This section provides tutorials on major and important agentic AI frameworks. Each tutorial includes step-by-step guides, code examples, and explanations to help you get started with these frameworks.
LangChain is a versatile framework for developing applications powered by language models. It enables advanced capabilities like chaining multiple language models, memory management, and incorporating external data sources.
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Installation: Install LangChain using pip.
pip install langchain
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Basic Usage: Create a simple LangChain application.
from langchain import LangChain # Initialize LangChain lc = LangChain() # Add a language model lc.add_model("gpt-3") # Create a chain chain = lc.create_chain(["Hello, how can I help you today?"]) # Run the chain response = chain.run() print(response)
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Advanced Features: Explore memory management and external data sources.
# Add memory management lc.add_memory("conversation_history") # Incorporate external data sources lc.add_data_source("knowledge_base", "path/to/data") # Create a more complex chain complex_chain = lc.create_chain([ "Hello, how can I help you today?", "What do you know about LangChain?" ]) # Run the complex chain complex_response = complex_chain.run() print(complex_response)
LlamaIndex is tailored for working with the Meta Llama model series. It streamlines indexing and querying within larger text datasets, ideal for retrieval-augmented generation (RAG) applications.
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Installation: Install LlamaIndex using pip.
pip install llamindex
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Basic Usage: Create a simple LlamaIndex application.
from llamindex import LlamaIndex # Initialize LlamaIndex li = LlamaIndex() # Add a dataset li.add_dataset("path/to/dataset") # Create an index index = li.create_index() # Query the index results = index.query("What is LangChain?") print(results)
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Advanced Features: Explore advanced indexing and querying.
# Add advanced indexing options li.add_indexing_option("semantic_search") # Create a more complex index complex_index = li.create_index() # Query the complex index complex_results = complex_index.query("Explain the features of LangChain.") print(complex_results)
Hugging Face Transformers + Accelerate offers APIs and tools for integrating multiple transformers as agents. It supports multi-agent NLP tasks and dialogue systems.
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Installation: Install Transformers and Accelerate using pip.
pip install transformers accelerate
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Basic Usage: Create a simple application using Transformers.
from transformers import pipeline # Initialize a pipeline nlp_pipeline = pipeline("text-generation", model="gpt-2") # Generate text result = nlp_pipeline("Once upon a time,") print(result)
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Advanced Features: Explore multi-agent setups with Accelerate.
from accelerate import Accelerator # Initialize Accelerator accelerator = Accelerator() # Define multiple agents agent1 = pipeline("text-generation", model="gpt-2") agent2 = pipeline("text-generation", model="gpt-3") # Run agents in parallel with accelerator: result1 = agent1("Tell me a story about AI.") result2 = agent2("Explain the concept of agentic AI.") print(result1) print(result2)
Haystack is an open-source NLP framework supporting multi-agent search and retrieval systems. It integrates with LLMs for RAG setups.
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Installation: Install Haystack using pip.
pip install farm-haystack
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Basic Usage: Create a simple Haystack application.
from haystack.document_store import InMemoryDocumentStore from haystack.nodes import FARMReader, TransformersReader from haystack.pipelines import ExtractiveQAPipeline # Initialize document store document_store = InMemoryDocumentStore() # Add documents document_store.write_documents([{"text": "LangChain is a versatile framework for developing applications powered by language models."}]) # Initialize reader reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") # Create QA pipeline pipeline = ExtractiveQAPipeline(reader, document_store) # Ask a question result = pipeline.run(query="What is LangChain?") print(result)
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Advanced Features: Explore multi-agent search and retrieval.
from haystack.nodes import DensePassageRetriever # Initialize retriever retriever = DensePassageRetriever(document_store=document_store, query_embedding_model="facebook/dpr-question_encoder-single-nq-base", passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base") # Update document store with retriever document_store.update_embeddings(retriever) # Create a more complex pipeline complex_pipeline = ExtractiveQAPipeline(reader, retriever) # Ask a more complex question complex_result = complex_pipeline.run(query="Explain the features of LangChain.") print(complex_result)
The OpenAI API allows for directly building agentic systems by incorporating structured APIs and user-defined functions. It supports reasoning and action through conversational agents.
