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Documentation Index

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Use this file to discover all available pages before exploring further.

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Functions

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parse_input()

Extract a JSON object from a text string, with optional dataclass validation. 
@overload
def parse_input(prompt: str) -> dict: ...

@overload
def parse_input(prompt: str, schema: type[T]) -> T: ...

def parse_input(prompt: str, schema: type | None = None) -> Any: ...
Parameters:
ParameterTypeRequiredDescription
promptstrYesThe enriched prompt text from Friday
schematype | NoneNoDataclass type for typed extraction
Returns: dict or dataclass instance Raises:
  • ValueError — No valid JSON object found in prompt
  • TypeError — Schema is not a dataclass
  • ValueError — JSON doesn’t match dataclass (missing required fields)
Example: 
from dataclasses import dataclass
from friday_agent_sdk import parse_input

# Plain dict extraction
data = parse_input(prompt)
repo = data.get("repository")
branch = data.get("branch", "main")

# Typed extraction
@dataclass
class Config:
    repository: str
    branch: str = "main"
    dry_run: bool = False

config = parse_input(prompt, Config)
# config.repository, config.branch, config.dry_run available with types

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parse_operation()

Extract an operation config using a discriminator field. 
def parse_operation(prompt: str, schemas: dict[str, type[T]]) -> T: ...
Parameters:
ParameterTypeRequiredDescription
promptstrYesThe enriched prompt text
schemasdict[str, type[T]]YesMap of operation name to dataclass type
Returns: Dataclass instance for the matched operation Raises: ValueError — No valid operation config found Example: 
from dataclasses import dataclass
from friday_agent_sdk import parse_operation

@dataclass
class CloneConfig:
    operation: str  # Must be "clone"
    repository: str
    branch: str = "main"

@dataclass
class DeployConfig:
    operation: str  # Must be "deploy"
    environment: str
    version: str

OPERATIONS = {
    "clone": CloneConfig,
    "deploy": DeployConfig,
}

config = parse_operation(prompt, OPERATIONS)

match config.operation:
    case "clone":
        # config is typed as CloneConfig
        handle_clone(config)
    case "deploy":
        # config is typed as DeployConfig
        handle_deploy(config)

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Extraction Strategy

Both functions search in this order:
  1. Balanced-brace JSON objects — Hand-rolled scanner handles arbitrary nesting
  2. Code-fenced JSON blocks — Extracts from ```json ... ```
  3. Full prompt — Attempts to parse entire prompt as JSON
For parse_operation(), only JSON objects containing an "operation" field are considered, and the discriminator value selects the schema.

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JSON in Markdown

Input may look like: 
Task: Deploy the application

Here is the configuration:

```json
{ "operation": "deploy", "environment": "production", "version": "1.2.3" }
```
Both parse_input() and parse_operation() extract the JSON block correctly.

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Dataclass Validation

When using a schema:
  • Only fields defined in the dataclass are extracted (unknown keys filtered)
  • Missing required fields raise ValueError with clear message
  • Type hints are not enforced at runtime (Python limitation)
@dataclass
class StrictConfig:
    required_field: str
    optional_field: int = 0

# Raises: ValueError: missing {'required_field'}
config = parse_input('{"optional_field": 5}', StrictConfig)

# Succeeds: required_field="value", optional_field=5
config = parse_input(
    '{"required_field": "value", "optional_field": 5, "extra": "ignored"}',
    StrictConfig,
)

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Error Messages

Clear errors for debugging: 
# No JSON found
parse_input("Just text without JSON")
# ValueError: No valid JSON object found in prompt. Prompt starts with: Just text...

# Invalid JSON
parse_input("{invalid json}")
# ValueError: No valid JSON object found...

# Schema mismatch
parse_input('{"wrong": "fields"}', Config)
# ValueError: JSON parsed but doesn't match Config: missing {'repository'}

# Operation not found
parse_operation('{"operation": "unknown"}', {"clone": CloneConfig})
# ValueError: No valid operation config found. Known operations: ['clone']...

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Real Example: Jira Agent

from dataclasses import dataclass
from friday_agent_sdk import agent, err, ok, parse_operation

@dataclass
class IssueViewConfig:
    operation: str
    issue_key: str

@dataclass
class IssueSearchConfig:
    operation: str
    jql: str
    max_results: int = 50

@dataclass
class IssueCreateConfig:
    operation: str
    project_key: str
    summary: str
    description: str | None = None
    issue_type: str = "Bug"

OPERATIONS = {
    "issue-view": IssueViewConfig,
    "issue-search": IssueSearchConfig,
    "issue-create": IssueCreateConfig,
}

@agent(id="jira", version="1.0.0", description="Jira operations")
def execute(prompt, ctx):
    try:
        config = parse_operation(prompt, OPERATIONS)
    except ValueError as e:
        return err(str(e))

    match config.operation:
        case "issue-view":
            return handle_view(config, ctx)
        case "issue-search":
            return handle_search(config, ctx)
        case "issue-create":
            return handle_create(config, ctx)
        case _:
            return err(f"Unknown operation: {config.operation}")

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When to Use

FunctionUse When
parse_input(prompt)Single configuration, flexible parsing, no operation types
parse_input(prompt, Schema)Single configuration, want typed fields
parse_operation(prompt, schemas)Multiple operations, discriminated by "operation" field

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Implementation Details

The balanced-brace scanner:
  • Handles arbitrary nesting depth (recursive objects/arrays)
  • Tracks string boundaries and escape sequences
  • Avoids miscounting braces inside string literals
  • Returns all valid JSON objects found, tries each in order
This hand-rolled approach is necessary because regex cannot handle arbitrary nesting depth reliably.

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See Also

How to Handle Structured Input

Task-oriented guide

Jira Agent Example

Full operation dispatch pattern shown above