Agent framework guide: From library to runtime

Agent frameworks are evolving beyond simple code libraries into powerful runtime environments. This architectural shift fundamentally changes how agents operate in production, enabling true isolation, efficient resource management, and enterprise-grade security guarantees that weren't previously possible.

The difference between library and runtime approaches determines whether your agents can scale to thousands of users or handle long-running tasks without degrading performance. In this article, we explore the key capabilities that differentiate production-ready agent frameworks from their predecessors and provide a roadmap for teams looking to build resilient multi-agent systems.

Understanding the shift from library to runtime

Agent frameworks exist in two distinct forms: libraries and runtimes. Libraries function as code packages imported into existing apps, providing functions for building agents within your codebase. Runtimes offer dedicated execution environments where agents operate independently from your main app, creating clear boundaries between agent code and app logic.

Most popular frameworks today function as libraries—they're straightforward to integrate but struggle at scale. Library-based agents share memory and compute resources with their host app, leading to resource contention, memory leaks, and frequent restart requirements. These limitations become critical when handling concurrent users or long-running tasks.

Runtime-based frameworks resolve these challenges through isolated execution environments for each agent. This isolation keeps failures contained and enables precise resource allocation. The runtime handles scaling, monitoring, and recovery automatically, dramatically reducing operational complexity.

Key elements of an agent framework

Effective agent frameworks address several core capabilities regardless of implementation approach.

1. Agent lifecycle management

Agent frameworks manage the complete lifecycle from creation through execution to termination. Libraries typically tie agent lifecycles to application processes, requiring custom state management. When apps restart, agent state must be manually restored.

Runtime-based frameworks maintain agent state independently of the calling application. They suspend inactive agents to free resources and resume them precisely where they left off when needed. This capability proves critical for agents handling sporadic interactions or long-running tasks.

WebAssembly-based runtimes resume agents with minimal overhead (under 10ms), creating seamless user experiences while dramatically reducing compute costs.

2. Context handling

Effective agents require both short-term memory (for current conversations) and long-term memory (for persistent knowledge). Libraries often struggle with long-term memory, relying on external databases that create additional failure points and latency.

Runtime frameworks integrate memory directly into the agent execution environment. Graph-based memory systems provide agents with rich context while maintaining performance. This integration enables agents to recall past interactions without complex external dependencies.

Agents with integrated graph memory make better decisions because they access relationship-rich context that vector databases alone cannot provide.

3. Tool invocation

Tool invocation requires secure mechanisms for agents to call external APIs and services. Libraries typically invoke tools within the same process as the agent, making permission enforcement difficult.

Runtime frameworks implement permission boundaries around tool invocation, controlling exactly what each agent can access. Sandboxed tool execution prevents compromised agents from accessing unauthorized resources, providing critical security guarantees.

The Model Context Protocol (MCP) standardizes tool definitions, but requires a runtime to enforce security boundaries between agents and the tools they access.

Why production-ready runtimes matter for scalability and security

Engineering teams building production-grade agents face challenges beyond basic functionality.

Runtime-based agent frameworks provide built-in approaches for horizontal scaling. They distribute agent workloads across multiple compute nodes automatically, without requiring complex orchestration code. This capability maintains consistent performance even during usage spikes.

Security concerns become paramount when executing potentially untrusted code. Runtime environments with proper sandboxing prevent agents from accessing resources beyond their authorized scope. WebAssembly-based runtimes provide strong security guarantees through memory isolation and capability-based security.

Resource efficiency becomes critical at scale. Runtime frameworks automatically scale resources up during peak usage and down during quiet periods. This dynamic scaling significantly reduces infrastructure costs compared to statically provisioned systems.

• Cost reduction: Agents release compute resources when inactive, reducing infrastructure costs by 50-80% compared to always-on deployments
• Security enhancement: Sandboxed execution prevents a compromised agent from accessing unauthorized resources
• Resilience improvement: Self-healing capabilities detect and recover from failures automatically

Selecting the right approach for multi-agent orchestration

Multi-agent systems—where domain experts collaborate on complex tasks—amplify the importance of choosing the right framework architecture.

1. Concurrency handling

Multi-agent orchestration requires efficient concurrency management. Agents operate simultaneously without blocking while still coordinating effectively. Libraries often struggle with concurrency, requiring complex threading models that introduce race conditions and deadlocks.

Runtime-based frameworks handle concurrency natively through isolated execution environments. Each agent runs independently, communicating through well-defined channels. This model eliminates many common concurrency bugs while maximizing throughput.

The orchestration layer manages message passing between agents, providing proper sequencing of operations without requiring complex synchronization code.

