The Konf platform addresses fundamental challenges in orchestrating multi-agent AI systems at scale: handling stochastic processes, maintaining contextual memory across interactions, and composing complex workflows declaratively. This work presents a modular architecture consisting of three core components—Sutra (workflow orchestration), Smrti (hierarchical memory), and a tool integration layer—designed to enable scalable, maintainable, and observable multi-agent systems. The platform demonstrates applicability beyond agentic AI, extending to domains such as federated learning, robotics coordination, and distributed stochastic process management.
1. System Architecture Overview
The Konf platform adopts a layered architecture where each component addresses a specific aspect of multi-agent coordination:
┌─────────────────────────────────────────────────────────────────┐
│ APPLICATION LAYER │
│ (Multi-Agent Workflows, Use Cases) │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ ORCHESTRATION LAYER │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│ │ SUTRA │ │ SMRTI │ │ TOOL REGISTRY │ │
│ │ Workflow │ │ Hierarchical│ │ (konf-tools) │ │
│ │ Engine │ │ Memory │ │ │ │
│ └──────────────┘ └──────────────┘ └──────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ INFRASTRUCTURE LAYER │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│ │ LLM │ │ Vector │ │ External APIs │ │
│ │ Providers │ │ Databases │ │ & Services │ │
│ └──────────────┘ └──────────────┘ └──────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
1.1 Design Principles
- Declarative Specification: Workflows defined in YAML, enabling version control, composition, and runtime validation
- Provider Agnosticism: Abstraction layer allows swapping LLM providers, memory backends, and tools without workflow changes
- Type Safety: JSON Schema-based validation catches errors at specification time rather than runtime
- Observability: Built-in tracing and metrics for debugging complex multi-agent interactions
- Modularity: Components operate independently, enabling incremental adoption and testing
2. Component Architecture
2.1 Sutra: Declarative Workflow Orchestration
Sutra provides a graph-based execution engine for multi-agent workflows. Agents are specified as directed acyclic graphs (DAGs) with support for cycles, conditional routing, and parallel execution.
# Example: Research Agent with ReAct Pattern
name: research_agent
version: "1.0"
format: advanced
entry: analyze_query
llm_profiles:
default:
model: openai/gpt-4
temperature: 0.7
timeout: 60
nodes:
- id: analyze_query
type: llm
profile: default
prompt: |
Query: {{ input }}
What information do we need to answer this?
List specific search queries.
edges:
- target: search_web
- id: search_web
type: tool
tool: web_search
args:
query: "{{ analyze_query }}"
edges:
- target: synthesize
- id: synthesize
type: llm
profile: default
prompt: |
Original query: {{ input }}
Search results: {{ search_web }}
Synthesize a comprehensive answer.
edges:
- target: END
Key Features:
- Support for sequential, parallel, and conditional execution patterns
- Built-in reasoning patterns: ReAct, Chain-of-Thought, Tree-of-Thoughts
- Dynamic agent composition at runtime
- Retry logic, timeout handling, and error recovery
- LangGraph compilation for execution
2.2 Smrti: Hierarchical Memory System
Smrti implements a multi-tier memory architecture inspired by cognitive science, providing working memory, semantic storage, and long-term episodic persistence.
