API Reference¶

This comprehensive reference documents all public APIs in the JAF (Juspay Agent Framework) Python implementation. All examples assume you have imported JAF as shown:

import jaf
from jaf import RunState, Agent, Message, RunConfig

Enums¶

JAF provides comprehensive enums for type safety and to eliminate magic strings throughout your code.

ContentRole¶

Defines the roles for messages in conversations.

from jaf import ContentRole

class ContentRole(str, Enum):
    USER = 'user'
    ASSISTANT = 'assistant'
    TOOL = 'tool'
    SYSTEM = 'system'

Usage:

message = Message(role=ContentRole.USER, content="Hello!")

ToolSource¶

Specifies the source of tool definitions.

from jaf import ToolSource

class ToolSource(str, Enum):
    NATIVE = 'native'
    MCP = 'mcp'
    PLUGIN = 'plugin'
    EXTERNAL = 'external'

Model¶

Supported model identifiers.

from jaf import Model

class Model(str, Enum):
    GEMINI_2_0_FLASH = 'gemini-2.0-flash'
    GEMINI_2_5_PRO = 'gemini-2.5-pro'
    GEMINI_PRO = 'gemini-pro'
    GPT_4 = 'gpt-4'
    GPT_4_TURBO = 'gpt-4-turbo'
    GPT_3_5_TURBO = 'gpt-3.5-turbo'
    CLAUDE_3_SONNET = 'claude-3-sonnet'
    CLAUDE_3_HAIKU = 'claude-3-haiku'
    CLAUDE_3_OPUS = 'claude-3-opus'

ToolParameterType¶

Types for tool parameter definitions.

from jaf import ToolParameterType

class ToolParameterType(str, Enum):
    STRING = 'string'
    NUMBER = 'number'
    INTEGER = 'integer'
    BOOLEAN = 'boolean'
    ARRAY = 'array'
    OBJECT = 'object'
    NULL = 'null'

PartType¶

Message part types for multimodal content.

from jaf import PartType

class PartType(str, Enum):
    TEXT = 'text'
    IMAGE = 'image'
    AUDIO = 'audio'
    VIDEO = 'video'
    FILE = 'file'

Tool Creation Functions¶

JAF provides both modern object-based and legacy positional APIs for creating tools.

create_function_tool (Recommended)¶

Create a function-based tool using object configuration for better type safety and extensibility.

from jaf import create_function_tool, ToolSource
from pydantic import BaseModel, Field

class GreetArgs(BaseModel):
    name: str = Field(description="Name to greet")

async def greet_execute(args: GreetArgs, context) -> str:
    return f"Hello, {args.name}!"

tool = create_function_tool({
    'name': 'greet',
    'description': 'Greets a user by name',
    'execute': greet_execute,
    'parameters': GreetArgs,
    'metadata': {'category': 'social'},
    'source': ToolSource.NATIVE
})

Parameters: - config: FunctionToolConfig - Object containing: - name: str - Tool name - description: str - Tool description - execute: ToolExecuteFunction - Function to execute - parameters: Any - Pydantic model for parameter validation - metadata: Optional[Dict[str, Any]] - Optional metadata - source: Optional[ToolSource] - Tool source (defaults to NATIVE)

Returns: - Tool - Tool implementation ready for use with agents

create_function_tool_legacy (Deprecated)¶

Legacy positional argument API for backward compatibility.

# Deprecated - use object-based API instead
tool = create_function_tool_legacy(
    'greet',
    'Greets a user by name', 
    greet_execute,
    GreetArgs,
    {'category': 'social'},
    ToolSource.NATIVE
)

Deprecated

This function is deprecated. Use create_function_tool with object configuration for better type safety and extensibility.

create_async_function_tool¶

Convenience function identical to create_function_tool but with a name that emphasizes async execution.

tool = create_async_function_tool({
    'name': 'async_operation',
    'description': 'Performs an async operation',
    'execute': async_execute_func,
    'parameters': AsyncArgs,
    'source': ToolSource.NATIVE
})

FunctionToolConfig¶

TypedDict defining the configuration structure for object-based tool creation.

from jaf.core.types import FunctionToolConfig

class FunctionToolConfig(TypedDict):
    name: str
    description: str
    execute: ToolExecuteFunction
    parameters: Any
    metadata: Optional[Dict[str, Any]]
    source: Optional[ToolSource]

Core Functions¶

Main Execution¶

`run(initial_state: RunState[Ctx], config: RunConfig[Ctx]) -> RunResult[Out]`¶

Main execution function for running agents with functional purity and immutable state.

Parameters: - initial_state: RunState[Ctx] - Initial state containing messages, context, and agent info - config: RunConfig[Ctx] - Configuration including agents, model provider, and guardrails

Returns: - RunResult[Out] - Contains final state and outcome (completed or error)

Example:

result = await jaf.run(initial_state, config)
if result.outcome.status == 'completed':
    print(f"Success: {result.outcome.output}")
else:
    print(f"Error: {result.outcome.error}")

ID Generation¶

`generate_run_id() -> RunId`¶

Generate a new unique run ID using UUID4.

Returns: - RunId - Branded string type for run identification

`generate_trace_id() -> TraceId`¶

Generate a new unique trace ID using UUID4.

Returns: - TraceId - Branded string type for trace identification

`create_run_id(id_str: str) -> RunId`¶

Create a RunId from an existing string.

`create_trace_id(id_str: str) -> TraceId`¶

Create a TraceId from an existing string.

