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The tradeoffs between constructing bespoke code-based brokers and the foremost agent frameworks.
Due to John Gilhuly for his contributions to this piece.
Brokers are having a second. With a number of new frameworks and recent funding within the area, fashionable AI brokers are overcoming shaky origins to quickly supplant RAG as an implementation precedence. So will 2024 lastly be the 12 months that autonomous AI methods that may take over writing our emails, reserving flights, speaking to our knowledge, or seemingly every other process?
Perhaps, however a lot work stays to get to that time. Any developer constructing an agent should not solely select foundations — which mannequin, use case, and structure to make use of — but additionally which framework to leverage. Do you go together with the long-standing LangGraph, or the newer entrant LlamaIndex Workflows? Or do you go the standard route and code the entire thing your self?
This submit goals to make that alternative a bit simpler. Over the previous few weeks, I constructed the identical agent in main frameworks to look at a number of the strengths and weaknesses of every at a technical stage. All the code for every agent is out there on this repo.
Background on the Agent Used for Testing
The agent used for testing consists of perform calling, a number of instruments or abilities, connections to exterior sources, and shared state or reminiscence.
The agent has the next capabilities:
Answering questions from a data baseTalking to knowledge: answering questions on telemetry knowledge of an LLM applicationAnalyzing knowledge: analyzing higher-level tendencies and patterns in retrieved telemetry knowledge
In an effort to accomplish these, the agent has three beginning abilities: RAG with product documentation, SQL technology on a hint database, and knowledge evaluation. A easy gradio-powered interface is used for the agent UI, with the agent itself structured as a chatbot.
The primary possibility you might have when growing an agent is to skip the frameworks totally and construct the agent absolutely your self. When embarking on this undertaking, this was the strategy I began with.
Pure Code Structure
The code-based agent under is made up of an OpenAI-powered router that makes use of perform calling to pick out the best ability to make use of. After that ability completes, it returns again to the router to both name one other ability or reply to the person.
The agent retains an ongoing listing of messages and responses that’s handed absolutely into the router on every name to protect context via cycles.
def router(messages):if not any(isinstance(message, dict) and message.get(“function”) == “system” for message in messages):system_prompt = {“function”: “system”, “content material”: SYSTEM_PROMPT}messages.append(system_prompt)
response = consumer.chat.completions.create(mannequin=”gpt-4o”,messages=messages,instruments=skill_map.get_combined_function_description_for_openai(),)
messages.append(response.selections[0].message)tool_calls = response.selections[0].message.tool_callsif tool_calls:handle_tool_calls(tool_calls, messages)return router(messages)else:return response.selections[0].message.content material
The talents themselves are outlined in their very own courses (e.g. GenerateSQLQuery) which are collectively held in a SkillMap. The router itself solely interacts with the SkillMap, which it makes use of to load ability names, descriptions, and callable features. This strategy signifies that including a brand new ability to the agent is so simple as writing that ability as its personal class, then including it to the listing of abilities within the SkillMap. The thought right here is to make it simple so as to add new abilities with out disturbing the router code.
class SkillMap:def __init__(self):abilities = [AnalyzeData(), GenerateSQLQuery()]
self.skill_map = {}for ability in abilities:self.skill_map[skill.get_function_name()] = (ability.get_function_dict(),ability.get_function_callable(),)
def get_function_callable_by_name(self, skill_name) -> Callable:return self.skill_map[skill_name][1]
def get_combined_function_description_for_openai(self):combined_dict = []for _, (function_dict, _) in self.skill_map.gadgets():combined_dict.append(function_dict)return combined_dict
def get_function_list(self):return listing(self.skill_map.keys())
def get_list_of_function_callables(self):return [skill[1] for ability in self.skill_map.values()]
def get_function_description_by_name(self, skill_name):return str(self.skill_map[skill_name][0][“function”])
Total, this strategy is pretty easy to implement however comes with just a few challenges.
Challenges with Pure Code Brokers
The primary issue lies in structuring the router system immediate. Typically, the router within the instance above insisted on producing SQL itself as an alternative of delegating that to the best ability. When you’ve ever tried to get an LLM to not do one thing, you understand how irritating that have might be; discovering a working immediate took many rounds of debugging. Accounting for the totally different output codecs from every step was additionally tough. Since I opted to not use structured outputs, I needed to be prepared for a number of totally different codecs from every of the LLM calls in my router and abilities.
