LLM Agent Frameworks

Feb 9, 2025 · 21 min read ·

Lightweight Frameworks for LLM-Powered AI Agents

Introduction

Building AI agents powered by large language models (LLMs) can be complex. Frameworks have emerged to simplify this process, but many are heavy with dependencies and steep learning curves. This report focuses on lightweight, Python-based frameworks for LLM agents that are open-source, actively maintained, well-documented, and minimalistic in design. We compare several frameworks on key features – ease of use, modularity/extensibility, tool and LLM integration, memory/context management, and customization – as well as community activity and documentation quality. The goal is to identify a lightweight framework that best meets the criteria, and update our recommendations accordingly.

The frameworks evaluated include LangChain, LlamaIndex, Phidata, PydanticAI, and Hugging Face SmolAgents. Each is Python-based and popular for building AI agents, but they vary greatly in complexity and focus. Below, we summarize each framework and how it measures up to the criteria, then conclude with our top recommendations.

TLDR

Criteria	SmolAgents (Hugging Face)	LangGraph (LangChain Graph)	Pydantic.AI	Phidata (Agno)	LlamaIndex (Workflows)
Architecture	Minimal, code-centric – Lightweight agent loop; LLM writes actions as code instead of JSON. No complex graphs or chains – just a simple iterative workflow controlled by the LLM’s outputs. Aims for minimal abstraction.	Graph-based orchestration – Uses a Pregel-like directed graph of nodes and edges. Each “agent” node (LLM call) and “tool” node forms part of the graph. Designed to allow loops/conditional branches via graph structure. Built on LangChain, reusing its components.	Pythonic & model-driven – Agents are defined through Python classes/functions with Pydantic models for I/O. Leverages Python’s native control flow and type system (“FastAPI for GenAI” feel). Internally can construct dependency graphs, but the developer mostly writes standard Python code.	Pure Python workflow – Emphasizes no explicit graphs or chains, just straightforward Python logic. Agents are simple Python objects orchestrating LLM calls, tools, memory, etc. Multi-modal by design (handles text, image, audio, video) natively. Focused on minimal framework overhead and direct coding of agent behavior.	Event-driven steps – Defines an agent as a Workflow: a series of steps triggered by events. Each step handles certain events and can emit new ones, forming a flexible flow. Integrated into LlamaIndex’s data-centric architecture (can incorporate indices, tools as events/steps).
Execution Flow	Cyclical & deterministic loop – Follows a ReAct-style loop: prompt → tool/code execution → observe → next prompt, until completion. The sequence is deterministic in structure but driven by the LLM’s outputs. No built-in concurrency; executes one action at a time in sequence.	Conditional graph traversal – Execution moves along graph edges based on LLM outputs. After each agent step, a condition decides whether to continue looping or finish. Supports branching logic via conditional edges. Flow is mostly sequential even if the graph allows loops.	Deterministic Python flow – Typically uses standard call/response cycles internally (e.g., `agent.run()` invokes LLM, possibly multiple tool calls, then returns). The developer can control flow using normal Python (loops, if/else).	Sequential or delegated – By default runs tools and LLM calls in a straightforward sequence. Agents can delegate subtasks to other agents (hierarchical execution) but each agent’s internal process is a step-by-step loop. Emphasizes performance and predictability.	Async event loop – Built to handle asynchronous, event-driven flows. The agent workflow runs as an async loop, waiting for events and resuming steps. Can handle cyclical flows and waiting on multiple events concurrently.
Tool & API Integration	Function-based tools – Tools are just Python functions with type hints and a `@tool` decorator. The LLM generates code that calls these functions, or uses a ToolCalling format. Built-in tools like web search are provided, and custom tools are easy to add.	LangChain tools via ToolNode – Provides a `ToolNode` that accepts a list of tool functions. The LLM can choose a function call, and the ToolNode executes it automatically, then returns control to the agent node. Essentially inherits LangChain’s tool integration (supporting OpenAI function calling, etc.).	Pydantic tools & DI – Supports tools as simple Python functions or methods, with dependency injection for external resources. LLM outputs are directly parsed into Pydantic models for reliable API calls, ensuring safe integration with external APIs or databases.	Flexible integration – Any model/provider and any tool can be used since you write the code. Agno has built-in support for multi-modal tools (vision, audio, etc.), and you can call external APIs or functions directly. Also supports knowledge base queries via built-in vector DB connectors.	Function tools & indices – Integrates with FunctionTool abstractions (wrappers for tool functions). The LLM can trigger a tool via event. Also, since it excels at data integration, many tools include index queries, retrieval from vector stores, etc. Provides many ready integrations as events or tools.
