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Autonomous AI Agents — AutoGPT, BabyAGI & More

Build autonomous AI agents powered by Amazon Bedrock models through stdapi.ai. Deploy self-directed agents that can break down complex tasks, use tools, and work independently—using enterprise-grade models instead of OpenAI.

About Autonomous AI Agents

Autonomous agents are AI systems that can independently plan, execute, and refine tasks to achieve goals. Most agent frameworks are built for OpenAI's API, making them perfect candidates for stdapi.ai integration.

🔗 AutoGPT: Website | GitHub | Documentation

🔗 BabyAGI: GitHub | Paper

🔗 AgentGPT: Website | GitHub

🔗 SuperAGI: Website | GitHub

🔗 CrewAI: Website | GitHub | Docs

🔗 LangGraph: Docs | GitHub

Why Autonomous Agents + stdapi.ai?

Autonomous AI

Run AI agents with Amazon Bedrock models for reasoning, longer context, and full control over your AI infrastructure.

Key Benefits:

  • Reasoning - Claude Sonnet excels at planning and complex task breakdown
  • Long context - Amazon Nova Pro supports 300K tokens for extensive agent memory
  • Cost control - AWS pricing for compute-intensive agent operations
  • Privacy & security - Agent data and conversations stay in your AWS environment
  • No rate limits - Avoid OpenAI's strict limits during intensive agent runs
  • Model selection - Choose optimal models for different agent tasks

Work in Progress

This integration guide is actively being developed and refined. While the configuration examples are based on documented APIs and best practices, they are pending practical validation. Complete end-to-end deployment examples will be added once testing is finalized.

🤖 AutoGPT Integration

About AutoGPT

AutoGPT is one of the most popular autonomous agent projects, capable of breaking down goals into tasks and executing them independently.

Installation:

git clone https://github.com/Significant-Gravitas/AutoGPT.git
cd AutoGPT
pip install -r requirements.txt

Configuration

.env Configuration

Create or edit .env file in the AutoGPT directory:

################################################################################
### LLM PROVIDER
################################################################################

## OPENAI (compatible with stdapi.ai)
OPENAI_API_KEY=your_stdapi_key_here
OPENAI_API_BASE_URL=https://YOUR_SERVER_URL/v1

## Model selection
SMART_LLM=anthropic.claude-sonnet-4-5-20250929-v1:0
FAST_LLM=amazon.nova-lite-v1:0

## Embeddings
EMBEDDING_MODEL=amazon.titan-embed-text-v2:0

################################################################################
### MEMORY
################################################################################

MEMORY_BACKEND=json_file
# MEMORY_BACKEND=pinecone  # For production

################################################################################
### AGENT SETTINGS
################################################################################

AI_SETTINGS_FILE=ai_settings.yaml
AGENT_NAME=AutoGPT-Bedrock

Usage

Running AutoGPT

# Start AutoGPT
python -m autogpt

# With custom goal
python -m autogpt --goal "Research and summarize the latest AI developments"

# Continue previous run
python -m autogpt --continue

Model Selection Strategy:

  • SMART_LLM — Used for complex reasoning and planning → Claude Sonnet
  • FAST_LLM — Used for simple tasks and confirmations → Nova Lite or Micro

👶 BabyAGI Integration

About BabyAGI

BabyAGI is a minimal autonomous agent that creates, prioritizes, and executes tasks based on objectives.

Installation:

git clone https://github.com/yoheinakajima/babyagi.git
cd babyagi
pip install -r requirements.txt

Configuration

Python Configuration

Edit babyagi.py or use environment variables:

import os
from openai import OpenAI

# Configure OpenAI client for stdapi.ai
client = OpenAI(
    api_key=os.environ.get("STDAPI_KEY"),
    base_url="https://YOUR_SERVER_URL/v1"
)

# Agent configuration
OBJECTIVE = os.environ.get("OBJECTIVE", "Research sustainable energy solutions")
INITIAL_TASK = os.environ.get("INITIAL_TASK", "Develop a task list")

# Model selection
LLM_MODEL = "anthropic.claude-sonnet-4-5-20250929-v1:0"
EMBEDDING_MODEL = "amazon.titan-embed-text-v2:0"

# Task execution function
def task_execution_agent(objective, task):
    response = client.chat.completions.create(
        model=LLM_MODEL,
        messages=[
            {"role": "system", "content": f"You are an AI agent helping with: {objective}"},
            {"role": "user", "content": task}
        ],
        max_tokens=2000
    )
    return response.choices[0].message.content

Usage

Running BabyAGI

# Set environment variables
export STDAPI_KEY=your_key_here
export OBJECTIVE="Create a marketing plan for a new product"

# Run agent
python babyagi.py

🎭 CrewAI Integration

About CrewAI

CrewAI enables creating teams of AI agents that work together on complex tasks, each with specific roles and goals.

