INTRODUCTION AND PROBLEM STATEMENT
The rapid evolution of Large Language Models has opened unprecedented opportunities for creating sophisticated multi-agent systems that can collaborate to solve complex problems. However, the current landscape of agentic AI development presents significant challenges for both technical and non-technical users who wish to create multi-agent solutions.
Existing frameworks like CrewAI, AutoGen, LangGraph, and OpenAI's experimental Swarm require substantial programming expertise and deep understanding of their respective architectures. CrewAI primarily uses Python objects and optional YAML configuration for defining agents and tasks. AutoGen employs a conversational approach with JSON specifications through AutoGen Studio. LangGraph utilizes graph-based architectures with complex state management. While these frameworks are powerful, they lack a unified, intuitive approach that allows users to focus on the problem domain rather than implementation details.
The challenge becomes even more complex when considering the integration of modern technologies such as Anthropic's Model Context Protocol, Retrieval Augmented Generation, GraphRAG, and the need to support various GPU architectures for local LLM inference. Users must navigate a fragmented ecosystem where each component requires specialized knowledge and custom integration work.
This article presents a comprehensive solution consisting of three interconnected components: a Domain-Specific Language for defining agentic AI solutions, a feature-complete framework for implementing these solutions, and a code generation tool that bridges the gap between high-level specifications and production-ready applications.
ANALYSIS OF EXISTING APPROACHES
Before designing our DSL, we must understand the strengths and limitations of current approaches in the multi-agent AI landscape.
CrewAI represents one of the most accessible frameworks, allowing users to define agents through Python objects with clear role definitions, goals, and backstories. The framework supports YAML configuration as an alternative to Python code, which serves as a rudimentary DSL. However, CrewAI's approach is limited in its expressiveness for complex workflows and lacks built-in support for advanced features like MCP integration or sophisticated RAG implementations.
AutoGen from Microsoft takes a different approach, focusing on conversational patterns between agents. Its AutoGen Studio provides a low-code interface with JSON specifications that act as a declarative DSL. The framework excels in creating dynamic multi-agent conversations but requires significant configuration overhead for complex scenarios. The JSON-based specification, while machine-readable, lacks the intuitive syntax that would make it accessible to domain experts without programming backgrounds.
LangGraph extends the LangChain ecosystem with a graph-based architecture that treats applications as directed graphs. Nodes represent units of work while edges define communication channels and transitions. This approach provides excellent control flow capabilities and state management but requires users to think in terms of graph structures, which may not align with how domain experts conceptualize their problems.
OpenAI's experimental Swarm framework emphasizes lightweight, stateless orchestration with dynamic handoffs between agents. While elegant in its simplicity, Swarm's experimental nature and limited feature set make it unsuitable for production environments requiring robust capabilities.
All these frameworks share common limitations: they require significant technical expertise, lack standardized integration patterns for emerging technologies like MCP, and provide limited support for declarative problem specification that non-technical users can understand and modify.
DSL DESIGN PRINCIPLES AND ARCHITECTURE
Our Domain-Specific Language for agentic AI solutions is built upon several fundamental design principles that address the limitations identified in existing approaches.
The first principle is declarative simplicity. Users should be able to express what they want to achieve rather than how to achieve it. The DSL abstracts away implementation details while providing sufficient expressiveness for complex scenarios. This approach allows domain experts to focus on problem-solving logic rather than technical infrastructure.
The second principle is technology agnosticism with intelligent defaults. The DSL should support various LLM providers, vector databases, and GPU architectures while providing sensible defaults for users who prefer not to specify technical details. When users do not specify particular technologies, the system defaults to open-source solutions to ensure accessibility and avoid vendor lock-in.
The third principle is extensibility and modularity. The DSL must accommodate future technologies and use cases without requiring fundamental redesign. This is achieved through a plugin architecture that allows new capabilities to be added seamlessly.
The fourth principle is dual representation support. The DSL supports both textual and graphical representations, allowing users to choose the interface that best matches their cognitive preferences and use case requirements.
The architecture consists of three layers: the specification layer where users define their agentic solutions, the translation layer that converts DSL specifications into executable code, and the runtime layer that provides the infrastructure for executing the generated applications.
DSL SYNTAX AND SEMANTICS DESIGN
The DSL syntax is designed to be intuitive and expressive while maintaining the precision necessary for generating production-ready code. The language uses a hierarchical structure that mirrors how users naturally think about multi-agent problems.
At the top level, users define a Solution that contains multiple Agents, Tasks, and Workflows. Each Agent has a role, capabilities, and configuration parameters. Tasks define specific objectives that agents must accomplish, while Workflows orchestrate the interaction between agents and tasks.