Advantages of a Pure Code Agent
A code-based strategy gives a great baseline and place to begin, providing an effective way to learn the way brokers work with out counting on canned agent tutorials from prevailing frameworks. Though convincing the LLM to behave might be difficult, the code construction itself is easy sufficient to make use of and would possibly make sense for sure use instances (extra within the evaluation part under).
LangGraph is without doubt one of the longest-standing agent frameworks, first releasing in January 2024. The framework is constructed to deal with the acyclic nature of current pipelines and chains by adopting a Pregel graph construction as an alternative. LangGraph makes it simpler to outline loops in your agent by including the ideas of nodes, edges, and conditional edges to traverse a graph. LangGraph is constructed on high of LangChain, and makes use of the objects and kinds from that framework.
LangGraph Structure
The LangGraph agent seems to be just like the code-based agent on paper, however the code behind it’s drastically totally different. LangGraph nonetheless makes use of a “router” technically, in that it calls OpenAI with features and makes use of the response to proceed to a brand new step. Nevertheless the way in which this system strikes between abilities is managed fully otherwise.
instruments = [generate_and_run_sql_query, data_analyzer]mannequin = ChatOpenAI(mannequin=”gpt-4o”, temperature=0).bind_tools(instruments)
def create_agent_graph():workflow = StateGraph(MessagesState)
tool_node = ToolNode(instruments)workflow.add_node(“agent”, call_model)workflow.add_node(“instruments”, tool_node)
workflow.add_edge(START, “agent”)workflow.add_conditional_edges(“agent”,should_continue,)workflow.add_edge(“instruments”, “agent”)
checkpointer = MemorySaver()app = workflow.compile(checkpointer=checkpointer)return app
The graph outlined right here has a node for the preliminary OpenAI name, known as “agent” above, and one for the instrument dealing with step, known as “instruments.” LangGraph has a built-in object known as ToolNode that takes a listing of callable instruments and triggers them based mostly on a ChatMessage response, earlier than returning to the “agent” node once more.
def should_continue(state: MessagesState):messages = state[“messages”]last_message = messages[-1]if last_message.tool_calls:return “instruments”return END
def call_model(state: MessagesState):messages = state[“messages”]response = mannequin.invoke(messages)return {“messages”: [response]}
After every name of the “agent” node (put one other manner: the router within the code-based agent), the should_continue edge decides whether or not to return the response to the person or go on to the ToolNode to deal with instrument calls.
All through every node, the “state” shops the listing of messages and responses from OpenAI, just like the code-based agent’s strategy.
Challenges with LangGraph
A lot of the difficulties with LangGraph within the instance stem from the necessity to use Langchain objects for issues to circulation properly.
Problem #1: Operate Name Validation
In an effort to use the ToolNode object, I needed to refactor most of my current Ability code. The ToolNode takes a listing of callable features, which initially made me assume I might use my current features, nonetheless issues broke down resulting from my perform parameters.
The talents had been outlined as courses with a callable member perform, that means that they had “self” as their first parameter. GPT-4o was good sufficient to not embody the “self” parameter within the generated perform name, nonetheless LangGraph learn this as a validation error resulting from a lacking parameter.
This took hours to determine, as a result of the error message as an alternative marked the third parameter within the perform (“args” on the info evaluation ability) because the lacking parameter:
pydantic.v1.error_wrappers.ValidationError: 1 validation error for data_analysis_toolSchemaargs area required (sort=value_error.lacking)
It’s price mentioning that the error message originated from Pydantic, not from LangGraph.
I ultimately bit the bullet and redefined my abilities as fundamental strategies with Langchain’s @instrument decorator, and was in a position to get issues working.
@tooldef generate_and_run_sql_query(question: str):”””Generates and runs an SQL question based mostly on the immediate.
Args:question (str): A string containing the unique person immediate.
Returns:str: The results of the SQL question.”””
Problem #2: Debugging
As talked about, debugging in a framework is tough. This primarily comes right down to complicated error messages and abstracted ideas that make it tougher to view variables.
The abstracted ideas primarily present up when making an attempt to debug the messages being despatched across the agent. LangGraph shops these messages in state[“messages”]. Some nodes inside the graph pull from these messages robotically, which may make it obscure the worth of messages when they’re accessed by the node.
LangGraph Advantages
One of many predominant advantages of LangGraph is that it’s simple to work with. The graph construction code is clear and accessible. Particularly if in case you have complicated node logic, having a single view of the graph makes it simpler to know how the agent is related collectively. LangGraph additionally makes it easy to transform an current utility in-built LangChain.