Installation:

pip install crewai crewai-tools openai

Configuration

Python - Multi-Agent Crew

import os
from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI

# Configure LLM with stdapi.ai
llm = ChatOpenAI(
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",
    openai_api_key=os.environ["STDAPI_KEY"],
    openai_api_base="https://YOUR_SERVER_URL/v1"
)

# Define agents
researcher = Agent(
    role="Senior Research Analyst",
    goal="Discover cutting-edge developments in AI",
    backstory="You're an expert at finding and analyzing information",
    verbose=True,
    llm=llm
)

writer = Agent(
    role="Tech Content Writer",
    goal="Create engaging content about technology",
    backstory="You're a skilled writer who makes complex topics accessible",
    verbose=True,
    llm=llm
)

# Define tasks
research_task = Task(
    description="Research the latest trends in AI agents",
    agent=researcher,
    expected_output="A detailed report on AI agent trends"
)

writing_task = Task(
    description="Write a blog post based on the research",
    agent=writer,
    expected_output="A 500-word engaging blog post"
)

# Create crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    verbose=True
)

# Execute
result = crew.kickoff()
print(result)

🕸️ LangGraph Integration

About LangGraph

LangGraph is a framework for building stateful, multi-agent applications with complex control flow.

Installation:

pip install langgraph langchain-openai

Configuration

Python - Agent with Memory

from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
import operator
import os

# Configure LLM
llm = ChatOpenAI(
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",
    openai_api_key=os.environ["STDAPI_KEY"],
    openai_api_base="https://YOUR_SERVER_URL/v1"
)

# Define state
class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    next_action: str

# Define nodes
def call_model(state):
    messages = state["messages"]
    response = llm.invoke(messages)
    return {"messages": [response]}

def decide_next_action(state):
    # Logic to determine next step
    last_message = state["messages"][-1].content
    if "FINISH" in last_message:
        return END
    return "call_model"

# Build graph
workflow = StateGraph(AgentState)
workflow.add_node("call_model", call_model)
workflow.add_edge("call_model", "decide_next_action")
workflow.set_entry_point("call_model")

app = workflow.compile()

# Run agent
result = app.invoke({
    "messages": [{"role": "user", "content": "Create a research plan"}]
})

🎯 Agent Design Patterns

Task Decomposition

Break complex goals into manageable sub-tasks.

Task Planning Pattern

def plan_tasks(objective):
    response = client.chat.completions.create(
        model="anthropic.claude-sonnet-4-5-20250929-v1:0",
        messages=[{
            "role": "user",
            "content": f"""Break down this objective into specific tasks:
            Objective: {objective}

            Provide a numbered list of 3-5 concrete tasks."""
        }]
    )
    return response.choices[0].message.content

Tool Usage

Enable agents to use external tools and APIs.

Agent with Tools

from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain.tools import Tool
from langchain_openai import ChatOpenAI

# Configure LLM
llm = ChatOpenAI(
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",
    openai_api_key=os.environ["STDAPI_KEY"],
    openai_api_base="https://YOUR_SERVER_URL/v1"
)

# Define tools
def web_search(query):
    """Search the web for information"""
    # Your search implementation
    return f"Search results for: {query}"

def calculator(expression):
    """Perform calculations"""
    try:
        return str(eval(expression))
    except:
        return "Invalid expression"

tools = [
    Tool(name="WebSearch", func=web_search, description="Search the web"),
    Tool(name="Calculator", func=calculator, description="Do math")
]

# Create agent
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run with tools
result = agent_executor.invoke({
    "input": "What is 15% of 1250? Then search for that amount in USD"
})

Memory Management

Implement different memory types for agent persistence.

Memory Types

Short-term Memory (Conversation): 

conversation_history = []

def add_to_memory(role, content):
    conversation_history.append({"role": role, "content": content})
    # Keep last 20 messages
    return conversation_history[-20:]

Long-term Memory (Vector Store): 

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="amazon.titan-embed-text-v2:0",
    openai_api_key=os.environ["STDAPI_KEY"],
    openai_api_base="https://YOUR_SERVER_URL/v1"
)

memory_store = Chroma(embedding_function=embeddings)

def store_memory(content):
    memory_store.add_texts([content])

def recall_memory(query, k=3):
    return memory_store.similarity_search(query, k=k)

📊 Model Selection by Agent Task

Choose models based on agent task requirements. These are examples—all Bedrock models are available.