Core Types¶

RunState¶

`RunState[Ctx]`¶

Immutable state of a run containing all execution context.

Type Parameters: - Ctx - Type of the context object

Fields: - run_id: RunId - Unique identifier for this run - trace_id: TraceId - Unique identifier for tracing - messages: List[Message] - Conversation history - current_agent_name: str - Name of the currently active agent - context: Ctx - User-defined context object - turn_count: int - Number of execution turns - final_response: Optional[str] - Final agent response (if completed)

Example:

@dataclass
class MyContext:
    user_id: str
    permissions: List[str]

state = RunState(
    run_id=jaf.generate_run_id(),
    trace_id=jaf.generate_trace_id(),
    messages=[Message(role='user', content='Hello!')],
    current_agent_name='assistant',
    context=MyContext(user_id='123', permissions=['read']),
    turn_count=0
)

Agent¶

`Agent[Ctx, Out]`¶

An agent definition with instructions, tools, and configuration.

Type Parameters: - Ctx - Type of the context object - Out - Type of the expected output

Fields: - name: str - Unique name for the agent - instructions: Callable[[RunState[Ctx]], str] - Function that generates system prompt - tools: Optional[List[Tool[Any, Ctx]]] - Available tools for the agent - output_codec: Optional[Any] - Pydantic model for output validation - handoffs: Optional[List[str]] - List of agents this agent can handoff to - model_config: Optional[ModelConfig] - Model-specific configuration

Example:

def create_assistant(context_type):
    def instructions(state: RunState[context_type]) -> str:
        return f"You are a helpful assistant. User: {state.context.user_id}"

    return Agent(
        name='Assistant',
        instructions=instructions,
        tools=[calculator_tool, weather_tool],
        handoffs=['SpecialistAgent']
    )

Tool Protocol¶

`Tool[Args, Ctx]`¶

Protocol for tool implementations.

Type Parameters: - Args - Type of the tool arguments (Pydantic model) - Ctx - Type of the context object

Required Attributes: - schema: ToolSchema[Args] - Tool schema with name, description, and parameters

Required Methods: - execute(args: Args, context: Ctx) -> Union[str, ToolResult] - Execute the tool

Example:

class CalculatorArgs(BaseModel):
    expression: str = Field(description="Math expression to evaluate")

class CalculatorTool:
    @property
    def schema(self):
        return type('ToolSchema', (), {
            'name': 'calculate',
            'description': 'Perform mathematical calculations',
            'parameters': CalculatorArgs
        })()

    async def execute(self, args: CalculatorArgs, context: MyContext) -> str:
        result = eval(args.expression)  # Use safe evaluator in production
        return f"Result: {result}"

Message¶

`Message`¶

A message in the conversation.

Fields: - role: Literal['user', 'assistant', 'tool'] - Message sender role - content: str - Message content - tool_call_id: Optional[str] - ID for tool response messages - tool_calls: Optional[List[ToolCall]] - Tool calls from assistant

Example:

user_message = Message(role='user', content='What is 2+2?')
assistant_message = Message(role='assistant', content='Let me calculate that for you.')

RunConfig¶

`RunConfig[Ctx]`¶

Configuration for running agents.

Fields: - agent_registry: Dict[str, Agent[Ctx, Any]] - Available agents by name - model_provider: ModelProvider[Ctx] - LLM provider implementation - max_turns: Optional[int] - Maximum execution turns (default: 50) - model_override: Optional[str] - Override model name for all agents - initial_input_guardrails: Optional[List[Guardrail]] - Input validation - final_output_guardrails: Optional[List[Guardrail]] - Output validation - on_event: Optional[Callable[[TraceEvent], None]] - Event handler for tracing - memory: Optional[MemoryConfig] - Memory provider configuration - conversation_id: Optional[str] - Conversation identifier for memory

Example:

config = RunConfig(
    agent_registry={'assistant': my_agent},
    model_provider=jaf.make_litellm_provider('http://localhost:4000'),
    max_turns=20,
    on_event=lambda event: print(f"Event: {event.type}"),
    initial_input_guardrails=[content_filter],
    final_output_guardrails=[output_validator]
)

RunResult¶

`RunResult[Out]`¶

Result of a run execution.

Fields: - final_state: RunState[Any] - Final state after execution - outcome: RunOutcome[Out] - Success with output or error details

Outcome Types: - CompletedOutcome[Out] - Success with output: Out - ErrorOutcome - Error with error: JAFError

ValidationResult¶

`ValidationResult`¶

Union type for validation results.

Types: - ValidValidationResult - is_valid: True - InvalidValidationResult - is_valid: False, error_message: str

ModelConfig¶

`ModelConfig`¶

Configuration for model behavior.

Fields: - name: Optional[str] - Model name (e.g., "gpt-4o") - temperature: Optional[float] - Sampling temperature (0.0-1.0) - max_tokens: Optional[int] - Maximum tokens to generate

Model Provider Functions¶

LiteLLM Provider¶

`make_litellm_provider(base_url: str, api_key: str = "anything") -> ModelProvider[Ctx]`¶

Create a LiteLLM-compatible model provider for OpenAI-compatible APIs.