Takeaway
When you use the whole lot within the framework, LangGraph works cleanly; for those who step exterior of it, put together for some debugging complications.
Workflows is a more recent entrant into the agent framework area, premiering earlier this summer time. Like LangGraph, it goals to make looping brokers simpler to construct. Workflows additionally has a selected concentrate on operating asynchronously.
Some parts of Workflows appear to be in direct response to LangGraph, particularly its use of occasions as an alternative of edges and conditional edges. Workflows use steps (analogous to nodes in LangGraph) to accommodate logic, and emitted and obtained occasions to maneuver between steps.
The construction above seems to be just like the LangGraph construction, save for one addition. I added a setup step to the Workflow to organize the agent context, extra on this under. Regardless of the same construction, there’s very totally different code powering it.
Workflows Structure
The code under defines the Workflow construction. Just like LangGraph, that is the place I ready the state and hooked up the abilities to the LLM object.
class AgentFlow(Workflow):def __init__(self, llm, timeout=300):tremendous().__init__(timeout=timeout)self.llm = llmself.reminiscence = ChatMemoryBuffer(token_limit=1000).from_defaults(llm=llm)self.instruments = []for func in skill_map.get_function_list():self.instruments.append(FunctionTool(skill_map.get_function_callable_by_name(func),metadata=ToolMetadata(identify=func, description=skill_map.get_function_description_by_name(func)),))
@stepasync def prepare_agent(self, ev: StartEvent) -> RouterInputEvent:user_input = ev.inputuser_msg = ChatMessage(function=”person”, content material=user_input)self.reminiscence.put(user_msg)
chat_history = self.reminiscence.get()return RouterInputEvent(enter=chat_history)
That is additionally the place I outline an additional step, “prepare_agent”. This step creates a ChatMessage from the person enter and provides it to the workflow reminiscence. Splitting this out as a separate step signifies that we do return to it because the agent loops via steps, which avoids repeatedly including the person message to the reminiscence.
Within the LangGraph case, I completed the identical factor with a run_agent methodology that lived exterior the graph. This modification is usually stylistic, nonetheless it’s cleaner for my part to accommodate this logic with the Workflow and graph as we’ve performed right here.
With the Workflow arrange, I then outlined the routing code:
@stepasync def router(self, ev: RouterInputEvent) -> ToolCallEvent | StopEvent:messages = ev.enter
if not any(isinstance(message, dict) and message.get(“function”) == “system” for message in messages):system_prompt = ChatMessage(function=”system”, content material=SYSTEM_PROMPT)messages.insert(0, system_prompt)
with using_prompt_template(template=SYSTEM_PROMPT, model=”v0.1″):response = await self.llm.achat_with_tools(mannequin=”gpt-4o”,messages=messages,instruments=self.instruments,)
self.reminiscence.put(response.message)
tool_calls = self.llm.get_tool_calls_from_response(response, error_on_no_tool_call=False)if tool_calls:return ToolCallEvent(tool_calls=tool_calls)else:return StopEvent(outcome=response.message.content material)
And the instrument name dealing with code:
@stepasync def tool_call_handler(self, ev: ToolCallEvent) -> RouterInputEvent:tool_calls = ev.tool_calls
for tool_call in tool_calls:function_name = tool_call.tool_namearguments = tool_call.tool_kwargsif “enter” in arguments:arguments[“prompt”] = arguments.pop(“enter”)
attempt:function_callable = skill_map.get_function_callable_by_name(function_name)besides KeyError:function_result = “Error: Unknown perform name”
function_result = function_callable(arguments)message = ChatMessage(function=”instrument”,content material=function_result,additional_kwargs={“tool_call_id”: tool_call.tool_id},)
self.reminiscence.put(message)
return RouterInputEvent(enter=self.reminiscence.get())
Each of those look extra just like the code-based agent than the LangGraph agent. That is primarily as a result of Workflows retains the conditional routing logic within the steps versus in conditional edges — strains 18–24 had been a conditional edge in LangGraph, whereas now they’re simply a part of the routing step — and the truth that LangGraph has a ToolNode object that does nearly the whole lot within the tool_call_handler methodology robotically.
Shifting previous the routing step, one factor I used to be very glad to see is that I might use my SkillMap and current abilities from my code-based agent with Workflows. These required no adjustments to work with Workflows, which made my life a lot simpler.
Challenges with Workflows
Problem #1: Sync vs Async
Whereas asynchronous execution is preferable for a stay agent, debugging a synchronous agent is far simpler. Workflows is designed to work asynchronously, and making an attempt to drive synchronous execution was very tough.
