PraisonAI API Reference

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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, AutoAgents, AutoRagAgent, ContextAgent, DeepResearchAgent, ImageAgent, PlanningAgent, PromptExpanderAgent, QueryRewriterAgent, create_context_agent

Methods:

Agent.agent_id() -> str
Agent.analyze_prompt(prompt: str) -> set
Agent.auto_memory() -> Optional[bool]
Agent.auto_memory(value: Optional[bool]) -> None
Agent.background() -> Optional[bool]
Agent.background(value: Optional[bool]) -> None
Agent.chat_with_context(message: str, context: 'ContextPack', **kwargs) -> str
Agent.checkpoints() -> Optional[bool]
Agent.checkpoints(value: Optional[bool]) -> None
Agent.console() -> Optional[Any]
Agent.context_manager() -> Optional[Any]
Agent.context_manager(value)
Agent.cost_summary() -> dict
Agent.diff(from_hash: Optional[str] = None)
Agent.display_name() -> str
Agent.from_template(uri: str, config: Optional[Dict[str, Any]] = None, offline: bool = False, **kwargs) -> 'Agent'
Agent.get_available_tools() -> List[Any]
Agent.get_learn_context() -> str
Agent.get_memory_context(query: Optional[str] = None) -> str
Agent.get_recommended_stage(prompt: str) -> str
Agent.get_rules_context(file_path: Optional[str] = None, include_manual: Optional[List[str]] = None) -> str
Agent.get_skills_prompt() -> str
Agent.handoff_to(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'
Agent.handoff_to_async(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'
Agent.llm_model() -> Optional[str]
Agent.output_style() -> Optional[str]
Agent.output_style(value: Optional[str]) -> None
Agent.policy() -> Optional[Any]
Agent.policy(value: Optional[Any]) -> None
Agent.query(question: str, **kwargs) -> 'RAGResult'
Agent.rag() -> Optional[Any]
Agent.rag_query(question: str, **kwargs) -> 'RAGResult'
Agent.redo() -> bool
Agent.retrieval_config() -> Optional[Any]
Agent.retrieve(query: str, **kwargs) -> 'ContextPack'
Agent.rules_manager() -> Optional[Any]
Agent.run_autonomous(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None, completion_promise: Optional[str] = None, clear_context: bool = False)
Agent.run_autonomous_async(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None, completion_promise: Optional[str] = None, clear_context: bool = False)
Agent.run_until(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'
Agent.run_until_async(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'
Agent.skill_manager() -> Optional[Any]
Agent.store_memory(content: str, memory_type: str = 'short_term', **kwargs: Any) -> None
Agent.stream_emitter() -> Optional[Any]
Agent.stream_emitter(value: Optional[Any]) -> None
Agent.thinking_budget() -> Optional[int]
Agent.thinking_budget(value: Optional[int]) -> None
Agent.total_cost() -> float
Agent.undo() -> bool
AutoAgents.astart()
AutoAgents.start()
AutoRagAgent.achat(message: str, **kwargs) -> str
AutoRagAgent.chat(message: str, **kwargs) -> str
AutoRagAgent.name() -> str
AutoRagAgent.rag() -> Optional['RAG']
ContextAgent.aanalyze_codebase(project_path: str) -> Dict[str, Any]
ContextAgent.acreate_implementation_blueprint(feature_request: str, context_analysis: Optional[Dict[str, Any]] = None) -> Dict[str, Any]
ContextAgent.agenerate_prp(feature_request: str, context_analysis: Optional[Dict[str, Any]] = None) -> str
ContextAgent.analyze_codebase(project_path: str) -> Dict[str, Any]
ContextAgent.analyze_codebase_with_gitingest(project_path: str) -> Dict[str, Any]
ContextAgent.analyze_integration_points(project_path: str) -> Dict[str, Any]
ContextAgent.analyze_test_patterns(project_path: str) -> Dict[str, Any]
ContextAgent.build_implementation_blueprint(feature_request: str, context_analysis: Dict[str, Any] = None) -> Dict[str, Any]
ContextAgent.compile_context_documentation(project_path: str) -> Dict[str, Any]
ContextAgent.create_implementation_blueprint(feature_request: str, context_analysis: Optional[Dict[str, Any]] = None) -> Dict[str, Any]
ContextAgent.create_quality_gates(requirements: List[str]) -> Dict[str, Any]
ContextAgent.create_validation_framework(project_path: str) -> Dict[str, Any]
ContextAgent.execute_prp(prp_file_path: str) -> Dict[str, Any]
ContextAgent.extract_implementation_patterns(project_path: str, ast_analysis: Dict[str, Any] = None) -> Dict[str, Any]
ContextAgent.generate_comprehensive_prp(feature_request: str, context_analysis: Dict[str, Any] = None) -> str
ContextAgent.generate_feature_prp(feature_request: str) -> str
ContextAgent.generate_prp(feature_request: str, context_analysis: Optional[Dict[str, Any]] = None) -> str
ContextAgent.get_agent_interaction_summary() -> Dict[str, Any]
ContextAgent.log_debug(message: str, **kwargs)
