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@@ -0,0 +1,57 @@
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name: Update API Reference
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||||
|
||||
on:
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push:
|
||||
branches: [ "main" ]
|
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paths:
|
||||
- 'src/praisonai-agents/praisonaiagents/**/*.py'
|
||||
- 'src/praisonai/praisonai/**/*.py'
|
||||
- 'src/praisonai-ts/src/**/*.ts'
|
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- '.github/workflows/update-api-md.yml'
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
|
||||
jobs:
|
||||
update-api-md:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
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with:
|
||||
ref: main
|
||||
persist-credentials: true
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fetch-depth: 0
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|
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- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
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with:
|
||||
python-version: '3.11'
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|
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- name: Generate api.md
|
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run: |
|
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cd $GITHUB_WORKSPACE
|
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python3 src/praisonai/scripts/generate_api_md.py
|
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|
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- name: Check for changes
|
||||
id: check_changes
|
||||
run: |
|
||||
if git diff --quiet api.md; then
|
||||
echo "changes=false" >> $GITHUB_OUTPUT
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echo "No changes to api.md"
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else
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echo "changes=true" >> $GITHUB_OUTPUT
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echo "api.md has been updated"
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git diff api.md | head -50
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fi
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- name: Commit and push changes
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if: steps.check_changes.outputs.changes == 'true'
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uses: stefanzweifel/git-auto-commit-action@v5
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with:
|
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commit_message: "docs: auto-update api.md [skip ci]"
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file_pattern: 'api.md'
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commit_user_name: "github-actions[bot]"
|
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commit_user_email: "41898282+github-actions[bot]@users.noreply.github.com"
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commit_author: "github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>"
|
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+2
-1
@@ -83,4 +83,5 @@ issue_details.json
|
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# MCP Registry tokens
|
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.mcpregistry_*
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|
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*.log
|
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*.log
|
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.internal
|
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@@ -0,0 +1,438 @@
|
||||
# PraisonAI API Reference
|
||||
|
||||
This file is auto-generated. Do not edit manually.
|
||||
Regenerate with: `praisonai docs api-md --write`
|
||||
|
||||
# Agents
|
||||
|
||||
Types:
|
||||
```python
|
||||
from praisonaiagents import Agent, Agents
|
||||
```
|
||||
|
||||
Methods:
|
||||
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">achat</a>(prompt: str, temperature = 1.0, tools = None, output_json = None, output_pydantic = None, reasoning_steps = False, task_name = None, task_description = None, task_id = None)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">aexecute</a>(task, context = None)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">agent_id</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">analyze_prompt</a>(prompt: str) -> set</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">arun</a>(prompt: str, **kwargs)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">astart</a>(prompt: str, **kwargs)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>(value)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">background</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">background</a>(value)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat</a>(prompt, temperature = 1.0, tools = None, output_json = None, output_pydantic = None, reasoning_steps = False, stream = None, task_name = None, task_description = None, task_id = None, config = None, force_retrieval = False, skip_retrieval = False)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat_with_context</a>(message: str, context: 'ContextPack', **kwargs) -> str</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>(value)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">clean_json_output</a>(output: str) -> str</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">clear_history</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">console</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>(value)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">display_generating</a>(content: str, start_time: float)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute</a>(task, context = None)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute_tool</a>(function_name, arguments)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute_tool_async</a>(function_name: str, arguments: Dict[str, Any]) -> Any</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">from_template</a>(uri: str, config: Optional[Dict[str, Any]] = None, offline: bool = False, **kwargs) -> 'Agent'</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">generate_task</a>() -> 'Task'</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_available_tools</a>() -> List[Any]</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_memory_context</a>(query: Optional[str] = None) -> str</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_recommended_stage</a>(prompt: str) -> str</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_rules_context</a>(file_path: Optional[str] = None, include_manual: Optional[List[str]] = None) -> str</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_skills_prompt</a>() -> str</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to_async</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">iter_stream</a>(prompt: str, **kwargs)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">launch</a>(path: str = '/', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">llm_model</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>(value)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">policy</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">policy</a>(value)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">query</a>(question: str, **kwargs) -> 'RAGResult'</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rag</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rag_query</a>(question: str, **kwargs) -> 'RAGResult'</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">retrieval_config</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">retrieve</a>(query: str, **kwargs) -> 'ContextPack'</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rules_manager</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run</a>(prompt: str, **kwargs)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_autonomous</a>(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">session_id</a>() -> Optional[str]</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">skill_manager</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">start</a>(prompt: str = None, **kwargs)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', **kwargs)</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">switch_model</a>(new_model: str) -> None</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>()</code>
