Agent | 1byteone - Java Backend / AI Application Developer

LangChain Memory: Designing Controlled Multi-Turn State

Fri, 21 Aug 2026 00:00:00 +0000

Whiteboard: LangChain — LangChain Memory.

The challenge of multi-turn chat is not putting every message back into the prompt. It is deciding which facts to retain, for how long, and who can delete them. Memory is explicit data, not an implicit model capability.

Core mental model

Short-term memory serves the current conversation, summaries compress earlier turns, and long-term memory stores consented preferences. All three need a conversation id, tenant isolation, and retention policy.

Key mechanics

Budget tokens before deciding whether to truncate or summarize. Filter PII and deduplicate before writing long-term memory. When reading memory, include source and timestamp so stale facts do not override newer ones.

Python example

from dataclasses import dataclass
from datetime import datetime

@dataclass
class MemoryItem:
 conversation_id: str
 text: str
 source: str
 created_at: datetime

def build_context(history: list[MemoryItem], max_chars: int = 6000) -> str:
 selected, size = [], 0
 for item in reversed(history):
 if size + len(item.text) > max_chars: break
 selected.append(f"[{item.source}] {item.text}")
 size += len(item.text)
 return "\n".join(reversed(selected))

Course focus

This article turns the whiteboard into explicit engineering boundaries: define inputs and outputs first, then decide how state, failures, and observability work. The examples use Python and focus on durable design principles rather than a particular provider version; verify APIs against the documentation for your installed dependencies.

Engineering practice

Keep business rules in the application layer instead of hiding them in untestable prompts or route handlers.
Add timeouts, bounded retries, and idempotency keys to external calls; retries are not a complete error strategy.
Record a request id, latency, input version, model/index version, and outcome without logging sensitive raw content.
Use a small fixed regression set first, then monitor quality and cost with sampled production traffic.

Common mistakes

Drawing only the happy path and omitting timeouts, empty results, rate limits, and rollback paths.
Letting one function parse input, call providers, build prompts, and persist data.
Replacing typed contracts with string conventions that can only be verified by manual integration.

Production checklist

Inputs, outputs, and error responses have explicit schemas
External dependencies have timeouts, bounded retries, rate limits, and fallbacks
Logs, metrics, and traces can be correlated to one request
Critical paths have unit tests, integration tests, and offline evaluation samples
Secrets, user content, and provider responses follow least-privilege and privacy rules

Practice

Implement the smallest loop shown on the whiteboard. Inject a timeout, an empty result, and a malformed payload, then check whether the system remains stable and diagnosable. Add one metric that proves your optimization improved quality or latency.

Hands-on exercise

Implement three context layers—recent turns, a conversation summary, and user preferences—and record which message ids each summary covers. When deleting a conversation, verify caches, vector stores, and audit copies are all removed.

Conclusion

An AI feature becomes maintainable when every arrow on the whiteboard maps to an input, an output, and a failure strategy.

LangChain Tool-Calling Agents: Separate Decisions from Execution

Fri, 21 Aug 2026 00:00:00 +0000

Whiteboard: LangChain — LangChain Tool-Calling Agents.

An agent should not give a model direct access to your system. The model proposes a structured tool intent; the application validates and executes it. The central boundary is: the model decides, the application executes.

Core mental model

The loop is Think → Call → Observe, while the host application controls iteration limits, tool allowlists, parameter validation, and permissions. Good descriptions improve selection but are not security policies.

Key mechanics

Tools should return machine-readable success or failure results; do not expose raw stack traces to the model or user. Require explicit confirmation for writes and emit an audit event for every call.

Python example

from langchain_core.tools import tool

@tool
def get_weather(city: str) -> dict:
 """Return current weather for an allowed city."""
 if city not in {"Shanghai", "Beijing"}:
 return {"ok": False, "error": "city_not_allowed"}
 return {"ok": True, "city": city, "celsius": 26}

llm_with_tools = model.bind_tools([get_weather])
message = llm_with_tools.invoke("Is Shanghai good for a run today?")
if message.tool_calls:
 result = get_weather.invoke(message.tool_calls[0]["args"])

Course focus

Engineering practice

Keep business rules in the application layer instead of hiding them in untestable prompts or route handlers.
Add timeouts, bounded retries, and idempotency keys to external calls; retries are not a complete error strategy.
Record a request id, latency, input version, model/index version, and outcome without logging sensitive raw content.
Use a small fixed regression set first, then monitor quality and cost with sampled production traffic.

