Comparing agent runtimes vs. frameworks

The architectural foundation you choose for building agent systems dramatically shapes development velocity, scalability potential, and long-term maintenance costs. Engineering teams frequently debate whether to invest in custom agent runtimes or leverage existing frameworks, with significant implications for both technical capabilities and business outcomes.

Selecting the right approach requires understanding the fundamental tradeoffs between control and convenience, customization and standardization. In this article, we examine the key differences between agent runtimes and frameworks, providing a conceptual framework for building multi-agent systems to help engineering teams select the most appropriate architecture for their specific requirements.

What does agent runtime and framework mean

Agent runtimes are execution environments that power agent functionality by managing resources, coordinating components, and executing agent logic. They provide the foundation layer with capabilities for memory management, tool execution, and state persistence. Frameworks operate at a higher level of abstraction, offering pre-built components, patterns, and development tooling that simplify agent creation.

The choice between runtimes and frameworks represents a fundamental architectural decision with significant implications. Runtimes grant greater control and customization potential at the cost of development effort, while frameworks accelerate development but may constrain flexibility in certain scenarios.

Engineering leaders gain clarity on these tradeoffs to select the appropriate approach based on specific technical requirements, team capabilities, and business objectives.

How does a custom-coded runtime differ

Custom agent runtimes provide granular control over execution environments, allowing engineering teams to tailor every aspect to their specific requirements. This control extends to memory management, threading models, execution patterns, and resource allocation strategies.

The primary advantage lies in optimization potential. Teams can design runtimes specifically for their workloads, eliminating unnecessary overhead and implementing specialized performance enhancements not possible with general-purpose frameworks.

Custom development requires significant engineering investment and ongoing maintenance responsibility. The development team is responsible for implementing all functionality that frameworks provide out-of-box, from basic execution environments to sophisticated orchestration capabilities.

1. Corporate constraints

Regulated industries face strict requirements around data handling, auditability, and compliance that standard frameworks may not adequately address. Custom runtimes allow engineering teams to build compliance directly into the execution environment.

Financial services, healthcare, and government sectors often require:

• Control: Fine-grained encryption, access controls, and data handling policies
• Audit: Detailed logging of all agent actions and decisions
• Residency: Guarantees about where data resides during processing

These requirements frequently extend beyond the configuration options available in existing frameworks, necessitating custom implementation.

2. Control over memory

Memory architecture fundamentally shapes agent capabilities and performance characteristics. Custom runtimes enable precisely tailored memory systems optimized for specific usage patterns and requirements.

Engineering teams can implement specialized approaches to:

• Context: Custom strategies for maintaining and retrieving relevant context
• Persistence: Purpose-built storage solutions for short and long-term memory
• Optimization: Specialized indexing and search capabilities for efficient memory access

These customizations become particularly valuable in scenarios with unique memory requirements, such as agents processing sensitive information or operating with strict latency constraints, illustrating why context for building effective agents is so crucial.

3. Tuning performance

Performance optimization represents a significant advantage of custom runtimes. Engineering teams can implement specialized execution patterns, caching strategies, and resource allocation approaches tailored to their specific workloads.

Custom runtimes enable optimization at multiple levels:

• Resources: Fine-tuned memory and compute resource management
• Execution: Specialized threading and parallel processing models
• Caching: Purpose-built caching mechanisms for frequently accessed data

These optimizations deliver particular value at scale, where even small efficiency improvements translate to significant cost savings and performance gains.

When to choose a framework instead

Frameworks like introducing hypermode agents path to production provide a faster path to production by abstracting complexity and offering standardized patterns for common agent functionality. They handle foundational challenges like tool integration, memory management, and execution orchestration through pre-built components.

Most engineering teams benefit from frameworks' accelerated development timelines unless they face highly specialized requirements. The productivity gains typically outweigh the flexibility limitations for standard agent implementations.

Frameworks continue to evolve rapidly, with new capabilities addressing previously challenging use cases. This evolution progressively narrows the gap between framework capabilities and custom runtime flexibility.

1. Speed of implementation

Frameworks dramatically reduce development time through ready-to-use components for common agent functionality. Engineering teams can leverage pre-built systems rather than implementing foundational capabilities from scratch.