Memory Handling	In-loop memory (minimal) – Keeps a history of the conversation, actions, and observations in memory (a Python list). No specialized long-term memory module by default – but you can integrate one (e.g., via a vector search tool).	LangChain memory via state – Maintains a message history in the agent’s state (stored in `state["messages"]`) throughout execution. Can leverage LangChain’s memory components or vector stores if needed.	Pydantic-managed state – Chat history and intermediate results are managed in the agent’s state using Pydantic models. Can easily inject external memory (like a DB) through dependency injection.	Built-in memory & storage – Provides memory management features out-of-the-box: stores user sessions and agent state in a database, and supports knowledge stores (vector DB integration) for long-term context.	Context and indices – Workflow agents maintain an internal memory (context) of the conversation. Integrates with databases/indices for long-term memory, allowing the agent to remember prior actions and use them in decision-making.
Multi-Agent Capabilities	Limited (single-agent focus) – Primarily designed for single-agent use. Coordinating multiple agents requires manual instantiation and external orchestration.	Single-agent graphs – LangGraph orchestrates one agent’s workflow. No built-in multi-agent system; multiple agents must be coordinated at the application level.	Supported (via composition) – Flexible enough to compose multiple Agent instances that interact or call each other; patterns exist for multi-agent collaboration, though no separate orchestrator is provided.	Yes – “team of agents” – Explicitly supports multi-agent systems by allowing delegation to specialized sub-agents as part of an agent team.	Yes – via AgentWorkflows – Supports multi-agent capability by coordinating multiple specialized agents within a single workflow that can exchange messages as events.
Concurrency & Parallelism	No native async/parallel – Runs synchronously, one action at a time. Concurrency must be managed externally (e.g., with threading or asyncio).	Mostly synchronous – Execution is sequential through graph nodes; while the graph can have parallel branches conceptually, current implementations are mostly sequential.	Synchronous (with DI flexibility) – Runs agent logic synchronously by default, though async execution can be implemented at the application level if needed.	High-performance, sync or async – Designed for speed; agent instantiation is fast and calls can be run concurrently through external management.	Built for async – Workflows are explicitly designed to be asynchronous, allowing agents to await tool events and handle multiple concurrent operations within a single workflow.
Ease of Use & Flexibility	Very easy & minimalist – A basic agent can be created in a few lines of code; minimal abstractions lead to transparency, but advanced features require manual implementation.	Moderate complexity, powerful – Graph abstraction offers powerful control flows but requires learning the node/edge model and debugging can be challenging. Highly customizable if already using LangChain.	Developer-friendly, type-safe – Provides a FastAPI-like experience with Pydantic models and dependency injection, ensuring robust and maintainable agent definitions.	High ease and performance – Simple to use with minimal DSL; built-in monitoring and UI enhance usability, making it suitable for both beginners and advanced users.	Structured but with a learning curve – Workflow/event-based model is powerful for complex designs but requires understanding the event-driven paradigm. Very flexible for advanced asynchronous agents.
Scalability	Lightweight (prototype to prod) – Minimal overhead makes it suitable for small-to-medium tasks; high concurrency or long sessions require external solutions.	Suited for complex apps – Best used when workflow complexity justifies it; added overhead from graph traversal may impact performance in simple tasks, but excels in orchestrating complex workflows.	Production-grade design – Built with type safety and reliability in mind; scales similarly to FastAPI apps and is suitable for high-throughput production environments.	Built for scale & speed – Emphasizes performance with fast initialization and low latency; supports persistent storage and multi-agent delegation, ideal for large production systems.	Enterprise-ready (data-centric) – Optimized for large knowledge bases and complex asynchronous workflows; scales well in data-intensive applications and multi-agent environments.
Best Use Cases	Simple tool-using agents; code execution tasks – Ideal for quick prototypes, data analysis assistants, or lightweight web-search QA bots where minimal overhead and transparency are key.	Complex reasoning with loops/conditions – Best for workflows requiring iterative decision-making, branching logic, or multi-step plans, especially if already using LangChain components.	Structured output and robust API agents – Excellent for agents interacting with structured data or requiring validated outputs (e.g., form-filling, report generation, DB queries) in production.	High-performance multi-modal systems – Best for systems that require speed, multi-agent orchestration, and multi-modal input (e.g., vision, audio) in production scenarios with low latency.	Knowledge-intensive and async workflows – Ideal for agents that ingest and reason over large datasets (e.g., research assistants, data integration bots) and require complex, asynchronous decision flows.