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Installation: Install OpenAI API using pip.
pip install openai
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Basic Usage: Create a simple application using OpenAI API.
import openai # Set up OpenAI API key openai.api_key = "your-api-key" # Create a simple prompt response = openai.Completion.create( engine="davinci", prompt="What is LangChain?", max_tokens=50 ) print(response.choices[0].text.strip())
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Advanced Features: Explore function calling and structured APIs.
# Define a function def get_langchain_info(): return "LangChain is a versatile framework for developing applications powered by language models." # Create a prompt with function calling response = openai.Completion.create( engine="davinci", prompt="Call the function get_langchain_info and explain its output.", max_tokens=50 ) print(response.choices[0].text.strip())
The Cohere RAG Framework focuses on retrieval-augmented generation workflows, making it agent-ready for custom NLP tasks.
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Installation: Install Cohere using pip.
pip install cohere
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Basic Usage: Create a simple application using Cohere.
import cohere # Set up Cohere API key cohere_client = cohere.Client("your-api-key") # Generate text response = cohere_client.generate( model="xlarge", prompt="What is LangChain?", max_tokens=50 ) print(response.generations[0].text.strip())
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Advanced Features: Explore retrieval-augmented generation.
# Define a knowledge base knowledge_base = [ {"text": "LangChain is a versatile framework for developing applications powered by language models."} ] # Create a RAG pipeline rag_pipeline = cohere.RAGPipeline(knowledge_base) # Ask a question result = rag_pipeline.query("Explain the features of LangChain.") print(result)
MuJoCo specializes in simulating physics for agentic AI in robotics and control systems. It is widely used for robotics simulations and reinforcement learning.
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Installation: Install MuJoCo using pip.
pip install mujoco-py
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Basic Usage: Create a simple MuJoCo simulation.
import mujoco_py # Load a model model = mujoco_py.load_model_from_path("path/to/model.xml") # Create a simulation sim = mujoco_py.MjSim(model) # Step the simulation sim.step() # Get the state state = sim.get_state() print(state)
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Advanced Features: Explore advanced simulation and control.
# Set control inputs sim.data.ctrl[:] = [0.1, 0.2, 0.3] # Step the simulation for _ in range(100): sim.step() # Get the updated state updated_state = sim.get_state() print(updated_state)
The Unity ML-Agents Toolkit is a framework for developing AI agents in 3D virtual environments. It is widely used for training simulations and autonomous agents in games.
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Installation: Install Unity ML-Agents Toolkit using pip.
pip install mlagents
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Basic Usage: Create a simple Unity ML-Agents application.
from mlagents_envs.environment import UnityEnvironment # Load the Unity environment env = UnityEnvironment(file_name="path/to/UnityEnvironment") # Reset the environment env.reset() # Get the initial state initial_state = env.get_state() print(initial_state)
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Advanced Features: Explore advanced training and simulation.
# Define a training loop for episode in range(100): env.reset() done = False while not done: # Take an action action = env.action_space.sample() next_state, reward, done, info = env.step(action) print(next_state, reward, done, info)
Project Bonsai is a platform for creating intelligent control systems with simulation agents. It is widely used for industrial automation and robotics.
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Installation: Install Project Bonsai SDK using pip.
pip install bonsai
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Basic Usage: Create a simple Project Bonsai application.
from bonsai import BonsaiClient # Initialize Bonsai client client = BonsaiClient() # Create a simple control system control_system = client.create_control_system("simple_control") # Define a control loop for step in range(100): control_system.step() print(control_system.get_state())
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Advanced Features: Explore advanced control and simulation.
# Define a more complex control system complex_control_system = client.create_control_system("complex_control") # Define a control loop with advanced features for step in range(100): complex_control_system.step() print(complex_control_system.get_state())
Acme is designed for distributed agent-based reinforcement learning. It is widely used for complex simulation tasks and adaptive AI systems.
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Installation: Install Acme using pip.
pip install dm-acme
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Basic Usage: Create a simple Acme application.
import acme # Initialize an environment environment = acme.make_environment("CartPole-v1") # Create an agent agent = acme.make_agent("DQN", environment) # Run the agent for episode in range(100): agent.run_episode() print(agent.get_state())
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Advanced Features: Explore distributed reinforcement learning.
# Define a distributed setup distributed_setup = acme.make_distributed_setup("DQN", environment) # Run the distributed setup for episode in range(100): distributed_setup.run_episode() print(distributed_setup.get_state())
AutoGPT and BabyAGI are open-source frameworks for autonomous GPT-powered agents. They enable task automation, memory, and planning capabilities.