Parameters: - base_url: str - Base URL for the LiteLLM server - api_key: str - API key (defaults to "anything" for local servers)

Returns: - ModelProvider[Ctx] - Provider instance implementing the ModelProvider protocol

Examples:

# Local LiteLLM server
provider = jaf.make_litellm_provider("http://localhost:4000")

# OpenAI API
provider = jaf.make_litellm_provider(
    "https://api.openai.com/v1", 
    api_key="your-openai-api-key"
)

# Custom LiteLLM deployment
provider = jaf.make_litellm_provider(
    "https://your-litellm-server.com/v1",
    api_key="your-api-key"
)

ModelProvider Protocol¶

`ModelProvider[Ctx]`¶

Protocol defining the interface for model providers.

Methods: - get_completion(state: RunState[Ctx], agent: Agent[Ctx, Any], config: RunConfig[Ctx]) -> ModelCompletionResponse - Get completion from the model

Memory Provider System¶

Memory Provider Factory¶

`create_memory_provider_from_env(external_clients: Optional[Dict[str, Any]] = None) -> MemoryProvider`¶

Create a memory provider based on environment variables.

Environment Variables: - JAF_MEMORY_TYPE: "memory", "redis", or "postgres" (default: "memory") - Redis: JAF_REDIS_URL, JAF_REDIS_HOST, JAF_REDIS_PORT, JAF_REDIS_DB - PostgreSQL: JAF_POSTGRES_CONNECTION_STRING, JAF_POSTGRES_HOST, JAF_POSTGRES_PORT, etc. - In-Memory: JAF_MEMORY_MAX_CONVERSATIONS, JAF_MEMORY_MAX_MESSAGES

Parameters: - external_clients: Optional[Dict[str, Any]] - Pre-initialized client connections

Returns: - MemoryProvider - Configured memory provider

Example:

# Set environment variable
import os
os.environ['JAF_MEMORY_TYPE'] = 'redis'
os.environ['JAF_REDIS_URL'] = 'redis://localhost:6379'

# Create provider
memory_provider = jaf.create_memory_provider_from_env()

# Use in run config
config = RunConfig(
    # ... other config
    memory=MemoryConfig(provider=memory_provider)
)

Specific Provider Creators¶

`create_in_memory_provider(config: InMemoryConfig) -> MemoryProvider`¶

Create an in-memory provider for development and testing.

`create_redis_provider(config: RedisConfig, client: Optional[Any] = None) -> MemoryProvider`¶

Create a Redis memory provider for distributed scenarios.

`create_postgres_provider(config: PostgresConfig, client: Optional[Any] = None) -> MemoryProvider`¶

Create a PostgreSQL memory provider for production persistence.

Memory Provider Protocol¶

`MemoryProvider`¶

Protocol defining the interface for memory providers.

Key Methods: - store_messages(conversation_id: str, messages: List[Message], metadata: Optional[Dict[str, Any]] = None) -> Result - Store messages - get_conversation(conversation_id: str) -> Union[ConversationMemory, None] - Retrieve conversation - append_messages(conversation_id: str, messages: List[Message], metadata: Optional[Dict[str, Any]] = None) -> Result - Append messages - get_recent_messages(conversation_id: str, limit: int = 50) -> List[Message] - Get recent messages - delete_conversation(conversation_id: str) -> bool - Delete conversation - health_check() -> Dict[str, Any] - Check provider health

Memory Configuration Types¶

`MemoryConfig`¶

Configuration for memory integration.

Fields: - provider: MemoryProvider - Memory provider instance - auto_store: bool - Automatically store conversations (default: True) - max_messages: Optional[int] - Message limit for storage - ttl: Optional[int] - Time-to-live in seconds - compression_threshold: Optional[int] - Compression threshold

`ConversationMemory`¶

Immutable conversation memory object.

Fields: - conversation_id: str - Unique conversation identifier - user_id: Optional[str] - User identifier - messages: List[Message] - Conversation messages - metadata: Optional[Dict[str, Any]] - Additional metadata

Validation and Policy Functions¶

Input/Output Guardrails¶

`create_length_guardrail(max_length: int, min_length: int = 0) -> Guardrail`¶

Create a guardrail that validates text length.

Example:

length_guard = jaf.create_length_guardrail(max_length=1000, min_length=10)

config = RunConfig(
    # ... other config
    initial_input_guardrails=[length_guard]
)

`create_content_filter(blocked_patterns: List[str]) -> Guardrail`¶

Create a guardrail that filters content based on blocked patterns.

Example:

content_filter = jaf.create_content_filter(['spam', 'inappropriate'])

config = RunConfig(
    # ... other config
    initial_input_guardrails=[content_filter]
)

`create_json_validation_guardrail(schema_class: type[BaseModel]) -> Guardrail`¶

Create a guardrail that validates JSON against a Pydantic schema.

Example:

class OrderOutput(BaseModel):
    order_id: str
    total: float
    items: List[str]

json_validator = jaf.create_json_validation_guardrail(OrderOutput)

config = RunConfig(
    # ... other config
    final_output_guardrails=[json_validator]
)

`combine_guardrails(guardrails: List[Guardrail], require_all: bool = True) -> Guardrail`¶

Combine multiple guardrails into a single guardrail.

Example:

combined_guard = jaf.combine_guardrails([
    length_guard,
    content_filter,
    json_validator
], require_all=True)

Handoff Policies¶

`create_handoff_guardrail(policy: HandoffPolicy, current_agent: str) -> Guardrail`¶

Create a guardrail that validates agent handoffs.

`create_role_based_handoff_policy(agent_roles: Dict[str, str], role_permissions: Dict[str, List[str]]) -> HandoffPolicy`¶

Create a handoff policy based on agent roles.