Here is an example of the basic DSL syntax:
Solution "Research and Analysis System" {
Configuration {
llm_provider: "local"
gpu_acceleration: "auto"
mcp_enabled: true
rag_enabled: true
}
Agent researcher {
role: "Research Specialist"
goal: "Gather comprehensive information on specified topics"
capabilities: ["web_search", "document_analysis"]
llm: "llama3.1:8b"
tools: ["web_search_mcp", "pdf_reader"]
}
Agent analyst {
role: "Data Analyst"
goal: "Analyze and synthesize research findings"
capabilities: ["data_analysis", "report_generation"]
llm: "llama3.1:8b"
memory: "vector_store"
}
Task research_task {
description: "Research the specified topic thoroughly"
assigned_to: researcher
input: "topic"
output: "research_findings"
max_iterations: 3
}
Task analysis_task {
description: "Analyze research findings and generate insights"
assigned_to: analyst
input: research_task.output
output: "analysis_report"
depends_on: [research_task]
}
Workflow main_workflow {
trigger: "user_request"
steps: [research_task, analysis_task]
parallel: false
}
}
The DSL supports advanced features through specialized blocks. For RAG integration, users can define knowledge bases and retrieval strategies:
KnowledgeBase company_docs {
type: "vector_store"
embedding_model: "sentence-transformers/all-MiniLM-L6-v2"
documents: ["./docs/**/*.pdf", "./docs/**/*.md"]
chunk_size: 512
overlap: 50
}
RAGConfig {
knowledge_base: company_docs
retrieval_strategy: "semantic_search"
top_k: 5
rerank: true
}
For GraphRAG support, the DSL provides constructs for defining knowledge graphs and relationship extraction:
GraphRAG entity_knowledge {
graph_store: "neo4j"
entity_extraction: true
relationship_mapping: true
community_detection: true
summarization_strategy: "hierarchical"
}
MCP integration is specified through server and client configurations:
MCPServer file_operations {
type: "filesystem"
root_path: "./workspace"
permissions: ["read", "write"]
}
MCPClient external_apis {
servers: ["github_mcp", "slack_mcp"]
timeout: 30
}
The DSL semantics ensure that specifications are unambiguous and complete. Type checking prevents common errors such as referencing non-existent agents or creating circular dependencies. The language also supports validation rules that check for best practices and potential performance issues.
FRAMEWORK ARCHITECTURE FOR LLM-BASED AGENTIC AI
The framework that implements DSL specifications consists of several interconnected components designed for modularity, scalability, and extensibility. The architecture follows clean architecture principles with clear separation of concerns and dependency inversion.
The core framework is built around an Agent Runtime Engine that manages agent lifecycle, communication, and resource allocation. This engine provides a standardized interface for different types of agents while allowing for specialized implementations based on agent capabilities and requirements.
The Communication Layer handles message passing between agents using a publish-subscribe pattern with support for both synchronous and asynchronous communication. This layer abstracts away the complexities of distributed communication while providing guarantees for message delivery and ordering when required.
class AgentRuntime:
def __init__(self, config):
self.config = config
self.agents = {}
self.message_bus = MessageBus()
self.task_scheduler = TaskScheduler()
self.resource_manager = ResourceManager()
def register_agent(self, agent_spec):
agent = self._create_agent(agent_spec)
self.agents[agent.id] = agent
self.message_bus.register_subscriber(agent)
def execute_workflow(self, workflow_spec):
tasks = self._parse_workflow(workflow_spec)
return self.task_scheduler.execute(tasks)
The LLM Abstraction Layer provides a unified interface for interacting with different language models, whether they are hosted locally or accessed through remote APIs. This layer handles model loading, GPU memory management, and request batching for optimal performance.
class LLMProvider:
def __init__(self, provider_type, model_config):
self.provider_type = provider_type
self.model_config = model_config
self.gpu_manager = GPUManager()
def generate(self, prompt, agent_context):
if self.provider_type == "local":
return self._local_inference(prompt, agent_context)
else:
return self._remote_inference(prompt, agent_context)
def _local_inference(self, prompt, context):
device = self.gpu_manager.get_optimal_device()
model = self._load_model_on_device(device)
return model.generate(prompt, context)
The Resource Management System monitors and allocates computational resources including GPU memory, CPU cores, and network bandwidth. This system ensures optimal performance while preventing resource conflicts between concurrent agents.
The Persistence Layer manages agent memory, conversation history, and intermediate results. It supports various storage backends including vector databases for semantic search, graph databases for relationship storage, and traditional databases for structured data.
FRAMEWORK IMPLEMENTATION DETAILS
The framework implementation prioritizes performance, reliability, and ease of integration. The codebase follows clean architecture principles with clear interfaces and dependency injection throughout.
The Agent class serves as the base abstraction for all agent types. Each agent maintains its own context, memory, and communication channels while participating in the broader multi-agent ecosystem.
class Agent:
def __init__(self, agent_id, role, goal, llm_provider):
self.agent_id = agent_id
self.role = role
self.goal = goal
self.llm_provider = llm_provider
self.memory = Memory()
self.tools = ToolRegistry()
self.context = AgentContext()
async def process_task(self, task):
context = self._build_context(task)
response = await self.llm_provider.generate(
self._create_prompt(task, context),
self.context
)
return self._process_response(response, task)
def _build_context(self, task):
relevant_memory = self.memory.retrieve(task.description)
return {
"role": self.role,
"goal": self.goal,
"task": task,
"memory": relevant_memory,
"available_tools": self.tools.list_available()
}
The Task Management System handles task creation, scheduling, and execution with support for dependencies, parallel execution, and error recovery. Tasks can be simple one-time operations or complex multi-step workflows requiring coordination between multiple agents.
class TaskScheduler:
def __init__(self):
self.pending_tasks = []
self.running_tasks = {}
self.completed_tasks = {}
self.dependency_graph = DependencyGraph()
async def execute(self, tasks):
self._build_dependency_graph(tasks)
ready_tasks = self._get_ready_tasks()
while ready_tasks or self.running_tasks:
for task in ready_tasks:
await self._start_task(task)
completed = await self._wait_for_completion()
self._update_dependencies(completed)
ready_tasks = self._get_ready_tasks()
return self._compile_results()
The Memory System provides both short-term and long-term memory capabilities for agents. Short-term memory maintains conversation context and immediate task state, while long-term memory stores experiences and learned patterns that can be retrieved for future tasks.
class Memory:
def __init__(self, config):
self.short_term = ShortTermMemory()
self.long_term = LongTermMemory(config.vector_store)
self.episodic = EpisodicMemory()
def store(self, content, memory_type="long_term"):
if memory_type == "short_term":
self.short_term.add(content)
elif memory_type == "long_term":
embedding = self._create_embedding(content)
self.long_term.store(content, embedding)
else:
self.episodic.add(content)
def retrieve(self, query, top_k=5):
query_embedding = self._create_embedding(query)
return self.long_term.search(query_embedding, top_k)
MCP INTEGRATION DESIGN AND IMPLEMENTATION
The Model Context Protocol integration provides standardized access to external tools and data sources while maintaining security and performance. Our framework implements both MCP client and server capabilities, allowing agents to consume external services and expose their own capabilities to other systems.
