Config Module

Feature configuration classes for agent-centric API

Module praisonaiagents.config

The config module provides dataclass-based configuration for all agent features. These classes offer type safety, sensible defaults, and IDE autocompletion.

Quick Start

from praisonaiagents import (
    MemoryConfig,
    OutputConfig,
    ExecutionConfig,
    PlanningConfig,
)

Or use the namespace style:

import praisonaiagents as pa

config = pa.config.MemoryConfig(user_id="user123")

Configuration Classes

MemoryConfig

Memory and session management

KnowledgeConfig

RAG and knowledge retrieval

OutputConfig

Output formatting and verbosity

ExecutionConfig

Agent execution parameters

PlanningConfig

Planning mode settings

ReflectionConfig

Self-reflection settings

GuardrailConfig

Safety and validation

WebConfig

Web search configuration

---

MemoryConfig

Configuration for agent memory and session management.

from praisonaiagents import MemoryConfig, MemoryBackend

# Simple usage
config = MemoryConfig(user_id="user123", session_id="session456")

# With backend selection
config = MemoryConfig(
    backend=MemoryBackend.SQLITE,
    user_id="user123",
    session_id="session456",
)

# Usage in Agent
from praisonaiagents import Agent
agent = Agent(
    instructions="...",
    memory=config,
)

Parameters

Parameter	Type	Default	Description
backend	MemoryBackend	FILE	Storage backend (FILE, SQLITE, REDIS, POSTGRES, MEM0, MONGODB)
user_id	str	None	User identifier for memory isolation
session_id	str	None	Session identifier
auto_memory	bool	False	Enable automatic memory extraction
claude_memory	bool	False	Use Claude memory format
learn	LearnConfig	None	Continuous learning configuration

---

OutputConfig

Configuration for output formatting and verbosity.

from praisonaiagents import OutputConfig

# Verbose output with markdown
config = OutputConfig(
    output="verbose",
    markdown=True,
    show_reasoning=True,
)

# Using presets (simpler)
from praisonaiagents import Agent
agent = Agent(instructions="...", output="verbose")  # Preset string

Parameters

Parameter	Type	Default	Description
verbose	bool	False	Enable verbose output
markdown	bool	True	Format output as markdown
show_reasoning	bool	False	Show agent reasoning steps
show_tool_calls	bool	True	Display tool call details
streaming	bool	False	Enable streaming output

Presets

Preset	Description
"silent"	Minimal output
"minimal"	Basic output only
"actions"	Show actions (default)
"verbose"	Detailed output

---

ExecutionConfig

Configuration for agent execution parameters.

from praisonaiagents import ExecutionConfig

config = ExecutionConfig(
    max_iter=20,
    max_retry_limit=5,
    timeout=300,
)

# Using presets
from praisonaiagents import Agent
agent = Agent(instructions="...", execution="thorough")

Parameters

Parameter	Type	Default	Description
max_iter	int	20	Maximum iterations
max_retry_limit	int	2	Max retries for retryable tool and guardrail failures
retry_initial_delay	float	1.0	First retry delay (seconds)
retry_backoff_factor	float	2.0	Exponential backoff multiplier
retry_jitter	float	0.1	Jitter fraction of base delay
timeout	int	None	Execution timeout in seconds
code_execution	bool	False	Enable code execution
code_mode	str	"safe"	Code execution mode (“safe” or “unsafe”)
code_tools	bool	False	When True, model-generated code may call the agent’s registered tools via injected proxies
code_tools_allow	Optional[List[str]]	None	Explicit per-run allow-list of tool names callable from code. None/empty = no tools exposed (safe default)
rate_limiter	Any	None	Rate limiter instance
allow_code_execution	bool	False	⚠️ Deprecated — use code_execution

Presets

Preset	Description
"fast"	Quick execution (max_iter=5)
"balanced"	Balanced (max_iter=20)
"thorough"	Thorough (max_iter=25)

---

KnowledgeConfig

Configuration for RAG and knowledge retrieval.

from praisonaiagents import KnowledgeConfig

config = KnowledgeConfig(
    sources=["docs/", "data.pdf"],
    retrieval_k=10,
    rerank=True,
)

Parameters

Parameter	Type	Default	Description
sources	List[str]	[]	Knowledge source paths
retrieval_k	int	5	Number of chunks to retrieve
retrieval_threshold	float	0.0	Minimum similarity threshold
rerank	bool	False	Enable reranking
auto_retrieve	bool	True	Automatic context retrieval

---

PlanningConfig

Configuration for planning mode.

from praisonaiagents import PlanningConfig

config = PlanningConfig(
    llm="gpt-4o",
    auto_approve=False,
    read_only=True,
)

Parameters

Parameter	Type	Default	Description
llm	str	None	LLM for planning (defaults to agent’s LLM)
tools	List	None	Tools available for planning
reasoning	bool	False	Enable reasoning in plans
auto_approve	bool	False	Auto-approve plans
read_only	bool	False	Only read-only operations

---

ReflectionConfig

Configuration for self-reflection.

from praisonaiagents import ReflectionConfig

config = ReflectionConfig(
    min_iterations=1,
    max_iterations=5,
    prompt="Evaluate your response for accuracy...",
)

Parameters

Parameter	Type	Default	Description
min_iterations	int	1	Minimum reflection iterations
max_iterations	int	3	Maximum reflection iterations
llm	str	None	LLM for reflection
prompt	str	None	Custom reflection prompt

---

GuardrailConfig

Configuration for guardrails and safety validation.

from praisonaiagents import GuardrailConfig, GuardrailAction

config = GuardrailConfig(
    validator=my_validator_fn,
    max_retries=5,
    on_fail=GuardrailAction.RAISE,
)

Parameters

Parameter	Type	Default	Description
validator	Callable	None	Validation function
description	str	None	Guardrail description for LLM-based validation
max_retries	int	3	Maximum retries on failure
on_fail	GuardrailAction	RETRY	Action on failure (RETRY, SKIP, RAISE)
policies	List[str]	[]	Policy strings

---

WebConfig

Configuration for web search capabilities.

from praisonaiagents import WebConfig, WebSearchProvider

config = WebConfig(
    provider=WebSearchProvider.TAVILY,
    max_results=10,
)

Parameters

Parameter	Type	Default	Description
provider	WebSearchProvider	DUCKDUCKGO	Search provider
max_results	int	5	Maximum search results
fetch_content	bool	True	Fetch page content

---

Bool/String/Config Pattern

All config classes follow the “progressive disclosure” pattern:

from praisonaiagents import Agent

# Level 1: Boolean toggle (simplest)
Agent(memory=True)

# Level 2: String preset
Agent(output="verbose")

# Level 3: Config object (full control)
Agent(memory=MemoryConfig(user_id="user123", backend=MemoryBackend.SQLITE))

This pattern applies to: memory, knowledge, planning, reflection, guardrails, web, output, execution, caching, hooks, skills, autonomy.

---

See Also

• Agent Configuration
• Memory Configuration
• praisonaiagents SDK