ContextAgent.perform_ast_analysis(project_path: str) -> Dict[str, Any]
ContextAgent.save_comprehensive_session_report()
ContextAgent.save_markdown_output(content: str, filename: str, section_title: str = 'Output')
ContextAgent.setup_logging()
ContextAgent.setup_output_directories()
ContextAgent.start(input_text: str) -> str
DeepResearchAgent.aresearch(query: str, instructions: Optional[str] = None, model: Optional[str] = None, summary_mode: Optional[Literal['auto', 'detailed', 'concise']] = None, web_search: Optional[bool] = None, code_interpreter: Optional[bool] = None, mcp_servers: Optional[List[Dict[str, Any]]] = None, file_ids: Optional[List[str]] = None, file_search: Optional[bool] = None, file_search_stores: Optional[List[str]] = None) -> DeepResearchResponse
DeepResearchAgent.async_openai_client()
DeepResearchAgent.clarify(query: str, model: Optional[str] = None) -> str
DeepResearchAgent.follow_up(query: str, previous_interaction_id: str, model: Optional[str] = None) -> DeepResearchResponse
DeepResearchAgent.gemini_client()
DeepResearchAgent.openai_client()
DeepResearchAgent.research(query: str, instructions: Optional[str] = None, model: Optional[str] = None, summary_mode: Optional[Literal['auto', 'detailed', 'concise']] = None, web_search: Optional[bool] = None, code_interpreter: Optional[bool] = None, mcp_servers: Optional[List[Dict[str, Any]]] = None, file_ids: Optional[List[str]] = None, file_search: Optional[bool] = None, file_search_stores: Optional[List[str]] = None, stream: bool = True) -> DeepResearchResponse
DeepResearchAgent.rewrite_query(query: str, model: Optional[str] = None) -> str
ImageAgent.achat(prompt: str, temperature: float = 0.2, tools: Optional[List[Any]] = None, output_json: Optional[str] = None, output_pydantic: Optional[Any] = None, reasoning_steps: bool = False, **kwargs) -> Union[str, Dict[str, Any]]
ImageAgent.aedit(image: str, prompt: str, mask: Optional[str] = None, n: int = 1, size: Optional[str] = None, **kwargs) -> Dict[str, Any]
ImageAgent.agenerate(prompt: str, **kwargs) -> Dict[str, Any]
ImageAgent.agenerate_image(prompt: str, **kwargs) -> Dict[str, Any]
ImageAgent.avariation(image: str, n: int = 1, size: Optional[str] = None, **kwargs) -> Dict[str, Any]
ImageAgent.chat(prompt: str, **kwargs) -> Dict[str, Any]
ImageAgent.edit(image: str, prompt: str, mask: Optional[str] = None, n: int = 1, size: Optional[str] = None, **kwargs) -> Dict[str, Any]
ImageAgent.generate(prompt: str, **kwargs) -> Dict[str, Any]
ImageAgent.generate_image(prompt: str, **kwargs) -> Dict[str, Any]
ImageAgent.litellm()
ImageAgent.variation(image: str, n: int = 1, size: Optional[str] = None, **kwargs) -> Dict[str, Any]
PlanningAgent.analyze_context(context: str) -> str
PlanningAgent.analyze_context_sync(context: str) -> str
PlanningAgent.create_plan(request: str, agents: List['Agent'], tasks: Optional[List['Task']] = None, context: Optional[str] = None) -> Plan
PlanningAgent.create_plan_sync(request: str, agents: List['Agent'], tasks: Optional[List['Task']] = None, context: Optional[str] = None) -> Plan
PlanningAgent.is_tool_allowed(tool_name: str) -> bool
PlanningAgent.refine_plan(plan: Plan, feedback: str) -> Plan
PlanningAgent.refine_plan_sync(plan: Plan, feedback: str) -> Plan
PromptExpanderAgent.agent()
PromptExpanderAgent.expand(prompt: str, strategy: ExpandStrategy = ..., context: Optional[str] = None) -> ExpandResult
PromptExpanderAgent.expand_basic(prompt: str, context: Optional[str] = None) -> ExpandResult
PromptExpanderAgent.expand_creative(prompt: str, context: Optional[str] = None) -> ExpandResult
PromptExpanderAgent.expand_detailed(prompt: str, context: Optional[str] = None) -> ExpandResult
PromptExpanderAgent.expand_structured(prompt: str, context: Optional[str] = None) -> ExpandResult
QueryRewriterAgent.add_abbreviation(abbrev: str, expansion: str) -> None
QueryRewriterAgent.add_abbreviations(abbreviations: Dict[str, str]) -> None
QueryRewriterAgent.agent()
QueryRewriterAgent.rewrite(query: str, strategy: RewriteStrategy = ..., chat_history: Optional[List[Dict[str, str]]] = None, context: Optional[str] = None, num_queries: int = None) -> RewriteResult
QueryRewriterAgent.rewrite_basic(query: str) -> RewriteResult
QueryRewriterAgent.rewrite_contextual(query: str, chat_history: List[Dict[str, str]]) -> RewriteResult
QueryRewriterAgent.rewrite_hyde(query: str) -> RewriteResult
QueryRewriterAgent.rewrite_multi_query(query: str, num_queries: int = None) -> RewriteResult
QueryRewriterAgent.rewrite_step_back(query: str) -> RewriteResult
QueryRewriterAgent.rewrite_sub_queries(query: str) -> RewriteResult
praisonaiagents.create_context_agent(llm: Optional[Union[str, Any]] = None, **kwargs) -> ContextAgent