The MCP Client implementation handles connection management, request routing, and response processing for external MCP servers. It provides a unified interface that abstracts away the complexities of different server implementations.
class MCPClient:
def __init__(self, server_configs):
self.servers = {}
self.connection_pool = ConnectionPool()
for config in server_configs:
server = MCPServerConnection(config)
self.servers[config.name] = server
async def call_tool(self, server_name, tool_name, parameters):
server = self.servers.get(server_name)
if not server:
raise MCPServerNotFound(server_name)
connection = await self.connection_pool.get_connection(server)
try:
request = MCPRequest(
method="tools/call",
params={
"name": tool_name,
"arguments": parameters
}
)
response = await connection.send(request)
return self._process_response(response)
finally:
self.connection_pool.return_connection(connection)
async def list_resources(self, server_name):
server = self.servers[server_name]
connection = await self.connection_pool.get_connection(server)
request = MCPRequest(method="resources/list")
response = await connection.send(request)
return response.result
The MCP Server implementation allows our agents to expose their capabilities to external systems. This bidirectional integration enables complex ecosystems where multiple agentic systems can collaborate.
class MCPServer:
def __init__(self, agent_registry, config):
self.agent_registry = agent_registry
self.config = config
self.tools = {}
self.resources = {}
self._register_agent_tools()
def _register_agent_tools(self):
for agent in self.agent_registry.get_all_agents():
for tool in agent.tools.list_available():
self.tools[f"{agent.agent_id}_{tool.name}"] = tool
async def handle_request(self, request):
if request.method == "tools/list":
return self._list_tools()
elif request.method == "tools/call":
return await self._call_tool(request.params)
elif request.method == "resources/list":
return self._list_resources()
else:
raise MCPMethodNotSupported(request.method)
async def _call_tool(self, params):
tool_name = params["name"]
arguments = params["arguments"]
tool = self.tools.get(tool_name)
if not tool:
raise MCPToolNotFound(tool_name)
result = await tool.execute(arguments)
return {"content": [{"type": "text", "text": str(result)}]}
RAG AND GRAPHRAG SUPPORT IMPLEMENTATION
The framework provides comprehensive support for both traditional RAG and GraphRAG approaches, allowing users to choose the most appropriate knowledge representation for their use case.
The RAG implementation supports multiple vector stores and embedding models with automatic optimization based on the data characteristics and query patterns. The system handles document ingestion, chunking, embedding generation, and retrieval with support for reranking and query expansion.
class RAGSystem:
def __init__(self, config):
self.config = config
self.vector_store = self._initialize_vector_store()
self.embedding_model = self._load_embedding_model()
self.text_splitter = TextSplitter(
chunk_size=config.chunk_size,
overlap=config.overlap
)
self.reranker = self._initialize_reranker() if config.rerank else None
def ingest_documents(self, documents):
for document in documents:
chunks = self.text_splitter.split(document.content)
embeddings = self.embedding_model.encode(chunks)
for chunk, embedding in zip(chunks, embeddings):
self.vector_store.add(
content=chunk,
embedding=embedding,
metadata=document.metadata
)
async def retrieve(self, query, top_k=5):
query_embedding = self.embedding_model.encode(query)
candidates = self.vector_store.search(query_embedding, top_k * 2)
if self.reranker:
reranked = self.reranker.rerank(query, candidates)
return reranked[:top_k]
return candidates[:top_k]
The GraphRAG implementation builds and maintains knowledge graphs from unstructured text, enabling more sophisticated reasoning about entities and relationships. The system supports automatic entity extraction, relationship identification, and community detection for hierarchical summarization.
class GraphRAGSystem:
def __init__(self, config):
self.config = config
self.graph_store = self._initialize_graph_store()
self.entity_extractor = EntityExtractor(config.llm_provider)
self.relationship_extractor = RelationshipExtractor(config.llm_provider)
self.community_detector = CommunityDetector()
async def ingest_documents(self, documents):
for document in documents:
entities = await self.entity_extractor.extract(document.content)
relationships = await self.relationship_extractor.extract(
document.content, entities
)
await self._store_entities(entities, document.metadata)
await self._store_relationships(relationships, document.metadata)
await self._detect_communities()
await self._generate_summaries()
async def retrieve(self, query, max_depth=2):
relevant_entities = await self._find_relevant_entities(query)
subgraph = await self._extract_subgraph(relevant_entities, max_depth)
context = await self._generate_context_from_subgraph(subgraph)
return context
CODE GENERATION TOOL IMPLEMENTATION
The code generation tool serves as the bridge between DSL specifications and executable applications. It parses DSL files, validates specifications, and generates production-ready code using the framework components.
