Semantic Kernel vs Letta: Which Is Better in 2026?

Semantic Kernel vs Letta: At a Glance

Semantic Kernel and Letta represent two distinct approaches to building AI agents, each addressing different architectural priorities. Semantic Kernel, developed by Microsoft, functions as an orchestration framework designed to integrate large language models with conventional programming code. The framework emphasizes enterprise integration patterns and works across multiple programming languages including C#, Python, and Java.

Letta takes a different approach by prioritizing stateful memory management for AI agents. The framework builds upon the concept that effective AI agents require persistent memory systems that extend beyond simple conversation history. Letta provides developers with tools to create agents that maintain context across extended interactions and can evolve their knowledge base over time.

Both frameworks are open-source and cost nothing to use directly, though operational costs will vary based on the underlying LLM services selected. The choice between them depends primarily on whether developers need Microsoft ecosystem integration and multi-language support (Semantic Kernel) or specialized memory architecture for stateful agents (Letta).

Features Compared

Orchestration Architecture

Semantic Kernel uses a plugin-based architecture where developers encapsulate functions as reusable components. The framework treats LLM interactions as one type of function alongside traditional code functions. This design allows the AI to orchestrate calls to both semantic functions (LLM prompts) and native functions (standard code) within a unified pipeline. The kernel manages prompt templates, function calling, and response parsing automatically.

Letta implements a memory-centric architecture where the agent's internal state persists across sessions. The framework divides memory into core memory (always accessible) and archival memory (retrieved as needed). This structure enables agents to maintain consistent personalities, remember user preferences, and build knowledge over extended periods without requiring all context in every prompt.

Memory Management

Semantic Kernel handles memory through a connector-based system that integrates with various storage backends. Developers can implement custom memory stores or use built-in connectors for services like Azure Cognitive Search or Chroma. The memory system supports semantic search capabilities, allowing agents to retrieve relevant information based on meaning rather than exact matches.

Letta's memory system is more opinionated and integrated into the core framework. The tool provides structured memory blocks with defined schemas, making it easier for agents to query and update their knowledge. The framework automatically manages memory compression when context windows fill up, determining what information to move from active to archival storage based on relevance and recency.

Tool Integration

Semantic Kernel's plugin system allows developers to expose existing APIs, databases, and services as tools the LLM can invoke. The framework handles parameter extraction from natural language, function calling, and result formatting. Plugins can be shared across different agents and projects, promoting code reuse.

Letta provides a tool registration system where developers define Python functions that agents can execute. The framework includes built-in tools for memory operations and supports custom tool creation. Tools can access and modify the agent's memory state, enabling more sophisticated behaviors than simple function calls.

Multi-Language Support

Semantic Kernel supports C#, Python, and Java with feature parity across languages. This cross-language compatibility allows organizations to integrate AI capabilities into existing codebases regardless of the technology stack. The framework's design patterns remain consistent across languages, reducing the learning curve when switching between implementations.

Letta focuses exclusively on Python, which allows for deeper integration with the Python AI ecosystem but limits adoption in polyglot environments. The Python-only approach enables Letta to leverage Python-specific features and maintain a more streamlined codebase.

LLM Provider Flexibility

Semantic Kernel abstracts LLM interactions through connectors, supporting OpenAI, Azure OpenAI, Hugging Face models, and custom implementations. Developers can switch between providers with minimal code changes, protecting against vendor lock-in. The framework handles API differences internally, presenting a consistent interface regardless of the underlying model.

Letta works with multiple LLM providers through standard interfaces, including OpenAI and local models via Ollama. The framework's memory management operates independently of the specific LLM used, allowing developers to select models based on cost, performance, or privacy requirements.

Prompt Engineering

Semantic Kernel uses templated prompts with variable substitution and includes a prompt template language for dynamic prompt construction. Developers can create reusable prompt templates that adapt based on context. The framework supports few-shot learning patterns and automatic prompt optimization.

Letta handles prompts through its memory system, where the framework constructs prompts by combining the user message, relevant memories, and system instructions. This approach reduces manual prompt engineering by automatically managing context injection based on the agent's memory state.

Pricing Compared

Both Semantic Kernel and Letta are open-source frameworks with no licensing fees. Organizations can use either tool without direct costs to the framework developers. The actual expenses come from the underlying infrastructure and services required to run AI agents.

Operational Costs

Users of both frameworks incur costs from the LLM API services they select. OpenAI's GPT-4 pricing, for example, charges based on token usage for both input and output. Azure OpenAI Service offers similar models with enterprise support and compliance guarantees at comparable rates. Local model deployment eliminates per-token costs but requires computing infrastructure.

