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The Trade-offs Between Building Bespoke Code-Based Agents and Major Agent Frameworks

Introduction to Agent Development

The Current Landscape
In today’s tech landscape, agents that harness the power of artificial intelligence (AI) are gaining traction rapidly. New frameworks are evolving, and the increasing investment in this field suggests that modern AI agents have the potential to redefine how we communicate and automate tasks. Are we nearing a future where AI systems independently handle numerous tasks—writing our emails, booking flights, and more? Although this prospect is exciting, the journey involves encountering numerous challenges and decisions.

Navigating the Choices
For developers venturing into the creation of AI agents, the landscape is rife with decisions. Beyond selecting models or determining the architecture, there’s the crucial choice of which agent framework to adopt. Should you choose an established framework like LangGraph, or opt for newer solutions like LlamaIndex Workflows? Alternatively, you could take the more labor-intensive, do-it-yourself approach by coding everything from scratch.

Purpose of This Analysis
This guide seeks to clarify these choices. Through building a sample agent in several frameworks, I’ve been able to explore and unpack the strengths and weaknesses of each at a technical level. Interested developers can find the complete code for these implementations available in the corresponding GitHub repository.

Background on the Agent Used for Testing

Testing Capabilities and Functionalities

Overview of the Agent’s Architecture
The agent employed in my testing process incorporates function calling, a variety of tools or skills, links to external data sources, and the capability to maintain a shared state or memory.

Key Functions of the Agent:

1. Knowledge Acquisition: Obtaining information from a knowledge base for accurate responses.
2. Data Interaction: Formulating answers based on telemetry data associated with applications powered by Large Language Models (LLMs).
3. Data Analysis: Deriving insights and identifying trends from scrutinized telemetry data.

User Interface
The user interface is driven by a straightforward Gradio-powered design that encapsulates the agent’s functionalities within a chatbot framework.

Pure Code Architecture

Conceptual Design of the Agent

Coding the Core Component
The fundamental code for this agent is driven by an OpenAI-powered router. This router utilizes function calls to identify which specific skill should be activated next. Upon completion of a skill, control returns to the router to potentially activate another skill or respond directly to the user’s input.

Maintaining Contextual Integrity
An ongoing list of messages is retained and passed into the router with every call, ensuring that context is preserved throughout the agent’s activities.

def router(messages):
    if not any(
        isinstance(message, dict) and message.get("role") == "system" for message in messages
    ):
        system_prompt = {"role": "system", "content": SYSTEM_PROMPT}
        messages.append(system_prompt)
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=skill_map.get_combined_function_description_for_openai(),
    )
    messages.append(response.choices[0].message)
    tool_calls = response.choices[0].message.tool_calls
    if tool_calls:
        handle_tool_calls(tool_calls, messages)
        return router(messages)
    else:
        return response.choices[0].message.content

Skills Engineering

The skills within this agent since the overall complexity is managed neatly using SkillMap, which provides an encapsulation of skill-related functionalities. Adding new skills requires merely writing a new class representing the skill and updating the SkillMap.

Challenges with Pure Code Agents

First Challenge: Structuring the Router System Prompt

While structuring the router is straightforward, errors can arise, particularly when specifying prompts. Getting the LLM to act appropriately requires finesse in prompting, which can be time-consuming and often frustrating during debugging.

Second Challenge: Output Format Handling

Handling diverse output formats generated from LLM calls presents another layer of complexity. As I chose not to utilize structured outputs, I needed to accommodate various output formats, leading to potential inconsistencies during interactions.

Benefits of a Pure Code Agent

This approach serves as an excellent entry point, allowing developers to learn how agents operate without becoming overly dependent on ready-made tutorials from established frameworks. Even if convincing the LLM to perform a desired action presents challenges, the overall simplicity provides value in certain specific use scenarios.

Framework Comparison

LangGraph: Structure and Functionality

Framework Overview
LangGraph, launched in January 2024, is designed to tackle complexities in existing pipelines by implementing a Pregel graph structure. This method provides enhanced flexibility in defining loops by introducing nodes, edges, and conditional edges.

LangGraph Architectures

The structural approach in LangGraph emphasizes its router concept, enabling OpenAI queries to be conducted via functions, which in turn initiate distinct skill actions in a coherent process.

def create_agent_graph():
    workflow = StateGraph(MessagesState)
    tool_node = ToolNode(tools)
    workflow.add_node("agent", call_model)
    workflow.add_node("tools", tool_node)
    workflow.add_edge(START, "agent")
    workflow.add_conditional_edges(
        "agent",
        should_continue,
    )
    workflow.add_edge("tools", "agent")

Challenges and Benefits of LangGraph

Challenges

The utilization of LangChain objects introduces additional complexity, particularly with function call validations that often require substantial refactoring of existing skills code.

Benefits

Conversely, LangGraph provides clarity and structure, which can be highly advantageous, especially in collaborative environments or for teams at the learning stage.

Workflows Architecture

Introduction to Workflows
Offering contemporary alternatives to LangGraph, Workflows prioritizes asynchronous execution while still maintaining flexibility. The layered event emission and reception adds a powerful dimension to manage complex interactions within applications.

Structural Elements
Workflows employs an event-driven architecture allowing communication between different operational steps within the agent.

Challenges of Workflows

Challenge #1: Synchronous vs Asynchronous Execution
Adapting a synchronous workflow in an inherently asynchronous framework can introduce debugging difficulties.

Challenge #2: Pydantic Validation Errors
Similar to LangGraph, challenges persist regarding unclear error messages around function definitions and validation errors.

Benefits of Workflows

Event-driven architecture promotes cleaner operations across numerous steps in more complex systems combining elements for vastly improved management. The simplicity and lightweight nature of Workflows are additional advantages, making it accessible to developers unfamiliar with strict structural conventions.

Conclusions and Recommendations

Comparison of Approaches

Ultimately, each framework comes with its distinct advantages. The choice of framework might depend significantly on a team’s familiarity with certain structures or existing projects.

How AI legalese decoder Can Assist

In legal scenarios where decisions on agent frameworks could have significant contractual implications or regulatory compliance requirements, the AI legalese decoder can be instrumental. This tool helps translate complex legal jargon into understandable language, thereby enabling developers and decision-makers to choose agent frameworks while remaining compliant and informed.

Questions to Guide Decision-Making

1. Existing Framework Usage: Are you currently utilizing LlamaIndex or LangChain?
2. Familiarity with Frameworks: Are you comfortable working within defined structures, or would you prefer flexibility?
3. Prior Experience: Have you previously built an agent? Familiarity can dictate your choice of frameworks and documentation you may rely on.

The journey to building and implementing AI agents involves many considerations, and leveraging tools like the AI legalese decoder can provide clarity amidst complexities.

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