Memory Tier Hierarchy:
┌──────────────────────────────────────────────────────────────┐
│ WORKING MEMORY (Context Window) │
│ - Current conversation state │
│ - Immediate context (last N turns) │
│ - TTL: Session duration │
└──────────────────────────────────────────────────────────────┘
▼
┌──────────────────────────────────────────────────────────────┐
│ SHORT-TERM MEMORY (Recent Context) │
│ - Recent interactions (hours to days) │
│ - Summarized conversation history │
│ - TTL: 24-72 hours │
└──────────────────────────────────────────────────────────────┘
▼
┌──────────────────────────────────────────────────────────────┐
│ EPISODIC MEMORY (Event Sequences) │
│ - Structured event logs │
│ - Agent action histories │
│ - TTL: 7-30 days │
└──────────────────────────────────────────────────────────────┘
▼
┌──────────────────────────────────────────────────────────────┐
│ SEMANTIC MEMORY (Knowledge Graph) │
│ - Entity relationships │
│ - Factual knowledge │
│ - TTL: Permanent with decay │
└──────────────────────────────────────────────────────────────┘
▼
┌──────────────────────────────────────────────────────────────┐
│ LONG-TERM MEMORY (Vector Embeddings) │
│ - Semantic similarity search │
│ - Document chunks │
│ - TTL: Permanent │
└──────────────────────────────────────────────────────────────┘
Memory Operations:
# Storing in appropriate tier
- id: store_insight
type: memory
operation: store
tier: SEMANTIC
data:
entities: ["{{ extracted_entities }}"]
relationships: ["{{ extracted_relations }}"]
timestamp: "{{ now }}"
# Retrieving with hybrid search
- id: retrieve_context
type: memory
operation: retrieve
tier: LONG_TERM
query: "{{ user_query }}"
strategy: hybrid # keyword + vector similarity
limit: 5
2.3 Tool Integration Layer
The tool registry provides a unified interface for external service integration with type safety and sandboxed execution.
Tool Registry
Dynamic discovery and registration of tools with JSON Schema validation
Adapter Pattern
Uniform interface for APIs, databases, and external services
Sandboxing
Jinja2-based templating with restricted evaluation context
3. Advanced Orchestration Patterns
3.1 Multi-Agent Coordination
Sutra enables complex multi-agent systems through declarative composition. Agents can be nested as tools, enabling hierarchical delegation:
# Coordinator agent delegates to specialists
nodes:
- id: classify_request
type: llm
prompt: |
Request: {{ input }}
Which specialist should handle this?
- research_agent: information gathering
- code_agent: programming tasks
- analysis_agent: data analysis
edges:
- target: research_specialist
condition: "'research' in classify_request"
- target: code_specialist
condition: "'code' in classify_request"
- id: research_specialist
type: tool
tool: agents:research_agent
args:
input: "{{ input }}"
edges:
- target: synthesize_results
- id: code_specialist
type: tool
tool: agents:code_agent
args:
input: "{{ input }}"
edges:
- target: synthesize_results
3.2 Reasoning Patterns
The platform implements several reasoning patterns as composable node types:
- ReAct (Reason + Act): Iterative reasoning with tool usage and reflection
- Chain-of-Thought: Multi-step decomposition of complex problems
- Tree-of-Thoughts: Parallel exploration of solution paths with evaluation
- Self-Critique: Iterative refinement through self-evaluation
# Tree-of-Thoughts example
- id: explore_solutions
type: tree_of_thoughts
branches: 3
depth: 2
prompt: "Generate solution approaches for: {{ problem }}"
evaluation_prompt: |
Rate this solution 1-10:
Solution: {{ solution }}
Criteria: feasibility, cost, time
selection_strategy: best
edges:
- target: implement_solution
4. Research Contributions and Vision
4.1 Problem Statement
Current multi-agent frameworks face several limitations:
- Lack of type safety leads to runtime failures in production
- No standardized memory hierarchy for context management
- Limited composability of reasoning patterns
- Insufficient observability for debugging complex agent interactions
- Tight coupling between orchestration logic and infrastructure
4.2 Proposed Solutions
The Konf platform addresses these challenges through:
- Static Analysis: YAML validation with JSON Schema catches errors before deployment
- Memory Abstraction: Hierarchical memory tiers with automatic consolidation policies
- Declarative Composition: Node types as composable building blocks
- Tracing Integration: Built-in observability with LangFuse/Langsmith
- Provider Abstraction: Dependency injection enables testing and provider swapping
4.3 Beyond Agentic AI
The declarative orchestration model extends to other domains requiring coordination of stochastic processes:
Federated Learning
Coordinate distributed model training across edge devices with heterogeneous hardware and unreliable networks
Robot Fleet Management
Orchestrate warehouse robots with collision avoidance, task allocation, and failure recovery
Clinical Trial Coordination
Manage multi-site trials with protocol adherence, adverse event monitoring, and adaptive protocols
Smart Manufacturing
Predictive maintenance, quality control, and supply chain optimization with real-time adaptation
5. Implementation Status
5.1 Production Projects
Two production applications demonstrate the platform's capabilities:
Aspire AI
A life assistant chatbot helping users with goal setting, task management, and personal development. Built with Sutra orchestration and Smrti memory system for context-aware conversations.
