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PraisonAI API Reference

This file is auto-generated. Do not edit manually. Regenerate with: praisonai docs api-md --write

Shared Types

Types:

from praisonaiagents import ContextPolicy, GuardrailResult, HandoffConfig, HandoffCycleError, HandoffDepthError, HandoffError, HandoffInputData, HandoffResult, HandoffTimeoutError, ReflectionOutput, StepResult, TaskOutput, ToolResult, ToolValidationError, WorkflowContext

Methods:

  • GuardrailResult.from_tuple(result: Tuple[bool, Any]) -> 'GuardrailResult'
  • HandoffConfig.from_dict(data: Dict[str, Any]) -> 'HandoffConfig'
  • HandoffConfig.to_dict() -> Dict[str, Any]
  • TaskOutput.json() -> Optional[str]
  • TaskOutput.to_dict() -> dict
  • ToolResult.to_dict() -> Dict[str, Any]

Agents

Types:

from praisonaiagents import Agent, Agents, AutoAgents, AutoRagAgent, ContextAgent, DeepResearchAgent, ImageAgent, PlanningAgent, PromptExpanderAgent, QueryRewriterAgent, create_context_agent

Methods:

Tools

Types:

from praisonaiagents import BaseTool, FunctionTool, ToolRegistry, Tools, get_registry, get_tool, register_tool, tool, validate_tool

Methods:

  • BaseTool.call(**kwargs) -> Any
  • BaseTool.get_schema() -> Dict[str, Any]
  • BaseTool.run(**kwargs) -> Any
  • BaseTool.safe_run(**kwargs) -> ToolResult
  • BaseTool.validate() -> bool
  • BaseTool.validate_class() -> bool
  • FunctionTool.call(*args, **kwargs) -> Any
  • FunctionTool.injected_params() -> Dict[str, Any]
  • FunctionTool.run(**kwargs) -> Any
  • ToolRegistry.clear() -> None
  • ToolRegistry.discover_plugins() -> int
  • ToolRegistry.get(name: str) -> Optional[Union[BaseTool, Callable]]
  • ToolRegistry.get_all() -> Dict[str, Union[BaseTool, Callable]]
  • ToolRegistry.list_base_tools() -> List[BaseTool]
  • ToolRegistry.list_tools() -> List[str]
  • ToolRegistry.register(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False) -> None
  • ToolRegistry.unregister(name: str) -> bool
  • Tools.internet_search(*args, **kwargs)
  • praisonaiagents.get_registry() -> ToolRegistry
  • praisonaiagents.get_tool(name: str) -> Optional[Union[BaseTool, Callable]]
  • praisonaiagents.register_tool(tool: Union[BaseTool, Callable], name: Optional[str] = None) -> None
  • praisonaiagents.tool(func: Optional[Callable] = None) -> Union[FunctionTool, Callable[[Callable], FunctionTool]]
  • praisonaiagents.validate_tool(tool: Any) -> bool

Workflows

Types:

from praisonaiagents import Loop, Parallel, Pipeline, Repeat, Route, Workflow, WorkflowStep, loop, parallel, repeat, route

Methods:

  • Workflow.arun(input: str = '', llm: Optional[str] = None, verbose: bool = False) -> Dict[str, Any]
  • Workflow.astart(input: str = '', llm: Optional[str] = None, verbose: bool = False) -> Dict[str, Any]
  • Workflow.from_template(uri: str, config: Optional[Dict[str, Any]] = None, offline: bool = False, **kwargs) -> 'Workflow'
  • Workflow.memory_config() -> Optional[Dict[str, Any]]
  • Workflow.on_step_complete() -> Optional[Callable]
  • Workflow.on_step_error() -> Optional[Callable]
  • Workflow.on_step_start() -> Optional[Callable]
  • Workflow.on_workflow_complete() -> Optional[Callable]
  • Workflow.on_workflow_start() -> Optional[Callable]
  • Workflow.planning_llm() -> Optional[str]
  • Workflow.reasoning() -> bool
  • Workflow.run(input: str = '', llm: Optional[str] = None, verbose: bool = False, stream: bool = None) -> Dict[str, Any]
  • Workflow.start(input: str = '', **kwargs) -> Dict[str, Any]
  • Workflow.stream() -> bool
  • Workflow.to_dict() -> Dict[str, Any]
  • Workflow.verbose() -> bool
  • Workflow.verbose(value: bool)
  • WorkflowStep.async_execution() -> bool
  • WorkflowStep.branch_condition() -> Optional[Dict[str, List[str]]]
  • WorkflowStep.context_from() -> Optional[List[str]]
  • WorkflowStep.evaluator()
  • WorkflowStep.executor()
  • WorkflowStep.max_retries() -> int
  • WorkflowStep.next_steps() -> Optional[List[str]]
  • WorkflowStep.on_error() -> str
  • WorkflowStep.output_file() -> Optional[str]
  • WorkflowStep.output_json() -> Optional[Any]
  • WorkflowStep.output_pydantic() -> Optional[Any]
  • WorkflowStep.output_variable() -> Optional[str]
  • WorkflowStep.quality_check() -> bool
  • WorkflowStep.rerun() -> bool
  • WorkflowStep.retain_full_context() -> bool
  • WorkflowStep.to_dict() -> Dict[str, Any]
  • praisonaiagents.loop(step: Any = None, steps: Optional[List[Any]] = None, over: Optional[str] = None, from_csv: Optional[str] = None, from_file: Optional[str] = None, var_name: str = 'item', parallel: bool = False, max_workers: Optional[int] = None, output_variable: Optional[str] = None) -> Loop
  • praisonaiagents.parallel(steps: List) -> Parallel
  • praisonaiagents.repeat(step: Any, until: Optional[Callable[[WorkflowContext], bool]] = None, max_iterations: int = 10) -> Repeat
  • praisonaiagents.route(routes: Dict[str, List], default: Optional[List] = None) -> Route

