-added databases subagents,-improved master agent prompt to invoke the correct subagent (#23)

Author: pavanjava

devops_agent/cli.py

@@ -6,7 +6,7 @@
 from rich.panel import Panel
 from rich.prompt import Prompt
 from devops_agent.core.master_agent import execute_master_agent
-from devops_agent.core.log_analysis_agent import execute_log_analysis_agent
+from devops_agent.loganalytics.log_analysis_agent import execute_log_analysis_agent
 
 console = Console()
 

devops_agent/core/master_agent.py

@@ -11,11 +11,14 @@
 from agno.vectordb.chroma import ChromaDb
 from agno.knowledge.embedder.fastembed import FastEmbedEmbedder
 from qdrant_client import QdrantClient
-from qdrant_client.http.models import VectorParams, Distance, models
-
-from devops_agent.core.devops_agent import execute_devops_agent
-from devops_agent.core.kubernetes_agent import execute_k8s_agent
-from devops_agent.core.terraform_agent import execute_terraform_agent
+from qdrant_client.http.models import VectorParams, Distance
+
+from devops_agent.devops.devops_agent import execute_devops_agent
+from devops_agent.k8s.kubernetes_agent import execute_k8s_agent
+from devops_agent.terraform.terraform_agent import execute_terraform_agent
+from devops_agent.database.db_architect import execute_db_architect_agent
+from devops_agent.database.db_optimizer import execute_db_optimization_agent
+from devops_agent.database.sql_pro import execute_db_sql_pro_agent
 from rich.console import Console
 from dotenv import load_dotenv, find_dotenv
 from devops_agent.utils.stream_handler import StreamingResponseHandler
@@ -68,13 +71,60 @@ def execute_master_agent(provider: str, model_str: str, user_query: str = None,
             execute_devops_agent(provider=provider, model=model_str, debug_mode=debug_mode, reasoning=reasoning),
             execute_k8s_agent(provider=provider, model=model_str, debug_mode=debug_mode, reasoning=reasoning),
             execute_terraform_agent(provider=provider, model=model_str, debug_mode=debug_mode, reasoning=reasoning),
+            execute_db_architect_agent(provider=provider, model=model_str, debug_mode=debug_mode, reasoning=reasoning),
+            execute_db_optimization_agent(provider=provider, model=model_str, debug_mode=debug_mode, reasoning=reasoning),
+            execute_db_sql_pro_agent(provider=provider, model=model_str, debug_mode=debug_mode, reasoning=reasoning),
         ],
         instructions=[
-            "You are a intelligent router that directs questions to the appropriate agent.",
-            "If the user asks in a non devops or k8s question whose agent is not a team member, respond in English with:",
-            "'I can only answer in the following technologies: Devops, terraform & Kubernetes Architecture on Multiple clouds. Please ask your question in one of these technologies.'",
-            "Always check the technology or domain of the user's input before routing to an agent.",
-            "For unsupported technologies like coding, flowcharts, analytics etc respond in English with the above message.",
+            "You are an intelligent router that analyzes user questions and directs them to the most appropriate specialist "
+            "agent based on their expertise domain.",
+
+            "AGENT SPECIALIZATIONS:",
+            "- DevOps Agent: CI/CD pipelines, cloud infrastructure automation, deployment strategies, monitoring, container orchestration workflows, multi-cloud DevOps practices",
+            "- Kubernetes Agent: K8s architecture, cluster management, workload deployment, service mesh, helm charts, operators, scaling strategies, troubleshooting",
+            "- Terraform Agent: Infrastructure as Code, Terraform/OpenTofu modules, state management, multi-cloud provisioning, resource automation, IaC best practices",
+            "- Database Architect Agent: Database technology selection, schema design from scratch, data modeling, migration planning, scalability architecture, greenfield/re-architecture projects",
+            "- Database Optimization Agent: Query performance tuning, indexing strategies, N+1 resolution, caching architectures, existing database optimization, bottleneck elimination",
+            "- SQL Pro Agent: Advanced SQL queries, analytical techniques, OLTP/OLAP optimization, cloud-native database queries, complex data analysis, reporting",
+
+            "ROUTING DECISION PROCESS:",
+            "1. Analyze the user's question to identify the primary technology domain and specific task",
+            "2. Determine if the question involves design/architecture vs optimization vs implementation",
+            "3. For database questions, distinguish between:",
+            "   - Architecture/Design (new systems, technology selection, schema design) → Database Architect",
+            "   - Performance/Optimization (slow queries, indexing, caching, tuning existing systems) → Database Optimization",
+            "   - Query Writing/Analysis (SQL development, complex queries, analytics) → SQL Pro",
+            "4. For infrastructure questions, distinguish between:",
+            "   - IaC/Provisioning (Terraform, resource creation, state management) → Terraform Agent",
+            "   - Container Orchestration (K8s workloads, pods, services, deployments) → Kubernetes Agent",
+            "   - General DevOps (CI/CD, automation, monitoring, deployments) → DevOps Agent",
+            "5. Route to the single most relevant agent - avoid over-routing to multiple agents unless truly necessary",
+
+            "DATABASE ROUTING EXAMPLES:",
+            "✓ 'Design a database for e-commerce platform' → Database Architect (greenfield design)",
+            "✓ 'My query is slow, how do I optimize it?' → Database Optimization (performance tuning)",
+            "✓ 'Write a SQL query for cohort analysis' → SQL Pro (query development)",
+            "✓ 'Should I use PostgreSQL or MongoDB?' → Database Architect (technology selection)",
+            "✓ 'Create indexes for better performance' → Database Optimization (optimization)",
+            "✓ 'Complex window function for analytics' → SQL Pro (advanced SQL)",
+
+            "INFRASTRUCTURE ROUTING EXAMPLES:",
+            "✓ 'Create Terraform module for AWS VPC' → Terraform Agent",
+            "✓ 'Deploy microservices on Kubernetes' → Kubernetes Agent",
+            "✓ 'Setup CI/CD pipeline for multi-cloud deployment' → DevOps Agent",
+            "✓ 'Troubleshoot pod crash loops' → Kubernetes Agent",
+            "✓ 'Implement blue-green deployment strategy' → DevOps Agent",
+
+            "UNSUPPORTED REQUESTS:",
+            "If the question is outside these domains (e.g., frontend development, mobile apps, data science, machine learning, general coding unrelated to infrastructure), respond with:",
+            "'I specialize in DevOps, Cloud Infrastructure, Kubernetes, Terraform/IaC, and Database Architecture/Optimization. Your question appears to be about [detected topic]. Please ask questions related to: cloud infrastructure automation, container orchestration, infrastructure as code, database design, query optimization, or SQL development.'",
+
+            "MULTI-AGENT SCENARIOS:",
+            "Only involve multiple agents when the question genuinely spans domains:",
+            "- 'Deploy database on Kubernetes with Terraform' → Terraform (infrastructure) + Kubernetes (deployment) + Database Architect (DB setup)",
+            "- 'Optimize database in containerized environment' → Database Optimization (tuning) + Kubernetes (container config)",
+
+            "Always prioritize the PRIMARY expertise needed and route to that agent first. Think step-by-step about which agent's core competency best matches the user's need."
         ],
         tools=[ReasoningTools()],  # Enable reasoning capabilities
         knowledge=knowledge,

