Best AI Agent Frameworks in 2025: Comparing LangGraph, DSPy, CrewAI, Agno, and More

As the ecosystem for LLM-powered agents matures in 2025, developers face an increasingly rich — and fragmented — set of choices when building production-ready AI agents. From open-source toolkits designed for fast experimentation, to enterprise-oriented frameworks aimed at robustness and observability, choosing the right AI agent framework today requires careful consideration of developer experience, abstraction design, tool integration, and alignment with emerging agentic patterns.

This guide offers a hands-on guide of leading AI agent frameworks, each assessed by implementing a simple but realistic customer support agent use case. The goal: compare side-by-side how intuitive, extensible, and production-capable these frameworks are — and identify their strengths, pain points, and best-fit use cases. The resulting analysis is meant to help AI developers, LLM engineers, and applied researchers select the right foundation for building and scaling agents in 2025 and beyond.

TL;DR: AI agent Framework Comparison

1. LangGraph – Structured yet a bit overwhelming

LangGraph brings together both low-level graph state management and higher-level agent building blocks, making it suitable for developers who want precision and flexibility in how an agent thinks, acts, and reacts. It supports a blend of reactive agents (e.g. create_react_agent) and custom workflows via functional APIs.

What stands out:

• Functional workflow design gives full control over steps, state, and flow

• Easy handling of sync/async and streaming responses with .invoke()

• Designed to be compatible with OpenAI tool-calling format

What slows teams down:

• Documentation is fragmented with multiple conflicting patterns

• Poor developer ergonomics: unclear errors, bloated imports from LangChain

• Lack of clear defaults or best practices slows down first implementations

Bottom line: LangGraph is a solid choice for structured agent workflows that require stateful flows and composable tasks, but it demands a learning curve due to abstraction layering and insufficient documentation cohesion.

2. DSPy – Fast results, abstracted internals

DSPy reimagines prompt orchestration by focusing on program synthesis for reasoning pipelines. It avoids conventional tool-calling or OpenAI-style message formatting, instead optimizing workflows for eval performance and latency.

What stands out:

• Produces high-quality outputs faster than most frameworks

• Embraces a "don't show the prompt" mindset for higher-level abstraction

• Encourages a model-centric rather than prompt-centric design

What slows teams down:

• Non-transparent execution: no native tool call logs, hard to debug

• Not aligned with OpenAI-compatible message workflows (e.g., for observability)

Bottom line: DSPy is a powerful choice for teams focused on performance and experimentation, especially when evaluation outcomes matter more than low-level traceability.

3. Google ADK – Ambitious vision, unfinished execution

Google’s Agent Development Kit (ADK) aims to provide a production-grade framework that supports agent lifecycle management, deployment, and interface integrations. But the current developer experience is still early-stage.

What stands out:

• Ambitious scaffolding, including built-in support for UI, session memory, and chat workflows

• Conceptual alignment with opinionated app frameworks like Ruby on Rails

What slows teams down:

• Silent failures, unclear APIs, and required directory structure assumptions

• Lack of clean API for simple agent invocations

• Docs are incomplete, with confusing naming (instruction vs global_instruction, etc.)

Bottom line: ADK has the bones of a future-ready framework, but in its current form, it’s better suited for experimentation than production unless you're deeply embedded in Google's stack.

4. InspectAI – Evaluation-first, functional, clean

InspectAI is purpose-built for evaluating agents and LLM systems against benchmarks. It prioritizes observability, introspection, and clean composition over multi-agent execution or deployment abstractions.

What stands out:

• Evals as a first-class citizen, tightly integrated into agent behavior

• Clean, composable API with functional design patterns

• Excellent error messages and developer tooling

What slows teams down:

• Not optimized for long-running or memory-rich agent deployments

• Lacks orchestration primitives for agent collaboration

Bottom line: Ideal for agent quality validation, regression testing, or research. Not yet a drop-in solution for production orchestration.

5. PydanticAI – Type-safe, lightweight, slightly rigid

PydanticAI leans into type safety, using familiar decorators and schema definitions to drive agent behavior. While intuitive for Python developers, its constraints become apparent in more dynamic workflows.

What stands out:

• Leverages Python type hints and Pydantic models to create structured tools

• Straightforward for developers familiar with schema-first design

What slows teams down:

• Tool decorators bind agents too tightly; order of declaration matters

• Manual flow control required (agent_run.next()), breaking composability

• Async behavior with Gemini and external models adds friction

Bottom line: PydanticAI is great for structured task agents and quick prototypes, but lacks ergonomic depth for large-scale agentic systems.

6. Agno – Production-ready with unique concepts

Agno offers one of the most intuitive developer experiences, combining clarity in docs with a well-structured API. It balances control and opinionation well, offering features like session memory, multiple instruction layers, and ReasoningTools.

What stands out:

• Built-in agent memory abstraction with clear session semantics

• Simple conversion of messages and agent state

• Excellent documentation and source code readability

What slows teams down:

• Requires manual stringification of tool outputs (e.g., json.dumps)

• Misleading variable names (e.g., response.messages includes full history)

Bottom line: Agno is a strong candidate for teams prioritizing clarity, memory management, and readable code. Production developers will appreciate its consistency.

7. Smolagents – Open-model friendly, docs-limited

Smolagents caters to developers focused on running smaller models or HuggingFace-hosted setups. While the framework emphasizes performance and openness, its onboarding flow is rough.

What stands out:

• Verbose tracing and metrics out-of-the-box

• Built-in memory with each agent instance

• Emphasizes open-source, small-model friendliness

What slows teams down:

• Lack of clear system prompt injection documentation

• Tooling patterns are hard to discover (e.g., ToolCallingAgent buried)

• Examples and tutorials are more conceptual than practical

Bottom line: A good fit for edge deployments and open model workflows, but needs better onboarding and standardization to reduce friction.

8. No Framework – Manual, transparent, educational

Sometimes the best way to learn is to build everything by hand. The no-framework route — using a loop, litellm, and basic JSON schema parsing — offers full transparency and total control.

What stands out:

• Excellent for understanding the fundamentals of tool calling and state tracking

• Lightweight setup with full flexibility over architecture

What slows teams down:

• No built-in memory, evaluation, or observability

• Repetitive boilerplate for prompt formatting and model calling

Bottom line: Recommended for early-stage prototyping, educational use, or when existing frameworks are too heavy. As complexity grows, migration becomes necessary.

Final Takeaways: Matching frameworks to use cases

The choice of AI agent framework should be guided by use case complexity, team experience, observability needs, and performance goals. Here’s a simplified mapping:

• LangGraph: Best for graph-based control flows, openai-compatible orchestration

• DSPy: Ideal for experiment-heavy workflows with eval-driven iteration

• Agno: Structured, memory-rich production agents

• InspectAI: Agent testing, evals, research, and benchmark comparison

• No Framework: Educational, transparent, and minimal-agent builds

New agentic use cases, from internal copilots to autonomous decision systems — demand both speed and reliability. This comparison surfaces the nuances in how today’s agent frameworks help (or hinder) those goals.

More side-by-side code examples and updates can be found at create-agent-app, an open-source repo with reference implementations across frameworks.

Whether you’re deploying agents to production, running evals at scale, or experimenting with open models, choosing the right starting point can make all the difference.

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