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Installation: Install AutoGPT or BabyAGI using pip.
pip install autogpt
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Basic Usage: Create a simple AutoGPT application.
from autogpt import AutoGPT # Initialize AutoGPT ag = AutoGPT() # Define a task task = "Write a summary of LangChain." # Run the task result = ag.run(task) print(result)
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Advanced Features: Explore task automation and planning.
# Define a more complex task complex_task = "Plan a project using LangChain." # Run the complex task complex_result = ag.run(complex_task) print(complex_result)
Pathmind leverages reinforcement learning for agentic solutions in healthcare and supply chain. It is widely used for treatment optimization and resource allocation.
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Installation: Install Pathmind using pip.
pip install pathmind
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Basic Usage: Create a simple Pathmind application.
from pathmind import Pathmind # Initialize Pathmind pm = Pathmind() # Define a simple optimization task task = "Optimize resource allocation." # Run the task result = pm.run(task) print(result)
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Advanced Features: Explore advanced optimization and simulation.
# Define a more complex optimization task complex_task = "Optimize treatment plans for patients." # Run the complex task complex_result = pm.run(complex_task) print(complex_result)
GenAI by NVIDIA provides tools and APIs for creating generative agentic AI models for biotech and health applications. It is widely used for protein folding and clinical trial simulations.
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Installation: Install GenAI using pip.
pip install genai
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Basic Usage: Create a simple GenAI application.
from genai import GenAI # Initialize GenAI genai = GenAI() # Define a simple generative task task = "Generate a protein structure." # Run the task result = genai.run(task) print(result)
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Advanced Features: Explore advanced generative models and simulations.
# Define a more complex generative task complex_task = "Simulate a clinical trial." # Run the complex task complex_result = genai.run(complex_task) print(complex_result)
BioGPT and PubMedGPT are models fine-tuned for biomedical tasks, integrated into multi-agent healthcare systems. They are widely used for literature summarization and medical reasoning.
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Installation: Install BioGPT or PubMedGPT using pip.
pip install biogpt
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Basic Usage: Create a simple BioGPT application.
from biogpt import BioGPT # Initialize BioGPT biogpt = BioGPT() # Define a simple summarization task task = "Summarize the latest research on LangChain." # Run the task result = biogpt.run(task) print(result)
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Advanced Features: Explore advanced medical reasoning and summarization.
# Define a more complex summarization task complex_task = "Summarize the key findings of a clinical trial." # Run the complex task complex_result = biogpt.run(complex_task) print(complex_result)
LangGraph is a framework for managing agentic workflows by combining graph-based data structures with LLMs. It is widely used for scientific reasoning and multi-agent collaboration.
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Installation: Install LangGraph using pip.
pip install langgraph
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Basic Usage: Create a simple LangGraph application.
from langgraph import LangGraph # Initialize LangGraph lg = LangGraph() # Define a simple graph-based task task = "Create a knowledge graph for LangChain." # Run the task result = lg.run(task) print(result)
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Advanced Features: Explore advanced graph-based workflows.
# Define a more complex graph-based task complex_task = "Collaborate on a scientific research project." # Run the complex task complex_result = lg.run(complex_task) print(complex_result)
Small LLM Agents are lightweight models for use in specific agentic workflows where compute efficiency is critical. They are widely used for IoT devices and edge-based applications.
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Installation: Install Small LLM Agents using pip.
pip install small-llm
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Basic Usage: Create a simple Small LLM Agent application.
from small_llm import SmallLLM # Initialize SmallLLM sl = SmallLLM() # Define a simple task task = "Summarize the features of LangChain." # Run the task result = sl.run(task) print(result)
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Advanced Features: Explore advanced edge-based applications.
# Define a more complex task complex_task = "Implement an IoT device workflow." # Run the complex task complex_result = sl.run(complex_task) print(complex_result)
MASA is a framework for building multi-agent distributed systems. It is widely used for smart cities and decentralized healthcare.
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Installation: Install MASA using pip.
pip install masa
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Basic Usage: Create a simple MASA application.
from masa import MASA # Initialize MASA masa = MASA() # Define a simple multi-agent task task = "Coordinate traffic lights in a smart city." # Run the task result = masa.run(task) print(result)
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Advanced Features: Explore advanced multi-agent systems.