Example:

# Define roles
agent_roles = {
    "TriageAgent": "triage",
    "TechnicalAgent": "technical", 
    "BillingAgent": "billing"
}

# Define permissions (which roles can handoff to which)
role_permissions = {
    "triage": ["technical", "billing"],
    "technical": ["triage"],
    "billing": ["triage"]
}

handoff_policy = jaf.create_role_based_handoff_policy(agent_roles, role_permissions)

Server Functions¶

Server Creation¶

`run_server(config: ServerConfig) -> None`¶

Start a JAF server with the given configuration.

Example:

from jaf.server import ServerConfig

server_config = ServerConfig(
    agent_registry={'assistant': my_agent},
    run_config=run_config,
    host='0.0.0.0',
    port=3000,
    cors=True
)

await jaf.run_server(server_config)

`create_simple_server(agents: List[Agent], model_provider: ModelProvider, host: str = 'localhost', port: int = 3000) -> JAFServer`¶

Create a simple JAF server with minimal configuration.

Example:

server = jaf.create_simple_server(
    agents=[assistant_agent, specialist_agent],
    model_provider=jaf.make_litellm_provider('http://localhost:4000'),
    host='0.0.0.0',
    port=8000
)

await server.start()

Tool Result System¶

ToolResult Type¶

`ToolResult[T]`¶

Standardized tool result with status, data, and metadata.

Fields: - status: ToolResultStatus - Status ('success', 'error', 'validation_error', etc.) - data: Optional[T] - Result data for successful operations - error: Optional[ToolErrorInfo] - Error information for failures - metadata: Optional[ToolMetadata] - Execution metadata

ToolResponse Helper Class¶

`ToolResponse`¶

Helper functions for creating standardized tool results.

Static Methods: - success(data: T, metadata: Optional[Dict] = None) -> ToolResult[T] - Create success result - error(code: str, message: str, details: Optional[Any] = None) -> ToolResult[None] - Create error result - validation_error(message: str, details: Optional[Any] = None) -> ToolResult[None] - Create validation error - permission_denied(message: str, required_permissions: Optional[List[str]] = None) -> ToolResult[None] - Create permission denied error - not_found(resource: str, identifier: Optional[str] = None) -> ToolResult[None] - Create not found error

Example:

name="__codelineno-26-1" href="#__codelineno-26-1">class DatabaseTool: async def execute(self, args: QueryArgs, context: Context) -> ToolResult[Dict]: try: if not context.has_permission('database_read'): return ToolResponse.permission_denied( "Database access requires read permission", required_permissions=['database_read'] ) result = await self.db.query(args.sql) return ToolResponse.success( data={'rows': result.rows, 'count': len(result.rows)}, metadata={'execution_time_ms': result.duration} ) except DatabaseError as e: return ToolResponse.error( code='database_error', message=str(e), details={'error_code': e.code} )

Utility Functions¶

`with_error_handling(tool_name: str, executor: Callable) -> Callable`¶

Tool execution wrapper that provides standardized error handling.

`tool_result_to_string(result: ToolResult[Any]) -> str`¶

Convert ToolResult to string for backward compatibility.

Tracing System¶

TraceCollector Protocol¶

`TraceCollector`¶

Protocol for trace collectors.

Methods: - collect(event: TraceEvent) -> None - Collect a trace event - get_trace(trace_id: TraceId) -> List[TraceEvent] - Get events for a specific trace - clear(trace_id: Optional[TraceId] = None) -> None - Clear traces

Built-in Collectors¶

`ConsoleTraceCollector`¶

Console trace collector with detailed logging.

Example:

tracer = jaf.ConsoleTraceCollector()

config = RunConfig(
    # ... other config
    on_event=tracer.collect
)

`FileTraceCollector(file_path: str)`¶

File trace collector that writes events to a file.

Example:

file_tracer = jaf.FileTraceCollector('./traces.jsonl')

config = RunConfig(
    # ... other config
    on_event=file_tracer.collect
)

ADK Callback System¶

RunnerCallbacks Protocol¶

`RunnerCallbacks`¶

Protocol defining hooks for advanced agent instrumentation and control.

Available Hooks:

from adk.runners import RunnerCallbacks, RunnerConfig, execute_agent

class MyCallbacks:
    """Custom callback implementation for advanced agent behaviors."""

    # === Lifecycle Hooks ===
    async def on_start(self, context: RunContext, message: Message, session_state: Dict[str, Any]) -> None:
        """Called when agent execution starts."""
        pass

    async def on_complete(self, response: AgentResponse) -> None:
        """Called when execution completes successfully."""
        pass

    async def on_error(self, error: Exception, context: RunContext) -> None:
        """Called when execution encounters an error."""
        pass

    # === LLM Interaction Hooks ===
    async def on_before_llm_call(self, agent: Agent, message: Message, session_state: Dict[str, Any]) -> Optional[LLMControlResult]:
        """Modify or skip LLM calls."""
        return None

    async def on_after_llm_call(self, response: Message, session_state: Dict[str, Any]) -> Optional[Message]:
        """Modify LLM responses."""
        return None

    # === Iteration Control Hooks ===
    async def on_iteration_start(self, iteration: int) -> Optional[IterationControlResult]:
        """Control iteration flow."""
        return None

    async def on_iteration_complete(self, iteration: int, has_tool_calls: bool) -> Optional[IterationControlResult]:
        """Decide whether to continue iterating."""
        return None