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.discover_single_file_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, Task, Workflow, loop, parallel, repeat, route

Methods:

Task.depends_on()
Task.depends_on(value)
Task.evaluate_when(context: Dict[str, Any]) -> bool
Task.execute_callback(task_output: TaskOutput) -> None
Task.execute_callback_sync(task_output: TaskOutput) -> None
Task.get_next_task(context: Dict[str, Any]) -> Optional[str]
Task.initialize_memory()
Task.store_in_memory(content: str, agent_name: str = None, task_id: str = None)
Task.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.close_connections()
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.compress_history(data: HandoffInputData) -> HandoffInputData
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) -> Tuple[bool, Union[str, 'TaskOutput']]

Planning

Types:

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

Methods:

ApprovalCallback.call(plan: 'Plan') -> bool
ApprovalCallback.always_approve(plan: 'Plan') -> bool
ApprovalCallback.always_reject(plan: 'Plan') -> bool
ApprovalCallback.approve_if_no_dangerous_tools(plan: 'Plan') -> bool
ApprovalCallback.approve_if_small(plan: 'Plan', max_steps: int = 5) -> bool
ApprovalCallback.async_call(plan: 'Plan') -> bool
ApprovalCallback.console_approval(plan: 'Plan') -> bool
Plan.add_step(step: PlanStep) -> None
Plan.approve() -> None
Plan.cancel() -> None
Plan.complete() -> None
Plan.completed_step_ids() -> List[str]
Plan.from_dict(data: Dict[str, Any]) -> 'Plan'
Plan.from_markdown(markdown: str) -> 'Plan'
Plan.get_next_steps() -> List[PlanStep]
Plan.get_step(step_id: str) -> Optional[PlanStep]
Plan.is_complete() -> bool
Plan.progress() -> float
Plan.remove_step(step_id: str) -> bool
Plan.start_execution() -> None
Plan.to_dict() -> Dict[str, Any]
Plan.to_markdown() -> str
Plan.update_step_status(step_id: str, status: str) -> bool
PlanStep.from_dict(data: Dict[str, Any]) -> 'PlanStep'
PlanStep.is_ready(completed_steps: List[str]) -> bool
PlanStep.mark_complete() -> None
PlanStep.mark_in_progress() -> None
PlanStep.mark_skipped() -> None
PlanStep.to_dict() -> Dict[str, Any]
PlanStorage.cleanup_old_plans(keep_count: int = 10) -> int
PlanStorage.delete_plan(plan_id: str) -> bool
PlanStorage.delete_todo(name: str) -> bool
PlanStorage.get_latest_plan() -> Optional[Plan]
PlanStorage.list_plans() -> List[Dict[str, Any]]
PlanStorage.list_todos() -> List[str]
PlanStorage.load_plan(plan_id: str) -> Optional[Plan]
PlanStorage.load_plan_from_file(path: str) -> Optional[Plan]
PlanStorage.load_todo(name: str = 'current') -> Optional[TodoList]
PlanStorage.save_plan(plan: Plan, filename: Optional[str] = None) -> str
PlanStorage.save_todo(todo: TodoList, name: str = 'current') -> str
PlanningConfig.to_dict() -> Dict[str, Any]
TodoItem.complete() -> None
TodoItem.from_dict(data: Dict[str, Any]) -> 'TodoItem'
TodoItem.is_ready(completed_ids: List[str]) -> bool
TodoItem.reset() -> None
TodoItem.start() -> None
TodoItem.to_dict() -> Dict[str, Any]
TodoList.add(item: Union[TodoItem, str]) -> TodoItem
TodoList.complete(item_id: str) -> bool
TodoList.completed() -> List[TodoItem]
TodoList.completed_ids() -> List[str]
TodoList.from_dict(data: Dict[str, Any]) -> 'TodoList'
TodoList.from_json(json_str: str) -> 'TodoList'
TodoList.from_markdown(markdown: str) -> 'TodoList'
TodoList.from_plan(plan: 'Plan') -> 'TodoList'
TodoList.get(item_id: str) -> Optional[TodoItem]
TodoList.get_ready_items() -> List[TodoItem]
TodoList.in_progress() -> List[TodoItem]
TodoList.is_complete() -> bool
TodoList.pending() -> List[TodoItem]
TodoList.progress() -> float
TodoList.remove(item_id: str) -> bool
TodoList.start(item_id: str) -> bool
TodoList.sync_with_plan(plan: 'Plan') -> None
TodoList.to_dict() -> Dict[str, Any]
TodoList.to_json() -> str
TodoList.to_markdown() -> str
TodoList.update_from_plan(plan: 'Plan') -> None

Skills

Types:

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

Methods:

SkillLoader.activate(skill: LoadedSkill) -> bool
SkillLoader.load(skill_path: str, activate: bool = False) -> Optional[LoadedSkill]
SkillLoader.load_all_resources(skill: LoadedSkill) -> None
SkillLoader.load_assets(skill: LoadedSkill) -> dict
SkillLoader.load_metadata(skill_path: str) -> Optional[LoadedSkill]
SkillLoader.load_references(skill: LoadedSkill) -> dict
SkillLoader.load_scripts(skill: LoadedSkill) -> dict
SkillManager.activate(skill: LoadedSkill) -> bool
SkillManager.activate_by_name(name: str) -> bool
SkillManager.add_skill(skill_path: str) -> Optional[LoadedSkill]
SkillManager.clear() -> None
SkillManager.discover(skill_dirs: Optional[List[str]] = None, include_defaults: bool = True) -> int
SkillManager.get_available_skills() -> List[SkillMetadata]
SkillManager.get_instructions(name: str) -> Optional[str]
SkillManager.get_skill(name: str) -> Optional[LoadedSkill]
SkillManager.load_resources(name: str) -> bool
SkillManager.skill_names() -> List[str]
SkillManager.skills() -> List[LoadedSkill]
SkillManager.to_prompt() -> str
SkillMetadata.from_properties(props: SkillProperties) -> 'SkillMetadata'
SkillProperties.to_dict() -> dict
SkillsConfig.to_dict() -> Dict[str, Any]

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:

MCP.get_tools() -> List[Callable]
MCP.shutdown()
MCP.to_openai_tool()

Telemetry

Types:

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

Methods:

MinimalTelemetry.flush()
MinimalTelemetry.get_metrics() -> Dict[str, Any]
MinimalTelemetry.shutdown()
MinimalTelemetry.track_agent_execution(agent_name: str = None, success: bool = True, async_mode: bool = False)
MinimalTelemetry.track_error(error_type: str = None)
MinimalTelemetry.track_feature_usage(feature_name: str)
MinimalTelemetry.track_task_completion(task_name: str = None, success: bool = True)
MinimalTelemetry.track_tool_usage(tool_name: str, success: bool = True, execution_time: float = None)
TelemetryCollector.get_metrics() -> Dict[str, Any]
TelemetryCollector.record_cost(cost: float, model: str = None)
TelemetryCollector.record_tokens(prompt_tokens: int, completion_tokens: int, model: str = None)
TelemetryCollector.start()
TelemetryCollector.stop()
TelemetryCollector.trace_agent_execution(agent_name: str, **attributes)
TelemetryCollector.trace_llm_call(model: str = None, **attributes)
TelemetryCollector.trace_task_execution(task_name: str, agent_name: str = None, **attributes)
TelemetryCollector.trace_tool_call(tool_name: str, **attributes)
praisonaiagents.cleanup_telemetry_resources()
praisonaiagents.disable_performance_mode()
praisonaiagents.disable_telemetry()
praisonaiagents.enable_performance_mode()
praisonaiagents.enable_telemetry()
praisonaiagents.get_telemetry() -> 'MinimalTelemetry'

Observability

Types:

from praisonaiagents import FlowDisplay, obs, track_workflow

Methods:

FlowDisplay.display()
FlowDisplay.start()
FlowDisplay.stop()
praisonaiagents.track_workflow()

Context

Types:

from praisonaiagents import ContextManager

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

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.effective_track_changes() -> bool
AutonomyConfig.from_dict(data: Dict[str, Any]) -> 'AutonomyConfig'
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 AgentAppConfig, AgentAppProtocol, AgentFlow, AgentManager, AgentOSConfig, AgentOSProtocol, AgentTeam, AutoApproveBackend, EmbeddingResult, aembedding, aembeddings, embedding, embeddings, get_dimensions

Methods:

AgentFlow.arun(input: str = '', llm: Optional[str] = None, verbose: bool = False) -> Dict[str, Any]
AgentFlow.astart(input: str = '', llm: Optional[str] = None, verbose: bool = False) -> Dict[str, Any]
AgentFlow.from_template(uri: str, config: Optional[Dict[str, Any]] = None, offline: bool = False, **kwargs) -> 'Workflow'
AgentFlow.get_history() -> List[Dict[str, Any]]
AgentFlow.memory_config() -> Optional[Dict[str, Any]]
AgentFlow.on_step_complete() -> Optional[Callable]
AgentFlow.on_step_error() -> Optional[Callable]
AgentFlow.on_step_start() -> Optional[Callable]
AgentFlow.on_workflow_complete() -> Optional[Callable]
AgentFlow.on_workflow_start() -> Optional[Callable]
AgentFlow.planning_llm() -> Optional[str]
AgentFlow.reasoning() -> bool
AgentFlow.run(input: str = '', llm: Optional[str] = None, verbose: bool = False, stream: bool = None) -> Dict[str, Any]
AgentFlow.start(input: str = '', **kwargs) -> Dict[str, Any]
AgentFlow.stream() -> bool
AgentFlow.to_dict() -> Dict[str, Any]
AgentFlow.verbose() -> bool
AgentFlow.verbose(value: bool)
AgentOSProtocol.get_app() -> Any
AgentOSProtocol.serve(host: Optional[str] = None, port: Optional[int] = None, reload: bool = False, **kwargs: Any) -> None
AgentTeam.add_task(task)
AgentTeam.aexecute_task(task_id)
AgentTeam.append_to_state(key: str, value: Any, max_length: Optional[int] = None) -> List[Any]
AgentTeam.arun_all_tasks()
AgentTeam.arun_task(task_id)
AgentTeam.astart(content = None, return_dict = False, **kwargs)
AgentTeam.clean_json_output(output: str) -> str
AgentTeam.clear_state() -> None
AgentTeam.context_manager()
AgentTeam.current_plan()
AgentTeam.default_completion_checker(task, agent_output)
AgentTeam.delete_state(key: str) -> bool
AgentTeam.display_token_usage()
AgentTeam.execute_task(task_id)
AgentTeam.get_agent_details(agent_name)
AgentTeam.get_all_state() -> Dict[str, Any]
AgentTeam.get_all_tasks_status()
AgentTeam.get_detailed_token_report() -> Dict[str, Any]
AgentTeam.get_plan_markdown() -> str
AgentTeam.get_state(key: str, default: Any = None) -> Any
AgentTeam.get_task_details(task_id)
AgentTeam.get_task_result(task_id)
AgentTeam.get_task_status(task_id)
AgentTeam.get_todo_markdown() -> str
AgentTeam.get_token_usage_summary() -> Dict[str, Any]
AgentTeam.has_state(key: str) -> bool
AgentTeam.increment_state(key: str, amount: float = 1, default: float = 0) -> float
AgentTeam.launch(path: str = '/agents', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')
AgentTeam.restore_session_state(session_id: str) -> bool
AgentTeam.run(content = None, return_dict = False, **kwargs)
AgentTeam.run_all_tasks()
AgentTeam.run_task(task_id)
AgentTeam.save_output_to_file(task, task_output)
AgentTeam.save_session_state(session_id: str, include_memory: bool = True) -> None
AgentTeam.set_state(key: str, value: Any) -> None
AgentTeam.start(content = None, return_dict = False, output = None, **kwargs)
AgentTeam.todo_list()
AgentTeam.update_plan_step_status(step_id: str, status: str) -> bool
AgentTeam.update_state(updates: Dict) -> None
AutoApproveBackend.request_approval(request: ApprovalRequest) -> ApprovalDecision
AutoApproveBackend.request_approval_sync(request: ApprovalRequest) -> ApprovalDecision
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.get_dimensions(model_name: str) -> int

Wrapper (praisonai)

Types:

from praisonai import AgentApp, AgentOS, 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 app app --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
praisonai bot agentmail --help
praisonai bot bot-callback --help
praisonai bot discord --help
praisonai bot email --help
praisonai bot slack --help
praisonai bot start --help
praisonai bot telegram --help
praisonai bot whatsapp --help
praisonai browser browser-callback --help
praisonai browser click --help
praisonai browser navigate --help
praisonai browser open --help
praisonai browser profiles --help
praisonai browser screenshot --help
praisonai browser snapshot --help
praisonai browser status --help
praisonai browser type --help
praisonai call call-main --help
praisonai chat --help
praisonai chat chat-main --help
praisonai claw claw --help
praisonai code --help
praisonai code code-main --help
praisonai commit commit-main --help
praisonai completion bash --help
praisonai completion completion-callback --help
praisonai completion fish --help
praisonai completion zsh --help
praisonai config doctor --help
praisonai config env --help
praisonai config get --help
praisonai config list --help
praisonai config path --help
praisonai config reset --help
praisonai config set --help
praisonai context add --help
praisonai context clear --help
praisonai context compact --help
praisonai context export --help
praisonai context grep --help
praisonai context list --help
praisonai context show --help
praisonai context stats --help
praisonai context tail --help
praisonai debug acp --help
praisonai debug debug-callback --help
praisonai debug interactive --help
praisonai debug lsp --help
praisonai debug trace --help
praisonai deploy aws --help
praisonai deploy azure --help
praisonai deploy docker --help
praisonai deploy gcp --help
praisonai diag diag-callback --help
praisonai diag export --help
praisonai docs api-md --help
praisonai docs generate --help
praisonai docs list --help
praisonai docs report --help
praisonai docs run --help
praisonai docs run-all --help
praisonai docs serve --help
praisonai docs stats --help
praisonai doctor cleanup --help
praisonai doctor config --help
praisonai doctor db --help
praisonai doctor doctor-callback --help
praisonai doctor env --help
praisonai doctor mcp --help
praisonai doctor network --help
praisonai doctor performance --help
praisonai doctor selftest --help
praisonai doctor tools --help
praisonai doctor troubleshoot --help
praisonai endpoints list --help
praisonai endpoints test --help
praisonai environment check --help
praisonai environment doctor --help
praisonai environment view --help
praisonai eval accuracy --help
praisonai eval judge --help
praisonai eval list-judges --help
praisonai eval performance --help
praisonai examples info --help
praisonai examples list --help
praisonai examples report --help
praisonai examples run --help
praisonai examples run-all --help
praisonai examples stats --help
praisonai flow flow-start --help
praisonai flow version --help
praisonai gateway channels --help
praisonai gateway gateway-callback --help
praisonai gateway send --help
praisonai gateway start --help
praisonai gateway status --help
praisonai hooks add --help
praisonai hooks list --help
praisonai hooks remove --help
praisonai knowledge add --help
praisonai knowledge index --help
praisonai knowledge list --help
praisonai knowledge search --help
praisonai loop help --help
praisonai loop loop-main --help
praisonai lsp logs --help
praisonai lsp lsp-callback --help
praisonai lsp start --help
praisonai lsp status --help
praisonai lsp stop --help
praisonai mcp add --help
praisonai mcp auth --help
praisonai mcp describe --help
praisonai mcp list --help
praisonai mcp logout --help
praisonai mcp mcp-callback --help
praisonai mcp remove --help
praisonai mcp run --help
praisonai mcp status --help
praisonai mcp sync --help
praisonai mcp test --help
praisonai mcp tools --help
praisonai memory add --help
praisonai memory clear --help
praisonai memory search --help
praisonai memory show --help
praisonai memory status --help
praisonai obs obs-fallback --help
praisonai package install --help
praisonai package list --help
praisonai package uninstall --help
praisonai paths show --help
praisonai plugins create --help
praisonai plugins disable --help
praisonai plugins discover --help
praisonai plugins doctor --help
praisonai plugins enable --help
praisonai plugins info --help
praisonai plugins install --help
praisonai plugins list --help
praisonai plugins plugins-callback --help
praisonai plugins remove --help
praisonai profile imports --help
praisonai profile optimize --help
praisonai profile profile-callback --help
praisonai profile query --help
praisonai profile snapshot --help
praisonai profile startup --help
praisonai profile suite --help
praisonai publish publish-main --help
praisonai publish pypi --help
praisonai rag chat --help
praisonai rag eval --help
praisonai rag index --help
praisonai rag query --help
praisonai rag serve --help
praisonai realtime realtime-main --help
praisonai recipe apply --help
praisonai recipe create --help
praisonai recipe info --help
praisonai recipe install --help
praisonai recipe judge --help
praisonai recipe list --help
praisonai recipe optimize --help
praisonai recipe run --help
praisonai recipe serve --help
praisonai registry list --help
praisonai registry serve --help
praisonai replay cleanup --help
praisonai replay context --help
praisonai replay dashboard --help
praisonai replay delete --help
praisonai replay flow --help
praisonai replay list --help
praisonai replay show --help
praisonai research research-main --help
praisonai retrieval index --help
praisonai retrieval query --help
praisonai retrieval search --help
praisonai rules add --help
praisonai rules clear --help
praisonai rules list --help
praisonai run --help
praisonai run run-main --help
praisonai sandbox explain --help
praisonai sandbox list --help
praisonai sandbox recreate --help
praisonai sandbox sandbox-callback --help
praisonai sandbox status --help
praisonai schedule add --help
praisonai schedule delete --help
praisonai schedule describe --help
praisonai schedule list --help
praisonai schedule logs --help
praisonai schedule restart --help
praisonai schedule schedule-callback --help
praisonai schedule start --help
praisonai schedule stats --help
praisonai schedule stop --help
praisonai serve a2a --help
praisonai serve a2u --help
praisonai serve acp --help
praisonai serve agents --help
praisonai serve docs --help
praisonai serve gateway --help
praisonai serve lsp --help
praisonai serve mcp --help
praisonai serve rag --help
praisonai serve recipe --help
praisonai serve registry --help
praisonai serve scheduler --help
praisonai serve serve-callback --help
praisonai serve start --help
praisonai serve status --help
praisonai serve stop --help
praisonai serve ui --help
praisonai serve unified --help
praisonai session delete --help
praisonai session export --help
praisonai session import --help
praisonai session list --help
praisonai session resume --help
praisonai session show --help
praisonai skills check --help
praisonai skills create --help
praisonai skills eligible --help
praisonai skills info --help
praisonai skills install --help
praisonai skills list --help
praisonai skills search --help
praisonai skills validate --help
praisonai standardise check --help
praisonai standardise fix --help
praisonai standardise init --help
praisonai standardise report --help
praisonai templates create --help
praisonai templates list --help
praisonai test info --help
praisonai test interactive --help
praisonai test run --help
praisonai todo add --help
praisonai todo done --help
praisonai todo list --help
praisonai tools info --help
praisonai tools list --help
praisonai tools test --help
praisonai tools validate --help
praisonai traces disable --help
praisonai traces enable --help
praisonai traces list --help
praisonai traces status --help
praisonai tracker batch --help
praisonai tracker judge --help
praisonai tracker run --help
praisonai tracker tools --help
praisonai tracker tracker-main --help
praisonai train agents --help
praisonai train apply --help
praisonai train list --help
praisonai train llm --help
praisonai train show --help
praisonai train train-callback --help
praisonai ui ui --help
praisonai version check --help
praisonai version show --help
praisonai version version-callback --help
praisonai workflow create --help
praisonai workflow list --help
praisonai workflow run --help