Frameworks Overview and Feature Comparison

LangChain

Focus: A comprehensive framework for composing LLM-based applications via chains and agents.

Ease of Setup & Usage: Installable via pip and quick to start, but the breadth of features means a higher learning curve. Simpler use cases require understanding its abstractions (chains, tools, memory, etc.), so it’s not the most lightweight in terms of mental overhead.
Modularity & Extensibility: Very high – LangChain provides a modular architecture with numerous components (prompts, models, memory, tools) that can be composed or extended. Developers can plug in custom tools or logic at many points, though the flexibility comes with added complexity.
Integration (Tools & LLMs): Extensive – it supports many LLM providers and a large suite of pre-built tools (search, calculators, code executors, etc.), plus integrations with vector stores and data connectors. This makes it powerful for complex agent workflows out-of-the-box.
Memory & Context Management: Yes – LangChain includes built-in memory classes for maintaining conversational context (e.g. chat history) and longer-term memory via vector stores. It seamlessly manages context injection into prompts, enabling agents to carry on multi-turn dialogues or recall past information.
Customization: High – you can define custom agents (even custom reasoning loops), or modify prompting strategies. LangChain supports patterns like ReAct and allows custom chain logic, though doing so may require delving into its framework internals.
Community Activity: Extremely active. LangChain is one of the most popular LLM frameworks with ~98k GitHub stars and hundreds of contributors. It’s frequently updated and benefits from a large ecosystem of community-contributed modules.
Documentation: Comprehensive – LangChain has extensive documentation, examples, and tutorials. However, because of the framework’s scope, the docs can be overwhelming. There is a large community (Discord, forums) for support, reflecting its widespread use.

LlamaIndex (formerly GPT Index)

Focus: A framework specialized for retrieval-augmented generation, i.e. connecting LLMs with external data via indices.

Ease of Setup & Usage: Easy to install and relatively straightforward for its intended use (knowledge retrieval). You create indices over your data and query them with natural language. Using it as an agent framework is possible (it can route queries and tools), but it’s more domain-specific than general agent orchestration.
Modularity & Extensibility: Moderate – LlamaIndex provides various index structures (tree, list, vector) and query interfaces you can swap out. It’s somewhat extensible (you can implement custom retrieval logic or integrate new data connectors), but it’s less about multi-step agent flows and more about plugin-like data access.
Integration (Tools & LLMs): Strong for data sources – it supports 160+ data connectors (API, PDF, databases, web, etc.) to ingest knowledge. It also works with multiple LLMs and can integrate with vector databases for storage. Tool integration beyond retrieval (e.g., using calculators or other agents) is not its primary focus, though you can extend it for those with additional coding.
Memory & Context Management: Focuses on knowledge rather than conversational memory. It doesn’t maintain a chat history by default; instead, it retrieves relevant context from data sources (documents, indexes) to augment LLM prompts. This makes it powerful for Q&A over documents or long-term knowledge, but it’s not a classic agent memory system.
Customization: High within its scope – you can customize indexing strategies, retrieval prompt templates, and even chain multiple indices or tools for complex queries. For example, developers can create custom query planners that decide which index or tool to use for a given question. Outside of retrieval tasks, however, you might need to combine LlamaIndex with another framework.
Community Activity: Very active. LlamaIndex has ~38k stars on GitHub and a growing contributor base. It gained popularity for enabling Retrieval-Augmented Generation (RAG) workflows and continues to be actively maintained with frequent releases and community plugins.
Documentation: Good documentation with clear tutorials on building indices, adding data sources, and examples of complex workflows (like question-answering bots). Since it’s focused, the docs are easier to navigate compared to a broad framework. There are also community examples demonstrating how to integrate LlamaIndex with agent executors (including LangChain) for more advanced use cases.

