LangChain Multi-Agent Project Structure

Multi-agent orchestration for complex workflows. Specialized agents with tools coordinated by supervisors.

#langchain #python #llm #ai #agents #multi-agent #tools

PNG PDF

Project Directory

myproject/

main.py

Orchestrator entry point

app/

Application code

__init__.py

config.py

Agent settings, API keys

agents/

Agent definitions

__init__.py

base.py

Base agent class

researcher.py

Web search agent

coder.py

Code generation agent

reviewer.py

Review/QA agent

tools/

Custom tools

__init__.py

search.py

Web search tool

code_executor.py

Sandboxed execution

file_tools.py

Read/write files

api_tools.py

External API calls

orchestration/

Agent coordination

__init__.py

supervisor.py

Task delegation

workflow.py

Agent graph/pipeline

state.py

Shared state management

prompts/

Agent prompts

__init__.py

agent_prompts.py

System prompts per agent

supervisor_prompt.py

memory/

Conversation and context

__init__.py

conversation.py

Chat history

shared_memory.py

Cross-agent context

workflows/

Predefined workflows

research_workflow.py

coding_workflow.py

tests/

__init__.py

test_agents.py

test_tools.py

requirements.txt

.env.example

.gitignore

README.md

Why This Structure?

Multi-agent systems split complex tasks across specialized agents. This structure separates agents, tools, and orchestration. A supervisor pattern coordinates agents, while shared memory enables cross-agent communication.

Key Directories

app/agents/-Specialized agents (researcher, coder, reviewer)
app/tools/-Tools agents can use (search, execute, file I/O)
app/orchestration/-Supervisor and workflow coordination
app/memory/-Shared state across agent conversations
workflows/-Predefined multi-agent pipelines

Getting Started

python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env (add API keys)
python main.py (run orchestrator)

Multi-Agent Flow

Supervisor-Receives task, decides which agent handles it
Agent-Executes subtask using tools
Tool-Performs concrete action (search, code, etc.)
Memory-Stores results for other agents
Loop-Supervisor evaluates, may delegate more

When To Use This

Complex tasks requiring multiple capabilities
Autonomous research and coding workflows
Systems where agents need different specializations
Projects exploring LangGraph patterns
Building AI assistants with tool use

Trade-offs

Complexity-Multi-agent adds debugging and coordination overhead
Cost-Multiple agents = multiple LLM calls per task
Latency-Sequential agent handoffs add response time

Best Practices

Keep agent responsibilities narrow and focused
Use structured output for agent-to-agent communication
Implement proper error handling for tool failures
Consider LangGraph for complex state machines
Log agent decisions for debugging