|
||||
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>(value)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">add_task</a>(task)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aexecute_task</a>(task_id)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">append_to_state</a>(key: str, value: Any, max_length: Optional[int] = None) -> List[Any]</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_all_tasks</a>()</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_task</a>(task_id)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">astart</a>(content = None, return_dict = False, **kwargs)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clean_json_output</a>(output: str) -> str</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clear_state</a>() -> None</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">context_manager</a>()</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">current_plan</a>()</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">default_completion_checker</a>(task, agent_output)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">delete_state</a>(key: str) -> bool</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">display_token_usage</a>()</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">execute_task</a>(task_id)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_agent_details</a>(agent_name)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_state</a>() -> Dict[str, Any]</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_tasks_status</a>()</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_detailed_token_report</a>() -> Dict[str, Any]</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_plan_markdown</a>() -> str</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_state</a>(key: str, default: Any = None) -> Any</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_details</a>(task_id)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_result</a>(task_id)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_status</a>(task_id)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_todo_markdown</a>() -> str</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_token_usage_summary</a>() -> Dict[str, Any]</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">has_state</a>(key: str) -> bool</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">increment_state</a>(key: str, amount: float = 1, default: float = 0) -> float</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">launch</a>(path: str = '/agents', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">restore_session_state</a>(session_id: str) -> bool</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run</a>(content = None, return_dict = False, **kwargs)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_all_tasks</a>()</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_task</a>(task_id)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, output = None, **kwargs)</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
|
||||
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
|
||||
|
||||
# Tools
|
||||
|
||||
Types:
|
||||
```python
|
||||
from praisonaiagents import Tools, tool
|
||||
```
|
||||
|
||||
Methods:
|
||||
|
||||
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">tool</a>(func: Optional[Callable] = None) -> Union[FunctionTool, Callable[[Callable], FunctionTool]]</code>
|
||||
|
||||
# Other
|
||||
|
||||
Types:
|
||||
```python
|
||||
from praisonaiagents import Task
|
||||
```
|
||||
|
||||
Methods:
|
||||
|
||||
* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback</a>(task_output: TaskOutput) -> None</code>
|
||||
* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback_sync</a>(task_output: TaskOutput) -> None</code>
|
||||
* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">initialize_memory</a>()</code>
|
||||
* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">store_in_memory</a>(content: str, agent_name: str = None, task_id: str = None)</code>
|
||||
|
||||
# Wrapper (praisonai)
|
||||
|
||||
Types:
|
||||
```python
|
||||
from praisonai import CloudProvider, Deploy, DeployConfig, DeployType, PraisonAI, __version__
|
||||
```
|
||||
|
||||
# CLI
|
||||
|
||||
Methods:
|
||||
|
||||
* <code title="cli">praisonai <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
|
||||
* <code title="cli">praisonai acp acp-main <a href="./src/praisonai/praisonai/cli/commands/acp.py">--help</a></code>
|
||||
* <code title="cli">praisonai agents create <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
|
||||
* <code title="cli">praisonai agents info <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
|
||||
* <code title="cli">praisonai agents list <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
|
||||
* <code title="cli">praisonai audit agent-centric <a href="./src/praisonai/praisonai/cli/commands/audit.py">--help</a></code>
|
||||
* <code title="cli">praisonai benchmark agent <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
|
||||
* <code title="cli">praisonai benchmark benchmark-callback <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
|
||||
* <code title="cli">praisonai benchmark cli <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
|
||||
* <code title="cli">praisonai benchmark compare <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
|
||||
* <code title="cli">praisonai benchmark litellm <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
|
||||
* <code title="cli">praisonai benchmark profile <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
|
||||
* <code title="cli">praisonai benchmark sdk <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
|
||||
* <code title="cli">praisonai benchmark workflow <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
|
||||
* <code title="cli">praisonai call call-main <a href="./src/praisonai/praisonai/cli/commands/call.py">--help</a></code>
|
||||
* <code title="cli">praisonai chat <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
|
||||
* <code title="cli">praisonai chat chat-main <a href="./src/praisonai/praisonai/cli/commands/chat.py">--help</a></code>
|
||||
* <code title="cli">praisonai code <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
|
||||
* <code title="cli">praisonai code code-main <a href="./src/praisonai/praisonai/cli/commands/code.py">--help</a></code>
|
||||
* <code title="cli">praisonai commit commit-main <a href="./src/praisonai/praisonai/cli/commands/commit.py">--help</a></code>
|
||||
* <code title="cli">praisonai completion bash <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
|
||||
* <code title="cli">praisonai completion completion-callback <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
|
||||
* <code title="cli">praisonai completion fish <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
|
||||
* <code title="cli">praisonai completion zsh <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
|
||||
* <code title="cli">praisonai config get <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
|
||||
* <code title="cli">praisonai config list <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
|
||||
* <code title="cli">praisonai config path <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
|
||||
* <code title="cli">praisonai config reset <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
|
||||
