The landscape of AI agent frameworks is growing, and choosing the right one can be overwhelming. Whether you're building a research assistant, a customer service bot, or work assistant, the framework you pick lays the foundation for how your agent will think, act, and improve over time.
Agent frameworks aren’t just libraries. They define how you structure reasoning, manage memory, and call tools. In addition, some offer rich orchestration, while others prioritize modularity, speed, or developer UX. So how do you choose?
This guide walks through nine widely-used or growing open-source agent frameworks: LangGraph, AutoGen, CrewAI, Agno, LlamaIndex, Semantic Kernel, SmolAgents, OpenAI Agents SDK, and Google Agent Development Kit. We break each down by architecture and real-world fit so you can find the framework that matches your needs. To help you ship more reliable agents, we’ll also share how observability and evaluation tools layer on top to catch bugs, understand failures, and improve agent performance.
Comparison Table of OSS Agent Frameworks
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Frameworks
LangGraph
LangGraph is a Python framework for building LLM agents as stateful programs with graph-based control flow. It models agents as finite state machines, where each node represents a reasoning or tool-use step, and transitions are determined by outputs.
This design makes LangGraph especially powerful for multi-turn, conditional, and retry-prone workflows. Developers define both the computation and its structure, making agents more inspectable and less reliant on emergent behavior.
LangGraph can be used standalone, but also integrates seamlessly with LangChain. Its structured execution model is particularly amenable to runtime monitoring and evaluation, making it ideal for production settings.
Great for: Multi-step planning, robust tool workflows, controllable agent behavior
Not ideal for: Informal experimentation
LangGraph Docs
AutoGen
AutoGen, developed by Microsoft Research, is a multi-agent framework built around the philosophy of collaborative reasoning through conversation. Agents interact via structured natural language messages, following a group chat-style logic. Each agent is assigned a role, goal, and optional tools.
This design emphasizes flexibility and expressiveness over determinism. Agents make decisions based on evolving conversations, often resembling how human collaborators brainstorm and iterate. While this makes AutoGen powerful for human-in-the-loop workflows and exploratory research, it can pose challenges for reproducibility and runtime control.
Its conversational logic can be difficult to debug or rerun consistently, especially when goals evolve mid-session or memory is implicit. Still, it excels when agent roles are clear.
Great for: Multi-agent coordination, research workflows. Has a native integration with Azure.
Not ideal for: Strict execution paths or structured planning
AutoGen GitHub
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CrewAI
CrewAI builds agents using a team-based, role-driven design. Inspired by human organizational structures, it encourages developers to define agents as specialized “crew members” with distinct roles like "Planner," "Researcher," or "Writer."
At its core, CrewAI treats agent systems as organizational units: each agent has a defined scope of work and toolset, and tasks are delegated by role. Unlike AutoGen's open-ended group chat approach, CrewAI enforces structured workflows with clear task delegation and role boundaries. This is particularly for tasks that naturally map to teams, such as drafting reports, researching questions, or summarizing content.
However, this abstraction can feel limiting for agents needing flexible planning or complex conditional logic. It’s best suited for use cases where team-like collaboration is natural.
Great for: Structured multi-agent flows, content generation pipelines
Not ideal for: Dynamic workflows or logic-heavy planning
CrewAI Docs
Agno
Agno (formerly Phidata) is an agent framework designed for composability, performance, and clean integration. It avoids boilerplate orchestration layers and instead offers direct, unified interfaces to 23+ model providers, multiple memory strategies, and toolchains, plus a powerful CLI and extensive out-of-the-box tools with strong type validation.
Agno supports multimodal inputs and outputs (text, image, audio, video) and cites advantages like ~10,000x faster agent instantiation and significantly lower memory usage compared to heavier frameworks. This makes it well-suited for environments prioritizing correctness, latency, and maintainability.
While it does not offer out-of-the-box multi-agent orchestration, its minimal surface makes it a strong fit for teams layering in their own monitoring and evaluation stacks, like Atla.
Great for: Customizable agent logic, model-agnostic execution, performance-critical systems
Not ideal for: Built-in multi-agent coordination or complex orchestration patterns
Agno Docs
LlamaIndex
LlamaIndex originated as a retrieval-augmented generation (RAG) framework, but has expanded into a broader platform for building document-aware agents. It emphasizes structured data ingestion, indexing, and querying, making it particularly strong in scenarios where agents need to reason over external knowledge.
With modules like AgentWorkflow and Workflows, LlamaIndex now supports basic orchestration capabilities. However, its strength remains in retrieval and document-grounded workflows rather than general-purpose agent design.
For teams building knowledge assistants, RAG pipelines, or API-aware agents, LlamaIndex offers strong built-in eval tooling and integration with tracing systems like Atla.
Great for: RAG workflows, document-grounded agents, enterprise knowledge apps
Not ideal for: Planning-heavy agents or general-purpose orchestration
LlamaIndex Framework
Semantic Kernel
Semantic Kernel is Microsoft's orchestration framework for embedding AI agents into enterprise software. It supports plugins, memory, and planning with support for C#, Python, and Java, and is well-aligned with .NET development environments.
