Prerequisites

  • Python 3.10 or higher
  • PraisonAI Agents package installed
  • Basic understanding of JSON format

​
JSON Tools

Use JSON Tools to process and manipulate JSON files with AI agents.

1

Install Dependencies

First, install the required package:

pip install praisonaiagents
2

Import Components

Import the necessary components:

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import (
    read_json, write_json, merge_json,
    validate_json, analyze_json, transform_json
)
3

Create Agent

Create a JSON processing agent:

json_agent = Agent(
    name="JSONProcessor",
    role="JSON Processing Specialist",
    goal="Process JSON files efficiently and accurately.",
    backstory="Expert in JSON file manipulation and validation.",
    tools=[
        read_json, write_json, merge_json,
        validate_json, analyze_json, transform_json
    ],
    self_reflect=False
)
4

Define Task

Define the JSON processing task:

json_task = Task(
    description="Parse and validate API response data.",
    expected_output="Validated and processed JSON data.",
    agent=json_agent,
    name="json_processing"
)
5

Run Agent

Initialize and run the agent:

agents = PraisonAIAgents(
    agents=[json_agent],
    tasks=[json_task],
    process="sequential"
)
agents.start()

​
Understanding JSON Tools

What are JSON Tools?

JSON Tools provide JSON processing capabilities for AI agents:

  • File reading and writing
  • Data validation
  • Schema validation
  • Data transformation
  • Structure analysis

​
Key Components

JSON Agent

Create specialized JSON agents:

Agent(tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json])

JSON Task

Define JSON tasks:

Task(description="json_operation")

Process Types

Sequential or parallel processing:

process="sequential"

JSON Options

Customize JSON operations:

indent=2, sort_keys=True

​
Available Functions

from praisonaiagents.tools import read_json
from praisonaiagents.tools import write_json
from praisonaiagents.tools import merge_json
from praisonaiagents.tools import validate_json
from praisonaiagents.tools import analyze_json
from praisonaiagents.tools import transform_json

​
Function Details

​
read_json(filepath: str, encoding: str = ‘utf-8’, validate_schema: Optional[Dict[str, Any]] = None)

Reads JSON files with schema validation:

  • Optional schema validation
  • Custom encoding support
  • Error handling
# Basic usage
data = read_json("config.json")

# With schema validation
schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    }
}
data = read_json(
    "user.json",
    encoding="utf-8",
    validate_schema=schema
)

# Returns: Dict[str, Any] (JSON data)
# Error case: Returns dict with 'error' key

​
write_json(data: Union[Dict[str, Any], List[Any]], filepath: str, encoding: str = ‘utf-8’, indent: int = 2, sort_keys: bool = False, ensure_ascii: bool = False)

Writes data to JSON files:

  • Pretty printing with indentation
  • Optional key sorting
  • Unicode support
  • Directory creation
# Basic usage
data = {"name": "Alice", "age": 30}
success = write_json(data, "output.json")

# With formatting options
data = {
    "users": [
        {"name": "Alice", "age": 30},
        {"name": "Bob", "age": 25}
    ]
}
success = write_json(
    data,
    "users.json",
    indent=4,
    sort_keys=True,
    ensure_ascii=False
)
# Returns: bool (True if successful)

​
merge_json(files: List[str], output_file: str, merge_arrays: bool = True, overwrite_duplicates: bool = True)

Merges multiple JSON files:

  • Deep merging of objects
  • Array handling options
  • Duplicate key handling
  • Nested structure support
# Merge configuration files
success = merge_json(
    files=["config1.json", "config2.json"],
    output_file="merged_config.json"
)

# Advanced merge with options
success = merge_json(
    files=["base.json", "override.json"],
    output_file="final.json",
    merge_arrays=True,
    overwrite_duplicates=False
)
# Returns: bool (True if successful)

​
validate_json(data: Union[Dict[str, Any], str], schema: Dict[str, Any])

Validates JSON against a schema:

  • JSON Schema support
  • Detailed error messages
  • File or data validation
# Validate data
schema = {
    "type": "object",
    "required": ["name", "email"],
    "properties": {
        "name": {"type": "string"},
        "email": {"type": "string", "format": "email"},
        "age": {"type": "integer", "minimum": 0}
    }
}

# Validate data directly
data = {"name": "Alice", "email": "alice@example.com", "age": 25}
is_valid, error = validate_json(data, schema)

# Validate file
is_valid, error = validate_json("user.json", schema)
# Returns: Tuple[bool, Optional[str]]

​
analyze_json(data: Union[Dict[str, Any], str], max_depth: int = 10)

Analyzes JSON structure:

  • Type information
  • Size metrics
  • Nested structure analysis
  • Sample data
# Analyze file
analysis = analyze_json("data.json", max_depth=5)

# Analyze data structure
data = {
    "users": [
        {"name": "Alice", "scores": [95, 87, 92]},
        {"name": "Bob", "scores": [88, 85, 90]}
    ]
}
analysis = analyze_json(data)

# Returns: Dict[str, Any]
# Example output:
# {
#   'analysis_time': '2023-01-01T12:00:00',
#   'structure': {
#     'type': 'dict',
#     'size': 1,
#     'keys': ['users'],
#     'children': {
#       'users': {
#         'type': 'list',
#         'length': 2,
#         'element_types': ['dict'],
#         'sample_elements': [...]
#       }
#     }
#   }
# }