2. Observability features

Debugging multi-agent systems requires comprehensive observability. Teams require visibility into each agent's decision-making process, tool usage, and communication patterns.

Runtime-based frameworks instrument agent execution automatically, capturing detailed telemetry without requiring manual logging code. This instrumentation provides insights into agent performance, resource usage, and error conditions.

Tracing capabilities in runtime frameworks allow teams to follow a request through multiple agents, understanding exactly how agents collaborate. This end-to-end visibility proves important for optimizing complex multi-agent workflows.

Practical steps for migrating from library to runtime

Teams considering a transition from library-based to runtime-based agent frameworks can follow a structured approach.

First, evaluate your current scaling challenges. If you experience memory leaks, inconsistent performance, or difficulty managing agent state across restarts, a runtime-based approach will provide significant benefits.

Next, identify your most critical agent workflows as migration candidates. Focus on agents that handle high-volume requests or maintain long-running conversations, as these benefit most from runtime capabilities.

When rewriting agent logic for runtime environments, extract business logic from framework-specific code. This separation makes migration easier and prevents lock-in. WebAssembly-based runtimes support multiple programming languages, allowing teams to use their preferred language for implementation.

• Evaluation criteria: Resource usage spikes, memory leaks, restart recovery time
• Migration candidates: High-volume agents, long-running conversations, critical business workflows
• Implementation approach: Separate business logic from framework code, leverage WebAssembly for language flexibility

Where to apply an agent framework in real workflows

Runtime-based agent frameworks excel in specific use cases where traditional approaches struggle.

1. Coordinating data pipelines

Data pipelines often involve long-running processes with complex dependencies. Runtime-based agents coordinate these pipelines while maintaining state across the entire workflow. If a pipeline step fails, the agent can retry just that step rather than restarting the entire process.

Agents make intelligent decisions about data routing based on content analysis, quality checks, or business rules. This intelligence reduces reliance on rigid, predefined workflows that cannot adapt to changing conditions.

The ability to maintain state across long-running processes makes runtime-based agents valuable for workflows spanning hours or days, such as multi-stage data enrichment or analysis tasks.

2. Integrating domain expert tasks

Domain experts often lack programming skills but possess valuable knowledge about specific business processes. Runtime-based frameworks provide interfaces that allow these experts to define agent behaviors without writing code.

WebAssembly enables flexible language support, allowing domain experts to use familiar tools while still deploying to a secure, scalable runtime. This flexibility removes barriers to agent adoption across different teams and skill levels.

Graph-based knowledge representation helps domain experts structure their knowledge in ways that agents can understand and apply. This structured knowledge becomes a valuable asset reused across multiple agents and use cases.

Next moves with agentic flows

Runtime-based agent frameworks represent the future of production agent deployments. They address the fundamental limitations of library-based approaches while providing the scalability, security, and observability features required for mission-critical systems.

Modus exemplifies this runtime-first approach, providing a WebAssembly-based execution environment specifically designed for agent workloads. As an open source project, Modus gives teams complete control over where and how they deploy their agents, from local development to global production environments.

The shift from libraries to runtimes parallels earlier transitions in software architecture, such as the move from monolithic apps to containerized microservices. Each transition brought similar benefits: improved isolation, more efficient resource usage, and better operational characteristics at scale.

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FAQs about agent frameworks

What is the difference between agent libraries and agent runtimes?

Agent libraries are code packages imported into your existing process, sharing memory and resources with your app. Agent runtimes provide dedicated execution environments with isolation boundaries, independent resource management, and built-in security features. Runtimes enable agents to scale independently of your main app and recover automatically from failures.

How do agent runtimes improve scalability?

Agent runtimes improve scalability by automatically managing compute resources based on actual usage. They release resources when agents are idle and allocate them when needed, enabling efficient handling of thousands of concurrent agents. Runtimes also support horizontal scaling across multiple machines without complex orchestration code, maintaining consistent performance during usage spikes.

Can agent runtimes work with existing language models?

Agent runtimes connect to all major language models including those from OpenAI, Anthropic, and open source alternatives. The runtime handles communication with these models while adding sandboxed execution, efficient resource management, and comprehensive observability. This compatibility lets teams use their preferred language models while gaining the operational benefits of runtime-based deployment.

What programming languages can be used with WebAssembly-based agent runtimes?

WebAssembly-based agent runtimes support multiple programming languages including Go, Rust, AssemblyScript, and C/C++. This language flexibility allows teams to use their existing skills and codebase when building agents. The compiled WebAssembly modules run in a secure sandbox with near-native performance, combining development flexibility with production-grade security and efficiency.

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