I initially thought I’d simply be capable to take away the “async” methodology designations and swap from “achat_with_tools” to “chat_with_tools”. Nevertheless, because the underlying strategies inside the Workflow class had been additionally marked as asynchronous, it was essential to redefine these in an effort to run synchronously. I ended up sticking to an asynchronous strategy, however this didn’t make debugging tougher.
Problem #2: Pydantic Validation Errors
In a repeat of the woes with LangGraph, comparable issues emerged round complicated Pydantic validation errors on abilities. Fortuitously, these had been simpler to deal with this time since Workflows was in a position to deal with member features simply fantastic. I in the end simply ended up having to be extra prescriptive in creating LlamaIndex FunctionTool objects for my abilities:
for func in skill_map.get_function_list(): self.instruments.append(FunctionTool(skill_map.get_function_callable_by_name(func), metadata=ToolMetadata(identify=func, description=skill_map.get_function_description_by_name(func))))
Excerpt from AgentFlow.__init__ that builds FunctionTools
Advantages of Workflows
I had a a lot simpler time constructing the Workflows agent than I did the LangGraph agent, primarily as a result of Workflows nonetheless required me to jot down routing logic and power dealing with code myself as an alternative of offering built-in features. This additionally meant that my Workflow agent appeared extraordinarily just like my code-based agent.
The most important distinction got here in using occasions. I used two customized occasions to maneuver between steps in my agent:
class ToolCallEvent(Occasion):tool_calls: listing[ToolSelection]
class RouterInputEvent(Occasion):enter: listing[ChatMessage]
The emitter-receiver, event-based structure took the place of instantly calling a number of the strategies in my agent, just like the instrument name handler.
In case you have extra complicated methods with a number of steps which are triggering asynchronously and would possibly emit a number of occasions, this structure turns into very useful to handle that cleanly.
Different advantages of Workflows embody the truth that it is vitally light-weight and doesn’t drive a lot construction on you (other than using sure LlamaIndex objects) and that its event-based structure gives a useful different to direct perform calling — particularly for complicated, asynchronous purposes.
Wanting throughout the three approaches, every one has its advantages.
The no framework strategy is the only to implement. As a result of any abstractions are outlined by the developer (i.e. SkillMap object within the above instance), preserving numerous sorts and objects straight is straightforward. The readability and accessibility of the code totally comes right down to the person developer nonetheless, and it’s simple to see how more and more complicated brokers might get messy with out some enforced construction.
LangGraph gives fairly a little bit of construction, which makes the agent very clearly outlined. If a broader staff is collaborating on an agent, this construction would offer a useful manner of implementing an structure. LangGraph additionally would possibly present a great place to begin with brokers for these not as accustomed to the construction. There’s a tradeoff, nonetheless — since LangGraph does fairly a bit for you, it will probably result in complications for those who don’t absolutely purchase into the framework; the code could also be very clear, however chances are you’ll pay for it with extra debugging.
Workflows falls someplace within the center. The event-based structure could be extraordinarily useful for some tasks, and the truth that much less is required when it comes to utilizing of LlamaIndex sorts gives larger flexibility for these not be absolutely utilizing the framework throughout their utility.
In the end, the core query could come right down to “are you already utilizing LlamaIndex or LangChain to orchestrate your utility?” LangGraph and Workflows are each so entwined with their respective underlying frameworks that the extra advantages of every agent-specific framework may not trigger you to change on benefit alone.
The pure code strategy will seemingly all the time be a sexy possibility. In case you have the rigor to doc and implement any abstractions created, then making certain nothing in an exterior framework slows you down is straightforward.
In fact, “it relies upon” isn’t a satisfying reply. These three questions ought to enable you determine which framework to make use of in your subsequent agent undertaking.
Are you already utilizing LlamaIndex or LangChain for important items of your undertaking?
If sure, discover that possibility first.
Are you accustomed to frequent agent constructions, or would you like one thing telling you the way it’s best to construction your agent?
When you fall into the latter group, attempt Workflows. When you actually fall into the latter group, attempt LangGraph.
Has your agent been constructed earlier than?
One of many framework advantages is that there are various tutorials and examples constructed with every. There are far fewer examples of pure code brokers to construct from.
Choosing an agent framework is only one alternative amongst many that can impression outcomes in manufacturing for generative AI methods. As all the time, it pays to have strong guardrails and LLM tracing in place — and to be agile as new agent frameworks, analysis, and fashions upend established methods.
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