The tradeoffs between constructing bespoke code-based brokers and the foremost agent frameworks.
Because of John Gilhuly for his contributions to this piece.
Brokers are having a second. With a number of new frameworks and contemporary funding within the area, trendy AI brokers are overcoming shaky origins to quickly supplant RAG as an implementation precedence. So will 2024 lastly be the yr that autonomous AI techniques that may take over writing our emails, reserving flights, speaking to our knowledge, or seemingly another activity?
Possibly, 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 in addition which framework to leverage. Do you go together with the long-standing LangGraph, or the newer entrant LlamaIndex Workflows? Or do you go the normal 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 few of the strengths and weaknesses of every at a technical degree. All the code for every agent is accessible in this repo.
Background on the Agent Used for Testing
The agent used for testing contains operate calling, a number of instruments or expertise, connections to exterior assets, and shared state or reminiscence.
The agent has the next capabilities:
- Answering questions from a data base
- Speaking to knowledge: answering questions on telemetry knowledge of an LLM utility
- Analyzing knowledge: analyzing higher-level tendencies and patterns in retrieved telemetry knowledge
To be able to accomplish these, the agent has three beginning expertise: 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 choice you may have when creating an agent is to skip the frameworks totally and construct the agent absolutely your self. When embarking on this mission, 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 operate calling to pick the suitable 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 consumer.
The agent retains an ongoing record of messages and responses that’s handed absolutely into the router on every name to protect context by way of 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 = shopper.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_calls
if tool_calls:
handle_tool_calls(tool_calls, messages)
return router(messages)
else:
return response.selections[0].message.content material
The abilities themselves are outlined in their very own lessons (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 capabilities. 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 record of expertise within the SkillMap. The thought right here is to make it straightforward so as to add new expertise with out disturbing the router code.
class SkillMap:
def __init__(self):
expertise = [AnalyzeData(), GenerateSQLQuery()]self.skill_map = {}
for ability in expertise:
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.objects():
combined_dict.append(function_dict)
return combined_dict
def get_function_list(self):
return record(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 a couple of challenges.
Challenges with Pure Code Brokers
The primary problem 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 suitable ability. For those who’ve ever tried to get an LLM not to do one thing, you understand how irritating that have will 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 expertise.
Advantages of a Pure Code Agent
A code-based strategy gives an excellent baseline and place to begin, providing an effective way to find out how brokers work with out counting on canned agent tutorials from prevailing frameworks. Though convincing the LLM to behave will be difficult, the code construction itself is straightforward sufficient to make use of and would possibly make sense for sure use circumstances (extra within the evaluation part under).
LangGraph is among the longest-standing agent frameworks, first releasing in January 2024. The framework is constructed to handle 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 appears to be like 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 capabilities and makes use of the response to proceed to a brand new step. Nonetheless the best way this system strikes between expertise is managed utterly in another way.
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 device dealing with step, known as “instruments.” LangGraph has a built-in object known as ToolNode that takes an inventory 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 ENDdef call_model(state: MessagesState):
messages = state["messages"]
response = mannequin.invoke(messages)
return {"messages": [response]}
After every name of the “agent” node (put one other means: the router within the code-based agent), the should_continue edge decides whether or not to return the response to the consumer or cross on to the ToolNode to deal with device calls.
All through every node, the “state” shops the record of messages and responses from OpenAI, just like the code-based agent’s strategy.
Challenges with LangGraph
Many of the difficulties with LangGraph within the instance stem from the necessity to use Langchain objects for issues to circulate properly.
Problem #1: Operate Name Validation
To be able to use the ToolNode object, I needed to refactor most of my current Ability code. The ToolNode takes an inventory of callable capabilities, which initially made me suppose I may use my current capabilities, nonetheless issues broke down attributable to my operate parameters.
The abilities have been outlined as lessons with a callable member operate, that means that they had “self” as their first parameter. GPT-4o was sensible sufficient to not embody the “self” parameter within the generated operate name, nonetheless LangGraph learn this as a validation error attributable to a lacking parameter.
This took hours to determine, as a result of the error message as an alternative marked the third parameter within the operate (“args” on the info evaluation ability) because the lacking parameter:
pydantic.v1.error_wrappers.ValidationError: 1 validation error for data_analysis_toolSchema
args area required (sort=value_error.lacking)
It’s price mentioning that the error message originated from Pydantic, not from LangGraph.
I finally bit the bullet and redefined my expertise as fundamental strategies with Langchain’s @device decorator, and was capable of get issues working.
@device
def 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 consumer immediate.
Returns:
str: The results of the SQL question.
"""
Problem #2: Debugging
As talked about, debugging in a framework is troublesome. 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 most important advantages of LangGraph is that it’s straightforward to work with. The graph construction code is clear and accessible. Particularly if in case you have advanced node logic, having a single view of the graph makes it simpler to grasp how the agent is linked collectively. LangGraph additionally makes it easy to transform an current utility inbuilt LangChain.