TypeScript

Types/Exports:

export { Agent, AgentTeam, Agents, PraisonAIAgents, Router } from "./agent";
export type { AgentTeamConfig, 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 { ContextPolicy, Handoff, HandoffCycleError, HandoffDepthError, HandoffError, HandoffTimeoutError, RECOMMENDED_PROMPT_PREFIX, handoff, handoffFilters, promptWithHandoffInstructions } 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, AutoTaskConfig, 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 (Python parity with praisonaiagents/context/fast)
  FastContext, // Flow Display
  FlowDisplay, // Git Integration
  GitManager, // Interactive TUI
  InteractiveTUI, // N8N Integration
  N8NIntegration, // Python parity additions
  type LineRange, // 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, addLineRangeToFileMatch, cliApprovalPrompt, createAutonomyManager, createCheckpointManager, createCostTracker, createDiffViewer, createExternalAgent, createFastContext, createFileCheckpointStorage, createFileJobStorage, createFileMatch, createFlowDisplay, createGitManager, createHistoryManager, createInteractiveTUI, createJobQueue, createLineRange, createN8NIntegration, createRepoMap, createSandboxExecutor, createScheduler, createSlashCommandHandler, createStatusDisplay, cronExpressions, estimateTokens, executeSlashCommand, externalAgentAsTool, formatCost, getExternalAgentRegistry, getLineCount, getQuickContext, getRepoTree, getTotalLines, isSlashCommand, mergeRanges, parseSlashCommand, rangesOverlap, registerCommand, renderWorkflow, sandboxExec, triggerN8NWebhook } from "./cli/features";
export { // Classes
  DictCondition, // Functions
  evaluateCondition, // Types
  type ConditionProtocol, ExpressionCondition, FunctionCondition, andConditions, createCondition, notCondition, orConditions } from "./conditions";
export { // Enums
  MemoryBackend, // Errors
  ConfigValidationError, // Parse utilities
  detect_url_scheme, // Presets
  MEMORY_PRESETS, // Resolver functions
  resolve, AUTONOMY_PRESETS, ArrayMode, CACHING_PRESETS, CONTEXT_PRESETS, ChunkingStrategy, EXECUTION_PRESETS, ExecutionPreset, FeatureMemoryConfig, GUARDRAIL_PRESETS, GuardrailAction, KNOWLEDGE_PRESETS, MEMORY_URL_SCHEMES, MULTI_AGENT_EXECUTION_PRESETS, MULTI_AGENT_OUTPUT_PRESETS, OUTPUT_PRESETS, OutputPreset, PLANNING_PRESETS, REFLECTION_PRESETS, WEB_PRESETS, WebSearchProvider, apply_config_defaults, clean_triple_backticks, get_config, get_config_path, get_default, get_defaults_config, get_plugins_config, is_path_like, is_policy_string, parse_policy_string, resolve_autonomy, resolve_caching, resolve_context, resolve_execution, resolve_guardrails, resolve_hooks, resolve_knowledge, resolve_memory, resolve_output, resolve_planning, resolve_reflection, resolve_routing, resolve_skills, resolve_web, suggest_similar, validate_config } from "./config";
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 { // Types
  type DisplayCallback, DisplayFlow, DisplayFlowConfig, asyncDisplayCallbacks, clearDisplayCallbacks, clearErrorLogs, displayError, displayGenerating, displayInstruction, displayInteraction, displaySelfReflection, displayToolCall, errorLogs, logError, registerDisplay, syncDisplayCallbacks } from "./display";
export { // Functions
  embed, // Types
  type EmbeddingResult, aembed, aembedding, aembeddings, cosineSimilarity, embedding, embeddings, euclideanDistance, getDimensions, normalizeEmbedding, setEmbeddingConfig } from "./embeddings";
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 { // Bot types
  type BotConfig, // Classes
  FailoverManager, // Enums
  SandboxStatus, // Gateway types
  type GatewayConfig, // Other types
  type ProviderStatus, AutonomyLevel, RagRetrievalPolicy } from "./gateway";
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 { AgentApp, AgentAppConfig, AgentAppProtocol, AgentOS, AgentOSConfig, AgentOSProtocol, DEFAULT_AGENTOS_CONFIG, mergeConfig } from "./os";
export type { AgentAppOptions, AgentOSOptions } from "./os";
export { // P0: Call Types (new)
  type MCPCall, // P0: Specialized Agent Configs (new)
  type AudioConfig, // P0: Specialized Agents (new)
  CodeAgent, // P1: Workflow Patterns (new)
  Chunking, // P2: Context Types (new - only items not already exported)
  ContextManager, // P3: Display callbacks (snake_case for Python parity)
  register_display_callback, // P3: Plugin functions
  get_plugin_manager, // P3: Trace & condition functions
