agents-towards-production

name	agents-towards-production
description	Build production-ready GenAI agents with stateful workflows, vector memory, deployment, and orchestration using LangGraph and LangChain
triggers	["how do I build a production-ready AI agent","show me how to deploy an agent with LangGraph","how to add memory to my AI agent","create a multi-agent system with coordination","how do I add web search to my agent","deploy an agent with Docker and FastAPI","implement agent observability and monitoring","build a RAG agent for production"]

Agents Towards Production

Skill by ara.so — AI Agent Skills collection.

This skill enables you to build production-grade GenAI agents from prototype to enterprise deployment. The repository provides 28+ end-to-end tutorials covering stateful workflows, vector memory, real-time web search, Docker deployment, FastAPI endpoints, security guardrails, GPU scaling, browser automation, multi-agent coordination, observability, evaluation, and UI development.

What It Does

Agents Towards Production is a comprehensive tutorial collection for building real-world AI agents that scale. It covers:

• Agent Frameworks: LangGraph, LangChain for stateful workflows and orchestration
• Memory Systems: Vector storage with Redis, Mem0 for persistent agent memory
• RAG Integration: Retrieval-augmented generation with Contextual AI
• Web Access: Real-time search APIs (Tavily), web scraping (Bright Data)
• Deployment: Docker, FastAPI, GPU scaling, production infrastructure
• Security: Guardrails, OAuth2, human-in-the-loop controls (Arcade)
• Multi-Agent: Coordination, orchestration, distributed workflows
• Observability: Monitoring, evaluation, debugging production agents
• UI Development: Browser automation, user interfaces

Installation

Clone the repository:

git clone https://github.com/NirDiamant/agents-towards-production.git
cd agents-towards-production

Install dependencies (each tutorial has its own requirements):

# For LangGraph tutorials
pip install langchain langgraph langchain-openai langchain-community

# For memory tutorials
pip install redis langchain-redis mem0ai

# For RAG tutorials
pip install contextual-client chromadb sentence-transformers

# For web access
pip install tavily-python brightdata-sdk

# For deployment
pip install fastapi uvicorn docker pydantic

# For observability
pip install langsmith weave opentelemetry

Key Tutorials and Usage Patterns

1. Basic LangGraph Agent

Create a stateful agent with LangGraph:

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

# Define state
class AgentState(TypedDict):
    messages: List[dict]
    current_step: str

# Initialize LLM
llm = ChatOpenAI(
    model="gpt-4",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Define agent nodes
def process_input(state: AgentState):
    """Process user input"""
    messages = state["messages"]
    response = llm.invoke(messages)
    return {
        "messages": messages + [{"role": "assistant", "content": response.content}],
        "current_step": "completed"
    }

# Build graph
workflow = StateGraph(AgentState)
workflow.add_node("process", process_input)
workflow.set_entry_point("process")
workflow.add_edge("process", END)

# Compile and run
app = workflow.compile()

result = app.invoke({
    "messages": [{"role": "user", "content": "Hello, how can you help me?"}],
    "current_step": "start"
})
print(result["messages"][-][])

2. Agent with Vector Memory (Redis)

Add persistent memory to your agent:

from langchain_redis import RedisVectorStore, RedisConfig
from langchain_openai import OpenAIEmbeddings
from langchain.schema import Document
import os

# Configure Redis
redis_config = RedisConfig(
    index_name="agent_memory",
    redis_url=os.getenv("REDIS_URL", "redis://localhost:6379"),
    distance_metric="COSINE"
)

# Initialize vector store
embeddings = OpenAIEmbeddings(api_key=os.getenv("OPENAI_API_KEY"))
vector_store = RedisVectorStore(
    config=redis_config,
    embedding=embeddings
)

# Store conversation memory
def store_memory(user_id: str, conversation: str, metadata: dict = None):
    """Store conversation in vector memory"""
    doc = Document(
        page_content=conversation,
        metadata={"user_id": user_id, **(metadata or {})}
    )
    vector_store.add_documents([doc])