DB

Types:

from praisonaiagents import db

Memory

Types:

from praisonaiagents import Memory, MemoryBackend, MemoryConfig

Methods:

  • Memory.build_context_for_task(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_in_output: Optional[bool] = None) -> str
  • Memory.calculate_quality_metrics(output: str, expected_output: str, llm: Optional[str] = None, custom_prompt: Optional[str] = None) -> Dict[str, float]
  • Memory.compute_quality_score(completeness: float, relevance: float, clarity: float, accuracy: float, weights: Dict[str, float] = None) -> float
  • Memory.delete_long_term(memory_id: str) -> bool
  • Memory.delete_memories(memory_ids: List[str]) -> int
  • Memory.delete_memories_matching(query: str, memory_type: Optional[str] = None, limit: int = 10) -> int
  • Memory.delete_memory(memory_id: str, memory_type: Optional[str] = None) -> bool
  • Memory.delete_short_term(memory_id: str) -> bool
  • Memory.finalize_task_output(content: str, agent_name: str, quality_score: float, threshold: float = 0.7, metrics: Dict[str, Any] = None, task_id: str = None)
  • Memory.get_all_memories() -> List[Dict[str, Any]]
  • Memory.get_learn_context() -> str
  • Memory.learn()
  • Memory.reset_all()
  • Memory.reset_entity_only()
  • Memory.reset_long_term()
  • Memory.reset_short_term()
  • Memory.reset_user_memory()
  • Memory.search(query: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, limit: int = 5, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]
  • Memory.search_entity(query: str, limit: int = 5) -> List[Dict[str, Any]]
  • Memory.search_long_term(query: str, limit: int = 5, relevance_cutoff: float = 0.0, min_quality: float = 0.0, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]
  • Memory.search_short_term(query: str, limit: int = 5, min_quality: float = 0.0, relevance_cutoff: float = 0.0, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]
  • Memory.search_user_memory(user_id: str, query: str, limit: int = 5, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]
  • Memory.search_with_quality(query: str, min_quality: float = 0.0, memory_type: Literal['short', 'long'] = 'long', limit: int = 5) -> List[Dict[str, Any]]
  • Memory.store_entity(name: str, type_: str, desc: str, relations: str)
  • Memory.store_long_term(text: str, metadata: Dict[str, Any] = None, completeness: float = None, relevance: float = None, clarity: float = None, accuracy: float = None, weights: Dict[str, float] = None, evaluator_quality: float = None)
  • Memory.store_quality(text: str, quality_score: float, task_id: Optional[str] = None, iteration: Optional[int] = None, metrics: Optional[Dict[str, float]] = None, memory_type: Literal['short', 'long'] = 'long') -> None
  • Memory.store_short_term(text: str, metadata: Dict[str, Any] = None, completeness: float = None, relevance: float = None, clarity: float = None, accuracy: float = None, weights: Dict[str, float] = None, evaluator_quality: float = None)
  • Memory.store_user_memory(user_id: str, text: str, extra: Dict[str, Any] = None)
  • MemoryConfig.to_dict() -> Dict[str, Any]

Knowledge

Types:

from praisonaiagents import Chunking, ChunkingStrategy, Knowledge, KnowledgeConfig

Methods:

  • Chunking.SUPPORTED_CHUNKERS() -> Dict[str, Any]
  • Chunking.call(text: Union[str, List[str]], **kwargs) -> Union[List[Any], List[List[Any]]]
  • Chunking.chunk(text: Union[str, List[str]], **kwargs) -> Union[List[Any], List[List[Any]]]
  • Chunking.chunker()
  • Chunking.embedding_model()
  • Knowledge.add(file_path, user_id = None, agent_id = None, run_id = None, metadata = None)
  • Knowledge.chunker()
  • Knowledge.config()
  • Knowledge.delete(memory_id)
  • Knowledge.delete_all(user_id = None, agent_id = None, run_id = None)
  • Knowledge.get(memory_id)
  • Knowledge.get_all(user_id = None, agent_id = None, run_id = None)
  • Knowledge.get_corpus_stats()
  • Knowledge.history(memory_id)
  • Knowledge.index(path: str, incremental: bool = True, force: bool = False, include_glob: list = None, exclude_glob: list = None, user_id: str = None, agent_id: str = None, run_id: str = None)
  • Knowledge.markdown()
  • Knowledge.memory()
  • Knowledge.normalize_content(content)
  • Knowledge.reset()
  • Knowledge.search(query, user_id = None, agent_id = None, run_id = None, rerank = None, **kwargs)
  • Knowledge.store(content, user_id = None, agent_id = None, run_id = None, metadata = None)
  • Knowledge.update(memory_id, data)
  • KnowledgeConfig.to_dict() -> Dict[str, Any]

RAG

Types:

from praisonaiagents import CitationsMode, ContextPack, RAG, RAGCitation, RAGConfig, RAGResult, RetrievalConfig, RetrievalPolicy

Methods:

  • ContextPack.format_for_prompt(include_sources: bool = True) -> str
  • ContextPack.from_dict(data: Dict[str, Any]) -> 'ContextPack'
  • ContextPack.has_citations() -> bool
  • ContextPack.to_dict() -> Dict[str, Any]
  • RAG.aquery(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> RAGResult
  • RAG.aretrieve(query: str, **kwargs) -> ContextPack
  • RAG.astream(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> AsyncIterator[str]
  • RAG.get_citations(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> List[Citation]
  • RAG.llm()
  • RAG.query(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> RAGResult
  • RAG.retrieve(query: str, **kwargs) -> ContextPack
  • RAG.stream(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> Iterator[str]
  • RAGCitation.from_dict(data: Dict[str, Any]) -> 'Citation'
  • RAGCitation.to_dict() -> Dict[str, Any]
  • RAGConfig.from_dict(data: Dict[str, Any]) -> 'RAGConfig'
  • RAGConfig.to_dict() -> Dict[str, Any]
  • RAGResult.format_answer_with_citations() -> str
  • RAGResult.from_dict(data: Dict[str, Any]) -> 'RAGResult'
  • RAGResult.has_citations() -> bool
  • RAGResult.to_dict() -> Dict[str, Any]
  • RetrievalConfig.from_dict(data: Dict[str, Any]) -> 'RetrievalConfig'
  • RetrievalConfig.get_strategy(corpus_stats = None)
  • RetrievalConfig.get_token_budget(model_name: Optional[str] = None)
  • RetrievalConfig.should_retrieve(query: str, force: bool = False, skip: bool = False) -> bool
  • RetrievalConfig.to_dict() -> Dict[str, Any]
  • RetrievalConfig.to_knowledge_config() -> Dict[str, Any]
  • RetrievalConfig.to_rag_config() -> Dict[str, Any]

Handoff

Types:

from praisonaiagents import Handoff, RECOMMENDED_PROMPT_PREFIX, handoff, handoff_filters, prompt_with_handoff_instructions

Methods:

  • Handoff.default_tool_description() -> str
  • Handoff.default_tool_name() -> str
  • Handoff.execute_async(source_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None) -> HandoffResult
  • Handoff.execute_programmatic(source_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None) -> HandoffResult
  • Handoff.to_tool_function(source_agent: 'Agent') -> Callable
  • Handoff.tool_description() -> str
  • Handoff.tool_name() -> str
  • praisonaiagents.handoff(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None) -> Handoff
  • handoff_filters.keep_last_n_messages(n: int) -> Callable[[HandoffInputData], HandoffInputData]
  • handoff_filters.remove_all_tools(data: HandoffInputData) -> HandoffInputData
  • handoff_filters.remove_system_messages(data: HandoffInputData) -> HandoffInputData
  • praisonaiagents.prompt_with_handoff_instructions(base_prompt: str, agent: 'Agent') -> str

Guardrails

Types:

from praisonaiagents import GuardrailAction, GuardrailConfig, LLMGuardrail

Methods:

  • GuardrailConfig.to_dict() -> Dict[str, Any]
  • LLMGuardrail.call(task_output: TaskOutput) -> Tuple[bool, Union[str, TaskOutput]]

Planning

Types:

from praisonaiagents import ApprovalCallback, Plan, PlanStep, PlanStorage, PlanningConfig, READ_ONLY_TOOLS, RESTRICTED_TOOLS, TodoItem, TodoList

Methods:

Skills

Types:

from praisonaiagents import SkillLoader, SkillManager, SkillMetadata, SkillProperties, SkillsConfig

Methods:

Session

Types:

from praisonaiagents import Session

Methods:

  • Session.Agent(name: str, role: str = 'Assistant', instructions: Optional[str] = None, tools: Optional[List[Any]] = None, memory: bool = True, knowledge: Optional[List[str]] = None, **kwargs) -> 'Agent'
  • Session.add_knowledge(source: str) -> None
  • Session.add_memory(text: str, memory_type: str = 'long', **metadata) -> None
  • Session.chat(message: str, **kwargs) -> str
  • Session.clear_memory(memory_type: str = 'all') -> None
  • Session.create_agent(*args, **kwargs) -> 'Agent'
  • Session.get_context(query: str, max_items: int = 3) -> str
  • Session.get_state(key: str, default: Any = None) -> Any
  • Session.increment_state(key: str, increment: int = 1, default: int = 0) -> None
  • Session.knowledge() -> 'Knowledge'
  • Session.memory() -> 'Memory'
  • Session.restore_state() -> Dict[str, Any]
  • Session.save_state(state_data: Dict[str, Any]) -> None
  • Session.search_knowledge(query: str, limit: int = 5) -> List[Dict[str, Any]]
  • Session.search_memory(query: str, memory_type: str = 'long', limit: int = 5) -> List[Dict[str, Any]]
  • Session.send_message(message: str, **kwargs) -> str
  • Session.set_state(key: str, value: Any) -> None

MCP

Types:

from praisonaiagents import MCP

Methods:

Telemetry

Types:

from praisonaiagents import MinimalTelemetry, TelemetryCollector, cleanup_telemetry_resources, disable_performance_mode, disable_telemetry, enable_performance_mode, enable_telemetry, get_telemetry

Methods:

Observability

Types:

from praisonaiagents import FlowDisplay, obs, track_workflow

Methods:

Context

Types:

from praisonaiagents import ContextManager, ManagerConfig

Methods:

  • ContextManager.capture_llm_boundary(messages: List[Dict[str, Any]], tools: List[Dict[str, Any]]) -> SnapshotHookData
  • ContextManager.emergency_truncate(messages: List[Dict[str, Any]], target_tokens: int) -> List[Dict[str, Any]]
  • ContextManager.estimate_tokens(text: str, validate: bool = False) -> Tuple[int, Optional[EstimationMetrics]]
  • ContextManager.get_history() -> List[Dict[str, Any]]
  • ContextManager.get_last_snapshot_hook() -> Optional[SnapshotHookData]
  • ContextManager.get_resolved_config() -> Dict[str, Any]
  • ContextManager.get_stats() -> Dict[str, Any]
  • ContextManager.get_tool_budget(tool_name: str) -> int
  • ContextManager.process(messages: List[Dict[str, Any]], system_prompt: str = '', tools: Optional[List[Dict[str, Any]]] = None, trigger: Literal['turn', 'tool_call', 'manual', 'overflow'] = 'turn') -> Dict[str, Any]
  • ContextManager.register_snapshot_callback(callback: Callable[[SnapshotHookData], None]) -> None
  • ContextManager.reset() -> None
  • ContextManager.set_tool_budget(tool_name: str, max_tokens: int, protected: bool = False) -> None
  • ContextManager.truncate_tool_output(tool_name: str, output: str) -> str
  • ManagerConfig.from_env() -> 'ManagerConfig'
  • ManagerConfig.merge(**overrides) -> 'ManagerConfig'
  • ManagerConfig.to_dict() -> Dict[str, Any]

Config

Types:

from praisonaiagents import AutonomyConfig, AutonomyLevel, CachingConfig, ExecutionConfig, ExecutionPreset, GuardrailConfig, HooksConfig, KnowledgeConfig, MemoryConfig, MultiAgentExecutionConfig, MultiAgentHooksConfig, MultiAgentMemoryConfig, MultiAgentOutputConfig, MultiAgentPlanningConfig, OutputConfig, OutputPreset, PlanningConfig, ReflectionConfig, SkillsConfig, TemplateConfig, WebConfig, WebSearchProvider

Methods:

  • AutonomyConfig.to_dict() -> Dict[str, Any]
  • CachingConfig.to_dict() -> Dict[str, Any]
  • ExecutionConfig.to_dict() -> Dict[str, Any]
  • GuardrailConfig.to_dict() -> Dict[str, Any]
  • HooksConfig.to_dict() -> Dict[str, Any]
  • KnowledgeConfig.to_dict() -> Dict[str, Any]
  • MemoryConfig.to_dict() -> Dict[str, Any]
  • MultiAgentExecutionConfig.to_dict() -> Dict[str, Any]
  • MultiAgentHooksConfig.to_dict() -> Dict[str, Any]
  • MultiAgentMemoryConfig.to_dict() -> Dict[str, Any]
  • MultiAgentOutputConfig.to_dict() -> Dict[str, Any]
  • MultiAgentPlanningConfig.to_dict() -> Dict[str, Any]
  • OutputConfig.to_dict() -> Dict[str, Any]
  • PlanningConfig.to_dict() -> Dict[str, Any]
  • ReflectionConfig.to_dict() -> Dict[str, Any]
  • SkillsConfig.to_dict() -> Dict[str, Any]
  • TemplateConfig.to_dict() -> Dict[str, Any]
  • WebConfig.to_dict() -> Dict[str, Any]