​
transform_json(data: Union[Dict[str, Any], str], transformations: List[Dict[str, Any]])

Transforms JSON data:

  • Multiple operations
  • Path-based modifications
  • Nested transformations
  • Operation types: set, delete, rename, move
# Transform data
transformations = [
    {
        "operation": "set",
        "path": "user.settings.theme",
        "value": "dark"
    },
    {
        "operation": "move",
        "path": "old.config",
        "value": "new.config"
    },
    {
        "operation": "delete",
        "path": "temporary.data"
    }
]

# Transform file
result = transform_json("config.json", transformations)

# Transform data directly
data = {"user": {"name": "Alice"}}
result = transform_json(data, transformations)
# Returns: Dict[str, Any] (transformed data)

​
Example Agent Configuration

from praisonaiagents import Agent
from praisonaiagents.tools import (
    read_json, write_json, merge_json,
    validate_json, analyze_json, transform_json
)

agent = Agent(
    name="JSONProcessor",
    description="An agent that processes JSON data",
    tools=[
        read_json, write_json, merge_json,
        validate_json, analyze_json, transform_json
    ]
)

​
Dependencies

The JSON tools require the following Python packages:

  • jsonschema: For JSON schema validation

These will be automatically installed when needed.

​
Error Handling

All functions include comprehensive error handling:

  • File I/O errors
  • JSON parsing errors
  • Schema validation errors
  • Transformation errors

Errors are handled consistently:

  • File operations return bool for success/failure
  • Data operations return error details in result
  • All errors are logged for debugging

​
Common Use Cases

  1. Configuration Management:
# Merge multiple config files
success = merge_json(
    ["base_config.json", "env_config.json", "user_config.json"],
    "final_config.json",
    merge_arrays=True,
    overwrite_duplicates=True
)

# Validate configuration
schema = {
    "type": "object",
    "required": ["database", "api"],
    "properties": {
        "database": {
            "type": "object",
            "required": ["host", "port"]
        },
        "api": {
            "type": "object",
            "required": ["key"]
        }
    }
}
is_valid, error = validate_json("final_config.json", schema)
  1. Data Analysis:
# Analyze data structure
analysis = analyze_json("large_dataset.json", max_depth=3)
print(f"Dataset size: {analysis['structure']['size']} entries")
print(f"Available fields: {analysis['structure']['keys']}")
  1. Data Transformation:
# Transform data format
transformations = [
    {"operation": "rename", "path": "firstName", "value": "first_name"},
    {"operation": "rename", "path": "lastName", "value": "last_name"},
    {"operation": "move", "path": "address.zip", "value": "address.postal_code"},
    {"operation": "delete", "path": "temporary_data"}
]
result = transform_json("user_data.json", transformations)
write_json(result, "transformed_data.json")

​
Understanding JSON Tools

What are JSON Tools?

JSON Tools provide JSON processing capabilities for AI agents:

  • File reading and writing
  • Data validation
  • Schema validation
  • Data transformation
  • Structure analysis

​
Key Components

JSON Agent

Create specialized JSON agents:

Agent(tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json])

JSON Task

Define JSON tasks:

Task(description="json_operation")

Process Types

Sequential or parallel processing:

process="sequential"

JSON Options

Customize JSON operations:

indent=2, sort_keys=True

​
Examples

​
Basic JSON Processing Agent

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import read_json, write_json, validate_json

# Create JSON processing agent
json_agent = Agent(
    name="JSONExpert",
    role="JSON Processor",
    goal="Process and validate JSON data efficiently.",
    backstory="Expert in JSON processing and validation.",
    tools=[read_json, write_json, validate_json],
    self_reflect=False
)

# Define JSON task
json_task = Task(
    description="Process and validate configuration files.",
    expected_output="Validated JSON configuration.",
    agent=json_agent,
    name="config_validation"
)

# Run agent
agents = PraisonAIAgents(
    agents=[json_agent],
    tasks=[json_task],
    process="sequential"
)
agents.start()

​
Advanced JSON Operations with Multiple Agents

# Create JSON validation agent
validation_agent = Agent(
    name="Validator",
    role="JSON Validation Specialist",
    goal="Ensure JSON data integrity and schema compliance.",
    tools=[validate_json, analyze_json],
    self_reflect=False
)

# Create JSON transformation agent
transform_agent = Agent(
    name="Transformer",
    role="JSON Transformation Specialist",
    goal="Transform and merge JSON data structures.",
    tools=[transform_json, merge_json],
    self_reflect=False
)

# Define tasks
validation_task = Task(
    description="Validate JSON data",
    agent=validation_agent,
    name="json_validation"
)   

transform_task = Task(
    description="Transform JSON data",
    agent=transform_agent,
    name="json_transformation"
)   

# Run agents
agents = PraisonAIAgents(
    agents=[validation_agent, transform_agent],
    tasks=[validation_task, transform_task],
    process="sequential"
)
agents.start()

​
Best Practices

​
Common Patterns

​
JSON Processing Pipeline

# Processing agent
processor = Agent(
    name="Processor",
    role="JSON Processor",
    tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json]
)

# Validation agent
validator = Agent(
    name="Validator",
    role="Data Validator"
)

# Define tasks
process_task = Task(
    description="Process JSON files",
    agent=processor
)

validate_task = Task(
    description="Validate processed data",
    agent=validator
)

# Run workflow
agents = PraisonAIAgents(
    agents=[processor, validator],
    tasks=[process_task, validate_task]
)