Agent Task Example Model Why
Planning & Strategy anthropic.claude-sonnet-4-5-20250929-v1:0 Superior reasoning and task decomposition
Task Execution amazon.nova-lite-v1:0 Fast, efficient for routine tasks
Long Context amazon.nova-pro-v1:0 300K token context for extensive memory
Code Generation anthropic.claude-sonnet-4-5-20250929-v1:0 Best for writing and debugging code
Quick Decisions amazon.nova-micro-v1:0 Fast responses for simple choices
Embeddings amazon.titan-embed-text-v2:0 High-quality semantic memory

💡 Best Practices

Agent Design

Clear Objectives: Define specific, measurable goals for agents

Task Constraints: Set time limits and iteration caps to prevent runaway execution

Human-in-the-Loop: Include checkpoints for human review and approval

Graceful Failure: Design agents to handle failures and ask for help

Performance Optimization

Model Selection: Use fast models for simple tasks, premium for complex reasoning

Parallel Execution: Run independent tasks concurrently when possible

Caching: Cache tool results and API responses to avoid redundant calls

Incremental Progress: Save state frequently to resume from failures

Safety & Control

Sandbox Environment: Test agents in isolated environments first

Rate Limiting: Prevent excessive API usage with throttling

Action Approval: Require confirmation for destructive actions

Logging: Comprehensive logs of all agent actions for debugging

Budget Limits: Set token usage caps to control costs

Production Deployment

Error Handling: Robust error recovery and retry logic

State Persistence: Store agent state in databases for resilience

Monitoring: Track agent performance, success rates, and costs

Version Control: Track agent configurations and prompt changes

🚀 Advanced Agent Patterns

Multi-Agent Collaboration

Have multiple specialized agents work together.

Agent Team Pattern

# Research agent
researcher = Agent(
    role="Researcher",
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",
    task="Gather information"
)

# Analyst agent
analyst = Agent(
    role="Analyst",
    model="amazon.nova-pro-v1:0",
    task="Analyze data"
)

# Writer agent
writer = Agent(
    role="Writer",
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",
    task="Create report"
)

# Coordinator
def coordinate_agents(objective):
    research = researcher.execute(objective)
    analysis = analyst.execute(research)
    report = writer.execute(analysis)
    return report

Feedback Loops

Implement self-improvement through iteration.

Iterative Refinement

def iterative_agent(task, max_iterations=3):
    result = initial_attempt(task)

    for i in range(max_iterations):
        critique = critique_result(result)

        if "ACCEPTABLE" in critique:
            break

        result = refine_result(result, critique)

    return result

🚀 Next Steps & Resources

Getting Started

  1. Choose Framework: Start with BabyAGI or CrewAI for simpler implementations
  2. Define Objectives: Start with small, well-defined goals
  3. Configure stdapi.ai: Set up credentials and model preferences
  4. Test Safely: Run in sandbox environment with supervision
  5. Monitor & Iterate: Track performance and refine agent behavior

Learn More

Additional Resources

Community & Support

Need Help?

  • 💬 Join framework-specific Discord communities
  • 📖 Review Amazon Bedrock documentation for model capabilities
  • 🐛 Report issues on the GitHub repository
  • 💡 Share agent patterns with the community

⚠️ Important Considerations

Model Availability

Regional Differences: Not all Amazon Bedrock models are available in every AWS region. Verify model availability before deploying agents.

Check availability: See the API Overview for supported models by region.

Safety & Ethics

Agent Supervision: Always supervise autonomous agents, especially in production

Action Limits: Restrict agents from performing destructive or irreversible actions

Data Privacy: Ensure agents don't process or expose sensitive information

Compliance: Follow applicable regulations for automated decision-making

Cost Management

Token Usage: Agents can consume many tokens—monitor usage closely

Iteration Limits: Set maximum iterations to prevent runaway costs

Model Selection: Use efficient models where possible to reduce costs

Caching: Cache intermediate results to avoid redundant processing

Performance Expectations

Response Times: Agents are slower than simple API calls due to iteration

Reliability: Agents may fail or produce unexpected results—plan accordingly

Determinism: Agent behavior can vary between runs—test thoroughly

Scaling: Resource usage scales with task complexity and iteration count