The generator follows a template-based approach with extensive customization capabilities. It analyzes the DSL specification to determine optimal configurations for the target deployment environment, including GPU architecture detection and resource allocation strategies.
class CodeGenerator:
def __init__(self):
self.template_engine = TemplateEngine()
self.validator = DSLValidator()
self.optimizer = ConfigurationOptimizer()
def generate(self, dsl_spec, output_path):
# Validate the DSL specification
validation_result = self.validator.validate(dsl_spec)
if not validation_result.is_valid:
raise DSLValidationError(validation_result.errors)
# Optimize configuration for target environment
optimized_config = self.optimizer.optimize(
dsl_spec.configuration,
self._detect_environment()
)
# Generate code components
agents = self._generate_agents(dsl_spec.agents)
tasks = self._generate_tasks(dsl_spec.tasks)
workflows = self._generate_workflows(dsl_spec.workflows)
# Create main application
main_app = self._generate_main_application(
agents, tasks, workflows, optimized_config
)
# Write generated code to files
self._write_generated_code(output_path, main_app)
def _detect_environment(self):
gpu_info = self._detect_gpu_architecture()
available_memory = self._get_available_memory()
cpu_cores = self._get_cpu_cores()
return EnvironmentInfo(
gpu_architecture=gpu_info,
memory_gb=available_memory,
cpu_cores=cpu_cores
)
The generator creates modular code that follows best practices for maintainability and extensibility. Each generated component includes comprehensive documentation and configuration options that allow users to fine-tune behavior without modifying the core logic.
def _generate_agent_class(self, agent_spec):
template = self.template_engine.get_template("agent_class.py.j2")
return template.render(
agent_name=agent_spec.name,
role=agent_spec.role,
goal=agent_spec.goal,
capabilities=agent_spec.capabilities,
llm_config=agent_spec.llm_config,
tools=agent_spec.tools,
memory_config=agent_spec.memory_config
)
def _generate_main_application(self, agents, tasks, workflows, config):
template = self.template_engine.get_template("main_app.py.j2")
return template.render(
agents=agents,
tasks=tasks,
workflows=workflows,
configuration=config,
imports=self._generate_imports(config),
initialization=self._generate_initialization(config)
)
GPU ARCHITECTURE SUPPORT AND OPTIMIZATION
The framework provides comprehensive support for different GPU architectures, automatically detecting and optimizing for NVIDIA CUDA, AMD ROCm, and Apple Metal Performance Shaders. This ensures optimal performance regardless of the target hardware.
The GPU Manager component handles device detection, memory management, and model loading strategies. It implements sophisticated algorithms for memory optimization and batch processing to maximize throughput while preventing out-of-memory errors.
class GPUManager:
def __init__(self):
self.architecture = self._detect_gpu_architecture()
self.devices = self._enumerate_devices()
self.memory_manager = MemoryManager(self.architecture)
def _detect_gpu_architecture(self):
if torch.cuda.is_available():
return "cuda"
elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
return "mps"
elif self._check_rocm_availability():
return "rocm"
else:
return "cpu"
def get_optimal_device(self, memory_required_mb):
if self.architecture == "cpu":
return torch.device("cpu")
best_device = None
max_free_memory = 0
for device in self.devices:
free_memory = self.memory_manager.get_free_memory(device)
if free_memory >= memory_required_mb and free_memory > max_free_memory:
max_free_memory = free_memory
best_device = device
return best_device or torch.device("cpu")
def load_model_optimized(self, model_path, device):
if self.architecture == "cuda":
return self._load_model_cuda(model_path, device)
elif self.architecture == "mps":
return self._load_model_mps(model_path, device)
elif self.architecture == "rocm":
return self._load_model_rocm(model_path, device)
else:
return self._load_model_cpu(model_path)
For CUDA environments, the framework leverages optimized kernels and memory management techniques specific to NVIDIA hardware. It supports features like tensor parallelism for large models and dynamic batching for improved throughput.
def _load_model_cuda(self, model_path, device):
# Enable optimized attention mechanisms
torch.backends.cuda.enable_flash_sdp(True)
# Load model with CUDA-specific optimizations
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map=device,
attn_implementation="flash_attention_2"
)
# Apply CUDA-specific optimizations
model = torch.compile(model, mode="max-autotune")
return model
For AMD ROCm environments, the framework uses HIP-compatible operations and ROCm-optimized libraries to ensure efficient execution on AMD hardware.
def _load_model_rocm(self, model_path, device):
# Configure ROCm-specific settings
os.environ["HSA_OVERRIDE_GFX_VERSION"] = "11.0.0"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map=device
)
# Apply ROCm-specific optimizations
if hasattr(torch, '_dynamo'):
model = torch.compile(model, backend="inductor")
return model
For Apple Silicon, the framework takes advantage of the unified memory architecture and Metal Performance Shaders for optimal performance on Mac hardware.
def _load_model_mps(self, model_path, device):
# Configure for Apple Silicon unified memory
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map=device,
low_cpu_mem_usage=True
)
# Enable MPS-specific optimizations
if hasattr(torch.backends.mps, 'is_built') and torch.backends.mps.is_built():
torch.mps.set_per_process_memory_fraction(0.8)
return model
COMPREHENSIVE RUNNING EXAMPLE
To demonstrate the complete system in action, we present a comprehensive example that showcases all major features of the DSL, framework, and code generation tool. This example implements a sophisticated research and analysis system that can handle complex multi-step workflows with RAG integration, MCP connectivity, and GPU optimization.
The DSL specification for our running example defines a multi-agent system capable of conducting research, analyzing findings, and generating comprehensive reports. The system includes specialized agents for different aspects of the research process, each with specific capabilities and tools.