Display

Types:

from praisonaiagents import async_display_callbacks, clean_triple_backticks, display_error, display_generating, display_instruction, display_interaction, display_self_reflection, display_tool_call, error_logs, register_display_callback, sync_display_callbacks

Methods:

  • praisonaiagents.clean_triple_backticks(text: str) -> str
  • praisonaiagents.display_error(message: str, console = None)
  • praisonaiagents.display_generating(content: str = '', start_time: Optional[float] = None)
  • praisonaiagents.display_instruction(message: str, console = None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None)
  • praisonaiagents.display_interaction(message, response, markdown = True, generation_time = None, console = None, agent_name = None, agent_role = None, agent_tools = None, task_name = None, task_description = None, task_id = None, metrics = None)
  • praisonaiagents.display_self_reflection(message: str, console = None)
  • praisonaiagents.display_tool_call(message: str, console = None, tool_name: str = None, tool_input: dict = None, tool_output: str = None, elapsed_time: float = None, success: bool = True)
  • praisonaiagents.register_display_callback(display_type: str, callback_fn, is_async: bool = False)

Utilities

Types:

from praisonaiagents import ArrayMode, is_policy_string, parse_policy_string, resolve, resolve_autonomy, resolve_caching, resolve_context, resolve_execution, resolve_guardrail_policies, resolve_guardrails, resolve_hooks, resolve_knowledge, resolve_memory, resolve_output, resolve_planning, resolve_reflection, resolve_routing, resolve_skills, resolve_web

Methods:

  • praisonaiagents.is_policy_string(value: str) -> bool
  • praisonaiagents.parse_policy_string(value: str) -> tuple
  • praisonaiagents.resolve(value: Any, param_name: str, config_class: Optional[Type] = None, presets: Optional[Dict[str, Any]] = None, default: Any = None, instance_check: Optional[Callable[[Any], bool]] = None, url_schemes: Optional[Dict[str, str]] = None, array_mode: Optional[str] = None, string_mode: Optional[str] = None) -> Any
  • praisonaiagents.resolve_autonomy(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_caching(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_context(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_execution(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_guardrail_policies(policies: list, config_class: Type) -> Any
  • praisonaiagents.resolve_guardrails(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_hooks(value: Any, config_class: Optional[Type] = None) -> Any
  • praisonaiagents.resolve_knowledge(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_memory(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_output(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_planning(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_reflection(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_routing(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_skills(value: Any, config_class: Type) -> Any
  • praisonaiagents.resolve_web(value: Any, config_class: Type) -> Any

Other

Types:

from praisonaiagents import EmbeddingResult, Task, aembedding, aembeddings, embedding, embeddings, get_dimensions

Methods:

  • Task.execute_callback(task_output: TaskOutput) -> None
  • Task.execute_callback_sync(task_output: TaskOutput) -> None
  • Task.initialize_memory()
  • Task.store_in_memory(content: str, agent_name: str = None, task_id: str = None)
  • praisonaiagents.aembedding(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult
  • praisonaiagents.aembeddings(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult
  • praisonaiagents.embedding(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult
  • praisonaiagents.embeddings(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult
  • praisonaiagents.get_dimensions(model_name: str) -> int

Wrapper (praisonai)

Types:

from praisonai import CloudProvider, Deploy, DeployConfig, DeployType, PraisonAI, __version__

CLI

Methods:

  • praisonai --help
  • praisonai acp acp-main --help
  • praisonai agents create --help
  • praisonai agents info --help
  • praisonai agents list --help
  • praisonai audit agent-centric --help
  • praisonai batch batch-run --help
  • praisonai batch list --help
  • praisonai batch report --help
  • praisonai batch stats --help
  • praisonai benchmark agent --help
  • praisonai benchmark benchmark-callback --help
  • praisonai benchmark cli --help
  • praisonai benchmark compare --help
  • praisonai benchmark litellm --help
  • praisonai benchmark profile --help
  • praisonai benchmark sdk --help
  • praisonai benchmark workflow --help
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  • praisonai plugins disable --help
  • praisonai plugins doctor --help
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  • praisonai serve docs --help
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  • praisonai serve rag --help
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  • praisonai workflow run --help

