AI Agent Frameworks Compared: ReAct, RAG Agents, and What to Actually Use

AI Agent Frameworks Compared: ReAct, RAG Agents, and What to Actually Use

Most "framework comparison" posts stack feature tables side by side and call it analysis. That is not useful once you have actually built something. What you need to know before you pick a framework is what the underlying execution pattern is, where it breaks, and which framework encodes that pattern in a way your team can maintain six months from now.

This post covers three things: the ReAct pattern (the reasoning loop nearly every agent framework builds on), RAG-agent architecture (how retrieval gets wired into an agent's action loop, not just its prompt), and a direct comparison of LangGraph, CrewAI, and Agno for teams deciding what to build on. If you want a broader tools listicle with pricing and pros/cons across eight frameworks, we already cover that ground in our best open-source AI agent frameworks roundup. This post is narrower and more technical: it is about the pattern underneath the tooling.

The ReAct pattern: what it actually is

ReAct (Reason + Act) comes from a 2022 paper by Yao et al. that interleaved chain-of-thought reasoning with tool calls in a single loop. Before ReAct, you had two separate approaches: chain-of-thought prompting, where a model reasons through a problem in text but never touches the outside world, and action-only agents, where a model calls tools but does not narrate why. ReAct's contribution was making the model alternate between "Thought," "Action," and "Observation" steps explicitly, so each tool call is grounded in a visible reasoning trace and each new piece of information updates that reasoning before the next action.

The loop looks like this in practice:

1. Thought: the model reasons about what it knows and what it still needs.
2. Action: it calls a tool (search, a database query, a calculator, an API).
3. Observation: the tool result gets appended to context.
4. Repeat until the model decides it has enough to produce a final answer.

This is the pattern underneath almost every "agent" you have used, whether it is exposed to you as ReAct explicitly or wrapped in a framework's own abstraction. LangChain's original AgentExecutor, OpenAI's function-calling loop, and most single-agent implementations in LangGraph, CrewAI, and Agno are ReAct with different scaffolding around it.

Where ReAct actually breaks

The pattern is simple, which is exactly why teams underestimate the failure modes:

• Looping without termination. A model can decide it needs "just one more search" indefinitely, especially on ambiguous questions where no single tool call resolves the task. Without an explicit step cap or a "confidence check" step, this burns tokens and time without producing an answer.
• Hallucinated tool arguments. The model reasons correctly about which tool to call but invents a parameter, a file path, or an ID that does not exist, then reasons from a fabricated observation as if it were real.
• Context drift on long loops. Every thought/action/observation cycle adds tokens to context. Past 10 to 15 iterations, models start losing track of the original goal, especially on models with weaker long-context attention, and the ReAct trace itself becomes noise that competes with the actual task.
• No error recovery pattern by default. Vanilla ReAct does not specify what happens when a tool call fails. Frameworks that just wrap the raw pattern will often feed the raw exception back as an observation and let the model figure it out, which works about half the time and produces garbage the other half.

This is exactly why production frameworks do not ship raw ReAct. They wrap it with state machines, step limits, structured output validation, and explicit error-handling branches. Understanding that ReAct is the substrate, not the product, is the single most useful mental model for evaluating any agent framework's marketing copy.

RAG-agent architecture: retrieval as an action, not a preprocessing step

Standard RAG (retrieval-augmented generation) is a pipeline: embed the query, retrieve the top-k chunks from a vector store, stuff them into the prompt, generate. It runs once per user turn and the model has no say in what gets retrieved.

A RAG agent is different. Retrieval becomes a tool the agent can call, reason about, and call again. Instead of retrieving once and hoping the top-k chunks are relevant, the agent decides:

• whether it needs to retrieve at all for this query
• what to search for, potentially reformulating the query based on partial results
• whether the retrieved chunks actually answer the question, or whether it needs a second, narrower search
• whether to combine retrieval with other tools (a calculator, a live API, a second knowledge base) before answering

This matters most on multi-hop questions, where the answer depends on information spread across documents that a single embedding search will not surface together. A static RAG pipeline retrieves once against the original query and fails silently when the first document only gets you halfway. A RAG agent can retrieve, notice the gap in its own reasoning trace, and issue a second targeted query, which is exactly the ReAct loop applied to retrieval instead of general tool use.

The practical architecture for a RAG agent looks like:

1. Query understanding step (sometimes a separate lightweight model call) to decide if retrieval is needed and how to phrase it.
2. Retrieval as a tool call, same as any other function the agent can invoke, with the vector search or hybrid search (BM25 plus embeddings) wrapped behind a typed interface.
3. A relevance-check step, either an explicit model judgment ("do these chunks answer the question?") or an implicit one baked into the next Thought step.
4. Re-retrieval with a reformulated query if the check fails, capped at 2 to 3 attempts to avoid the same looping problem raw ReAct has.
5. Synthesis with citations traced back to the specific chunks used, which matters for any use case where you need to show your work (support, legal, compliance, internal knowledge bases).

The tradeoff against static RAG is cost and latency: a RAG agent can make 3 to 5 model calls where static RAG makes one. For high-volume, well-defined queries (an FAQ bot answering questions against a stable knowledge base), static RAG is usually the right call because the query distribution is narrow enough that top-k retrieval works fine. For open-ended internal tools where questions are unpredictable and wrong answers are expensive, the extra latency of a RAG agent is worth it.

LangGraph vs CrewAI vs Agno: what each one is actually for

These three come up constantly in the same conversation, but they solve different problems. Picking based on GitHub stars instead of architecture fit is how teams end up rewriting their agent layer six months in.