Solution "Advanced Research Analysis System" {
Configuration {
name: "research_system"
version: "1.0.0"
llm_provider: "local"
gpu_acceleration: "auto"
mcp_enabled: true
rag_enabled: true
graphrag_enabled: true
max_concurrent_agents: 4
memory_limit_gb: 16
}
LLMConfig {
default_model: "llama3.1:8b"
temperature: 0.7
max_tokens: 2048
context_window: 8192
}
Agent research_coordinator {
role: "Research Coordinator"
goal: "Orchestrate research activities and ensure comprehensive coverage"
capabilities: ["task_planning", "resource_allocation", "quality_control"]
llm: "llama3.1:8b"
memory_type: "episodic"
tools: ["task_manager", "progress_tracker"]
max_iterations: 5
}
Agent web_researcher {
role: "Web Research Specialist"
goal: "Gather information from web sources and online databases"
capabilities: ["web_search", "content_extraction", "source_validation"]
llm: "llama3.1:8b"
memory_type: "vector_store"
tools: ["web_search_mcp", "content_parser", "url_validator"]
concurrent_requests: 3
}
Agent document_analyst {
role: "Document Analysis Expert"
goal: "Analyze documents and extract relevant information"
capabilities: ["document_parsing", "information_extraction", "summarization"]
llm: "llama3.1:8b"
memory_type: "vector_store"
tools: ["pdf_reader", "doc_parser", "text_extractor"]
supported_formats: ["pdf", "docx", "txt", "md"]
}
Agent data_synthesizer {
role: "Data Synthesis Specialist"
goal: "Synthesize findings from multiple sources into coherent insights"
capabilities: ["data_integration", "pattern_recognition", "insight_generation"]
llm: "llama3.1:8b"
memory_type: "graph_store"
tools: ["data_merger", "pattern_analyzer", "insight_extractor"]
}
Agent report_generator {
role: "Report Generation Expert"
goal: "Create comprehensive reports from synthesized data"
capabilities: ["report_writing", "visualization", "formatting"]
llm: "llama3.1:8b"
memory_type: "vector_store"
tools: ["report_writer", "chart_generator", "formatter"]
output_formats: ["markdown", "pdf", "html"]
}
KnowledgeBase research_corpus {
type: "hybrid"
vector_store: {
provider: "chroma"
embedding_model: "sentence-transformers/all-MiniLM-L6-v2"
collection_name: "research_documents"
}
graph_store: {
provider: "neo4j"
database: "research_graph"
username: "neo4j"
password: "research123"
}
documents: ["./knowledge_base/**/*.pdf", "./knowledge_base/**/*.md"]
chunk_size: 512
overlap: 50
update_frequency: "daily"
}
RAGConfig {
knowledge_base: research_corpus
retrieval_strategy: "hybrid"
vector_top_k: 10
graph_max_depth: 3
rerank: true
rerank_model: "cross-encoder/ms-marco-MiniLM-L-6-v2"
query_expansion: true
}
GraphRAG entity_knowledge {
graph_store: research_corpus.graph_store
entity_extraction: {
enabled: true
entity_types: ["person", "organization", "concept", "technology"]
extraction_model: "llama3.1:8b"
}
relationship_mapping: {
enabled: true
relationship_types: ["collaborates_with", "develops", "influences", "part_of"]
confidence_threshold: 0.7
}
community_detection: {
enabled: true
algorithm: "leiden"
resolution: 1.0
}
summarization_strategy: "hierarchical"
}
MCPServer research_tools {
name: "research_tools_server"
port: 8080
exposed_tools: ["document_search", "entity_lookup", "relationship_query"]
authentication: "api_key"
}
MCPClient external_services {
servers: [
{
name: "arxiv_search"
url: "http://localhost:8081"
timeout: 30
},
{
name: "pubmed_search"
url: "http://localhost:8082"
timeout: 30
},
{
name: "github_search"
url: "http://localhost:8083"
timeout: 30
}
]
}
Task initial_planning {
description: "Analyze research request and create execution plan"
assigned_to: research_coordinator
input: "research_topic"
output: "research_plan"
priority: "high"
timeout: 300
}
Task web_research {
description: "Conduct comprehensive web research on the topic"
assigned_to: web_researcher
input: initial_planning.output
output: "web_findings"
depends_on: [initial_planning]
max_iterations: 3
parallel_searches: 5
}
Task document_analysis {
description: "Analyze relevant documents from knowledge base"
assigned_to: document_analyst
input: initial_planning.output
output: "document_insights"
depends_on: [initial_planning]
rag_enabled: true
max_documents: 20
}
Task data_synthesis {
description: "Synthesize findings from all sources"
assigned_to: data_synthesizer
input: [web_research.output, document_analysis.output]
output: "synthesized_insights"
depends_on: [web_research, document_analysis]
graphrag_enabled: true
}
Task report_generation {
description: "Generate comprehensive research report"
assigned_to: report_generator
input: data_synthesis.output
output: "final_report"
depends_on: [data_synthesis]
format: "markdown"
include_citations: true
}
Workflow research_workflow {
name: "comprehensive_research"
trigger: "api_request"
steps: [
initial_planning,
{
parallel: [web_research, document_analysis]
},
data_synthesis,
report_generation
]
error_handling: "retry_with_backoff"
max_retries: 3
timeout: 3600
}
Monitoring {
enabled: true
metrics: ["task_duration", "agent_utilization", "memory_usage", "gpu_utilization"]
logging_level: "info"
export_format: "prometheus"
}
}
The generated code from this DSL specification creates a complete, production-ready application with all the specified capabilities. The main application file orchestrates the entire system:
import asyncio
import logging
from typing import Dict, List, Any
from datetime import datetime
from framework.agent_runtime import AgentRuntime
from framework.llm_provider import LLMProvider
from framework.gpu_manager import GPUManager
from framework.rag_system import RAGSystem
from framework.graphrag_system import GraphRAGSystem
from framework.mcp_client import MCPClient
from framework.mcp_server import MCPServer
from framework.task_scheduler import TaskScheduler
from framework.monitoring import MonitoringSystem
class AdvancedResearchAnalysisSystem:
def __init__(self, config_path: str):
self.config = self._load_config(config_path)
self.gpu_manager = GPUManager()
self.llm_provider = LLMProvider(
provider_type="local",
model_config=self.config.llm_config,
gpu_manager=self.gpu_manager
)
# Initialize core systems
self.agent_runtime = AgentRuntime(self.config)
self.task_scheduler = TaskScheduler()
self.monitoring = MonitoringSystem(self.config.monitoring)
# Initialize RAG and GraphRAG systems
self.rag_system = RAGSystem(self.config.rag_config)
self.graphrag_system = GraphRAGSystem(self.config.graphrag_config)
# Initialize MCP components
self.mcp_client = MCPClient(self.config.mcp_client.servers)
self.mcp_server = MCPServer(
self.agent_runtime.agent_registry,
self.config.mcp_server
)
# Register agents
self._register_agents()
# Start monitoring
self.monitoring.start()
def _register_agents(self):
"""Register all agents defined in the DSL specification."""