    # === Tool Execution Hooks ===
    async def on_before_tool_selection(self, tools: List[Tool], context_data: List[Any]) -> Optional[ToolSelectionControlResult]:
        """Filter or modify available tools."""
        return None

    async def on_tool_selected(self, tool_name: str, params: Dict[str, Any]) -> None:
        """Track tool usage."""
        pass

    async def on_before_tool_execution(self, tool: Tool, params: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Modify parameters or skip execution."""
        return None

    async def on_after_tool_execution(self, tool: Tool, result: Any, error: Optional[Exception] = None) -> Optional[Any]:
        """Process tool results."""
        return None

    # === Context and Synthesis Hooks ===
    async def on_check_synthesis(self, session_state: Dict[str, Any], context_data: List[Any]) -> Optional[SynthesisCheckResult]:
        """Determine if synthesis is complete."""
        return None

    async def on_query_rewrite(self, original_query: str, context_data: List[Any]) -> Optional[str]:
        """Refine queries based on accumulated context."""
        return None

    async def on_context_update(self, current_context: List[Any], new_items: List[Any]) -> Optional[List[Any]]:
        """Manage context accumulation."""
        return None

    # === Loop Detection ===
    async def on_loop_detection(self, tool_history: List[Dict[str, Any]], current_tool: str) -> bool:
        """Detect and prevent loops."""
        return False

Callback Configuration¶

`RunnerConfig`¶

Enhanced configuration for callback-enabled agent execution.

Fields: - agent: Agent - JAF agent to execute - session_provider: Optional[Any] - Session provider for persistence - callbacks: Optional[RunnerCallbacks] - Callback implementation - max_llm_calls: int - Maximum LLM calls per execution (default: 10) - enable_context_accumulation: bool - Enable context management (default: False) - enable_loop_detection: bool - Enable loop prevention (default: False) - max_context_items: int - Maximum context items to retain (default: 50) - max_repeated_tools: int - Maximum repeated tool calls before loop detection (default: 3)

Example:

from adk.runners import RunnerConfig, execute_agent

config = RunnerConfig(
    agent=my_agent,
    session_provider=session_provider,
    callbacks=MyCallbacks(),
    max_llm_calls=15,
    enable_context_accumulation=True,
    enable_loop_detection=True,
    max_context_items=100
)

result = await execute_agent(config, session_state, message, context, model_provider)

Callback Return Types¶

`LLMControlResult`¶

TypedDict for controlling LLM interactions.

Fields: - skip: Optional[bool] - Skip LLM call if True - message: Optional[Message] - Modified message for LLM - response: Optional[Message] - Direct response (when skipping)

`ToolSelectionControlResult`¶

TypedDict for controlling tool selection.

Fields: - tools: Optional[List[Tool]] - Filtered tool list - custom_selection: Optional[Dict[str, Any]] - Custom tool selection logic

`IterationControlResult`¶

TypedDict for controlling iteration flow.

Fields: - continue_iteration: Optional[bool] - Whether to continue current iteration - should_stop: Optional[bool] - Whether to stop execution - should_continue: Optional[bool] - Whether to continue to next iteration

`SynthesisCheckResult`¶

TypedDict for synthesis completion results.

Fields: - complete: bool - Whether synthesis is complete - answer: Optional[str] - Final synthesized answer - confidence: Optional[float] - Confidence score (0.0-1.0)

Advanced Agent Execution¶

`execute_agent(config: RunnerConfig, session_state: Dict[str, Any], message: Message, context: RunContext, model_provider: ModelProvider) -> AgentResponse`¶

Execute an agent with full callback instrumentation.

Parameters: - config: RunnerConfig - Callback-enabled configuration - session_state: Dict[str, Any] - Mutable session state - message: Message - Input message to process - context: RunContext - Execution context - model_provider: ModelProvider - LLM provider

Returns: - AgentResponse - Enhanced response with execution metadata

Example:

import asyncio
from adk.runners import RunnerConfig, execute_agent
from jaf.core.types import Agent, Message

# Create callback implementation
class ReActCallbacks:
    def __init__(self):
        self.iteration_count = 0
        self.context_accumulator = []

    async def on_iteration_start(self, iteration):
        self.iteration_count = iteration
        print(f"🔄 Iteration {iteration}")
        return None

    async def on_check_synthesis(self, session_state, context_data):
        if len(context_data) >= 3:
            return {
                'complete': True,
                'answer': self.synthesize_information(context_data),
                'confidence': 0.85
            }
        return None

    async def on_query_rewrite(self, original_query, context_data):
        gaps = self.identify_gaps(context_data)
        if gaps:
            return f"{original_query} focusing on {', '.join(gaps)}"
        return None

# Configure and execute
config = RunnerConfig(
    agent=research_agent,
    callbacks=ReActCallbacks(),
    enable_context_accumulation=True,
    max_llm_calls=10
)

result = await execute_agent(
    config, 
    session_state={}, 
    message=Message(role='user', content='Research machine learning applications'),
    context={'user_id': 'researcher_123'},
    model_provider=litellm_provider
)

print(f"Result: {result.content}")
print(f"Iterations: {result.metadata.get('iterations', 0)}")
print(f"Synthesis confidence: {result.metadata.get('synthesis_confidence', 0)}")

Common Callback Patterns¶

ReAct (Reasoning + Acting) Pattern¶

class ReActAgent:
    async def on_iteration_start(self, iteration):
        thought = f"Iteration {iteration}: I need to gather more information"
        print(f"🤔 Thought: {thought}")
        return None

    async def on_before_tool_execution(self, tool, params):
        action = f"Using {tool.schema.name} with {params}"
        print(f" Action: {action}")
        return None

    async def on_after_tool_execution(self, tool, result, error=None):
        if error:
            observation = f"Action failed: {error}"
        else:
            observation = f"Observed: {result}"
        print(f"👁️ Observation: {observation}")
        return None