Common mistakes

Drawing only the happy path and omitting timeouts, empty results, rate limits, and rollback paths.
Letting one function parse input, call providers, build prompts, and persist data.
Replacing typed contracts with string conventions that can only be verified by manual integration.

Production checklist

Inputs, outputs, and error responses have explicit schemas
External dependencies have timeouts, bounded retries, rate limits, and fallbacks
Logs, metrics, and traces can be correlated to one request
Critical paths have unit tests, integration tests, and offline evaluation samples
Secrets, user content, and provider responses follow least-privilege and privacy rules

Practice

Hands-on exercise

Add one read-only search tool and one side-effecting refund tool. Auto-run the first, return requires_confirmation for the second, and test that the agent cannot bypass confirmation.

Conclusion

An AI feature becomes maintainable when every arrow on the whiteboard maps to an input, an output, and a failure strategy.

Building AI Agents with LangChain: Tool Integration and Multi-Turn Conversations

Fri, 10 Jul 2026 00:00:00 +0000

AI agents are revolutionizing how we build intelligent applications. Unlike simple chatbots, agents can use tools, maintain context, and make decisions. This guide covers building production-ready agents with LangChain.

Above: LangChain Agent architecture — User Input → Reasoning Loop → Tool Calling → Multi-turn Conversation

What are AI Agents?

Agents combine LLMs with tools and reasoning capabilities:

User Input → Agent (Reasoning) → Tool Selection → Tool Execution → Response

Key characteristics:

Tool Usage - Can interact with external systems
Reasoning - Decides which tools to use and when
Memory - Maintains context across interactions
Autonomy - Can chain multiple actions together

LangChain Agent Framework

LangChain provides a powerful framework for building agents:

from langchain.agents import create_openai_tools_agent
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Initialize LLM
llm = ChatOpenAI(model="gpt-4", temperature=0)

# Define prompt
prompt = ChatPromptTemplate.from_messages([
 ("system", "You are a helpful assistant with access to tools."),
 ("human", "{input}"),
 ("placeholder", "{agent_scratchpad}")
])

# Create agent
agent = create_openai_tools_agent(llm, tools, prompt)

Custom Tool Development

Build tools specific to your use case:

from langchain_core.tools import tool
from typing import Optional

@tool
def search_knowledge_base(query: str) -> str:
 """Search the private knowledge base for information."""
 # Implementation here
 results = vector_store.similarity_search(query, k=3)
 return "\n".join([doc.page_content for doc in results])

@tool
def get_product_info(product_id: str) -> str:
 """Get detailed product information by ID."""
 # Database query here
 product = db.get_product(product_id)
 return f"Product: {product.name}, Price: {product.price}"

# Register tools
tools = [search_knowledge_base, get_product_info]

Tool Best Practices

Clear Descriptions - Help the agent understand when to use each tool
Error Handling - Gracefully handle tool failures
Input Validation - Validate inputs before processing
Rate Limiting - Prevent abuse of external APIs

Memory Management

Implement conversation memory for context persistence:

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain

# Initialize memory
memory = ConversationBufferMemory(return_messages=True)

# Create conversation chain
conversation = ConversationChain(
 llm=llm,
 memory=memory,
 verbose=True
)

# Use in agent
agent = create_openai_tools_agent(
 llm, tools, prompt,
 memory=memory
)

Memory Types

Type	Use Case	Trade-offs
Buffer	Simple conversations	Limited context
Summary	Long conversations	Information loss
Vector	Semantic search	Storage overhead
Entity	Entity tracking	Complexity