Phidata

Focus: An all-in-one framework for building multi-modal agents with memory, knowledge, and tool integration, plus a web UI for interaction. It emphasizes quick development of agent workflows.

Ease of Setup & Usage: Very easy to get started. Phidata can spin up a basic agent in just a few lines of code – for example, creating a web search agent with one or two tool calls in ~10 lines. It comes with sensible defaults (e.g. agents use a reasonable reasoning loop by default) which makes initial usage simple. Additionally, it offers a built-in UI to chat with agents, simplifying testing.
Modularity & Extensibility: High-level and modular. Phidata provides built-in components for common needs: different models (OpenAI, local, etc.), tools (web search, databases, etc.), knowledge stores (vector DBs for retrieval), and multi-agent orchestration. These pieces can be configured or extended – e.g., you can add custom tools or swap the memory backend. The architecture is less granular than LangChain’s, but it’s designed to cover end-to-end agent system needs.
Integration (Tools & LLMs): Broad and easy. It’s model-agnostic, supporting any model/provider (OpenAI, Anthropic, local models) without lock-in. It natively supports multi-modal inputs (text, images, audio, video) and provides ready-to-use tools like DuckDuckGo search, PDF readers, databases, etc. Integration with vector stores for knowledge (e.g. LanceDB, Pinecone) is built-in for retrieval-augmented generation. In short, Phidata tries to include all the integrations you need for an AI agent in one package.
Memory & Context Management: First-class support. Phidata agents have memory management out-of-the-box – user sessions and agent state can be stored (e.g., in a database) to maintain context. It also supports persistent knowledge via vector databases, effectively giving agents long-term memory or reference knowledge. This means an agent can remember past interactions and use stored knowledge when reasoning, with minimal setup by the developer.
Customization: Flexible. You can customize an agent’s behavior by defining its role/instructions, adding or removing tools, or even composing multiple agents that collaborate. Because Phidata supports multi-agent teams, you can orchestrate specialized agents (for example, one agent handling web research, another handling calculations) working together. Lower-level customization (like changing the agent reasoning algorithm) is less exposed than in LangChain, but the provided abstractions cover most use cases with simple configuration.
Community Activity: Very high and recent. Phidata (sometimes referred to by its code name “Agno”) has rapidly grown in popularity – it has on the order of 15–18k GitHub stars as of early 2025, with dozens of contributors and an active Discord/community. It’s under active development, frequently updating with new features (e.g. “Reasoning agents” features, new integrations) and bug fixes, indicating strong maintenance.
Documentation: Good. There is an official documentation site with guides and an API reference. The docs include quick start tutorials (how to build your first agent, how to use memory, etc.) and a “cookbook” of examples. Given the framework’s focus on usability, documentation is generally clear with step-by-step examples. Additionally, blog posts and community tutorials (Medium, YouTube) provide beginner-friendly walkthroughs of building agents with Phidata.

PydanticAI

Focus: A type-safe AI agent framework built on Pydantic, aiming to bring robust data validation and structured outputs to LLM applications. It’s designed by the team behind the Pydantic library.