  evaluate_condition, EmbeddingAgent, If, Knowledge, MCP, OCRAgent, Parallel, RealtimeAgent, Route, Session, VideoAgent, VisionAgent, async_display_callbacks, cleanup_telemetry_resources, create_context_agent, disable_performance_mode, disable_telemetry, discover_and_load_plugins, discover_plugins, display_error, display_generating, display_instruction, display_interaction, display_self_reflection, display_tool_call, enable_performance_mode, enable_telemetry, ensure_plugin_dir, error_logs, get_default_plugin_dirs, get_dimensions, get_plugin_template, get_telemetry, handoff_filters, load_plugin, parse_plugin_header, parse_plugin_header_from_file, prompt_with_handoff_instructions, resolve_guardrail_policies, sync_display_callbacks, trace_context, track_workflow, when } from "./parity";
export { // Core classes
  Plan, // Python parity additions
  ApprovalCallback, PlanStep, PlanStorage, PlanningAgent, READ_ONLY_TOOLS, RESEARCH_TOOLS, RESTRICTED_TOOLS, TaskAgent, TodoItem, TodoList, createApprovalCallback, createPlan, createPlanStorage, createPlanningAgent, createTaskAgent, createTodoList } from "./planning";
export { // Classes
  Plugin, // Enums
  PluginHook, // Functions
  getPluginManager, // Interfaces
  type PluginMetadata, FunctionPlugin, PluginManager, PluginParseError, PluginType, disablePlugins, discoverAndLoadPlugins, discoverPlugins, enablePlugins, ensurePluginDir, getDefaultPluginDirs, getPluginTemplate, isPluginEnabled, listPlugins, loadPlugin, parsePluginHeader, parsePluginHeaderFromFile } from "./plugins";
export { // A2A Protocol
  A2ATaskState, // AGUI Protocol
  AGUI, // AgentManager alias type
  type AgentManager, // Global singletons
  config, // Guardrail policies
  type GuardrailPolicy, // Tools class
  type ToolDefinition, A2A, A2ARole, AutoRagAgent, AutoRetrievalPolicy, DEFAULT_AUTO_KEYWORDS, GUARDRAIL_POLICY_PRESETS, Tools, memory, obs, resolveGuardrailPolicies, workflows } from "./protocols";
export { CitationsMode, DEFAULT_RAG_TEMPLATE, RAG, RAGCitation, RAGContextPack, RetrievalPolicy, RetrievalStrategy, createCitation, createContextPack, createRAG, createRAGConfig, createRAGResult, createRetrievalConfig, createSimpleRetrievalConfig, createSmartRetrievalConfig, formatAnswerWithCitations, formatCitation, formatContextPackForPrompt, hasCitations } from "./rag";
export { // Python parity additions
  SkillLoader, SkillManager, createSkillLoader, createSkillManager, createSkillProperties, parseSkillFile } from "./skills";
export { AgentTask, AgentTaskConfig, BaseTask, createTaskOutput } from "./task";
export { // Python parity additions
  MinimalTelemetry, AgentTelemetry, PerformanceMonitor, TelemetryCollector, TelemetryIntegration, cleanupTelemetry, cleanupTelemetryResources, createAgentTelemetry, createConsoleSink, createHTTPSink, createPerformanceMonitor, createTelemetryIntegration, disablePerformanceMode, disableTelemetry, enablePerformanceMode, enableTelemetry, getMinimalTelemetry, 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 { BudgetExceededError, MissingDependencyError, MissingEnvVarError, ToolsRegistry, composeMiddleware, createLoggingMiddleware, createRateLimitMiddleware, createRedactionMiddleware, createRetryMiddleware, createTimeoutMiddleware, createToolsRegistry, createTracingMiddleware, createValidationMiddleware, getRegistry, getTool, getToolsRegistry, get_registry, get_tool, registerTool, register_tool, resetToolsRegistry, validateTool, validate_tool } 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 { // Classes
  TraceSink, // Enums
  ContextEventType, // Functions
  createContextEvent, // Types
  type ContextEvent, ContextListSink, ContextNoOpSink, ContextTraceEmitter, ContextTraceSink, EventType, MessageType, TraceCtx, traceContext, trackWorkflow } from "./trace";
export { // New: Python-parity Loop and Repeat classes
  Loop, // Task class
  Task, AgentFlow, Pipeline, Repeat, Workflow, loop, loopPattern, parallel, repeat, repeatPattern, route } from "./workflows";
export type { LoopConfig, LoopResult, RepeatConfig, RepeatContext, RepeatResult, StepContextConfig, StepExecutionConfig, StepOutputConfig, StepResult, StepRoutingConfig, TaskConfig, WorkflowContext } 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.

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PraisonAI API Reference

Shared Types

Agents

Tools

Workflows

DB

Memory

Knowledge

RAG

Handoff

Guardrails

Planning

Skills

Session

MCP

Telemetry

Observability

Context

Config

Display

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Optional Plugins

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PraisonAI API Reference

Shared Types

Agents

Tools

Workflows

DB

Memory

Knowledge

RAG

Handoff

Guardrails

Planning

Skills

Session

MCP

Telemetry

Observability

Context

Config

Display

Utilities

Other

Wrapper (praisonai)

CLI

TypeScript

Optional Plugins