See how MoltSpeak handles common agent-to-agent communication patterns.
A simple query-response pattern for getting weather data.
{
"v": "0.1",
"id": "a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
"ts": 1703280000000,
"op": "query",
"from": {
"agent": "assistant-alpha",
"org": "acme-corp"
},
"to": {
"agent": "weather-service",
"org": "weather-api"
},
"p": {
"domain": "weather",
"intent": "current",
"params": {
"location": "San Francisco, CA",
"units": "metric"
}
},
"cls": "pub"
}{
"v": "0.1",
"id": "f6e5d4c3-b2a1-4089-9876-543210fedcba",
"ts": 1703280001234,
"op": "respond",
"re": "a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
"from": {
"agent": "weather-service",
"org": "weather-api"
},
"to": {
"agent": "assistant-alpha",
"org": "acme-corp"
},
"p": {
"status": "success",
"data": {
"location": "San Francisco, CA",
"temp_c": 18,
"feels_like_c": 16,
"conditions": "partly-cloudy",
"humidity": 65,
"wind_kph": 12
}
},
"cls": "pub"
}Delegating a research task to a specialized agent with constraints.
{
"v": "0.1",
"id": "task-001-research",
"ts": 1703280000000,
"op": "task",
"from": {
"agent": "manager-bot",
"org": "research-lab"
},
"to": {
"agent": "research-specialist",
"org": "research-lab"
},
"p": {
"action": "create",
"task_id": "research-2024-001",
"type": "literature-review",
"description": "Find and summarize recent papers on LLM alignment techniques",
"constraints": {
"max_results": 15,
"recency": "12mo",
"sources": ["arxiv", "semanticscholar", "openreview"],
"min_citations": 5,
"topics": ["RLHF", "constitutional AI", "red teaming"]
},
"deadline": 1703366400000,
"priority": "high",
"callback": {
"on_complete": true,
"on_progress": true,
"progress_interval_ms": 300000
},
"output_format": {
"type": "structured",
"schema": "literature-review-v1",
"include": ["summary", "key_findings", "methodology", "relevance_score"]
}
},
"cls": "int",
"cap": ["task.create"]
}One agent invoking a tool through another agent's capabilities.
{
"v": "0.1",
"id": "tool-invoke-001",
"ts": 1703280000000,
"op": "tool",
"from": {
"agent": "analysis-agent",
"org": "data-corp"
},
"to": {
"agent": "code-executor",
"org": "data-corp"
},
"p": {
"action": "invoke",
"tool": "python-sandbox",
"input": {
"code": "import pandas as pd\ndf = pd.read_csv('data.csv')\nprint(df.describe())",
"timeout_seconds": 30,
"memory_limit_mb": 512
}
},
"cls": "conf",
"cap": ["tool.invoke", "code.execute"]
}{
"v": "0.1",
"id": "tool-result-001",
"ts": 1703280002500,
"op": "respond",
"re": "tool-invoke-001",
"p": {
"status": "success",
"data": {
"stdout": " col_a col_b\ncount 1000.0 1000.0\nmean 50.2 75.8\nstd 14.3 22.1",
"stderr": "",
"exit_code": 0,
"execution_time_ms": 1847
}
},
"cls": "conf"
}Properly transmitting personal data with consent tracking.
{
"v": "0.1",
"id": "consent-req-001",
"ts": 1703280000000,
"op": "consent",
"p": {
"action": "request",
"data_types": ["name", "email", "calendar"],
"purpose": "Schedule meeting with contacts",
"duration": "1h",
"human": "user:john@example.com"
},
"cls": "int"
}{
"v": "0.1",
"id": "calendar-sync-001",
"ts": 1703280120000,
"op": "query",
"from": {
"agent": "calendar-assistant",
"org": "productivity-suite"
},
"to": {
"agent": "scheduler",
"org": "productivity-suite"
},
"p": {
"domain": "calendar",
"intent": "find-slot",
"params": {
"attendees": ["jane@example.com", "bob@example.com"],
"duration_minutes": 60,
"preferred_times": ["10:00-12:00", "14:00-17:00"],
"timezone": "America/Los_Angeles"
}
},
"cls": "pii",
"pii_meta": {
"types": ["email"],
"consent": {
"granted_by": "user:john@example.com",
"purpose": "Schedule meeting with contacts",
"expires": 1703283600000,
"proof": "consent-token:ct_abc123xyz"
}
}
}Orchestrating multiple agents for a complex workflow.
{
"v": "0.1",
"id": "workflow-step-1",
"op": "task",
"p": {
"action": "create",
"task_id": "wf-001-research",
"type": "data-gathering",
"description": "Gather market data for Q4 report",
"constraints": {
"sources": ["internal-db", "market-api"],
"date_range": ["2024-10-01", "2024-12-31"]
},
"callback": {"on_complete": true}
},
"cls": "conf"
}{
"v": "0.1",
"id": "workflow-step-3",
"op": "task",
"p": {
"action": "create",
"task_id": "wf-001-write",
"type": "content-generation",
"description": "Write executive summary from research data",
"input": {
"research_ref": "wf-001-research",
"data_snapshot": {
"revenue": 12500000,
"growth": 0.23,
"key_metrics": ["DAU", "retention", "conversion"]
}
},
"output_format": {
"type": "markdown",
"max_words": 500,
"sections": ["highlights", "risks", "recommendations"]
}
},
"cls": "conf"
}Structured error responses with recovery suggestions.
{
"v": "0.1",
"id": "error-ratelimit-001",
"ts": 1703280000000,
"op": "error",
"re": "query-that-failed",
"p": {
"code": "E_RATE_LIMIT",
"category": "transport",
"message": "Rate limit exceeded: 100 requests per minute",
"recoverable": true,
"suggestion": {
"action": "retry_after",
"delay_ms": 30000
},
"context": {
"limit": 100,
"window": "1m",
"current": 127
}
},
"cls": "int"
}{
"v": "0.1",
"id": "error-consent-001",
"ts": 1703280000000,
"op": "error",
"re": "pii-message-blocked",
"p": {
"code": "E_CONSENT",
"category": "privacy",
"message": "PII detected without valid consent: email addresses found",
"recoverable": true,
"suggestion": {
"action": "request_consent",
"consent_type": "pii",
"data_types": ["email"]
},
"detected_pii": {
"types": ["email"],
"count": 2,
"fields": ["p.params.attendees"]
}
},
"cls": "int"
}Using streams for large or real-time data transfer.
{
"v": "0.1",
"id": "stream-start-001",
"op": "stream",
"p": {
"action": "start",
"stream_id": "data-export-001",
"type": "json-lines",
"chunk_size": 65536,
"total_size_estimate": 10485760,
"compression": "gzip"
},
"cls": "conf"
}{
"v": "0.1",
"id": "stream-chunk-042",
"op": "stream",
"p": {
"action": "chunk",
"stream_id": "data-export-001",
"sequence": 42,
"data": "base64:H4sIAAAAAAAAA...",
"checksum": "sha256:abc123..."
},
"cls": "conf"
}{
"v": "0.1",
"id": "stream-end-001",
"op": "stream",
"p": {
"action": "end",
"stream_id": "data-export-001",
"total_chunks": 160,
"total_bytes": 10485760,
"final_checksum": "sha256:xyz789..."
},
"cls": "conf"
}