* <code title="cli">praisonai config set <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
|
||||
* <code title="cli">praisonai context add <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai context clear <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai context compact <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai context export <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai context grep <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai context list <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai context show <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai context stats <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai context tail <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
|
||||
* <code title="cli">praisonai debug acp <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
|
||||
* <code title="cli">praisonai debug debug-callback <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
|
||||
* <code title="cli">praisonai debug interactive <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
|
||||
* <code title="cli">praisonai debug lsp <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
|
||||
* <code title="cli">praisonai debug trace <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
|
||||
* <code title="cli">praisonai deploy aws <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
|
||||
* <code title="cli">praisonai deploy azure <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
|
||||
* <code title="cli">praisonai deploy docker <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
|
||||
* <code title="cli">praisonai deploy gcp <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
|
||||
* <code title="cli">praisonai diag diag-callback <a href="./src/praisonai/praisonai/cli/commands/diag.py">--help</a></code>
|
||||
* <code title="cli">praisonai diag export <a href="./src/praisonai/praisonai/cli/commands/diag.py">--help</a></code>
|
||||
* <code title="cli">praisonai docs api-md <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
|
||||
* <code title="cli">praisonai docs generate <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
|
||||
* <code title="cli">praisonai docs list <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
|
||||
* <code title="cli">praisonai docs report <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
|
||||
* <code title="cli">praisonai docs run <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
|
||||
* <code title="cli">praisonai docs run-all <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
|
||||
* <code title="cli">praisonai docs serve <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
|
||||
* <code title="cli">praisonai docs stats <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor config <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor db <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor doctor-callback <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor env <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor mcp <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor network <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor performance <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor selftest <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai doctor tools <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
|
||||
* <code title="cli">praisonai endpoints list <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
|
||||
* <code title="cli">praisonai endpoints test <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
|
||||
* <code title="cli">praisonai environment check <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
|
||||
* <code title="cli">praisonai environment doctor <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
|
||||
* <code title="cli">praisonai environment view <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
|
||||
* <code title="cli">praisonai eval accuracy <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
|
||||
* <code title="cli">praisonai eval performance <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
|
||||
* <code title="cli">praisonai examples info <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
|
||||
* <code title="cli">praisonai examples list <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
|
||||
* <code title="cli">praisonai examples report <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
|
||||
* <code title="cli">praisonai examples run <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
|
||||
* <code title="cli">praisonai examples run-all <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
|
||||
* <code title="cli">praisonai examples stats <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
|
||||
* <code title="cli">praisonai hooks add <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
|
||||
* <code title="cli">praisonai hooks list <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
|
||||
* <code title="cli">praisonai hooks remove <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
|
||||
* <code title="cli">praisonai knowledge add <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
|
||||
* <code title="cli">praisonai knowledge index <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
|
||||
* <code title="cli">praisonai knowledge list <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
|
||||
* <code title="cli">praisonai knowledge search <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
|
||||
* <code title="cli">praisonai lsp logs <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
|
||||
* <code title="cli">praisonai lsp lsp-callback <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
|
||||
* <code title="cli">praisonai lsp start <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
|
||||
* <code title="cli">praisonai lsp status <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
|
||||
* <code title="cli">praisonai lsp stop <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp add <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp describe <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp list <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp mcp-callback <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp remove <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp status <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp sync <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp test <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai mcp tools <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
|
||||
* <code title="cli">praisonai memory add <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
|
||||
* <code title="cli">praisonai memory clear <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
|
||||
* <code title="cli">praisonai memory search <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
|
||||
* <code title="cli">praisonai memory show <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
|
||||
* <code title="cli">praisonai memory status <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
|
||||
* <code title="cli">praisonai package install <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
|
||||
* <code title="cli">praisonai package list <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
|
||||
* <code title="cli">praisonai package uninstall <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
|
||||
* <code title="cli">praisonai profile imports <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
|
||||
* <code title="cli">praisonai profile optimize <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
|
||||
* <code title="cli">praisonai profile profile-callback <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
|
||||
* <code title="cli">praisonai profile query <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
|
||||
* <code title="cli">praisonai profile snapshot <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
|