Ease of Setup & Usage: Straightforward installation (pip install pydantic-ai) and quick to start if you’re familiar with Python data classes. Defining an agent feels similar to defining a Pydantic model or a FastAPI endpoint, which many developers find intuitive. Simple “Hello World” examples show minimal boilerplate: you specify the LLM model and a schema or output model, and the framework handles prompt generation and parsing. Overall, it’s fairly easy to use for basic use cases, though leveraging advanced features (like dependency injection) has a learning curve.
Modularity & Extensibility: Python-centric and modular. PydanticAI encourages composing logic using standard Python functions and classes rather than a new DSL. It offers an optional dependency injection system to manage components like tools or external resources. Because it’s built on Pydantic, you can extend it by defining custom data models or validators that shape the agent’s inputs/outputs. The framework is less about chaining predefined modules and more about letting you stitch together Python code, which is very extensible if you are comfortable coding the flow yourself.
Integration (Tools & LLMs): Model-agnostic with broad support – it supports many LLM providers (OpenAI, Anthropic, Cohere, Mistral, etc.) out-of-the-box. Adding a new model provider is straightforward via a simple interface. Tool integration is achieved by treating tools as functions that can be injected or called by the agent; you describe tool interfaces (using Pydantic for input/output schemas) so the LLM can use them safely. This design lets you integrate arbitrary Python functions or APIs as tools while benefiting from Pydantic’s type enforcement.
Memory & Context Management: Limited built-in memory – PydanticAI’s current version emphasizes structured I/O and reliability over providing a built-in conversational memory module. Agents can maintain context in conversation (for example, you could keep a history and feed it into the prompt) but the framework doesn’t yet have a high-level API for long-term memory or vector-store knowledge out-of-the-box. (There is community discussion about adding memory features comparable to other frameworks.) For now, developers can implement memory by storing conversation state externally or by leveraging Pydantic models to structure a history, but it requires manual work.
Customization: Very high. Because you essentially script the agent’s behavior in Python (while the framework handles prompt formatting and validation), you have freedom to implement custom logic. You can design custom prompts with expected response schemas, enforce certain formats, or route between different sub-agents manually. This gives experienced developers fine-grained control to build exactly the workflow they want. The trade-off is that PydanticAI doesn’t provide as many pre-built behaviors – you might need to write more code for complex agent logic, albeit in a straightforward, Pythonic way.
Community Activity: Strong and growing. Although newer (released in late 2024), it already has about 5.6k GitHub stars and is backed by the popular Pydantic project. The core Pydantic team is actively maintaining it, and there’s significant interest from developers who value type safety. Issues and discussions on the repo show prompt responses. As it’s quite new, the community is smaller than LangChain’s, but very enthusiastic (early adopters, blog posts, etc.).
Documentation: Well-documented. There’s an official docs site with guides and API reference for PydanticAI, including a “Hello World” tutorial and examples of using tools and dependency injection. The documentation is clear and leverages the familiarity of Pydantic’s style (e.g., showing how to define expected output schemas). Additionally, because it’s inspired by FastAPI’s developer experience, many concepts feel documented through analogy (if you know FastAPI/Pydantic, the docs make PydanticAI usage very intuitive). There are also several blog articles and videos emerging that demonstrate building agents with PydanticAI in practice.

Hugging Face SmolAgents

Focus: A minimalist multi-agent framework (on the order of 1,000 lines of core code) that lets LLMs act by generating Python code to perform actions. Launched by Hugging Face in late 2024, it prioritizes simplicity and extensibility with minimal abstractions.

