Soorma Common

Common models and DTOs shared across Soorma platform services.

Installation

pip install -e .

Usage

from soorma_common.models import (
    AgentDefinition,
    AgentCapability,
    EventDefinition,
    SemanticMemoryCreate,
    EpisodicMemoryCreate,
    WorkingMemorySet,
)

# Create an agent definition (v0.8.1+: consumed_event and produced_events require EventDefinition objects)
agent = AgentDefinition(
    agent_id="my-agent",
    name="My Agent",
    description="A sample agent",
    capabilities=[
        AgentCapability(
            task_name="process_data",
            description="Process incoming data",
            consumed_event=EventDefinition(
                event_name="data.received",
                topic="action-requests",
                description="Incoming data event"
            ),
            produced_events=[
                EventDefinition(
                    event_name="data.processed",
                    topic="action-results",
                    description="Successfully processed data"
                ),
                EventDefinition(
                    event_name="data.error",
                    topic="action-results",
                    description="Data processing error"
                ),
            ]
        )
    ]
)

# Create semantic memory (knowledge storage)
semantic = SemanticMemoryCreate(
    agent_id="researcher",
    content="Python is a high-level programming language",
    metadata={"category": "programming", "source": "textbook"}
)

# Create episodic memory (interaction history)
episodic = EpisodicMemoryCreate(
    agent_id="assistant",
    role="user",
    content="What is the weather like today?",
    metadata={"session_id": "abc-123"}
)

# Create working memory (plan state)
working = WorkingMemorySet(
    value={"current_step": 2, "total_steps": 5, "status": "in_progress"}
)

Models

Agent Registry

• AgentCapability - Describes a single capability or task an agent can perform
• AgentDefinition - Defines a single agent in the system
• AgentRegistrationRequest - Request to register a new agent
• AgentRegistrationResponse - Response after registering an agent
• AgentQueryRequest - Request to query agents
• AgentQueryResponse - Response containing agent definitions
• DiscoveredAgent - Agent discovery result; includes full AgentCapability list with get_consumed_schemas() / get_produced_schemas() helpers

Schema Registry (v0.8.1+)

• PayloadSchema - Schema definition with semantic versioning (schema_name, version, json_schema)
• PayloadSchemaRegistration - Request to register a new schema (body; auth headers provide tenant/user)
• PayloadSchemaResponse - Registration response with schema_name, version, success, message
• PayloadSchemaRegistrationRequest - Envelope wrapping PayloadSchema in schema key
• PayloadSchemaListResponse - Response containing a list of PayloadSchema entries

Event Registry

• EventDefinition - Defines a single event in the system (v0.8.1+: add payload_schema_name / response_schema_name for Schema Registry references)
• EventRegistrationRequest - Request to register a new event
• EventRegistrationResponse - Response after registering an event
• EventQueryRequest - Request to query events
• EventQueryResponse - Response containing event definitions

A2A (Agent-to-Agent Protocol, v0.8.1+)

Import from soorma_common.a2a

• A2AAgentCard - Agent capability advertisement card (name, description, URL, skills)
• A2ASkill - Individual skill entry advertised in an agent card
• A2AAuthentication - Authentication descriptor for an A2A agent card
• A2ATask - A2A task representation (id, messages, current status)
• A2ATaskStatus - Enum: submitted, working, completed, failed, canceled
• A2AMessage - Message in an A2A task conversation (role + parts)
• A2APart - Content part of an A2A message (text or data)
• A2ATaskResponse - Response payload returned from an A2A task handler
• A2AAuthType - Enum: none, bearer_token, api_key

Memory Service (CoALA Framework)

The Memory Service implements the CoALA (Cognitive Architectures for Language Agents) framework with four memory types:

Authentication Note: Memory Service supports dual authentication:

• JWT Token (User sessions): Provides tenant_id + user_id from token
• API Key (Agent operations): Provides tenant_id + agent_id, requires explicit user_id in request parameters

See Memory Service SDK documentation for details.

Semantic Memory (Knowledge Base)

• SemanticMemoryCreate - Add knowledge to semantic memory
• SemanticMemoryResponse - Semantic memory entry with similarity score
• Use cases: Store facts, documentation, learned information
• Features: Vector search, RAG (Retrieval-Augmented Generation)
• Scoping: Tenant-level (shared across users in tenant)

Episodic Memory (Interaction History)

• EpisodicMemoryCreate - Log an interaction or event
• EpisodicMemoryResponse - Episodic memory entry with timestamp
• Use cases: Conversation history, user interactions, audit logs
• Features: Temporal recall, role-based filtering (user/assistant/system/tool)
• Scoping: Tenant + User + Agent (user-specific conversation history)

Procedural Memory (Skills & Procedures)

• ProceduralMemoryResponse - Skill or procedure with trigger conditions
• Use cases: Dynamic prompts, few-shot examples, user-specific agent customization
• Features: Context-aware retrieval, trigger-based activation, personalization
• Scoping: Tenant + User + Agent (enables per-user agent customization)

Working Memory (Plan State)

• WorkingMemorySet - Store plan-scoped state
• WorkingMemoryResponse - Working memory entry
• Use cases: Multi-agent collaboration, plan execution state, shared variables
• Features: Plan-scoped isolation, key-value storage
• Scoping: Tenant + Plan (shared state within plan execution)

Memory Service Examples

Semantic Memory (Knowledge Storage)

from soorma_common.models import SemanticMemoryCreate

# Store knowledge
memory = SemanticMemoryCreate(
    agent_id="researcher",
    content="FastAPI is a modern web framework for Python",
    metadata={"category": "web-dev", "language": "python"}
)

Episodic Memory (Interaction History)

from soorma_common.models import EpisodicMemoryCreate

# Log user interaction
memory = EpisodicMemoryCreate(
    agent_id="chatbot",
    role="user",  # user, assistant, system, tool
    content="How do I deploy to production?",
    metadata={"session_id": "session-123", "timestamp": "2025-12-23T10:00:00Z"}
)

Working Memory (Plan State)

from soorma_common.models import WorkingMemorySet

# Store plan execution state
state = WorkingMemorySet(
    value={
        "plan_id": "research-plan-1",
        "current_phase": "data_collection",
        "completed_tasks": ["search", "filter"],
        "pending_tasks": ["analyze", "report"],
        "research_summary": "Found 50 relevant papers..."
    }
)

Development

# Install in editable mode
pip install -e .

# Run tests
pytest

# Build package
python -m build

Version History

See CHANGELOG.md for version history and release notes.