Its Agent Framework can dynamically compose multi-step plans and workflows, making it ideal for deterministic automation inside structured systems like CRM or business tooling. However, its setup is heavier and less suited to fast iteration or informal experimentation.
Great for: Enterprise workflow automation, internal tools, structured agent planning. Microsoft/Azure ecosystem integration.
Not ideal for: Lightweight prototyping or exploratory agents
Semantic Kernel GitHub
SmolAgents
SmolAgents is a minimalist framework by Hugging Face that aims to keep agent design simple, fast, and transparent. With roughly 1,000 lines of code, it supports both tool-calling and code-writing agents, favoring Pythonic transparency over abstraction.
It follows a "small pieces loosely joined" philosophy and integrates well with Hugging Face models, local LLMs, and containerized tool environments. Its code-first approach makes it great for rapid iteration, but it lacks the orchestration features and eval hooks needed for production.
Great for: Demos, experiments, learning, code agents
Not ideal for: Complex orchestration, production-grade reliability
SmolAgents Site
OpenAI Agents SDK
The OpenAI Agents SDK is an open-source Python framework for building multi-agent workflows, released recently in March 2025 as a production-ready upgrade to OpenAI's experimental Swarm project. It provides a lightweight toolkit with minimal abstractions focused on four core primitives: agents, handoffs, guardrails, and built-in tracing.
Rather than being limited to OpenAI's ecosystem, the SDK is provider-agnostic and supports 100+ LLMs through OpenAI-compatible APIs. It emphasizes simplicity with powerful capabilities, allowing developers to build complex agent relationships while maintaining control and visibility through comprehensive built-in tracing and debugging tools.
The SDK is designed for self-hosting and custom orchestration, offering developers full control over their agent workflows with automatic schema generation, Pydantic validation, and extensible tracing to external platforms.
Great for: Multi-agent workflows, custom orchestration, self-hosted agents
Not ideal for: No-code solutions or simple single-agent tasks
OpenAI Agents GitHub
Google Agent Development Kit (ADK)
Google's Agent Development Kit (ADK) is an open-source Python framework launched at Google Cloud NEXT 2025 for building production-grade AI agents and multi-agent systems. Built on the same foundation powering Google's own applications like Agentspace, ADK provides a model-agnostic, deployment-agnostic framework with enterprise-grade capabilities.
ADK emphasizes a software-engineering-first approach with rich abstractions, built-in testing harness, CLI tooling, and seamless deployment options. While optimized for Google's ecosystem (Gemini, Vertex AI), it supports 100+ LLMs through integrations and includes enterprise connectors for systems like BigQuery, AlloyDB, and third-party APIs.
The framework supports multi-agent orchestration, bidirectional audio/video streaming, comprehensive tracing, and includes safety features like response moderation and scoped permissions—making it suitable for both experimentation and production deployment across various environments.
Great for: Production-grade agents , enterprise deployment, Google Cloud integration
Not ideal for: Simple single-agent tasks or teams avoiding Google ecosystem dependencies
Google ADK GitHub
When to Skip Frameworks Entirely
Not every agent project needs a framework. If you're building cutting-edge research agents, implementing novel architectures, or need sub-millisecond performance, you might be better off with raw LLM APIs and custom code. Many sophisticated AI labs avoid frameworks because they need precise control over reasoning patterns, memory systems, or multi-modal workflows that don't fit standard abstractions. The trade-off is development speed for complete flexibility. Still for most production use cases, frameworks provide significant value through battle-tested patterns and community support.
Tracing and Evaluation: What Happens After You Ship
Agent frameworks help you build behavior, but monitoring, evaluation, and debugging tools help you improve it. Regardless of which framework you choose, observability becomes essential once your agent moves beyond the lab.
Atla provides an agent-agnostic evaluation layer that works across these frameworks to deliver:
- Tracing: Visualize agent steps, tool calls, state transitions, and memory access.
- Failure tagging and error identification: Identify where your agent breaks down and auto-identify error patterns to improve reliability fast.
- Automated evaluation: Critiques of agent actions.
Whether your agents follow a state machine, chat loop, or code-generation approach, Atla gives you visibility, feedback, and the confidence to scale.
Takeaways
Choosing a framework sets the rules for how your agents operate. Here’s what to remember:
- LangGraph and Semantic Kernel excel at structured control and long-term planning.
- AutoGen and CrewAI make it easy to coordinate agent roles and conversations.
- Agno and LlamaIndex offer composable, high-performance solutions for real-world deployment.
- SmolAgents is great for learning, testing, and building code agents quickly.
- OpenAI Agents SDK and Google ADK provide production-ready frameworks with enterprise-grade tooling and comprehensive observability.
But whichever framework you choose, monitoring and evaluation determine how far you go. Agent development doesn’t stop at execution. It continues through debugging and iteration, to help you create a truly reliable agent.
Atla helps teams trace, evaluate, and improve agents in production.