# Research Coordinator Agent
research_coordinator = ResearchCoordinatorAgent(
agent_id="research_coordinator",
role="Research Coordinator",
goal="Orchestrate research activities and ensure comprehensive coverage",
llm_provider=self.llm_provider,
capabilities=["task_planning", "resource_allocation", "quality_control"],
tools=["task_manager", "progress_tracker"],
memory_type="episodic",
max_iterations=5
)
self.agent_runtime.register_agent(research_coordinator)
# Web Researcher Agent
web_researcher = WebResearcherAgent(
agent_id="web_researcher",
role="Web Research Specialist",
goal="Gather information from web sources and online databases",
llm_provider=self.llm_provider,
capabilities=["web_search", "content_extraction", "source_validation"],
tools=["web_search_mcp", "content_parser", "url_validator"],
memory_type="vector_store",
concurrent_requests=3,
mcp_client=self.mcp_client
)
self.agent_runtime.register_agent(web_researcher)
# Document Analyst Agent
document_analyst = DocumentAnalystAgent(
agent_id="document_analyst",
role="Document Analysis Expert",
goal="Analyze documents and extract relevant information",
llm_provider=self.llm_provider,
capabilities=["document_parsing", "information_extraction", "summarization"],
tools=["pdf_reader", "doc_parser", "text_extractor"],
memory_type="vector_store",
supported_formats=["pdf", "docx", "txt", "md"],
rag_system=self.rag_system
)
self.agent_runtime.register_agent(document_analyst)
# Data Synthesizer Agent
data_synthesizer = DataSynthesizerAgent(
agent_id="data_synthesizer",
role="Data Synthesis Specialist",
goal="Synthesize findings from multiple sources into coherent insights",
llm_provider=self.llm_provider,
capabilities=["data_integration", "pattern_recognition", "insight_generation"],
tools=["data_merger", "pattern_analyzer", "insight_extractor"],
memory_type="graph_store",
graphrag_system=self.graphrag_system
)
self.agent_runtime.register_agent(data_synthesizer)
# Report Generator Agent
report_generator = ReportGeneratorAgent(
agent_id="report_generator",
role="Report Generation Expert",
goal="Create comprehensive reports from synthesized data",
llm_provider=self.llm_provider,
capabilities=["report_writing", "visualization", "formatting"],
tools=["report_writer", "chart_generator", "formatter"],
memory_type="vector_store",
output_formats=["markdown", "pdf", "html"]
)
self.agent_runtime.register_agent(report_generator)
async def process_research_request(self, research_topic: str) -> Dict[str, Any]:
"""Process a research request through the complete workflow."""
start_time = datetime.now()
self.monitoring.log_request_start(research_topic, start_time)
try:
# Create workflow tasks
tasks = self._create_workflow_tasks(research_topic)
# Execute workflow
results = await self.task_scheduler.execute(tasks)
# Compile final results
final_result = self._compile_results(results)
end_time = datetime.now()
duration = (end_time - start_time).total_seconds()
self.monitoring.log_request_completion(
research_topic,
duration,
final_result
)
return final_result
except Exception as e:
self.monitoring.log_error(research_topic, str(e))
raise
def _create_workflow_tasks(self, research_topic: str) -> List[Task]:
"""Create tasks based on the workflow definition."""
tasks = []
# Initial Planning Task
initial_planning = Task(
task_id="initial_planning",
description="Analyze research request and create execution plan",
assigned_agent="research_coordinator",
input_data={"research_topic": research_topic},
priority="high",
timeout=300
)
tasks.append(initial_planning)
# Web Research Task
web_research = Task(
task_id="web_research",
description="Conduct comprehensive web research on the topic",
assigned_agent="web_researcher",
depends_on=["initial_planning"],
max_iterations=3,
parallel_searches=5
)
tasks.append(web_research)
# Document Analysis Task
document_analysis = Task(
task_id="document_analysis",
description="Analyze relevant documents from knowledge base",
assigned_agent="document_analyst",
depends_on=["initial_planning"],
rag_enabled=True,
max_documents=20
)
tasks.append(document_analysis)
# Data Synthesis Task
data_synthesis = Task(
task_id="data_synthesis",
description="Synthesize findings from all sources",
assigned_agent="data_synthesizer",
depends_on=["web_research", "document_analysis"],
graphrag_enabled=True
)
tasks.append(data_synthesis)
# Report Generation Task
report_generation = Task(
task_id="report_generation",
description="Generate comprehensive research report",
assigned_agent="report_generator",
depends_on=["data_synthesis"],
format="markdown",
include_citations=True
)
tasks.append(report_generation)
return tasks
def _compile_results(self, task_results: Dict[str, Any]) -> Dict[str, Any]:
"""Compile results from all tasks into final output."""