Semantic Kernel users in enterprise environments may incur additional Azure service costs if they leverage Microsoft's cloud ecosystem for memory storage, monitoring, or deployment. Azure Cognitive Search, often paired with Semantic Kernel for semantic memory, has its own pricing based on index size and query volume.

Letta users need to budget for persistent storage if they want agent memories to survive between sessions. The framework can use local storage for development or cloud databases for production deployments. Vector databases for memory retrieval also carry costs when hosted as services.

Hidden Costs

Both frameworks require developer time for implementation and maintenance. Semantic Kernel's broader scope and multi-language support may require more initial learning but can reduce long-term costs through code reuse across projects. Letta's specialized focus on memory management may accelerate development for specific use cases but could require more custom code for general-purpose applications.

Infrastructure costs vary significantly based on deployment choices. Self-hosted solutions require server management but provide more cost predictability. Serverless deployments reduce operational overhead but can become expensive at scale without careful optimization.

Who Should Use Semantic Kernel?

Enterprise Developers

Organizations already invested in the Microsoft ecosystem benefit from Semantic Kernel's native Azure integration. The framework works seamlessly with Azure services, supports enterprise authentication patterns, and aligns with Microsoft's security practices. Companies using C# or .NET for backend services can integrate AI capabilities without introducing new technology stacks.

Multi-Language Teams

Development teams working across C#, Python, and Java can standardize on Semantic Kernel for AI orchestration. This consistency reduces training costs and enables knowledge sharing across language boundaries. Teams can write shared plugins that work across different service implementations.

Platform Builders

Developers creating AI platforms or frameworks for other developers benefit from Semantic Kernel's extensibility. The plugin architecture allows platform creators to define standard interfaces while letting end users implement custom behaviors. The abstraction layers make it easier to support multiple LLM providers and swap implementations as the market evolves.

Projects Requiring Function Orchestration

Applications that need to combine LLM reasoning with existing business logic fit Semantic Kernel's orchestration model. The framework excels at scenarios where the AI needs to call multiple services, process intermediate results, and make decisions based on external data. Examples include automated workflow systems, intelligent APIs, and complex business process automation.

Who Should Use Letta?

Conversational AI Developers

Teams building chatbots, virtual assistants, or customer service agents benefit from Letta's memory architecture. The framework's ability to maintain context across sessions creates more natural interactions. Agents can remember previous conversations, learn user preferences, and provide personalized responses without requiring manual context management in every interaction.

Research Teams

Researchers exploring agent memory, long-term learning, or autonomous systems find Letta's opinionated architecture useful for experimentation. The framework provides primitives for memory operations that would otherwise require significant custom development. The structured memory system makes it easier to analyze how agents use and update information over time.

Python-Centric Organizations

Development teams standardized on Python for AI development can leverage Letta without introducing language complexity. The framework integrates naturally with the Python data science and machine learning ecosystem, including libraries like pandas, scikit-learn, and PyTorch.

Stateful Applications

Projects requiring agents that evolve over time benefit from Letta's persistence model. Educational applications where agents track student progress, personal productivity tools that learn user habits, or simulation environments where agents accumulate experiences all align with Letta's design philosophy.

Privacy-Focused Deployments

Organizations with strict data privacy requirements can run Letta with local models and on-premise storage. The framework's memory management works independently of cloud services, making it suitable for environments where data cannot leave corporate infrastructure.

The Verdict

Semantic Kernel and Letta serve different architectural priorities in the AI agent development landscape. Neither framework is universally superior; the optimal choice depends on specific project requirements and organizational context.

Semantic Kernel delivers the most value in enterprise environments where Microsoft ecosystem integration matters, multi-language support is required, or teams need to orchestrate complex workflows combining AI reasoning with traditional software functions. The framework's maturity, backed by Microsoft's resources, provides confidence for production deployments. Organizations building platforms or products that will integrate AI capabilities into existing C# or Java codebases should evaluate Semantic Kernel first.

Letta excels in scenarios where agent memory and statefulness are primary concerns. The framework reduces development time for conversational applications that need to maintain context across extended interactions. Python teams building specialized agents, research projects exploring memory architectures, or applications requiring sophisticated personalization will find Letta's opinionated design accelerates development.

The frameworks can coexist within an organization's toolkit. Teams might use Semantic Kernel for backend orchestration services while deploying Letta-based agents for user-facing conversational interfaces. The open-source nature of both tools allows developers to examine the source code, contribute improvements, and avoid vendor lock-in.

Developers should prototype with both frameworks when project requirements fall in the middle ground between orchestration and memory management. The time invested in evaluation pays dividends by ensuring architectural alignment with long-term project goals.

See the full comparison on ToolSpotter.