# Retrieve relevant memories
def retrieve_memory(user_id: str, query: str, k: int = 3):
    """Retrieve relevant past conversations"""
    results = vector_store.similarity_search(
        query,
        k=k,
        filter={"user_id": user_id}
    )
    return [doc.page_content for doc in results]

# Usage
store_memory(
    user_id="user123",
    conversation="User asked about Python tutorials. Agent provided resources.",
    metadata={: , : }
)

memories = retrieve_memory(, )
(, memories)

3. RAG Agent with Contextual AI

Build a retrieval-augmented generation agent:

from contextual_client import ContextualClient
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
import os

# Initialize Contextual client
contextual = ContextualClient(api_key=os.getenv("CONTEXTUAL_API_KEY"))

# Create knowledge base
kb = contextual.create_knowledge_base(
    name="product_docs",
    description="Product documentation and FAQs"
)

# Index documents
documents = [
    {"content": "Our API supports REST and GraphQL endpoints.", "metadata": {"type": "api"}},
    {"content": "Authentication uses OAuth2 with JWT tokens.", "metadata": {"type": "auth"}},
]

contextual.index_documents(knowledge_base_id=kb.id, documents=documents)

# RAG query function
def rag_query(question: str):
    """Query with retrieval-augmented generation"""
    # Retrieve relevant context
    results = contextual.search(
        knowledge_base_id=kb.id,
        query=question,
        top_k=3
    )
    
    context = "\n".join([r.content for r in results])
    
    # Generate response with context
    prompt = ChatPromptTemplate.from_template("""
    Answer the question based on the following context:
    
    Context: {context}
    
    Question: {question}
    
    Answer:
    """)
    
    llm = ChatOpenAI(model="gpt-4", api_key=os.getenv("OPENAI_API_KEY"))
    chain = prompt | llm
    
    response = chain.invoke({: context, : question})
     response.content


answer = rag_query()
(answer)

4. Web Search Agent with Tavily

Add real-time web search capabilities:

from tavily import TavilyClient
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
import os

# Initialize Tavily
tavily = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))

def web_search_agent(query: str):
    """Agent with real-time web search"""
    # Search the web
    search_results = tavily.search(
        query=query,
        search_depth="advanced",
        max_results=5,
        include_domains=None,
        exclude_domains=None
    )
    
    # Format results
    context = "\n\n".join([
        f"Source: {r['url']}\n{r['content']}"
        for r in search_results.get('results', [])
    ])
    
    # Generate response
    prompt = ChatPromptTemplate.from_template("""
    Based on the following web search results, answer the user's question:
    
    Search Results:
    {context}
    
    Question: {question}
    
    Provide a comprehensive answer with sources:
    """)
    
    llm = ChatOpenAI(model="gpt-4", api_key=os.getenv("OPENAI_API_KEY"))
    chain = prompt | llm
    
    response = chain.invoke({"context": context, "question": query})
    return {
        "answer": response.content,
        "sources": [r['url'] for r in search_results.get(, [])]
    }


result = web_search_agent()
(result[])
(, result[])

5. Multi-Agent Coordination

Coordinate multiple specialized agents:

from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from typing import TypedDict, List, Literal
import os

class MultiAgentState(TypedDict):
    messages: List[dict]
    task: str
    current_agent: str
    results: dict

# Define specialized agents
llm = ChatOpenAI(model="gpt-4", api_key=os.getenv("OPENAI_API_KEY"))

def research_agent(state: MultiAgentState):
    """Research specialist"""
    prompt = f"Research the following: {state['task']}"
    response = llm.invoke([{"role": "user", "content": prompt}])
    state["results"]["research"] = response.content
    return state

def analysis_agent(state: MultiAgentState):
    """Analysis specialist"""
    research = state["results"].get("research", "")
    prompt = f"Analyze this research:\n{research}"
    response = llm.invoke([{"role": "user", "content": prompt}])
    state["results"]["analysis"] = response.content
     state

 ():
    
    analysis = state[].get(, )
    prompt = 
    response = llm.invoke([{: , : prompt}])
    state[][] = response.content
     state


 () -> [, , , ]:
    
        state[]:
         
        state[]:
         
        state[]:
         