LangGraph: explicit state machines for agents that need control flow

LangGraph models an agent (or a multi-agent system) as a graph: nodes are functions or LLM calls, edges define transitions, and a shared state object flows through the whole graph. The core value is that you write the control flow explicitly instead of hoping the model's own reasoning produces the right sequence of steps.

This matters when you need:

• Conditional branching that depends on real logic, not just model judgment ("if the retrieved confidence score is below X, route to human review instead of a fourth retry").
• Human-in-the-loop checkpoints, where the graph pauses, waits for a person to approve or edit state, then resumes. LangGraph's checkpointing (persisting state to a database between steps) is built for exactly this.
• Cycles with hard guarantees, like "retry this node at most 3 times, then escalate," expressed as actual graph structure instead of prompt instructions the model might ignore.

The cost is verbosity. You are writing and maintaining an actual state graph, defining the state schema, and reasoning about every edge case in code rather than delegating that reasoning to the model. For a single agent doing a simple ReAct loop, LangGraph is overkill. For a workflow with real branching logic, approval gates, or long-running processes that need to survive a restart, it is close to the only sane choice among these three.

CrewAI: role-based orchestration for multi-agent collaboration

CrewAI's abstraction is a team: you define agents with roles, goals, and backstories, give them tools, and assign them tasks that can depend on each other's output. A "Researcher" agent hands its findings to a "Writer" agent, which hands a draft to an "Editor" agent, and CrewAI manages the sequencing.

This is the right fit when your problem naturally decomposes into specialized roles with a clear handoff order, think content pipelines, research-then-synthesize workflows, or multi-step data processing where each stage has a distinct "job description." The role framing also makes prompts easier to write and debug for teams without deep agent-engineering experience, because "you are a researcher whose job is X" maps to something a non-specialist can reason about and tune.

Where it strains: CrewAI's process management (sequential vs hierarchical crews) is less explicit than LangGraph's graph structure. Conditional logic and error recovery across agents work, but you are working within CrewAI's task/crew abstractions rather than writing arbitrary control flow. For workflows with heavy branching or that need to persist and resume mid-execution, teams often hit the edges of what the role-based model expresses cleanly.

Agno: a lean runtime for high-throughput single or few-agent systems

Agno (formerly Phidata) takes the opposite bet from LangGraph: instead of exposing more control-flow primitives, it strips overhead to make agents fast and cheap to run at scale. Agent initialization measured in microseconds and a memory footprint in single-digit kilobytes per instance matters when you are running thousands of concurrent agent instances rather than orchestrating one complex multi-step workflow. We cover Agno's full architecture, its Teams and Workflows abstractions, and how it fits into a broader AI-native engineering stack in our AI-native dev team stack post, so we will not repeat that ground here.

For this comparison, the relevant point is fit: Agno is the right pick when your bottleneck is throughput and cost per agent instance, not orchestration complexity. If you need heavy conditional branching or long-running human-in-the-loop workflows, LangGraph's explicit graph model handles that better than Agno's lighter abstraction.

A practical decision framework

None of these are mutually exclusive in a mature stack. It is common to see LangGraph orchestrating the top-level workflow while individual nodes call out to a leaner runtime for specific high-volume subtasks. The mistake is picking the heaviest-looking framework because it seems "more production-ready," when a two-node ReAct loop with a step cap would have shipped the same feature in a fraction of the code.

Building vs buying: when "just pick a framework" is not the actual bottleneck

Framework choice matters, but for most teams it is not the thing standing between them and a working agent in production. The harder problems are usually: getting tool definitions accurate enough that the model does not hallucinate arguments, building the evaluation harness that tells you whether a change to your prompt or graph actually improved outcomes, and hardening the retry and error-handling paths so a single flaky API call does not cascade into a broken multi-step workflow.

If your team is evaluating whether to build this in-house or bring in help, our AI agent development page covers what that engagement typically looks like, and our custom AI agent development post walks through the build process in more detail. Either way, the framework decision above should come after you have scoped the actual control-flow and retrieval requirements, not before.

FAQ

Is ReAct a framework or a pattern?

It is a pattern, not a framework or a product. LangGraph, CrewAI, Agno, and most agent tooling implement variations of the ReAct loop internally, with different amounts of structure wrapped around it. Understanding ReAct helps you read what any framework is actually doing under its abstractions.

Do I need a RAG agent, or is static RAG enough?

If your queries are predictable and your knowledge base is stable (an FAQ bot, a support tool answering against a fixed product doc set), static RAG is usually enough and costs less per query. If questions are open-ended, multi-hop, or the cost of a wrong answer is high, the extra retrieval rounds a RAG agent can perform are worth the added latency.

Can I use LangGraph and CrewAI together?

Yes, though it is more common to pick one as your primary orchestration layer and use the other's ideas rather than literally mixing both runtimes in one system. Some teams use LangGraph for the top-level workflow graph and implement individual nodes with simpler, framework-agnostic logic inspired by CrewAI's role decomposition.

What is hermes-agent?

Hermes refers to a family of open-weight models (from Nous Research) fine-tuned for function-calling and agentic tool use, distinct from LangGraph, CrewAI, or Agno, which are orchestration frameworks that can run on top of any capable model, including Hermes variants. If you are searching for "hermes-agent" while evaluating frameworks, the more useful question is usually which orchestration layer you will run on top of whatever model you choose, which is what this post covers.

How many tool-call iterations should I cap a ReAct loop at?

There is no universal number, but most production implementations cap between 5 and 15 iterations depending on task complexity, with an explicit fallback ("return your best answer so far" or "escalate to a human") when the cap is hit rather than letting the loop fail silently.