Atomic Agents Agent Configuration

The AtomicAgent is the core class for LLM interactions in the Atomic Agents framework. It handles structured input/output, conversation history, and system prompt management.

Basic Agent Setup

python

import instructor
import openai
from atomic_agents.agents.base_agent import AtomicAgent, AgentConfig
from atomic_agents.lib.components.system_prompt_generator import SystemPromptGenerator
from atomic_agents.lib.components.chat_history import ChatHistory

# 1. Create instructor-wrapped client
client = instructor.from_openai(openai.OpenAI())

# 2. Configure the agent
config = AgentConfig(
    client=client,
    model="gpt-4o-mini",
    history=ChatHistory(),
    system_prompt_generator=SystemPromptGenerator(
        background=["You are an expert assistant."],
        steps=["1. Analyze the input.", "2. Generate response."],
        output_instructions=["Be concise and helpful."],
    ),
)

# 3. Create the agent with type parameters
agent = AtomicAgent[InputSchema, OutputSchema](config=config)

AgentConfig Parameters

Parameter	Type	Required	Description
`client`	Instructor client	Yes	Instructor-wrapped LLM client
`model`	str	Yes	Model identifier (e.g., "gpt-4o-mini")
`history`	ChatHistory	No	Conversation history manager
`system_prompt_generator`	SystemPromptGenerator	No	System prompt configuration
`input_schema`	BaseIOSchema	No	Override input schema
`output_schema`	BaseIOSchema	No	Override output schema
`model_api_parameters`	dict	No	Additional API parameters

LLM Provider Setup

OpenAI

python

import instructor
import openai

client = instructor.from_openai(openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY")))
model = "gpt-4o-mini"  # or "gpt-4o", "gpt-4-turbo"

Anthropic

python

import instructor
import anthropic

client = instructor.from_anthropic(anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY")))
model = "claude-sonnet-4-20250514"

Groq

python

import instructor
from groq import Groq

client = instructor.from_groq(Groq(api_key=os.getenv("GROQ_API_KEY")), mode=instructor.Mode.JSON)
model = "llama-3.1-70b-versatile"

Ollama (Local)

python

import instructor
import openai

client = instructor.from_openai(
    openai.OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
)
model = "llama3.1"

Agent Execution Methods

Synchronous

python

output = agent.run(InputSchema(message="Hello"))

Asynchronous

python

output = await agent.run_async(InputSchema(message="Hello"))

Streaming (Sync)

python

for partial in agent.run_stream(InputSchema(message="Hello")):
    print(partial)  # Partial responses as they arrive

Streaming (Async)

python

async for partial in agent.run_async_stream(InputSchema(message="Hello")):
    print(partial)

ChatHistory Management

python

from atomic_agents.lib.components.chat_history import ChatHistory

# Create history
history = ChatHistory()

# Use with agent
config = AgentConfig(client=client, model=model, history=history)
agent = AtomicAgent[InputSchema, OutputSchema](config=config)

# Run multiple turns (history accumulates)
agent.run(InputSchema(message="Hello"))
agent.run(InputSchema(message="Tell me more"))

# Reset history
agent.reset_history()

# Save/load history
history_data = history.to_dict()
new_history = ChatHistory.from_dict(history_data)

Context Providers

Dynamic context injection into system prompts:

python

from atomic_agents.lib.components.system_prompt_generator import BaseDynamicContextProvider

class UserContextProvider(BaseDynamicContextProvider):
    def __init__(self):
        super().__init__(title="User Context")
        self.user_name = ""

    def get_info(self) -> str:
        return f"Current user: {self.user_name}"

# Register with agent
provider = UserContextProvider()
agent.register_context_provider("user", provider)

# Update context dynamically
provider.user_name = "Alice"
agent.run(input_data)  # System prompt now includes user context

Token Counting

python

# Get token usage
token_info = agent.get_context_token_count()

print(f"Total: {token_info.total}")
print(f"System prompt: {token_info.system_prompt}")
print(f"History: {token_info.history}")
print(f"Utilization: {token_info.utilization:.1%}")

Hooks for Monitoring

python

def on_response(response):
    print(f"Got response: {response}")

def on_error(error):
    print(f"Error: {error}")

agent.register_hook("completion:response", on_response)
agent.register_hook("completion:error", on_error)
agent.register_hook("parse:error", on_error)

Hook events:

•completion:kwargs - Before API call
•completion:response - After successful response
•completion:error - On API error
•parse:error - On parsing/validation error

Model API Parameters

Pass additional parameters to the LLM:

python

config = AgentConfig(
    client=client,
    model="gpt-4o",
    model_api_parameters={
        "max_tokens": 1000,
        "temperature": 0.7,
        "top_p": 0.9,
    },
)

Best Practices

•Always wrap with instructor - Required for structured outputs
•Use environment variables - Never hardcode API keys
•Initialize history when needed - Only if conversation state matters
•Type your agents - AtomicAgent[Input, Output] for type safety
•Use streaming for long responses - Better user experience
•Monitor with hooks - Track errors and performance
•Reset history appropriately - Prevent context overflow

References

See references/ for:

•multi-provider.md - Detailed provider configurations
•async-patterns.md - Async and streaming patterns

See examples/ for:

•basic-agent.py - Minimal agent setup
•streaming-agent.py - Streaming implementation