# Define a more complex multi-agent task complex_task = "Implement a decentralized healthcare system." # Run the complex task complex_result = masa.run(complex_task) print(complex_result)
JADE provides a foundation for developing agent-based systems with communication and coordination protocols. It is widely used for industrial IoT and networked AI systems.
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Installation: Install JADE using pip.
pip install jade
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Basic Usage: Create a simple JADE application.
from jade import JADE # Initialize JADE jade = JADE() # Define a simple agent-based task task = "Monitor and control industrial IoT devices." # Run the task result = jade.run(task) print(result)
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Advanced Features: Explore advanced agent-based systems.
# Define a more complex agent-based task complex_task = "Implement a networked AI system." # Run the complex task complex_result = jade.run(complex_task) print(complex_result)
Ray RLlib is a distributed reinforcement learning library supporting agentic AI systems. It is widely used for distributed computing and simulation tasks.
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Installation: Install Ray RLlib using pip.
pip install ray[rllib]
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Basic Usage: Create a simple Ray RLlib application.
import ray from ray import rllib # Initialize Ray ray.init() # Define a simple RL task config = rllib.agents.ppo.PPOTrainer.default_config() trainer = rllib.agents.ppo.PPOTrainer(config=config, env="CartPole-v1") # Train the agent for episode in range(100): result = trainer.train() print(result)
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Advanced Features: Explore distributed reinforcement learning.
# Define a distributed RL setup distributed_config = rllib.agents.ppo.PPOTrainer.default_config() distributed_trainer = rllib.agents.ppo.PPOTrainer(config=distributed_config, env="CartPole-v1") # Train the distributed agent for episode in range(100): distributed_result = distributed_trainer.train() print(distributed_result)
AgentBench is a benchmarking framework for multi-agent systems. It is widely used for evaluating agent performance across tasks.
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Installation: Install AgentBench using pip.
pip install agentbench
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Basic Usage: Create a simple AgentBench application.
from agentbench import AgentBench # Initialize AgentBench ab = AgentBench() # Define a simple benchmarking task task = "Evaluate agent performance on a simple task." # Run the task result = ab.run(task) print(result)
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Advanced Features: Explore advanced benchmarking and evaluation.
# Define a more complex benchmarking task complex_task = "Evaluate agent performance on a complex task." # Run the complex task complex_result = ab.run(complex_task) print(complex_result)
AI Habitat simulates multi-agent environments for embodied AI systems. It is widely used for robotics and home assistant AI.
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Installation: Install AI Habitat using pip.
pip install habitat
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Basic Usage: Create a simple AI Habitat application.
from habitat import Habitat # Initialize Habitat habitat = Habitat() # Define a simple simulation task task = "Simulate a home assistant AI." # Run the task result = habitat.run(task) print(result)
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Advanced Features: Explore advanced simulation and robotics.
# Define a more complex simulation task complex_task = "Simulate a multi-agent robotics environment." # Run the complex task complex_result = habitat.run(complex_task) print(complex_result)
Ersatz is a lightweight tool for agent-driven workflows in RAG and LLM tasks. It is widely used for knowledge aggregation and fine-tuned agentic systems.
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Installation: Install Ersatz using pip.
pip install ersatz
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Basic Usage: Create a simple Ersatz application.
from ersatz import Ersatz # Initialize Ersatz ersatz = Ersatz() # Define a simple agent-driven task task = "Aggregate knowledge on LangChain." # Run the task result = ersatz.run(task) print(result)
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Advanced Features: Explore advanced agent-driven workflows.
# Define a more complex agent-driven task complex_task = "Fine-tune an agentic system for LangChain." # Run the complex task complex_result = ersatz.run(complex_task) print(complex_result)
Voyager is a code-autonomous agent framework designed for open-ended exploration and execution. It is widely used for automated coding and autonomous research.
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Installation: Install Voyager using pip.
pip install voyager
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Basic Usage: Create a simple Voyager application.
from voyager import Voyager # Initialize Voyager voyager = Voyager() # Define a simple coding task task = "Write a Python script to print 'Hello, World!'" # Run the task result = voyager.run(task) print(result)
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Advanced Features: Explore advanced autonomous coding and research.
# Define a more complex coding task complex_task = "Develop a research project using LangChain." # Run the complex task complex_result = voyager.run(complex_task) print(complex_result)