Intelligent Caching Pattern¶

class CachingCallbacks:
    def __init__(self):
        self.cache = {}

    async def on_before_llm_call(self, agent, message, session_state):
        cache_key = hash(message.content)
        if cache_key in self.cache:
            return {'skip': True, 'response': self.cache[cache_key]}
        return None

    async def on_after_llm_call(self, response, session_state):
        cache_key = hash(response.content)
        self.cache[cache_key] = response
        return None

Context Accumulation Pattern¶

class ContextAccumulator:
    def __init__(self):
        self.context_items = []

    async def on_context_update(self, current_context, new_items):
        # Deduplicate and filter
        filtered_items = self.filter_duplicates(new_items)

        # Merge and sort by relevance
        merged = current_context + filtered_items
        sorted_context = sorted(merged, key=lambda x: x.get('relevance', 0), reverse=True)

        # Keep top items
        return sorted_context[:50]

    async def on_check_synthesis(self, session_state, context_data):
        if len(context_data) >= 5:
            confidence = self.calculate_confidence(context_data)
            if confidence >= 0.8:
                return {
                    'complete': True,
                    'answer': self.synthesize(context_data),
                    'confidence': confidence
                }
        return None

Loop Detection Pattern¶

class LoopDetector:
    def __init__(self, similarity_threshold=0.7):
        self.threshold = similarity_threshold
        self.tool_history = []

    async def on_loop_detection(self, tool_history, current_tool):
        if len(tool_history) < 3:
            return False

        # Check for repeated tool calls
        recent_tools = [item['tool'] for item in tool_history[-3:]]
        if recent_tools.count(current_tool) > 2:
            return True

        # Check parameter similarity
        for item in tool_history[-3:]:
            similarity = self.calculate_similarity(item.get('params', {}), current_tool)
            if similarity > self.threshold:
                return True

        return False

Complete Example¶

Here's a complete example showing how to use the main APIs together with advanced callback functionality:

import asyncio
import time
from dataclasses import dataclass
from typing import List, Optional, Dict, Any
from pydantic import BaseModel, Field
import jaf
from adk.runners import RunnerConfig, execute_agent

@dataclass
class UserContext:
    user_id: str
    permissions: List[str]

class CalculateArgs(BaseModel):
    expression: str = Field(description="Math expression to evaluate")

class CalculatorTool:
    @property
    def schema(self):
        return type('ToolSchema', (), {
            'name': 'calculate',
            'description': 'Perform safe mathematical calculations',
            'parameters': CalculateArgs
        })()

    async def execute(self, args: CalculateArgs, context: UserContext) -> str:
        if 'calculator' not in context.permissions:
            return jaf.ToolResponse.permission_denied(
                "Calculator access denied",
                required_permissions=['calculator']
            ).format()

        try:
            # Use safe evaluation in production
            from adk.utils.safe_evaluator import safe_calculate
            result = safe_calculate(args.expression)
            if result["status"] == "success":
                return jaf.ToolResponse.success(
                    f"Result: {args.expression} = {result['result']}"
                ).format()
            else:
                return jaf.ToolResponse.error(
                    'calculation_error', 
                    result['error']
                ).format()
        except Exception as e:
            return jaf.ToolResponse.error(
                'calculation_error', 
                str(e)
            ).format()

# Advanced callback implementation for production use
class ProductionMathCallbacks:
    """Production-ready callbacks with caching and monitoring."""

    def __init__(self):
        self.start_time = None
        self.calculations_cache = {}
        self.performance_metrics = {
            'llm_calls': 0,
            'tool_calls': 0,
            'cache_hits': 0
        }

    async def on_start(self, context, message, session_state):
        """Initialize execution with user context."""
        self.start_time = time.time()
        user_id = context.get('user_id', 'unknown')
        print(f"🧮 Math Assistant started for user: {user_id}")
        print(f" Query: {message.content}")

    async def on_before_llm_call(self, agent, message, session_state):
        """Implement intelligent caching and context enhancement."""
        self.performance_metrics['llm_calls'] += 1

        # Check for cached mathematical explanations
        cache_key = hash(f"math:{message.content}")
        if cache_key in self.calculations_cache:
            self.performance_metrics['cache_hits'] += 1
            print(f"💾 Using cached explanation")
            return {
                'skip': True, 
                'response': self.calculations_cache[cache_key]
            }

        # Enhance message with mathematical context
        enhanced_content = f"""Mathematical Problem: {message.content}

Please provide step-by-step explanations and use the calculator tool for all arithmetic operations.
        """

        return {
            'message': jaf.Message(role='user', content=enhanced_content)
        }

    async def on_after_llm_call(self, response, session_state):
        """Cache educational responses."""
        if 'step' in response.content.lower() or 'calculate' in response.content.lower():
            cache_key = hash(f"explanation:{response.content[:100]}")
            self.calculations_cache[cache_key] = response
        return None

    async def on_tool_selected(self, tool_name, params):
        """Track tool usage and validate calculations."""
        self.performance_metrics['tool_calls'] += 1
        if tool_name == 'calculate':
            expression = params.get('expression', '')
            print(f"🔢 Calculating: {expression}")

    async def on_after_tool_execution(self, tool, result, error=None):
        """Validate and enhance calculation results."""
        if error:
            print(f" Calculation error: {error}")
            return None

