Master framework for developing specialized AI agents with domain supremacy
graph TD
classDef agent fill:#f9f,stroke:#333,stroke-width:2px
classDef knowledge fill:#bbf,stroke:#333,stroke-width:2px
classDef protocol fill:#bfb,stroke:#333,stroke-width:2px
classDef governance fill:#fbb,stroke:#333,stroke-width:2px
classDef monitoring fill:#ffb,stroke:#333,stroke-width:2px
classDef core fill:#ddd,stroke:#333,stroke-width:2px
subgraph Core["Core Components"]
MCA["Master Control Agent"]
GE["Governance Engine"]
MS["Monitoring System"]
P["Protocols"]
end
subgraph Agents["Domain-Specific Agents"]
DSA1["PowerPoint Agent"]
DSA2["Data Analysis Agent"]
DSAn["Custom Domain Agent"]
end
subgraph Knowledge["Knowledge Bases"]
KB1["PowerPoint Knowledge"]
KB2["Data Analysis Knowledge"]
KBn["Custom Domain Knowledge"]
end
subgraph Protocols["Interaction Protocols"]
DP["Direct Protocol"]
MP["Mediated Protocol"]
CP["Collaborative Protocol"]
SP["Supervised Protocol"]
end
subgraph Governance["Governance & Compliance"]
DR["Domain Rules"]
VR["Validation Rules"]
PR["Performance Rules"]
end
subgraph Monitoring["Performance Monitoring"]
RT["Response Time"]
A["Accuracy"]
PM["Performance Metrics"]
end
Human["Human User"] --> CLI
CLI["Command Line Interface"] --> MCA
MCA --> DSA1
MCA --> DSA2
MCA --> DSAn
DSA1 --> KB1
DSA2 --> KB2
DSAn --> KBn
P --> DP
P --> MP
P --> CP
P --> SP
GE --> DR
GE --> VR
GE --> PR
MS --> RT
MS --> A
MS --> PM
MCA --> GE
MCA --> MS
MCA --> P
class MCA,GE,MS,P core
class DSA1,DSA2,DSAn agent
class KB1,KB2,KBn knowledge
class DP,MP,CP,SP protocol
class DR,VR,PR governance
class RT,A,PM monitoring
Elementum DSA is a comprehensive framework for creating, deploying, and managing domain-specific AI agents that excel in targeted knowledge domains. The framework enforces robust knowledge structures, standardized interaction protocols, and strict governance policies.
- Standardized agent implementation templates
- Version-controlled knowledge bases
- Cross-agent collaboration protocols
- Performance monitoring systems
- Governance and compliance frameworks
/core- Core framework components and interfaces/agents- Agent templates and implementations/knowledge- Knowledge base structures and schemas/protocols- Interaction and communication protocols/examples- Example implementations/docs- Comprehensive documentation/tests- Test frameworks and validation tools
# Clone the repository
git clone https://github.com/PWereh/elementum-dsa-project.git
cd elementum-dsa-project
# Install in development mode with all dependencies
pip install -e ".[dev]"# Clone the repository
git clone https://github.com/PWereh/elementum-dsa-project.git
cd elementum-dsa-project
# Install the package
pip install .# Process a single query
elementum-dsa --query "Generate presentation structure for technical overview" --domain "presentation_development"
# Process queries from a file
elementum-dsa --query-file example_queries.json --output-file responses.json
# List available agents
elementum-dsa --list-agentsfrom elementum_dsa import ElementumDSA
# Initialize the framework
framework = ElementumDSA()
# Process a query
response = framework.process_query(
"Generate presentation structure for technical overview",
domain="presentation_development"
)
print(response)
# Get available agents
agents = framework.get_available_agents()
print(agents)from elementum_dsa import create_custom_agent
# Define knowledge implementation
def custom_knowledge():
return {
"core_knowledge": {
"concepts": {
"custom_concept": "Definition of custom concept"
},
"terminology": {
"custom_term": "Definition of custom term"
},
"principles": {
"custom_principle": "Description of custom principle"
}
},
"rules": {
"constraints": {
"custom_constraint": "Description of custom constraint"
},
"requirements": {
"custom_requirement": "Description of custom requirement"
},
"guidelines": {
"custom_guideline": "Description of custom guideline"
}
},
"best_practices": {
"recommended": {
"custom_practice": "Description of recommended practice"
},
"optional": {
"custom_practice": "Description of optional practice"
},
"discouraged": {
"custom_practice": "Description of discouraged practice"
}
},
"validation": {
"input": {
"custom_rule": "Description of input validation rule"
},
"process": {
"custom_rule": "Description of process validation rule"
},
"output": {
"custom_rule": "Description of output validation rule"
}
},
"integration": {
"apis": {
"custom_api": "Description of custom API"
},
"services": {
"custom_service": "Description of custom service"
},
"data_sources": {
"custom_source": "Description of custom data source"
}
}
}
# Create custom agent
custom_agent = create_custom_agent(
domain="custom_domain",
version=1.0,
capabilities=["capability_1", "capability_2", "capability_3"],
knowledge_implementation=custom_knowledge
)
# Register with framework
framework = ElementumDSA()
framework.register_agent(custom_agent)
# Use custom agent
response = framework.process_query(
"Process with capability_1",
domain="custom_domain"
)
print(response)from elementum_dsa import ElementumDSA
# Initialize framework
framework = ElementumDSA()
# Collaborative query
response = framework.collaborate(
"Create a presentation with statistical analysis",
primary_agent_id="Elementum-DSA-PRESENTATION_DEVELOPMENT-AGENT-V1.0",
support_agent_ids=["Elementum-DSA-DATA_ANALYSIS-AGENT-V1.0"]
)
print(response)from elementum_dsa import ElementumDSA
# Initialize framework
framework = ElementumDSA()
# Get performance metrics
metrics = framework.get_performance_metrics()
print(metrics)- Agent Development Guide - Detailed guide for agent development
- Knowledge Development Guide - Guide for knowledge base development
- Protocol Development Guide - Guide for protocol implementation
- Governance Guide - Governance and compliance guidelines
- Integration Guide - System integration guidelines
Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.
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