Apartmint
An intelligent real estate chatbot that helps users find housing listings through natural conversation. Demonstrates multi-step reasoning and tool integration for property search and filtering.
5.2 Component Status
Component Status (October 2025): Sutra (Orchestration) ████████████████████ 100% ✅ ├─ YAML specification Complete ├─ Graph execution Complete ├─ LLM adapters Complete (OpenAI, Anthropic, Gemini) ├─ Tool integration Complete └─ Tracing Complete Smrti (Memory) ███████████████░░░░░ 75% 🚧 ├─ Tier architecture Complete ├─ Vector storage Complete ├─ Consolidation logic In progress └─ Query optimization Planned Tool Integration ████████████████░░░░ 80% 🚧 ├─ Registry system Complete ├─ Basic tools Complete ├─ MCP integration In progress └─ Sandboxing Planned Gateway (API) ███████████░░░░░░░░░ 55% 🚧 ├─ Session management Complete ├─ Multi-tenancy In progress ├─ Rate limiting Planned └─ Deployment tools Planned
6. Future Research Directions
6.1 Static Analysis Engine
Develop compile-time validation for agent specifications, including:
- Type checking between node inputs/outputs
- Variable resolution and scope analysis
- Reachability analysis for detecting unreachable nodes
- Cycle detection with stop condition validation
- Performance analysis and optimization suggestions
6.2 Vak Language
Development of a domain-specific language for agent workflows (~70% complete) that provides:
- True compile-time type safety
- Native protocol definitions for multi-agent contracts
- First-class memory operations
- Semantic type system for agent capabilities
6.3 Formal Verification
Investigate formal methods for verifying agent properties:
- Termination guarantees for iterative reasoning patterns
- Safety properties for tool execution
- Liveness properties for multi-agent protocols
7. References and Related Work
- LangChain/LangGraph: Graph-based orchestration, lacks declarative specification
- AutoGPT: Autonomous agents, limited composability
- CrewAI: Role-based agents, no memory hierarchy
- Semantic Kernel: Microsoft's agent framework, tight Azure coupling
- DSPy: Programming framework for LLM applications, different abstraction level
8. Repository Structure
konf-platform/
├── sutra/ # Orchestration engine
│ ├── src/sutra/
│ │ ├── declarative/ # YAML spec parsing
│ │ ├── executor/ # Graph execution
│ │ ├── llm/ # Provider adapters
│ │ └── tools/ # Tool registry
│ └── docs/ # User guides, examples
│
├── smrti/ # Memory system
│ ├── src/
│ │ ├── tiers/ # Memory tier implementations
│ │ ├── consolidation/ # Memory lifecycle
│ │ └── query/ # Retrieval strategies
│ └── docs/
│
├── konf-tools/ # Tool integration
│ ├── src/
│ │ ├── registry/ # Tool discovery
│ │ ├── adapters/ # External service adapters
│ │ └── validation/ # JSON Schema validation
│ └── docs/
│
├── konf-gateway/ # API gateway
│ ├── src/
│ │ ├── api/ # REST endpoints
│ │ ├── session/ # Session management
│ │ └── auth/ # Multi-tenancy
│ └── docs/
│
└── ideas-and-docs/ # Research documentation
├── docs/design/ # Architecture docs
├── docs/research/ # Research papers
└── docs/examples/ # Use cases