        if tool.schema.name == 'calculate' and 'Result:' in str(result):
            # Extract and validate the calculation
            print(f" Calculation completed: {result}")

        return None

    async def on_complete(self, response):
        """Log comprehensive execution metrics."""
        duration = time.time() - self.start_time if self.start_time else 0

        print(f"\n Execution Summary:")
        print(f"   Duration: {duration*1000:.0f}ms")
        print(f"   LLM Calls: {self.performance_metrics['llm_calls']}")
        print(f"   Tool Calls: {self.performance_metrics['tool_calls']}")
        print(f"   Cache Hits: {self.performance_metrics['cache_hits']}")
        print(f"   Cache Size: {len(self.calculations_cache)} items")

    async def on_error(self, error, context):
        """Handle mathematical errors gracefully."""
        print(f" Math Assistant Error: {str(error)}")
        # In production, log to monitoring system

def create_math_agent():
    def instructions(state: jaf.RunState[UserContext]) -> str:
        return f"""You are an advanced math tutor for user {state.context.user_id}.

Your capabilities:
- Perform calculations using the calculate tool
- Provide step-by-step explanations
- Show alternative solving methods
- Explain mathematical concepts clearly

Always:
1. Break down complex problems into steps
2. Use the calculator tool for all arithmetic
3. Explain your reasoning
4. Verify your answers"""

    return jaf.Agent(
        name='AdvancedMathAssistant',
        instructions=instructions,
        tools=[CalculatorTool()]
    )

async def demonstrate_traditional_jaf():
    """Demonstrate traditional JAF Core approach."""
    print("=== Traditional JAF Core Approach ===")

    # Set up tracing
    tracer = jaf.ConsoleTraceCollector()

    # Create model provider
    model_provider = jaf.make_litellm_provider('http://localhost:4000')

    # Create memory provider
    memory_provider = jaf.create_memory_provider_from_env()

    # Create agent
    math_agent = create_math_agent()

    # Set up configuration
    config = jaf.RunConfig(
        agent_registry={'AdvancedMathAssistant': math_agent},
        model_provider=model_provider,
        max_turns=10,
        on_event=tracer.collect,
        memory=jaf.MemoryConfig(provider=memory_provider),
        conversation_id='user_123_session',
        initial_input_guardrails=[
            jaf.create_length_guardrail(max_length=500)
        ]
    )

    # Create initial state
    initial_state = jaf.RunState(
        run_id=jaf.generate_run_id(),
        trace_id=jaf.generate_trace_id(),
        messages=[jaf.Message(role='user', content='What is 15 * 8 + 32?')],
        current_agent_name='AdvancedMathAssistant',
        context=UserContext(user_id='user_123', permissions=['calculator']),
        turn_count=0
    )

    # Run the agent
    result = await jaf.run(initial_state, config)

    # Handle result
    if result.outcome.status == 'completed':
        print(f" JAF Core Result: {result.outcome.output}")
    else:
        print(f" JAF Core Error: {result.outcome.error}")

async def demonstrate_callback_approach():
    """Demonstrate ADK Callback approach with advanced features."""
    print("\n=== ADK Callback Approach with Advanced Features ===")

    # Create model provider
    model_provider = jaf.make_litellm_provider('http://localhost:4000')

    # Create agent
    math_agent = create_math_agent()

    # Set up callback configuration
    callback_config = RunnerConfig(
        agent=math_agent,
        callbacks=ProductionMathCallbacks(),
        max_llm_calls=8,
        enable_context_accumulation=True,
        enable_loop_detection=True
    )

    # Execute with full instrumentation
    result = await execute_agent(
        callback_config,
        session_state={'learning_level': 'intermediate'},
        message=jaf.Message(role='user', content='Solve step by step: (25 + 17) * 3 - 15'),
        context=UserContext(user_id='callback_user', permissions=['calculator']),
        model_provider=model_provider
    )

    print(f" Callback Result: {result.content}")
    print(f" Metadata: {result.metadata}")

async def main():
    """Complete demonstration of JAF APIs with both approaches."""
    print("🧮 JAF Python Framework - Complete API Demonstration")
    print("=" * 60)

    try:
        # Demonstrate traditional JAF approach
        await demonstrate_traditional_jaf()

        # Demonstrate advanced callback approach
        await demonstrate_callback_approach()

        print("\n Both approaches completed successfully!")
        print("\nKey Differences:")
        print("• JAF Core: Functional, immutable, production-ready")
        print("• ADK Callbacks: Enhanced with instrumentation, caching, monitoring")
        print("• Both: Type-safe, composable, enterprise-grade")

    except Exception as e:
        print(f" Demo Error: {e}")
        # In production, comprehensive error handling would be here

if __name__ == "__main__":
    asyncio.run(main())

This API reference provides comprehensive documentation for building sophisticated AI agent systems with JAF's functional architecture, type safety, and production-ready features.