TypeScript

Types/Exports:

export { Agent, Agents, PraisonAIAgents, Router } from "./agent";
export type { PraisonAIAgentsConfig, SimpleAgentConfig, SimpleRouteConfig, SimpleRouterConfig } from "./agent";
export { AudioAgent, createAudioAgent } from "./agent/audio";
export type { AudioAgentConfig, AudioProvider, AudioSpeakOptions, AudioSpeakResult, AudioTranscribeOptions, AudioTranscribeResult } from "./agent/audio";
export { ContextAgent, createContextAgent } from "./agent/context";
export { Handoff, handoff, handoffFilters } from "./agent/handoff";
export { ImageAgent, createImageAgent } from "./agent/image";
export { PromptExpanderAgent, createPromptExpanderAgent } from "./agent/prompt-expander";
export { QueryRewriterAgent, createQueryRewriterAgent } from "./agent/query-rewriter";
export { DeepResearchAgent, createDeepResearchAgent } from "./agent/research";
export { RouterAgent, createRouter, routeConditions } from "./agent/router";
export { // Agent loop
  createAgentLoop, // DevTools
  enableDevTools, // MCP
  createMCP, // Middleware (renamed to avoid conflicts)
  createCachingMiddleware, // Models
  createModel, // Multimodal
  createImagePart, // Next.js
  createRouteHandler, // OAuth for MCP
  type OAuthClientProvider, // Server adapters
  createHttpHandler, // Speech & Transcription
  generateSpeech, // Telemetry (AI SDK v6 parity)
  configureTelemetry, // Tool Approval (AI SDK v6 parity)
  ApprovalManager, // Tools
  defineTool, // UI Message (AI SDK v6 parity)
  convertToModelMessages, AIAgentStep, AIEmbedManyResult, AIEmbedOptions, AIEmbedResult, AIFilePart, AIGenerateImageOptions, AIGenerateImageResult, AIGenerateObjectOptions, AIGenerateObjectResult, AIGenerateTextOptions, AIGenerateTextResult, AIImagePart, AIMiddleware, AIMiddlewareConfig, AIModelMessage, AISpan, AISpanKind, AISpanOptions, AISpanStatus, AIStreamObjectOptions, AIStreamObjectResult, AIStreamTextOptions, AIStreamTextResult, AITelemetryEvent, AITelemetrySettings, AITextPart, AIToolDefinition, AITracer, AgentLoop, DANGEROUS_PATTERNS, MCPClientType, MODEL_ALIASES, SPEECH_MODELS, TRANSCRIPTION_MODELS, ToolApprovalDeniedError, ToolApprovalTimeoutError, aiEmbed, aiEmbedMany, aiGenerateImage, aiGenerateObject, aiGenerateText, aiStreamObject, aiStreamText, applyMiddleware, autoEnableDevTools, base64ToUint8Array, clearAICache, clearEvents, closeAllMCPClients, closeMCPClient, convertToUIMessages, createAILoggingMiddleware, createAISpan, createApprovalResponse, createDangerousPatternChecker, createDevToolsMiddleware, createExpressHandler, createFastifyHandler, createFilePart, createHonoHandler, createMultimodalMessage, createNestHandler, createPagesHandler, createPdfPart, createSystemMessage, createTelemetryMiddleware, createTelemetrySettings, createTextMessage, createTextPart, createToolSet, disableAITelemetry, disableDevTools, enableAITelemetry, functionToTool, getAICacheStats, getApprovalManager, getDevToolsState, getDevToolsUrl, getEvents, getMCPClient, getModel, getTelemetrySettings, getToolsNeedingApproval, getTracer, hasModelAlias, hasPendingApprovals, initOpenTelemetry, isDangerous, isDataUrl, isDevToolsEnabled, isTelemetryEnabled, isUrl, listModelAliases, mcpToolsToAITools, parseModel, pipeUIMessageStreamToResponse, recordEvent, resolveModelAlias, safeValidateUIMessages, setApprovalManager, stopAfterSteps, stopWhen, stopWhenNoToolCalls, toMessageContent, toUIMessageStreamResponse, transcribe, uint8ArrayToBase64, validateUIMessages, withApproval, withSpan, wrapModel } from "./ai";
export { AutoAgents, createAutoAgents } from "./auto";
export { BaseCache, FileCache, MemoryCache, createFileCache, createMemoryCache } from "./cache";
export { CLI_SPEC_VERSION, executeCommand, parseArgs } from "./cli";
export { // Autonomy Mode
  AutonomyManager, // Background Jobs
  JobQueue, // Checkpoints
  CheckpointManager, // Cost Tracker
  CostTracker, // External Agents
  BaseExternalAgent, // Fast Context
  FastContext, // Flow Display
  FlowDisplay, // Git Integration
  GitManager, // Interactive TUI
  InteractiveTUI, // N8N Integration
  N8NIntegration, // Repo Map
  RepoMap, // Sandbox Executor
  SandboxExecutor, // Scheduler
  Scheduler, // Slash Commands
