Building Agents with Frameworks

Why Use Frameworks?

Building agents from scratch is complex. Frameworks provide:

• Pre-built patterns (ReAct, CoT)
• Tool integration
• Memory management
• Orchestration logic

LangChain

Popular Python framework for building LLM applications.

Components:
1. LLM (OpenAI, Anthropic, etc.)
2. Tools (functions agent can use)
3. Agent executor (orchestration)
4. Memory (conversation history)
5. Chains (sequences of steps)

Example:
from langchain.agents import initialize_agent
from langchain.llms import OpenAI

llm = OpenAI(api_key="...")
tools = [SearchTool(), Calculator(), Python]

agent = initialize_agent(
  llm=llm,
  tools=tools,
  agent_type="ReAct",
  memory=ConversationMemory()
)

result = agent.run("What's 2^10 + sqrt(16)?")
# Output: 1028 (computed by agent)

AutoGPT Pattern

Autonomous agent that:

1. Sets goals
2. Breaks into tasks
3. Executes tasks
4. Reviews progress
5. Iterates

Loop:
Agent thinks: "Goal: Write essay"
  → Task 1: Research (execute)
  → Task 2: Outline (execute)
  → Task 3: Write (execute)
  → Review: Essay complete?
  → If not done: Create new task
  → If done: Return result

OpenAI Function Calling

Modern approach: Function calling in LLM API.

Define tools:
{
  "name": "search",
  "description": "Search the web"
}

LLM decides to use tool:
{
  "function_name": "search",
  "arguments": {"query": "..."}
}

System executes and returns result

Comparison: Frameworks

Choosing a Framework

Choose LangChain if:
- Need flexibility
- Building production system
- Python preferred

Choose AutoGPT if:
- Want fully autonomous behavior
- Don't need customization

Choose built-in APIs if:
- Simple use case
- No external dependencies