Ease of Setup & Usage: Extremely easy. Install with pip install smolagents, and you can create an agent in just a few lines of code. For example, you can instantiate a CodeAgent with a list of tools and a model, then call agent.run(prompt). The design is intentionally simple: there’s very little new syntax or framework-specific configuration to learn. This makes the barrier to entry low – if you know how to call an LLM and write Python functions, you can use SmolAgents.
Modularity & Extensibility: Lightweight core, highly extensible. The core logic is kept minimal and close to raw Python, meaning developers can easily understand and modify how the agent works. SmolAgents operates on the principle that the agent’s reasoning is done via generated code, so adding new capabilities often means providing a new tool (which is just a Python function with a docstring) or even instructing the agent to import and use new libraries. Because the abstractions are few, you can extend the framework by leveraging Python itself – for instance, you could create a new Agent class by subclassing or add new helper functions without dealing with a complex API. This is a very “build-your-own-agent” approach, with the framework just guiding the LLM to produce and execute code safely.
Integration (Tools & LLMs): Flexible and agnostic. It’s LLM-agnostic – you can use any model (OpenAI, Anthropic, local Transformers, etc.) by choosing the appropriate model wrapper. SmolAgents provides convenient integrations: HfApiModel to use models on HuggingFace Hub, LiteLLMModel to access 100+ models via the Litellm library, OpenAIServerModel for OpenAI or compatible endpoints, TransformersModel for local models, and so on. Tool integration is equally flexible: it includes a few basic tools like DuckDuckGoSearchTool out-of-the-box, and it can incorporate tools from other ecosystems (it can even load tools from LangChain or use a HuggingFace Hub Space as a tool). Essentially, any function can be a tool – the agent will generate code to call that function. This means integrating new tools is as simple as defining a Python function and adding it to the agent’s tool list.
Memory & Context Management: Minimal by design. SmolAgents does not include a built-in complex memory module – the philosophy is to keep the framework minimal and let the developer handle state as needed. The agent’s prompt and code execution loop can include context, and you could implement memory by, for example, writing a tool that stores/retrieves conversation history or using the agent’s code to append to a memory variable. For multi-turn conversations, the agent (especially the ChatAgent or similar) can be given the conversation history as part of prompt context, but SmolAgents doesn’t automatically manage long-term memory or vector stores. This lean approach means more work if your application needs extensive memory, but it keeps the core framework simple.
Customization: Very high. Since the agent literally writes Python code to decide actions, a developer can customize what the agent can do by controlling the environment and available tools. You can constrain or guide the agent by providing templates or examples of code, and because you can inspect the code the agent writes, you have transparency into its reasoning. Multi-agent scenarios are supported simply by allowing agents to call each other or run concurrently, which you can manage using normal Python logic. The framework’s minimal interference means you can craft very custom agent behaviors without fighting a rigid structure – essentially, you customize by writing or editing code rather than using a complex API.
Community Activity: Rapidly growing. SmolAgents was released in late 2024 and quickly gained traction – on GitHub it reached over 5.6k stars within the first month. Being a Hugging Face project, it has strong backing and attracted many contributors (50+ even in early stages). The community is active in discussing new tool integrations and improvements (the project is open-source Apache-2.0). Given Hugging Face’s involvement, we can expect ongoing maintenance and community support. It’s newer than others in this report, but its popularity and lightweight nature suggest a growing, enthusiastic user base.
Documentation: Clear and concise. The initial documentation includes a detailed README and an official blog post introducing SmolAgents with examples. The README demonstrates usage and lists key features in a very straightforward way. There are also tutorials on the Hugging Face website (covering text and even vision use-cases) and a command-line interface guide. While it may not have a huge dedicated docs site yet, the code is small enough that many users find it easy to read the source for understanding. Overall, the documentation and examples focus on simplicity, reflecting the framework’s ethos.

Final Recommendation

After evaluating the above frameworks, it’s clear that the best choice depends on your project’s needs. For comprehensive agent capabilities and a mature ecosystem, LangChain remains a top choice – it provides every tool and integration imaginable, albeit with significant complexity. LlamaIndex is excellent if your agent requires heavy retrieval-augmented generation (leveraging external data sources effectively). Phidata stands out for rapid development of multi-modal agents with memory; it offers an easy, high-level API to get an agent with long-term memory and tools running quickly.

When it comes to truly lightweight frameworks, we highlight two options: PydanticAI and Hugging Face SmolAgents. PydanticAI brings the reliability of schema validation and is ideal for developers who want type safety and structured outputs in production-grade applications. However, if we had to choose one lightweight framework that best embodies simplicity and minimal overhead, SmolAgents is our recommendation. SmolAgents provides a minimal yet powerful core, letting you build functional LLM-driven agents with very little code or dependency bloat. Despite its young age, it’s actively maintained and well-supported by Hugging Face, and it integrates easily with various models and tools.

In summary, for most users we recommend a two-pronged approach: use LangChain, LlamaIndex, or Phidata when you need a full-featured solution with lots of built-in capabilities, but consider SmolAgents (along with PydanticAI for type-safe needs) for a lightweight, flexible alternative. This combination allows you to balance functionality with simplicity, choosing the framework that best fits your project’s complexity. By selecting the right tool for the job – from the rich feature set of LangChain to the minimalistic elegance of SmolAgents – developers can accelerate development of robust LLM-powered agents without unnecessary overhead.