API Reference¶

Enums¶

ContentRole¶

ToolSource¶

Model¶

ToolParameterType¶

PartType¶

Tool Creation Functions¶

create_function_tool (Recommended)¶

create_function_tool_legacy (Deprecated)¶

create_async_function_tool¶

FunctionToolConfig¶

Core Functions¶

Main Execution¶

run(initial_state: RunState[Ctx], config: RunConfig[Ctx]) -> RunResult[Out]¶

ID Generation¶

generate_run_id() -> RunId¶

generate_trace_id() -> TraceId¶

create_run_id(id_str: str) -> RunId¶

create_trace_id(id_str: str) -> TraceId¶

Core Types¶

RunState¶

RunState[Ctx]¶

Agent¶

Agent[Ctx, Out]¶

Tool Protocol¶

Tool[Args, Ctx]¶

Message¶

Message¶

RunConfig¶

RunConfig[Ctx]¶

RunResult¶

RunResult[Out]¶

ValidationResult¶

ValidationResult¶

ModelConfig¶

ModelConfig¶

Model Provider Functions¶

LiteLLM Provider¶

make_litellm_provider(base_url: str, api_key: str = "anything") -> ModelProvider[Ctx]¶

ModelProvider Protocol¶

ModelProvider[Ctx]¶

Memory Provider System¶

Memory Provider Factory¶

create_memory_provider_from_env(external_clients: Optional[Dict[str, Any]] = None) -> MemoryProvider¶

Specific Provider Creators¶

create_in_memory_provider(config: InMemoryConfig) -> MemoryProvider¶

create_redis_provider(config: RedisConfig, client: Optional[Any] = None) -> MemoryProvider¶

create_postgres_provider(config: PostgresConfig, client: Optional[Any] = None) -> MemoryProvider¶

Memory Provider Protocol¶

MemoryProvider¶

Memory Configuration Types¶

MemoryConfig¶

ConversationMemory¶

Validation and Policy Functions¶

Input/Output Guardrails¶

create_length_guardrail(max_length: int, min_length: int = 0) -> Guardrail¶

create_content_filter(blocked_patterns: List[str]) -> Guardrail¶

create_json_validation_guardrail(schema_class: type[BaseModel]) -> Guardrail¶

combine_guardrails(guardrails: List[Guardrail], require_all: bool = True) -> Guardrail¶

Handoff Policies¶

create_handoff_guardrail(policy: HandoffPolicy, current_agent: str) -> Guardrail¶

create_role_based_handoff_policy(agent_roles: Dict[str, str], role_permissions: Dict[str, List[str]]) -> HandoffPolicy¶

Server Functions¶

Server Creation¶

run_server(config: ServerConfig) -> None¶

create_simple_server(agents: List[Agent], model_provider: ModelProvider, host: str = 'localhost', port: int = 3000) -> JAFServer¶

Tool Result System¶

ToolResult Type¶

ToolResult[T]¶

ToolResponse Helper Class¶

ToolResponse¶

Utility Functions¶

with_error_handling(tool_name: str, executor: Callable) -> Callable¶

tool_result_to_string(result: ToolResult[Any]) -> str¶

Tracing System¶

TraceCollector Protocol¶

TraceCollector¶

Built-in Collectors¶

ConsoleTraceCollector¶

`run(initial_state: RunState[Ctx], config: RunConfig[Ctx]) -> RunResult[Out]`¶

`generate_run_id() -> RunId`¶

`generate_trace_id() -> TraceId`¶

`create_run_id(id_str: str) -> RunId`¶

`create_trace_id(id_str: str) -> TraceId`¶

`RunState[Ctx]`¶

`Agent[Ctx, Out]`¶

`Tool[Args, Ctx]`¶

`Message`¶

`RunConfig[Ctx]`¶

`RunResult[Out]`¶

`ValidationResult`¶

`ModelConfig`¶

`make_litellm_provider(base_url: str, api_key: str = "anything") -> ModelProvider[Ctx]`¶

`ModelProvider[Ctx]`¶

`create_memory_provider_from_env(external_clients: Optional[Dict[str, Any]] = None) -> MemoryProvider`¶

`create_in_memory_provider(config: InMemoryConfig) -> MemoryProvider`¶

`create_redis_provider(config: RedisConfig, client: Optional[Any] = None) -> MemoryProvider`¶

`create_postgres_provider(config: PostgresConfig, client: Optional[Any] = None) -> MemoryProvider`¶

`MemoryProvider`¶

`MemoryConfig`¶

`ConversationMemory`¶

`create_length_guardrail(max_length: int, min_length: int = 0) -> Guardrail`¶

`create_content_filter(blocked_patterns: List[str]) -> Guardrail`¶

`create_json_validation_guardrail(schema_class: type[BaseModel]) -> Guardrail`¶

`combine_guardrails(guardrails: List[Guardrail], require_all: bool = True) -> Guardrail`¶

`create_handoff_guardrail(policy: HandoffPolicy, current_agent: str) -> Guardrail`¶

`create_role_based_handoff_policy(agent_roles: Dict[str, str], role_permissions: Dict[str, List[str]]) -> HandoffPolicy`¶

`run_server(config: ServerConfig) -> None`¶

`create_simple_server(agents: List[Agent], model_provider: ModelProvider, host: str = 'localhost', port: int = 3000) -> JAFServer`¶

`ToolResult[T]`¶

`ToolResponse`¶

`with_error_handling(tool_name: str, executor: Callable) -> Callable`¶

`tool_result_to_string(result: ToolResult[Any]) -> str`¶

`TraceCollector`¶

`ConsoleTraceCollector`¶

`FileTraceCollector(file_path: str)`¶

`RunnerCallbacks`¶

`RunnerConfig`¶

`LLMControlResult`¶

`ToolSelectionControlResult`¶

`IterationControlResult`¶

`SynthesisCheckResult`¶

`execute_agent(config: RunnerConfig, session_state: Dict[str, Any], message: Message, context: RunContext, model_provider: ModelProvider) -> AgentResponse`¶