  SlashCommandHandler, AiderAgent, ClaudeCodeAgent, CodexCliAgent, CommandValidator, CostTokenUsage, DEFAULT_BLOCKED_COMMANDS, DEFAULT_BLOCKED_PATHS, DEFAULT_IGNORE_PATTERNS, DiffViewer, FileCheckpointStorage, FileJobStorage, GeminiCliAgent, GenericExternalAgent, HistoryManager, MODEL_PRICING, MODE_POLICIES, MemoryCheckpointStorage, MemoryJobStorage, StatusDisplay, cliApprovalPrompt, createAutonomyManager, createCheckpointManager, createCostTracker, createDiffViewer, createExternalAgent, createFastContext, createFileCheckpointStorage, createFileJobStorage, createFlowDisplay, createGitManager, createHistoryManager, createInteractiveTUI, createJobQueue, createN8NIntegration, createRepoMap, createSandboxExecutor, createScheduler, createSlashCommandHandler, createStatusDisplay, cronExpressions, estimateTokens, executeSlashCommand, externalAgentAsTool, formatCost, getExternalAgentRegistry, getQuickContext, getRepoTree, isSlashCommand, parseSlashCommand, registerCommand, renderWorkflow, sandboxExec, triggerN8NWebhook } from "./cli/features";
export { createDbAdapter, db, getDefaultDbAdapter, setDefaultDbAdapter } from "./db";
export type { DbAdapter, DbConfig, DbMessage, DbRun, DbTrace } from "./db";
export { MemoryPostgresAdapter, NeonPostgresAdapter, PostgresSessionStorage, createMemoryPostgres, createNeonPostgres, createPostgresSessionStorage } from "./db/postgres";
export { MemoryRedisAdapter, UpstashRedisAdapter, createMemoryRedis, createUpstashRedis } from "./db/redis";
export { SQLiteAdapter, createSQLiteAdapter } from "./db/sqlite";
export { // LLM-as-Judge
  Judge, AccuracyJudge, CriteriaJudge, EvalResults, EvalSuite, Evaluator, RecipeJudge, accuracyEval, addJudge, addOptimizationRule, containsKeywordsCriterion, createDefaultEvaluator, createEvalResults, createEvaluator, getJudge, getOptimizationRule, lengthCriterion, listJudges, listOptimizationRules, noHarmfulContentCriterion, parseJudgeResponse, performanceEval, relevanceCriterion, reliabilityEval, removeJudge, removeOptimizationRule } from "./eval";
export { AgentEventBus, AgentEvents, EventEmitterPubSub, PubSub, createEventBus, createPubSub } from "./events";
export { LLMGuardrail, createLLMGuardrail } from "./guardrails/llm-guardrail";
export { DisplayTypes, HooksManager, WorkflowHooksExecutor, clearAllCallbacks, clearApprovalCallback, createHooksManager, createLoggingOperationHooks, createLoggingWorkflowHooks, createTimingWorkflowHooks, createValidationOperationHooks, createWorkflowHooks, executeCallback, executeSyncCallback, getRegisteredDisplayTypes, hasApprovalCallback, registerApprovalCallback, registerDisplayCallback, requestApproval, unregisterDisplayCallback } from "./hooks";
export { // Computer Use
  createComputerUse, ComputerUseClient, createCLIApprovalPrompt, createComputerUseAgent } from "./integrations/computer-use";
export { BaseObservabilityProvider, ConsoleObservabilityProvider, LangfuseObservabilityProvider, MemoryObservabilityProvider, ObservabilityTraceContext, createConsoleObservability, createLangfuseObservability, createMemoryObservability } from "./integrations/observability";
export { // Natural Language Postgres
  createNLPostgres, NLPostgresClient, NLPostgresConfig, createPostgresTool } from "./integrations/postgres";
export { // Slack
  createSlackBot, SlackBot, parseSlackMessage, verifySlackSignature } from "./integrations/slack";
export { BaseVectorStore, ChromaVectorStore, MemoryVectorStore, PineconeVectorStore, QdrantVectorStore, VectorQueryResult, WeaviateVectorStore, createChromaStore, createMemoryVectorStore, createPineconeStore, createQdrantStore, createWeaviateStore } from "./integrations/vector";
export { BaseVoiceProvider, ElevenLabsVoiceProvider, OpenAIVoiceProvider, createElevenLabsVoice, createOpenAIVoice } from "./integrations/voice";
export { GraphRAG, GraphStore, createGraphRAG } from "./knowledge/graph-rag";
export { BaseReranker, CohereReranker, CrossEncoderReranker, LLMReranker, createCohereReranker, createCrossEncoderReranker, createLLMReranker } from "./knowledge/reranker";
export { // Provider classes
  OpenAIProvider, // Provider factory and utilities
  createProvider, // Provider registry (extensibility API)
  ProviderRegistry, // Types
  type LLMProvider, AnthropicProvider, BaseProvider, GoogleProvider, ProviderMessage, ProviderToolDefinition, createProviderRegistry, getAvailableProviders, getDefaultProvider, getDefaultRegistry, hasProvider, isProviderAvailable, listProviders, parseModelString, registerBuiltinProviders, registerProvider, unregisterProvider } from "./llm/providers";