devops_agent/database/__init__.py

@@ -0,0 +1,39 @@
+from textwrap import dedent
+
+from agno.agent import Agent
+from rich.console import Console
+from rich.panel import Panel
+from devops_agent.utils.model_provider import get_model
+
+from devops_agent.utils.prompt_generator_from_poml import prompt_from_poml
+
+db_architect_prompt = prompt_from_poml('db_architect.poml')
+
+console = Console()
+
+
+def execute_db_architect_agent(provider: str, model: str, debug_mode: bool = False, reasoning: bool = False) -> Agent:
+    console.print(Panel.fit(
+        "[bold cyan]DB Architect Agent Invoking...[/bold cyan]",
+        border_style="cyan"
+    ))
+
+    model = get_model(provider=provider, model_str=model)
+
+    db_optmization_assist = Agent(
+        name="DB Architect Agent",
+        model=model,
+        description=dedent("""\
+        You are Expert database architect specializing in data layer design from scratch, technology selection, schema modeling,
+        and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies,
+        migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of
+        existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.\
+        """),
+        instructions=db_architect_prompt,
+        stream_intermediate_steps=True,
+        markdown=True,
+        debug_mode=debug_mode,
+        reasoning=reasoning
+    )
+
+    return db_optmization_assist

devops_agent/database/db_architect.py

@@ -0,0 +1,39 @@
+from textwrap import dedent
+
+from agno.agent import Agent
+from rich.console import Console
+from rich.panel import Panel
+from devops_agent.utils.model_provider import get_model
+
+from devops_agent.utils.prompt_generator_from_poml import prompt_from_poml
+
+db_optimization_prompt = prompt_from_poml('db_optimizer.poml')
+
+console = Console()
+
+
+def execute_db_optimization_agent(provider: str, model: str, debug_mode: bool = False, reasoning: bool = False) -> Agent:
+    console.print(Panel.fit(
+        "[bold cyan]DB Optimization Agent Invoking...[/bold cyan]",
+        border_style="cyan"
+    ))
+
+    model = get_model(provider=provider, model_str=model)
+
+    db_optmization_assist = Agent(
+        name="DB Optimization  Agent",
+        model=model,
+        description=dedent("""\
+        You are Expert database optimizer with comprehensive knowledge of modern database performance tuning, query optimization
+        , and scalable architecture design. Masters multi-database platforms, advanced indexing strategies, caching 
+        architectures, and performance monitoring. Specializes in eliminating bottlenecks, optimizing complex queries, 
+        and designing high-performance database systems.\
+        """),
+        instructions=db_optimization_prompt,
+        stream_intermediate_steps=True,
+        markdown=True,
+        debug_mode=debug_mode,
+        reasoning=reasoning
+    )
+
+    return db_optmization_assist

devops_agent/database/db_optimizer.py

@@ -0,0 +1,38 @@
+from textwrap import dedent
+
+from agno.agent import Agent
+from rich.console import Console
+from rich.panel import Panel
+from devops_agent.utils.model_provider import get_model
+
+from devops_agent.utils.prompt_generator_from_poml import prompt_from_poml
+
+db_sql_pro_prompt = prompt_from_poml('db_sql_pro.poml')
+
+console = Console()
+
+
+def execute_db_sql_pro_agent(provider: str, model: str, debug_mode: bool = False, reasoning: bool = False) -> Agent:
+    console.print(Panel.fit(
+        "[bold cyan]Sql Pro Agent Invoking...[/bold cyan]",
+        border_style="cyan"
+    ))
+
+    model = get_model(provider=provider, model_str=model)
+
+    db_optmization_assist = Agent(
+        name="Sql Pro Agent",
+        model=model,
+        description=dedent("""\
+        You are Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. 
+        Expert in performance tuning, data modeling, and hybrid analytical systems. Use PROACTIVELY for database 
+        optimization or complex analysis.\
+        """),
+        instructions=db_sql_pro_prompt,
+        stream_intermediate_steps=True,
+        markdown=True,
+        debug_mode=debug_mode,
+        reasoning=reasoning
+    )
+
+    return db_optmization_assist