The time savings prove substantial across the development lifecycle:

• Initial setup: Framework environments typically require hours or days versus weeks for custom runtime foundations
• Core functionality: Pre-built components for memory, tools, and orchestration eliminate significant development work
• Integration: Standardized patterns simplify connecting agents to external systems

These acceleration factors enable teams to focus engineering resources on unique agent capabilities rather than infrastructure components.

2. Community and plugins

Popular agent frameworks benefit from active communities that contribute extensions, integrations, and knowledge resources. These ecosystem assets further accelerate development through ready-made solutions for common requirements.

Community resources provide multiple advantages:

• Plugins and extensions: Pre-built components that extend framework capabilities
• Documentation and examples: Guidance for implementing common patterns
• Problem-solving resources: Community forums and knowledge bases for troubleshooting

These resources reduce development risk by providing tested solutions and implementation guidance, particularly valuable for teams new to agent development.

Key factors for your selection

Several critical factors guide the decision between custom runtimes and frameworks. These considerations help engineering leaders assess which approach best aligns with their specific requirements and constraints.

1. Complexity and domain specifics

Task complexity and domain-specific requirements significantly impact the runtime versus framework decision. Simple agents with straightforward workflows rarely justify custom runtime investment, while highly specialized domains often benefit from customization.

Consider developing custom runtimes when:

• Unique interaction patterns: Agents require specialized dialog or reasoning flows
• Domain-specific optimizations: Performance requirements demand customizations for particular use cases
• Novel agent architectures: Implementation approaches differ substantially from common patterns

Frameworks typically prove sufficient for standard agent implementations following established patterns, even in complex domains.

2. Integration requirements

Existing technology ecosystems shape integration requirements and influence the runtime versus framework decision. Engineering teams evaluate how agent architectures connect with data sources, APIs, and other systems.

Custom runtimes offer advantages when:

• Legacy system integration: Connections to specialized or older systems require custom approaches
• Complex data transformations: Unique data processing needs fall outside standard patterns
• Real-time requirements: Integration with high-throughput or low-latency systems demands optimization

Frameworks typically provide sufficient integration capabilities for standard APIs and data sources through built-in connectors or extension mechanisms.

3. Iteration and maintainability

Long-term maintenance represents a critical consideration when choosing between runtimes and frameworks. Engineering leaders assess how their choice affects the ability to evolve agent capabilities over time.

Frameworks generally provide:

• Structured extension patterns: Clear approaches for adding new capabilities
• Version management: Simplified updates to core components
• Compatibility guarantees: Assurance that extensions will continue working with future versions

Custom runtimes require teams to design their own extension mechanisms and manage all aspects of the system as requirements evolve, potentially increasing long-term maintenance costs.

4. Security and governance

Security and governance requirements significantly influence the runtime versus framework decision. Engineering teams must evaluate how their choice impacts their ability to implement appropriate controls around data access, model usage, and agent behavior.

Custom runtimes enable:

• Built-in security controls: Security mechanisms integrated directly into execution environments
• Specialized audit capabilities: Purpose-built logging and monitoring for specific compliance needs
• Fine-grained permissions: Custom access control mechanisms tailored to organizational structures

Frameworks typically provide configurable security features that satisfy common requirements but may not support highly specialized needs without significant customization.

Side-by-side comparison

This comparison highlights key differences between agent runtimes and frameworks across several important dimensions.

Ease of setup

Requires significant development effort

Quick setup with documentation and examples

Memory management

Custom implementation with complete control

Pre-built systems with configuration options

Tool orchestration

Custom integration patterns

Standardized patterns with plugin architecture

Resource overhead

Optimized for specific use cases

Generally higher but more predictable

1. Ease of setup

Custom runtimes require weeks or months of development before teams can begin building agent functionality. Engineers must implement core infrastructure components like memory management, tool integration, and execution orchestration before focusing on agent capabilities.

Frameworks provide a much faster path to productivity. Teams can typically set up a framework environment in hours or days, with documentation and examples guiding implementation. This difference in setup time represents one of the most significant advantages of frameworks for teams aiming to move quickly.

The setup time gap continues to widen as frameworks mature, with improved documentation, starter templates, and automation tools further accelerating initial implementation.

2. Memory management

Custom runtimes allow teams to implement memory systems precisely tailored to their requirements. Engineers can design specialized data structures, persistence layers, and retrieval mechanisms optimized for specific use cases.