export { ADAPTERS, AISDK_PROVIDERS, COMMUNITY_PROVIDERS, PROVIDER_ALIASES } from "./llm/providers/ai-sdk/types";
export { MCPClient, MCPSecurity, MCPServer, MCPSessionManager, createApiKeyPolicy, createMCPClient, createMCPSecurity, createMCPServer, createMCPSession, createRateLimitPolicy, getMCPTools } from "./mcp";
export { AutoMemory, AutoMemoryKnowledgeBase, AutoMemoryVectorStore, DEFAULT_POLICIES, createAutoMemory, createLLMSummarizer } from "./memory/auto-memory";
export { DocsManager, createDocsManager } from "./memory/docs-manager";
export { FileMemory, createFileMemory } from "./memory/file-memory";
export { MemoryHooks, createEncryptionHooks, createLoggingHooks, createMemoryHooks, createValidationHooks } from "./memory/hooks";
export { Memory, createMemory } from "./memory/memory";
export type { MemoryConfig, MemoryEntry } from "./memory/memory";
export { RulesManager, createRulesManager, createSafetyRules } from "./memory/rules-manager";
export { // Adapters
  NoopObservabilityAdapter, // Constants
  OBSERVABILITY_TOOLS, // Global adapter management
  setObservabilityAdapter, // Types
  type SpanKind, ConsoleObservabilityAdapter, MemoryObservabilityAdapter, clearAdapterCache, createConsoleAdapter, createMemoryAdapter, createObservabilityAdapter, getObservabilityAdapter, getObservabilityToolInfo, hasObservabilityToolEnvVar, listObservabilityTools, noopAdapter, resetObservabilityAdapter, trace } from "./observability";
export { Plan, PlanStep, PlanStorage, PlanningAgent, TaskAgent, TodoItem, TodoList, createPlan, createPlanStorage, createPlanningAgent, createTaskAgent, createTodoList } from "./planning";
export { SkillManager, createSkillManager, parseSkillFile } from "./skills";
export { AgentTelemetry, PerformanceMonitor, TelemetryCollector, TelemetryIntegration, cleanupTelemetry, createAgentTelemetry, createConsoleSink, createHTTPSink, createPerformanceMonitor, createTelemetryIntegration, disableTelemetry, enableTelemetry, getTelemetry } from "./telemetry";
export { // Subagent Tool (agent-as-tool pattern)
  SubagentTool, BaseTool, FunctionTool, ToolRegistry, ToolResult, ToolValidationError, createDelegator, createSubagentTool, createSubagentTools, createTool, getRegistry, getTool, registerTool, tool, validateTool } from "./tools";
export { airweaveSearch, bedrockBrowserClick, bedrockBrowserFill, bedrockBrowserNavigate, bedrockCodeInterpreter, codeExecution, codeMode, createCustomTool, exaSearch, firecrawlCrawl, firecrawlScrape, parallelSearch, perplexitySearch, registerCustomTool, registerLocalTool, registerNpmTool, superagentGuard, superagentRedact, superagentVerify, tavilyCrawl, tavilyExtract, tavilySearch, valyuBioSearch, valyuCompanyResearch, valyuEconomicsSearch, valyuFinanceSearch, valyuPaperSearch, valyuPatentSearch, valyuSecSearch, valyuWebSearch } from "./tools/builtins";
export { MissingDependencyError, MissingEnvVarError, ToolsRegistry, composeMiddleware, createLoggingMiddleware, createRateLimitMiddleware, createRedactionMiddleware, createRetryMiddleware, createTimeoutMiddleware, createToolsRegistry, createTracingMiddleware, createValidationMiddleware, getToolsRegistry, resetToolsRegistry } from "./tools/registry";
export type { InstallHints, PraisonTool, RedactionHooks, RegisteredTool, ToolCapabilities, ToolExecutionContext, ToolExecutionResult, ToolFactory, ToolHooks, ToolInstallStatus, ToolLimits, ToolLogger, ToolMetadata, ToolMiddleware, ToolParameterProperty, ToolParameterSchema } from "./tools/registry";
export { registerBuiltinTools, tools } from "./tools/tools";
export { // New: Python-parity Loop and Repeat classes
  Loop, // WorkflowStep class
  WorkflowStep, Repeat, Workflow, loop, loopPattern, parallel, repeat, repeatPattern, route } from "./workflows";
export type { LoopConfig, LoopResult, RepeatConfig, RepeatContext, RepeatResult, StepContextConfig, StepExecutionConfig, StepOutputConfig, StepResult, StepRoutingConfig, WorkflowContext, WorkflowStepConfig } from "./workflows";
export { createWorkflowFromYAML, loadWorkflowFromFile, parseYAMLWorkflow, validateWorkflowDefinition } from "./workflows/yaml-parser";

Optional Plugins

External tools are available via praisonai-tools package:

pip install praisonai-tools

See PraisonAI-tools for available tools.