str = "A test tool" + +__all__ = ['MyTool'] +""", + ) + tools = utils.extract_available_exports(dir_path=temp_project_dir) + assert [{"name": "MyTool"}] == tools + + +def test_extract_available_exports_valid_tool_decorator(temp_project_dir): + create_init_file( + temp_project_dir, + """from crewai.tools import tool + +@tool +def my_tool_function(text: str) -> str: + \"\"\"A test tool function\"\"\" + return text + +__all__ = ['my_tool_function'] +""", + ) + tools = utils.extract_available_exports(dir_path=temp_project_dir) + assert [{"name": "my_tool_function"}] == tools + + +def test_extract_available_exports_multiple_valid_tools(temp_project_dir): + create_init_file( + temp_project_dir, + """from crewai.tools import BaseTool, tool + +class MyTool(BaseTool): + name: str = "my_tool" + description: str = "A test tool" + +@tool +def my_tool_function(text: str) -> str: + \"\"\"A test tool function\"\"\" + return text + +__all__ = ['MyTool', 'my_tool_function'] +""", + ) + tools = utils.extract_available_exports(dir_path=temp_project_dir) + assert [{"name": "MyTool"}, {"name": "my_tool_function"}] == tools + + +def test_extract_available_exports_with_invalid_tool_decorator(temp_project_dir): + create_init_file( + temp_project_dir, + """from crewai.tools import BaseTool + +class MyTool(BaseTool): + name: str = "my_tool" + description: str = "A test tool" + +def not_a_tool(): + pass + +__all__ = ['MyTool', 'not_a_tool'] +""", + ) + tools = utils.extract_available_exports(dir_path=temp_project_dir) + assert [{"name": "MyTool"}] == tools + + +def test_extract_available_exports_import_error(temp_project_dir, capsys): + create_init_file( + temp_project_dir, + """from nonexistent_module import something + +class MyTool(BaseTool): + pass + +__all__ = ['MyTool'] +""", + ) + with pytest.raises(SystemExit): + utils.extract_available_exports(dir_path=temp_project_dir) + captured = capsys.readouterr() + + assert "nonexistent_module" in captured.out + + +def test_extract_available_exports_syntax_error(temp_project_dir, capsys): + create_init_file( + temp_project_dir, + """from crewai.tools import BaseTool + +class MyTool(BaseTool): + # Missing closing parenthesis + def __init__(self, name: + pass + +__all__ = ['MyTool'] +""", + ) + with pytest.raises(SystemExit): + utils.extract_available_exports(dir_path=temp_project_dir) + captured = capsys.readouterr() + + assert "was never closed" in captured.out diff --git a/tests/cli/tools/test_main.py b/tests/cli/tools/test_main.py index 28659a80a..79bb171b4 100644 --- a/tests/cli/tools/test_main.py +++ b/tests/cli/tools/test_main.py @@ -85,6 +85,36 @@ def test_install_success(mock_get, mock_subprocess_run, capsys, tool_command): env=unittest.mock.ANY, ) +@patch("crewai.cli.tools.main.subprocess.run") +@patch("crewai.cli.plus_api.PlusAPI.get_tool") +def test_install_success_from_pypi(mock_get, mock_subprocess_run, capsys, tool_command): + mock_get_response = MagicMock() + mock_get_response.status_code = 200 + mock_get_response.json.return_value = { + "handle": "sample-tool", + "repository": {"handle": "sample-repo", "url": "https://example.com/repo"}, + "source": "pypi", + } + mock_get.return_value = mock_get_response + mock_subprocess_run.return_value = MagicMock(stderr=None) + + tool_command.install("sample-tool") + output = capsys.readouterr().out + assert "Successfully installed sample-tool" in output + + mock_get.assert_has_calls([mock.call("sample-tool"), mock.call().json()]) + mock_subprocess_run.assert_any_call( + [ + "uv", + "add", + "sample-tool", + ], + capture_output=False, + text=True, + check=True, + env=unittest.mock.ANY, + ) + @patch("crewai.cli.plus_api.PlusAPI.get_tool") def test_install_tool_not_found(mock_get, capsys, tool_command): @@ -135,7 +165,9 @@ def test_publish_when_not_in_sync(mock_is_synced, capsys, tool_command): ) @patch("crewai.cli.plus_api.PlusAPI.publish_tool") @patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=False) +@patch("crewai.cli.tools.main.extract_available_exports", return_value=["SampleTool"]) def test_publish_when_not_in_sync_and_force( + mock_available_exports, mock_is_synced, mock_publish, mock_open, @@ -168,6 +200,7 @@ def test_publish_when_not_in_sync_and_force( version="1.0.0", description="A sample tool", encoded_file=unittest.mock.ANY, + available_exports=["SampleTool"], ) @@ -183,7 +216,9 @@ def test_publish_when_not_in_sync_and_force( ) @patch("crewai.cli.plus_api.PlusAPI.publish_tool") @patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=True) +@patch("crewai.cli.tools.main.extract_available_exports", return_value=["SampleTool"]) def test_publish_success( + mock_available_exports, mock_is_synced, mock_publish, mock_open, @@ -216,6 +251,7 @@ def test_publish_success( version="1.0.0", description="A sample tool", encoded_file=unittest.mock.ANY, + available_exports=["SampleTool"], ) @@ -230,7 +266,9 @@ def test_publish_success( read_data=b"sample tarball content", ) @patch("crewai.cli.plus_api.PlusAPI.publish_tool") +@patch("crewai.cli.tools.main.extract_available_exports", return_value=["SampleTool"]) def test_publish_failure( + mock_available_exports, mock_publish, mock_open, mock_listdir, @@ -266,7 +304,9 @@ def test_publish_failure( read_data=b"sample tarball content", ) @patch("crewai.cli.plus_api.PlusAPI.publish_tool") +@patch("crewai.cli.tools.main.extract_available_exports", return_value=["SampleTool"]) def test_publish_api_error( + mock_available_exports, mock_publish, mock_open, mock_listdir,