Function Calling Interface

Chronologue’s function calling interface allows agents, language models, and interactive tools to invoke structured actions through a well-defined schema. This layer bridges natural language intent with precise, executable instructions—powering planning, memory logging, calendar scheduling, and feedback loops.

Function calls can be invoked via:

• OpenAI’s functions or tool_choice API
• Internal agent runtime (/functions/call)
• Manual interface triggers (e.g., UI buttons or editable tables)

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2. Use Cases

• Translate user prompts into agent_plan actions
• Reflect on memory with structured create_reflection calls
• Log new observations or task completions
• Schedule calendar events based on structured parameters
• Edit traces or plans via interactive table or UI interface

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3. Function Calling Schema

Each function includes:

Example:

{
  "name": "schedule_task",
  "description": "Create an agent plan to schedule a task with a specific time and duration.",
  "parameters": {
    "type": "object",
    "properties": {
      "content": { "type": "string" },
      "scheduled_for": { "type": "string", "format": "date-time" },
      "duration_minutes": { "type": "integer" }
    },
    "required": ["content", "scheduled_for"]
  }
}

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4. Defined Functions in Chronologue

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5. API Integration with FastAPI

Function calls are processed via:

POST /functions/call

Request body:

{
  "name": "schedule_task",
  "arguments": {
    "content": "Write CUDA blog post",
    "scheduled_for": "2025-05-13T16:00:00Z",
    "duration_minutes": 90
  }
}

The API dispatches the call, validates parameters with Pydantic, and returns a result:

• A new memory trace
• A calendar_event block
• A confirmation or summary message

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6. Function Calling with LLMs

Chronologue registers these functions with tool-enabled LLM runtimes (e.g. OpenAI, Claude).

Prompt:

“Schedule a writing session for tomorrow afternoon.”

Resulting call:

{
  "function_call": {
    "name": "schedule_task",
    "arguments": {
      "content": "Writing session",
      "scheduled_for": "2025-05-13T14:00:00Z",
      "duration_minutes": 120
    }
  }
}

The returned trace is optionally previewed before saving.

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7. Design Principles

• Schema-driven: Validated with JSON Schema and Pydantic
• Composable: Output can be stored, revised, or chained
• Reversible: Every function call result is a trace
• Minimal: Only expose essential agent operations

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8. Error Handling and Feedback

When a function call fails:

• Returns a 400 or 422 response with detail
• trace_id is included for log correlation
• Suggestions (hint) may be returned for retry

Example error response:

{
  "error": "Missing required field 'scheduled_for'",
  "status": 422,
  "hint": "Include a valid ISO timestamp for the schedule."
}

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9. Manual Function Calls from UI (Editable Calendar/Table)

Chronologue supports invoking functions directly from:

• Calendar editors (e.g., dragging a block)
• Markdown tables in chat interface (editable fields)

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Example Table (Editable in UI):

On interaction, each Edit button triggers a corresponding function call:

• propose_revision
• reschedule_task
• update_trace_field

This interface allows users to manually invoke functions, not just through LLMs.

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10. Related Modules

• Agent DSL and Execution Model
• Memory Trace Schema
• API Endpoints Overview
• OpenAI Function Calling Guide

Chronologue’s function calling interface enables structured, transparent, and user-aligned interaction with agents. Whether invoked by an LLM, clicked in a UI, or scheduled via planner, each function brings natural language closer to structured, traceable execution.