Agent Framework Comparisons

Solana Agent vs OpenAI Agents SDK

Feature	Solana Agent	OpenAI Agents SDK
Architecture	Service-oriented with query routing	Agent-based with explicit handoffs
Configuration	JSON-based config with minimal code	Python code-based agent definitions
Multi-Agent	Automatic specialization routing	Direct agent-to-agent handoffs
Memory	Integrated MongoDB/Zep persistence	In-context memory within message history
Multi-Modal	Full audio/text streaming built-in	Optional voice support via add-on package
Model Support	OpenAI only	Any provider with OpenAI-compatible API
Tool Integration	Class-based tools with registry	Function decorators with @function_tool
Debugging	Pydantic Logfire, OpenAI logging, and terminal	Tracing with visualization
Output Handling	Streaming yield pattern	Structured output types with validation
Business Focus	Business mission/values framework	General purpose agent framework

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Solana Agent vs LangGraph

Feature	Solana Agent	LangGraph
Architecture	Service-oriented with agents	Graph-based state machine
Workflow Design	Implicit routing by specialization	Explicit node connections and state transitions
Learning Curve	Simple setup with config objects	Steeper with graph concepts and states
Streaming	Native streaming for all I/O	Requires additional configuration
Visualization	None built-in	Graph visualization of agent workflows
State Management	Implicit state via memory	Explicit state transitions and persistence
Integration	Standalone framework	Part of LangChain ecosystem
Flexibility	Fixed routing paradigm	Highly customizable flow control

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Solana Agent vs CrewAI

Feature	Solana Agent	CrewAI
Multi-Agent Design	Specialist agents with router	Agent crews with explicit roles
Agent Interaction	Query router determines agent	Direct agent-to-agent delegation
Configuration	JSON-based configuration	Python class-based agent definitions
Task Structure	Query-based interactions	Task-based with goals and workflows
Memory Sharing	Shared memory store	Agent-specific memories
Human Interaction	Built for direct human queries	Designed for autonomous operation
Streaming	Native streaming support	Limited streaming support
Team Dynamics	Flat specialist structure	Hierarchical with managers and workers

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Solana Agent vs PydanticAI

Feature	Solana Agent	PydanticAI
Multi-Modal	Full audio/text streaming built-in	Text output only, input depends on LLM
Memory	Built-in conversation history	Not included
Multi-Agent	First-class multi-agent support	Single agent focus with composition patterns
Tool Creation	Python classes with execute method	Function decorators with schema
Model Support	OpenAI only	Integrates with many LLMs
Flow Control	Implicit routing	Python control flow with graph support

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When to Use Each Framework

Choose Solana Agent when:

• You need a simple, quick-to-deploy agent system.
• Multi-modal support (text/audio) is essential.
• You want automatic routing between specialized agents.
• Business mission alignment is important.
• You prefer configuration over code.
• Persistent memory across conversations is needed.
• You want streaming responses out of the box.

Choose OpenAI Agents SDK when:

• You need detailed tracing for debugging complex agent workflows.
• You want explicit control over agent handoffs.
• Your architecture requires structured output validation.
• You're using multiple LLM providers with OpenAI-compatible APIs.
• You prefer a code-first approach to agent definition.

Choose LangGraph when:

• You need complex, multi-step workflows with branching logic.
• Your use case requires explicit state management.
• You want to visualize the flow of your agent system.
• You're already in the LangChain ecosystem.
• You need fine-grained control over agent decision paths.
• Your application has complex conditional flows.
• You want to model your agent system as a state machine.

Choose CrewAI when:

• You need agents to work together with minimal human input.
• Your use case involves complex team collaboration.
• You need hierarchical task delegation between agents.
• You want agents with specific roles and responsibilities.
• Your application requires autonomous operation.
• You need explicit agent-to-agent communication.
• Your workflow involves complex multi-step tasks.

Choose PydanticAI when:

• You want to use multiple LLM providers in one codebase.
• You require structured responses with validation guarantees.
• Your application needs dependency injection for easier testing.
• You want to leverage your existing Pydantic knowledge.
• You need both simple control flow and complex graph capabilities.