Framework Comparison
How Specrun compares to LangChain, CrewAI, AutoGen, and other popular AI agent frameworks.
There are many excellent frameworks for building AI agents. This page compares their design philosophies and trade-offs to help you decide which fits your use case.
Last updated March 2026.
| Framework | Language | Definition Style | License |
|---|
| Specrun | Any (tools are executables) | Declarative (YAML/JSON) | MIT |
| LangChain / LangGraph | Python, JS/TS | Imperative (code) | MIT |
| CrewAI | Python | Imperative (code + YAML config) | MIT |
| AutoGen | Python, .NET | Imperative (code) | MIT |
| Pydantic AI | Python | Imperative (code) | MIT |
| Smolagents | Python | Imperative (code) | Apache 2.0 |
| OpenAI Agents SDK | Python | Imperative (code) | MIT |
| Google ADK | Python | Imperative (code) | Apache 2.0 |
| Mastra | TypeScript | Imperative (code) | MIT |
| AG2 | Python | Imperative (code) | Apache 2.0 |
| Framework | LLM Provider Support | Tool Integration |
|---|
| Specrun | Any OpenAI-compatible, vLLM, Ollama | Standalone executables (stdin/stdout), any language |
| LangChain / LangGraph | 50+ providers via integrations | Python/JS functions or integration classes |
| CrewAI | OpenAI, Ollama, Azure, others via LiteLLM | Python functions with decorator |
| AutoGen | OpenAI, Azure, local models | Python functions |
| Pydantic AI | OpenAI, Anthropic, Gemini, Groq, Mistral, Ollama | Python functions with type annotations |
| Smolagents | HF Inference, OpenAI, Anthropic, local models | Python functions or classes |
| OpenAI Agents SDK | OpenAI | Python functions with decorator |
| Google ADK | Gemini, others via LiteLLM | Python functions, built-in Google tools |
| Mastra | OpenAI, Anthropic, Google, others | TypeScript functions with Zod schemas |
| AG2 | OpenAI, Azure, local models | Python functions |
| Framework | Multi-Agent | Execution Model |
|---|
| Specrun | Yes (multi-node flows) | Local CLI |
| LangChain / LangGraph | Yes (graph-based) | Library (embed in your app) |
| CrewAI | Yes (role-based crews) | Library / CLI |
| AutoGen | Yes (conversation-based) | Library |
| Pydantic AI | Yes (agent delegation) | Library |
| Smolagents | Yes (managed agents) | Library |
| OpenAI Agents SDK | Yes (handoffs) | Library |
| Google ADK | Yes (agent hierarchy) | Library / CLI / Cloud |
| Mastra | Yes (workflows) | Library / Server |
| AG2 | Yes (conversation-based) | Library |
| Framework | Learning Curve | Key Strength |
|---|
| Specrun | Low | Declarative specs, language-agnostic tools, no backend needed |
| LangChain / LangGraph | Moderate to High | Massive ecosystem with extensive integrations |
| CrewAI | Low to Moderate | Intuitive role-based multi-agent design |
| AutoGen | Moderate | Flexible multi-agent conversation patterns |
| Pydantic AI | Low to Moderate | Type safety and clean Python-native design |
| Smolagents | Low | Minimal API surface, code-based agents |
| OpenAI Agents SDK | Low | Tight OpenAI integration with simple API |
| Google ADK | Moderate | Google ecosystem integration and cloud deployment |
| Mastra | Moderate | TypeScript-native with built-in eval and observability |
| AG2 | Moderate | Community-driven with flexible agent patterns |
- You want to define workflows as data, not code — Specrun workflows are YAML/JSON files, not Python scripts.
- Your team uses multiple languages — Tools are standalone executables. Write them in Python, Go, Rust, or anything else. No SDK dependency.
- You want to run agents locally — A single
specrun run command compiles, validates, and executes your workflow. No backend or service required.
- You want to swap LLM providers without changing your workflow — Provider configuration is separate from workflow logic.
- You want a portable spec — Workflows follow the Open Agent Specification, making them shareable and framework-independent.