     


workflow = StateGraph(MultiAgentState)
workflow.add_node(, research_agent)
workflow.add_node(, analysis_agent)
workflow.add_node(, synthesis_agent)

workflow.set_entry_point()
workflow.add_edge(, )
workflow.add_edge(, )
workflow.add_edge(, END)

app = workflow.()


result = app.invoke({
    : [],
    : ,
    : ,
    : {}
})

(, result[][])

6. FastAPI Deployment

Deploy your agent as a REST API:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing import List, Dict
import os
import uvicorn

app = FastAPI(title="Production Agent API")

# Request/Response models
class ChatRequest(BaseModel):
    message: str
    user_id: str
    session_id: str

class ChatResponse(BaseModel):
    response: str
    session_id: str
    metadata: Dict

# Agent state
class AgentState(BaseModel):
    messages: List[Dict]
    user_id: str
    session_id: str

# Initialize agent
llm = ChatOpenAI(model="gpt-4", api_key=os.getenv("OPENAI_API_KEY"))

def create_agent():
    """Create agent workflow"""
    def process(state: dict):
        messages = state["messages"]
        response = llm.invoke(messages)
        state[].append({
            : ,
            : response.content
        })
         state
    
    workflow = StateGraph()
    workflow.add_node(, process)
    workflow.set_entry_point()
    workflow.add_edge(, END)
     workflow.()

agent = create_agent()



  ():
    
    :
        result = agent.invoke({
            : [{: , : request.message}],
            : request.user_id,
            : request.session_id
        })
        
         ChatResponse(
            response=result[][-][],
            session_id=request.session_id,
            metadata={: request.user_id}
        )
     Exception  e:
         HTTPException(status_code=, detail=(e))


  ():
    
     {: }

 __name__ == :
    uvicorn.run(app, host=, port=)

7. Docker Deployment

Deploy with Docker:

# Dockerfile
FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY . .

# Expose port
EXPOSE 8000

# Environment variables (set via docker run or docker-compose)
ENV OPENAI_API_KEY=""
ENV REDIS_URL="redis://redis:6379"

# Run application
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

# docker-compose.yml
version: '3.8'

services:
  agent:
    build: .
    ports:
      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - REDIS_URL=redis://redis:6379
      - TAVILY_API_KEY=${TAVILY_API_KEY}
    depends_on:
      - redis
    restart: unless-stopped

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redis_data:/data
    restart: unless-stopped

volumes:
  redis_data:

Deploy:

# Build and run
docker-compose up -d

# View logs
docker-compose logs -f agent

# Scale agents
docker-compose up -d --scale agent=3

8. Agent with Observability

Add monitoring and observability:

from langsmith import Client
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
import os
from datetime import datetime

# Initialize LangSmith
langsmith_client = Client(api_key=os.getenv("LANGSMITH_API_KEY"))

class ObservableAgent:
    def __init__(self, project_name: str):
        self.project_name = project_name
        self.llm = ChatOpenAI(
            model="gpt-4",
            api_key=os.getenv("OPENAI_API_KEY")
        )
    
    def create_workflow(self):
        """Create observable workflow"""
        def process_with_tracing(state: dict):
            # Start trace
            run_id = langsmith_client.create_run(
                name="agent_process",
                run_type="chain",
                inputs={"messages": state["messages"]},
                project_name=self.project_name
            )
            
            try:
                # Process
                response = self.llm.invoke(state["messages"])
                state["messages"].append({
                    "role": "assistant",
                    : response.content
                })
                
                
                langsmith_client.update_run(
                    run_id=run_id.,
                    outputs={: response.content},
                    end_time=datetime.now()
                )
             Exception  e:
                
                langsmith_client.update_run(
                    run_id=run_id.,
                    error=(e),
                    end_time=datetime.now()
                )
                
            
             state
        
        workflow = StateGraph()
        workflow.add_node(, process_with_tracing)
        workflow.set_entry_point()
        workflow.add_edge(, END)
         workflow.()


agent = ObservableAgent(project_name=)
app = agent.create_workflow()

result = app.invoke({
    : [{: , : }]
})

Configuration

Key environment variables:

# LLM API Keys
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"

# Vector Stores
export REDIS_URL="redis://localhost:6379"
export PINECONE_API_KEY="your-pinecone-key"

# Web Access
export TAVILY_API_KEY="your-tavily-key"
export BRIGHTDATA_API_KEY="your-brightdata-key"

# RAG Platforms
export CONTEXTUAL_API_KEY="your-contextual-key"

# Observability
export LANGSMITH_API_KEY="your-langsmith-key"
export LANGSMITH_PROJECT="your-project-name"

# Security
export ARCADE_API_KEY="your-arcade-key"

# Deployment
export REDIS_HOST="localhost"
export REDIS_PORT="6379"
export API_PORT="8000"

Common Patterns

Agent with Tools

from langchain.tools import Tool
from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI
import os

def calculator(expression: str) -> str:
    """Evaluate mathematical expression"""
    try:
        return str(eval(expression))
    except:
        return "Invalid expression"

def web_search(query: str) -> str:
    """Search the web"""
    # Implementation with Tavily
    return f"Results for: {query}"

tools = [
    Tool(
        name="Calculator",
        func=calculator,
        description="Evaluate mathematical expressions"
    ),
    Tool(
        name="WebSearch",
        func=web_search,
        description="Search the web for information"
    )
]

llm = ChatOpenAI(model="gpt-4", api_key=os.getenv("OPENAI_API_KEY"))
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

result = agent.run("What is 25 * 17? Then search for information about AI agents.")

Streaming Responses

from langchain_openai import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

llm = ChatOpenAI(
    model="gpt-4",
    api_key=os.getenv("OPENAI_API_KEY"),
    streaming=True,
    callbacks=[StreamingStdOutCallbackHandler()]
)

# Stream response
for chunk in llm.stream("Tell me about AI agents"):
    print(chunk.content, end="", flush=True)

Error Handling and Retries

from tenacity import retry, stop_after_attempt, wait_exponential
from langchain_openai import ChatOpenAI
import os

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
def call_llm_with_retry(prompt: str):
    """Call LLM with automatic retries"""
    llm = ChatOpenAI(model="gpt-4", api_key=os.getenv("OPENAI_API_KEY"))
    return llm.invoke([{"role": "user", "content": prompt}])

try:
    response = call_llm_with_retry("Hello, how are you?")
    print(response.content)
except Exception as e:
    print(f"Failed after retries: {e}")

Troubleshooting

Common Issues

Agent not responding:

# Check API key is set
import os
assert os.getenv("OPENAI_API_KEY"), "OPENAI_API_KEY not set"

# Enable verbose mode
from langchain.globals import set_verbose
set_verbose(True)

Memory not persisting:

# Verify Redis connection
import redis
r = redis.from_url(os.getenv("REDIS_URL", "redis://localhost:6379"))
r.ping()  # Should return True

# Check vector store configuration
from langchain_redis import RedisVectorStore
# Ensure index_name is consistent across runs

Rate limiting:

# Add rate limiting middleware
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback

with get_openai_callback() as cb:
    # Your agent code here
    print(f"Total Tokens: {cb.total_tokens}")
    print(f"Total Cost: ${cb.total_cost}")

Docker deployment issues:

# Check container logs
docker-compose logs -f agent

# Test health endpoint
curl http://localhost:8000/health

# Verify environment variables
docker-compose exec agent env | grep API_KEY

LangGraph state not updating:

# Ensure state is properly typed
from typing import TypedDict
from langgraph.graph import StateGraph

class State(TypedDict):
    messages: list  # Must be mutable type

# Return updated state, don't modify in place
def node(state: State):
    return {"messages": state["messages"] + [new_message]}

Additional Resources

• LangGraph Documentation
• LangChain Docs
• Repository Tutorials
• Discord Community

Best Practices

1. Always use environment variables for API keys and secrets
2. Implement retry logic for external API calls
3. Add observability from day one (LangSmith, traces)
4. Use typed state in LangGraph for better debugging
5. Implement health checks in production deployments
6. Store conversation history in vector databases for context
7. Set token limits to control costs
8. Test with different LLM models for cost/performance tradeoffs
9. Add input validation to prevent prompt injection
10. Monitor usage and costs in production