Frameworks provide pre-built memory systems with configuration options to adapt to different requirements. These systems typically handle common patterns effectively but may not support highly specialized needs without significant customization.

The memory management capabilities of leading frameworks continue to evolve, with increasing flexibility and performance improvements narrowing the gap with custom implementations for many use cases.

3. Tool orchestration

Tool orchestration enables agents to interact with external systems and execute complex operations. Custom runtimes allow teams to implement precisely tailored orchestration patterns with specialized optimizations for specific tool combinations or execution flows.

Frameworks provide standardized patterns for tool integration and orchestration. These patterns simplify implementation but may impose constraints on how tools can be combined or executed. Most frameworks offer plugin architectures that allow for extending the available tools.

The orchestration capabilities of frameworks have advanced significantly, with flexible execution models and rich extension mechanisms supporting increasingly complex tool interactions.

4. Resource overhead

Custom runtimes can be optimized for specific resource constraints, potentially reducing overhead compared to general-purpose frameworks. Teams can implement efficient execution patterns tailored to their specific workloads.

Frameworks typically introduce some resource overhead due to their generalized nature. However, they often provide more predictable resource usage patterns and may include built-in monitoring and optimization features that help teams manage resources effectively.

The resource efficiency gap continues to narrow as frameworks mature, with performance optimizations and improved resource management capabilities reducing overhead for common use cases.

Common pitfalls and how to avoid them

Engineering teams implementing agent architectures often encounter similar challenges regardless of whether they choose runtimes or frameworks. Understanding these common pitfalls can help teams avoid costly mistakes.

1. Over-engineering

Teams frequently build unnecessarily complex systems that exceed actual requirements. This complexity increases development time, maintenance burden, and operational challenges without providing proportional benefits.

Avoid this pitfall by:

• Simple: Begin with the minimal architecture that satisfies core requirements
• Validation: Test hypotheses about required capabilities before implementing
• Enhancement: Add complexity only when proven necessary through real-world usage

This measured approach reduces initial development time and provides clearer paths for targeted enhancements based on validated needs.

2. Failing to plan for scale

Initial architecture decisions can limit future growth if teams don't consider how their systems will evolve. Agents that work well with small datasets or limited users may encounter performance bottlenecks as usage increases.

Design for scalability by:

• Isolation: Create clear boundaries between system components
• Scaling: Design for distributed execution across multiple instances
• Memory: Consider how memory usage will grow with increased usage

These design principles align with the twelve-factor agentic app and enable graceful scaling as agent usage expands, preventing costly rearchitecture efforts as demand grows.

Moving forward with your agentic flow

Selecting the right approach for your agent architecture requires balancing technical requirements, resource constraints, and business objectives. Custom runtimes offer greater control and optimization potential but require significant development investment. Frameworks accelerate development but may impose constraints on flexibility and performance.

For many teams, a hybrid approach provides the best balance, as demonstrated by introducing hypermode agents. Using a framework as a foundation while customizing specific components for unique requirements combines development speed with necessary flexibility.

At Hypermode, we've designed our AI development platform to support both approaches through a component architecture. Teams can use Modus, an agent runtime for production, for orchestration, ModusGraph for agent memory, and Dgraph for implementing knowledge graphs as strategic ai infrastructure, combining these components to build powerful agent architectures tailored to their specific needs.

Frequently asked questions about agent runtimes vs frameworks

Can a hybrid approach work?

Yes, organizations can effectively combine custom runtime components with framework elements to create hybrid architectures. This approach allows teams to use frameworks for standard functionality while implementing custom components for areas with unique requirements. For example, a team might use a framework for basic agent orchestration while implementing a custom memory system optimized for their specific use case.

Are agent runtimes easier to run on-premises?

Custom runtimes can offer advantages for on-premises deployment when designed specifically for private infrastructure. They can be optimized for specific hardware configurations and security requirements common in on-premises environments. However, this advantage depends entirely on implementation details rather than being inherent to the runtime approach.

Do frameworks limit domain experts?

The impact on domain expert participation varies significantly between frameworks. Some frameworks provide visual builders or natural language interfaces that enable domain experts to contribute directly to agent development, following a stay in language natural agentic development approach. Others require significant technical expertise, limiting non-technical participation. The best frameworks provide multiple interfaces tailored to different user types, allowing both developers and domain experts to contribute effectively.