return {
"research_plan": task_results.get("initial_planning", {}),
"web_findings": task_results.get("web_research", {}),
"document_insights": task_results.get("document_analysis", {}),
"synthesized_insights": task_results.get("data_synthesis", {}),
"final_report": task_results.get("report_generation", {}),
"metadata": {
"timestamp": datetime.now().isoformat(),
"agents_used": list(task_results.keys()),
"total_processing_time": sum(
result.get("processing_time", 0)
for result in task_results.values()
)
}
}
async def start_server(self, host: str = "0.0.0.0", port: int = 8000):
"""Start the research system server."""
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
app = FastAPI(title="Advanced Research Analysis System")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.post("/research")
async def conduct_research(request: Dict[str, str]):
try:
research_topic = request.get("topic")
if not research_topic:
raise HTTPException(status_code=400, detail="Topic is required")
result = await self.process_research_request(research_topic)
return result
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health")
async def health_check():
return {
"status": "healthy",
"timestamp": datetime.now().isoformat(),
"agents": len(self.agent_runtime.agents),
"gpu_available": self.gpu_manager.architecture != "cpu"
}
@app.get("/metrics")
async def get_metrics():
return self.monitoring.get_current_metrics()
# Start MCP server
await self.mcp_server.start()
# Start main application server
config = uvicorn.Config(app, host=host, port=port)
server = uvicorn.Server(config)
await server.serve()
Each agent in the system is implemented as a specialized class that inherits from the base Agent class while providing domain-specific functionality:
class ResearchCoordinatorAgent(Agent):
def __init__(self, agent_id, role, goal, llm_provider, capabilities, tools, memory_type, max_iterations):
super().__init__(agent_id, role, goal, llm_provider)
self.capabilities = capabilities
self.tools = tools
self.memory_type = memory_type
self.max_iterations = max_iterations
self.task_tracker = TaskTracker()
async def process_task(self, task):
"""Process coordination tasks with planning and oversight capabilities."""
if task.description.startswith("Analyze research request"):
return await self._create_research_plan(task)
elif task.description.startswith("Monitor progress"):
return await self._monitor_progress(task)
else:
return await super().process_task(task)
async def _create_research_plan(self, task):
"""Create a comprehensive research plan based on the topic."""
research_topic = task.input_data.get("research_topic")
planning_prompt = f"""
As a Research Coordinator, create a comprehensive research plan for the topic: "{research_topic}"
Consider the following aspects:
1. Key research questions to investigate
2. Potential sources of information (web, academic papers, documents)
3. Timeline and resource allocation
4. Quality control measures
5. Expected deliverables
Provide a structured plan that other agents can follow.
"""
context = self._build_context(task)
response = await self.llm_provider.generate(planning_prompt, context)
# Parse the response and create structured plan
research_plan = self._parse_research_plan(response)
# Store plan in memory for future reference
self.memory.store(research_plan, "long_term")
return {
"research_plan": research_plan,
"processing_time": task.get_processing_time(),
"agent_id": self.agent_id
}
def _parse_research_plan(self, response):
"""Parse LLM response into structured research plan."""
# Implementation would parse the response and create structured data
# This is a simplified version for demonstration
return {
"research_questions": self._extract_research_questions(response),
"information_sources": self._extract_sources(response),
"timeline": self._extract_timeline(response),
"quality_measures": self._extract_quality_measures(response),
"deliverables": self._extract_deliverables(response)
}
The Web Researcher Agent demonstrates integration with MCP clients for accessing external search services:
class WebResearcherAgent(Agent):
def __init__(self, agent_id, role, goal, llm_provider, capabilities, tools, memory_type, concurrent_requests, mcp_client):
super().__init__(agent_id, role, goal, llm_provider)
self.capabilities = capabilities
self.tools = tools
self.memory_type = memory_type
self.concurrent_requests = concurrent_requests
self.mcp_client = mcp_client
self.search_history = []
async def process_task(self, task):
"""Process web research tasks with parallel search capabilities."""
if "web research" in task.description.lower():
return await self._conduct_web_research(task)
else:
return await super().process_task(task)
async def _conduct_web_research(self, task):
"""Conduct comprehensive web research using multiple sources."""
research_plan = task.input_data.get("research_plan", {})
research_questions = research_plan.get("research_questions", [])
# Create search queries from research questions
search_queries = self._generate_search_queries(research_questions)
# Conduct parallel searches
search_tasks = []
for query in search_queries[:self.concurrent_requests]:
search_task = self._search_with_mcp(query)
search_tasks.append(search_task)
search_results = await asyncio.gather(*search_tasks, return_exceptions=True)
# Process and synthesize results
synthesized_findings = await self._synthesize_search_results(search_results)
# Store findings in memory
self.memory.store(synthesized_findings, "long_term")
return {
"web_findings": synthesized_findings,
"search_queries_used": search_queries,
"sources_consulted": len(search_results),
"processing_time": task.get_processing_time(),
"agent_id": self.agent_id
}
async def _search_with_mcp(self, query):
"""Perform search using MCP client with multiple services."""