Multi-turn Conversations

Handle complex conversation flows:

from langchain.agents import AgentExecutor

# Create agent executor
agent_executor = AgentExecutor(
 agent=agent,
 tools=tools,
 verbose=True,
 max_iterations=10, # Prevent infinite loops
 handle_parsing_errors=True
)

# Conversation with context
def chat(user_input: str, conversation_id: str):
 # Load conversation history
 history = load_conversation_history(conversation_id)

 # Run agent
 result = agent_executor.invoke({
 "input": user_input,
 "chat_history": history
 })

 # Save to history
 save_conversation_history(conversation_id, user_input, result["output"])

 return result["output"]

Production Deployment

1. FastAPI Integration

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

class ChatRequest(BaseModel):
 message: str
 conversation_id: str

class ChatResponse(BaseModel):
 response: str
 conversation_id: str

@app.post("/chat", response_model=ChatResponse)
async def chat_endpoint(request: ChatRequest):
 try:
 response = chat(request.message, request.conversation_id)
 return ChatResponse(
 response=response,
 conversation_id=request.conversation_id
 )
 except Exception as e:
 raise HTTPException(status_code=500, detail=str(e))

2. Streaming Responses

from fastapi.responses import StreamingResponse

@app.post("/chat/stream")
async def chat_stream(request: ChatRequest):
 async def generate():
 async for event in agent_executor.astream_events(
 {"input": request.message},
 version="v1"
 ):
 if event["event"] == "on_chat_model_stream":
 content = event["data"]["chunk"].content
 if content:
 yield f"data: {content}\n\n"
 yield "data: [DONE]\n\n"

 return StreamingResponse(generate(), media_type="text/event-stream")

3. Error Handling

@app.middleware("http")
async def error_handler(request, call_next):
 try:
 response = await call_next(request)
 return response
 except Exception as e:
 logger.error(f"Agent error: {e}")
 return JSONResponse(
 status_code=500,
 content={"error": "Internal server error"}
 )

Real-World Example: Agricultural Q&A Agent

Here’s how I built an agricultural knowledge base agent:

from langchain.agents import create_openai_tools_agent
from langchain.tools import Tool

# Define agricultural tools
tools = [
 Tool(
 name="SearchKnowledgeBase",
 func=search_agricultural_knowledge,
 description="Search private agricultural manuals for crop diseases, treatments, and best practices"
 ),
 Tool(
 name="GeneralAgriculturalKnowledge",
 func=get_general_knowledge,
 description="Get general agricultural information when private knowledge base doesn't have the answer"
 )
]

# System prompt emphasizing private data priority
system_prompt = """You are an agricultural expert assistant.

IMPORTANT: Always search the private knowledge base FIRST. Only use general knowledge if the private search returns no relevant results.

When answering:
1. Cite the source document when possible
2. Be specific about crop names, disease symptoms, and treatments
3. If unsure, say "I recommend consulting a local agricultural expert"
"""

Conclusion

Building AI agents requires:

Tool Design - Clear, well-documented tools
Memory Management - Appropriate memory strategy for your use case
Error Handling - Graceful failure management
User Experience - Streaming and clear feedback

The complete agricultural agent code is available on .

Questions? Reach out on or email me at !

Agent | 1byteone - Java Backend / AI Application Developer

LangChain Memory: Designing Controlled Multi-Turn State

Core mental model

Key mechanics

Python example

Course focus

Engineering practice

Common mistakes

Production checklist

Practice

Hands-on exercise

Conclusion

LangChain Tool-Calling Agents: Separate Decisions from Execution

Core mental model

Key mechanics

Python example

Course focus

Engineering practice

Common mistakes

Production checklist

Practice

Hands-on exercise

Conclusion

Building AI Agents with LangChain: Tool Integration and Multi-Turn Conversations

Table of Contents

What are AI Agents?

LangChain Agent Framework

Custom Tool Development

Tool Best Practices

Memory Management

Memory Types

Multi-turn Conversations

Production Deployment

1. FastAPI Integration

2. Streaming Responses

3. Error Handling

Real-World Example: Agricultural Q&A Agent

Conclusion