Agent Framework Scorecard (Explained)

This is the scorecard framework explanation.

Score Interpretation

• 4.5 - 5.0: Industry Leading

• 4.0 - 4.4: Production Ready

• 3.5 - 3.9: Mature

• 3.0 - 3.4: Developing

• 2.0 - 2.9: Basic

• Below 2.0: Early Stage

Different Dimensions of the Scorecard

1. Learning Experience

• Documentation Quality (1-5)

  • Comprehensiveness

  • Up-to-date content

  • Code examples and tutorials

• Learning Curve (1-5)

  • Time to basic proficiency - How easy is it to get started with the framework and understand the core concepts?

  • Complexity of concepts

  • Available learning resources

• Developer Tools (1-5)

  • IDE support

  • Debugging capabilities

  • Testing frameworks

2. Architecture & Design

• Deployability (1-5)

How easy is it to deploy the framework in a distributed environment, different cloud providers (Azure, GCP, and AWS) and scale its performance?

• Scalability (1-5)

  • Handling of concurrent agents

  • Resource management

  • Performance under load

  • Consider the support for asynchronous messaging.

• Flexibility (1-5)

  • Ability to handle different types of tasks

  • Customization options

  • Integration capabilities

3. Capabilities & Features

• Core Agent Features (1-5)

  • Planning and reasoning

  • Memory management

    • Look at the ability to monitor trends in the number of calls and latency, and the ability to view traces from the application.

    • Consider if it is easy to avoid memory overflow.

    • Look at options for memory, such as short-term and RAG on memory.

  • Decision-making capabilities

• Tool Integration (1-5)

  • Built-in tools

  • Ease of adding new tools

  • Consider the ease of using both closed-source and open-source models.

  • Consider the level of support for different tools, or whether a 'proxy' server is needed to integrate.

  • See whether the framework supports remote tool execution.

  • API integrations

• Important Features (1-5)

  • Streaming: Does the framework support streaming of tokens or messages?

  • Human-in-the-Loop: How well does it support human intervention?

  • Languages: What programming languages does it support?

  • Low-Code Tools: Does it offer a low-code interface for creating agents?

• Language Model Support (1-5)

  • Number of supported LLMs

  • Ease of switching between models

  • Implementation quality

4. Production Readiness

• Stability (1-5)

  • Production track record

  • Bug frequency

  • Update reliability

• Security (1-5)

  • Authentication & authorization

  • Data protection

  • Security best practices

• Monitoring & Observability (1-5)

  • Logging capabilities

  • Performance metrics

    • Look at the ability to monitor trends in the number of calls and latency, and the ability to view traces from the application.

  • Debugging tools

  • Time Travel: Can you go back in time after a task is completed to see how the agent behaved?

  • Look for the ability to monitor tokens used, latency, and error rates.

5. Community & Support

• Community Size (1-5)

  • GitHub stars/forks

  • Active contributors

  • Community engagement

  • Market place for Agents

• Support Quality (1-5)

  • Issue response time

  • Commercial support options

  • Community help availability

• Ecosystem (1-5)

  • Third-party plugins

  • Integration examples

  • Community resources