search_services = ["arxiv_search", "pubmed_search", "github_search"]
results = {}
for service in search_services:
try:
result = await self.mcp_client.call_tool(
service,
"search",
{"query": query, "max_results": 10}
)
results[service] = result
except Exception as e:
logging.warning(f"Search failed for {service}: {e}")
results[service] = {"error": str(e)}
return {"query": query, "results": results}
The Document Analyst Agent showcases RAG integration for analyzing documents from the knowledge base:
class DocumentAnalystAgent(Agent):
def __init__(self, agent_id, role, goal, llm_provider, capabilities, tools, memory_type, supported_formats, rag_system):
super().__init__(agent_id, role, goal, llm_provider)
self.capabilities = capabilities
self.tools = tools
self.memory_type = memory_type
self.supported_formats = supported_formats
self.rag_system = rag_system
async def process_task(self, task):
"""Process document analysis tasks with RAG support."""
if task.rag_enabled:
return await self._analyze_with_rag(task)
else:
return await super().process_task(task)
async def _analyze_with_rag(self, task):
"""Analyze documents using RAG for relevant information retrieval."""
research_plan = task.input_data.get("research_plan", {})
research_questions = research_plan.get("research_questions", [])
document_insights = {}
for question in research_questions:
# Retrieve relevant documents
relevant_docs = await self.rag_system.retrieve(
question,
top_k=task.max_documents or 20
)
# Analyze retrieved documents
analysis = await self._analyze_documents(question, relevant_docs)
document_insights[question] = analysis
# Compile comprehensive insights
comprehensive_insights = await self._compile_insights(document_insights)
# Store insights in memory
self.memory.store(comprehensive_insights, "long_term")
return {
"document_insights": comprehensive_insights,
"documents_analyzed": sum(len(docs) for docs in document_insights.values()),
"processing_time": task.get_processing_time(),
"agent_id": self.agent_id
}
async def _analyze_documents(self, question, documents):
"""Analyze a set of documents for a specific research question."""
analysis_prompt = f"""
Research Question: {question}
Analyze the following documents and extract relevant information:
{self._format_documents_for_analysis(documents)}
Provide:
1. Key findings related to the research question
2. Supporting evidence and citations
3. Gaps or limitations in the available information
4. Confidence level in the findings
"""
context = self._build_context_with_documents(question, documents)
response = await self.llm_provider.generate(analysis_prompt, context)
return self._parse_document_analysis(response, documents)
The system includes comprehensive error handling, monitoring, and optimization features that ensure robust operation in production environments. The monitoring system tracks performance metrics, resource utilization, and system health:
class MonitoringSystem:
def __init__(self, config):
self.config = config
self.metrics = {}
self.start_time = datetime.now()
self.request_count = 0
self.error_count = 0
def log_request_start(self, topic, start_time):
"""Log the start of a research request."""
self.request_count += 1
self.metrics[f"request_{self.request_count}"] = {
"topic": topic,
"start_time": start_time,
"status": "in_progress"
}
def log_request_completion(self, topic, duration, result):
"""Log the completion of a research request."""
request_id = f"request_{self.request_count}"
if request_id in self.metrics:
self.metrics[request_id].update({
"status": "completed",
"duration": duration,
"result_size": len(str(result))
})
def get_current_metrics(self):
"""Get current system metrics."""
return {
"uptime": (datetime.now() - self.start_time).total_seconds(),
"total_requests": self.request_count,
"error_rate": self.error_count / max(self.request_count, 1),
"average_response_time": self._calculate_average_response_time(),
"system_health": self._assess_system_health()
}
CONCLUSION
This comprehensive article has presented a complete solution for designing and implementing LLM-based agentic AI systems through a Domain-Specific Language approach. The solution addresses the key challenges in the current landscape by providing an intuitive DSL for specification, a robust framework for implementation, and an intelligent code generation tool that produces production-ready applications.
The DSL design prioritizes declarative simplicity while maintaining the expressiveness necessary for complex multi-agent scenarios. By supporting both textual and graphical representations, the language accommodates different user preferences and use cases. The integration of modern technologies such as MCP, RAG, and GraphRAG ensures that generated applications can leverage the latest advances in AI and knowledge management.
The framework architecture follows clean architecture principles with clear separation of concerns, making it maintainable and extensible. The comprehensive support for different GPU architectures ensures optimal performance regardless of the target hardware, while the modular design allows for easy customization and extension.
The code generation tool bridges the gap between high-level specifications and production-ready code, automatically optimizing configurations based on the target environment and user requirements. The generated applications include comprehensive error handling, monitoring, and optimization features that ensure robust operation in production environments.
The running example demonstrates the practical application of all these concepts in a realistic scenario, showing how the complete system can handle complex multi-agent workflows with sophisticated knowledge management and external service integration. The generated code is production-ready and follows best practices for maintainability and scalability.
This solution represents a significant advancement in making agentic AI accessible to a broader audience while maintaining the sophistication necessary for complex real-world applications. By abstracting away implementation complexities while preserving full control over system behavior, it enables both technical and non-technical users to create powerful multi-agent systems that can solve complex problems across various domains.
The open-source approach ensures that the solution remains accessible and can evolve with the rapidly advancing field of AI, while the modular architecture allows for easy integration of new technologies and capabilities as they emerge. This foundation provides a solid base for the future development of even more sophisticated agentic AI systems.