Takeaway
For those who use every thing within the framework, LangGraph works cleanly; when you step exterior of it, put together for some debugging complications.
Workflows is a more moderen entrant into the agent framework area, premiering earlier this summer season. Like LangGraph, it goals to make looping brokers simpler to construct. Workflows additionally has a selected concentrate on working 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 deal with logic, and emitted and acquired occasions to maneuver between steps.
The construction above appears to be like 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 may be very totally different code powering it.
Workflows Structure
The code under defines the Workflow construction. Much like LangGraph, that is the place I ready the state and connected the talents to the LLM object.
class AgentFlow(Workflow):
def __init__(self, llm, timeout=300):
tremendous().__init__(timeout=timeout)
self.llm = llm
self.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)
),
)
)@step
async def prepare_agent(self, ev: StartEvent) -> RouterInputEvent:
user_input = ev.enter
user_msg = ChatMessage(function="consumer", 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 consumer 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 by way of steps, which avoids repeatedly including the consumer 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 deal with this logic with the Workflow and graph as we’ve completed right here.
With the Workflow arrange, I then outlined the routing code:
@step
async def router(self, ev: RouterInputEvent) -> ToolCallEvent | StopEvent:
messages = ev.enterif 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(end result=response.message.content material)
And the device name dealing with code:
@step
async def tool_call_handler(self, ev: ToolCallEvent) -> RouterInputEvent:
tool_calls = ev.tool_callsfor tool_call in tool_calls:
function_name = tool_call.tool_name
arguments = tool_call.tool_kwargs
if "enter" in arguments:
arguments["prompt"] = arguments.pop("enter")
strive:
function_callable = skill_map.get_function_callable_by_name(function_name)
besides KeyError:
function_result = "Error: Unknown operate name"
function_result = function_callable(arguments)
message = ChatMessage(
function="device",
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 — traces 18–24 have 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 every thing within the tool_call_handler methodology robotically.
Transferring previous the routing step, one factor I used to be very joyful to see is that I may use my SkillMap and current expertise from my code-based agent with Workflows. These required no modifications 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 pressure synchronous execution was very troublesome.
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”. Nonetheless, because the underlying strategies inside the Workflow class have been additionally marked as asynchronous, it was essential to redefine these with the intention to run synchronously. I ended up sticking to an asynchronous strategy, however this didn’t make debugging harder.
Problem #2: Pydantic Validation Errors
In a repeat of the woes with LangGraph, comparable issues emerged round complicated Pydantic validation errors on expertise. Luckily, these have been simpler to handle this time since Workflows was capable of deal with member capabilities simply effective. I finally simply ended up having to be extra prescriptive in creating LlamaIndex FunctionTool objects for my expertise:
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 gear dealing with code myself as an alternative of offering built-in capabilities. This additionally meant that my Workflow agent seemed extraordinarily just like my code-based agent.
The largest distinction got here in the usage of occasions. I used two customized occasions to maneuver between steps in my agent:
class ToolCallEvent(Occasion):
tool_calls: record[ToolSelection]class RouterInputEvent(Occasion):
enter: record[ChatMessage]
The emitter-receiver, event-based structure took the place of instantly calling a few of the strategies in my agent, just like the device name handler.
You probably have extra advanced techniques 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 rather light-weight and doesn’t pressure a lot construction on you (except for the usage of sure LlamaIndex objects) and that its event-based structure gives a useful various to direct operate calling — particularly for advanced, asynchronous purposes.
Trying 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 varied varieties and objects straight is straightforward. The readability and accessibility of the code totally comes right down to the person developer nonetheless, and it’s straightforward to see how more and more advanced brokers may 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 group is collaborating on an agent, this construction would offer a useful means of implementing an structure. LangGraph additionally would possibly present an excellent place to begin with brokers for these not as conversant in the construction. There’s a tradeoff, nonetheless — since LangGraph does fairly a bit for you, it might probably result in complications when you 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 may be extraordinarily useful for some initiatives, and the truth that much less is required by way of utilizing of LlamaIndex varieties gives better flexibility for these not be absolutely utilizing the framework throughout their utility.
Finally, 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 modify on benefit alone.
The pure code strategy will probably all the time be a gorgeous choice. You probably have the rigor to doc and implement any abstractions created, then guaranteeing nothing in an exterior framework slows you down is straightforward.
After all, “it relies upon” is rarely a satisfying reply. These three questions ought to assist you determine which framework to make use of in your subsequent agent mission.
Are you already utilizing LlamaIndex or LangChain for important items of your mission?
If sure, discover that choice first.
Are you conversant in widespread agent constructions, or would you like one thing telling you the way it’s best to construction your agent?
For those who fall into the latter group, strive Workflows. For those who actually fall into the latter group, strive 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 may impression outcomes in manufacturing for generative AI techniques. As all the time, it pays to have sturdy guardrails and LLM tracing in place — and to be agile as new agent frameworks, analysis, and fashions upend established methods.