||||
* <code title="cli">praisonai profile startup <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
|
||||
* <code title="cli">praisonai profile suite <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
|
||||
* <code title="cli">praisonai rag chat <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
|
||||
* <code title="cli">praisonai rag eval <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
|
||||
* <code title="cli">praisonai rag index <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
|
||||
* <code title="cli">praisonai rag query <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
|
||||
* <code title="cli">praisonai rag serve <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
|
||||
* <code title="cli">praisonai realtime realtime-main <a href="./src/praisonai/praisonai/cli/commands/realtime.py">--help</a></code>
|
||||
* <code title="cli">praisonai recipe install <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
|
||||
* <code title="cli">praisonai recipe list <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
|
||||
* <code title="cli">praisonai recipe run <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
|
||||
* <code title="cli">praisonai registry list <a href="./src/praisonai/praisonai/cli/commands/registry.py">--help</a></code>
|
||||
* <code title="cli">praisonai registry serve <a href="./src/praisonai/praisonai/cli/commands/registry.py">--help</a></code>
|
||||
* <code title="cli">praisonai research research-main <a href="./src/praisonai/praisonai/cli/commands/research.py">--help</a></code>
|
||||
* <code title="cli">praisonai retrieval index <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
|
||||
* <code title="cli">praisonai retrieval query <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
|
||||
* <code title="cli">praisonai retrieval search <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
|
||||
* <code title="cli">praisonai rules add <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
|
||||
* <code title="cli">praisonai rules clear <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
|
||||
* <code title="cli">praisonai rules list <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
|
||||
* <code title="cli">praisonai run <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
|
||||
* <code title="cli">praisonai run run-main <a href="./src/praisonai/praisonai/cli/commands/run.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule delete <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule describe <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule list <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule logs <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule restart <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule schedule-callback <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule start <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule stats <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai schedule stop <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
|
||||
* <code title="cli">praisonai serve serve-callback <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
|
||||
* <code title="cli">praisonai serve start <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
|
||||
* <code title="cli">praisonai serve status <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
|
||||
* <code title="cli">praisonai serve stop <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
|
||||
* <code title="cli">praisonai session delete <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
|
||||
* <code title="cli">praisonai session export <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
|
||||
* <code title="cli">praisonai session import <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
|
||||
* <code title="cli">praisonai session list <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
|
||||
* <code title="cli">praisonai session resume <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
|
||||
* <code title="cli">praisonai session show <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
|
||||
* <code title="cli">praisonai skills create <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
|
||||
* <code title="cli">praisonai skills info <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
|
||||
* <code title="cli">praisonai skills install <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
|
||||
* <code title="cli">praisonai skills list <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
|
||||
* <code title="cli">praisonai skills search <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
|
||||
* <code title="cli">praisonai skills validate <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
|
||||
* <code title="cli">praisonai standardise check <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
|
||||
* <code title="cli">praisonai standardise fix <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
|
||||
* <code title="cli">praisonai standardise init <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
|
||||
* <code title="cli">praisonai standardise report <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
|
||||
* <code title="cli">praisonai templates create <a href="./src/praisonai/praisonai/cli/commands/templates.py">--help</a></code>
|
||||
* <code title="cli">praisonai templates list <a href="./src/praisonai/praisonai/cli/commands/templates.py">--help</a></code>
|
||||
* <code title="cli">praisonai test info <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>
|
||||
* <code title="cli">praisonai test interactive <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>
|
||||
* <code title="cli">praisonai test run <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>
|
||||
* <code title="cli">praisonai todo add <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
|
||||
* <code title="cli">praisonai todo done <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
|
||||
* <code title="cli">praisonai todo list <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
|
||||
* <code title="cli">praisonai tools info <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
|
||||
* <code title="cli">praisonai tools list <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
|
||||
* <code title="cli">praisonai tools test <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
|
||||
* <code title="cli">praisonai traces disable <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
|
||||
* <code title="cli">praisonai traces enable <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
|
||||
* <code title="cli">praisonai traces list <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
|
||||
* <code title="cli">praisonai traces status <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
|
||||
* <code title="cli">praisonai train train-main <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
|
||||
* <code title="cli">praisonai ui chat <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
|
||||
* <code title="cli">praisonai ui code <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
|
||||
* <code title="cli">praisonai ui gradio <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
|
||||
* <code title="cli">praisonai ui realtime <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
|
||||
* <code title="cli">praisonai ui ui-main <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
|
||||
* <code title="cli">praisonai version check <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
|
||||
* <code title="cli">praisonai version show <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
|
||||
* <code title="cli">praisonai version version-callback <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
|
||||
* <code title="cli">praisonai workflow create <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
|
||||
* <code title="cli">praisonai workflow list <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
|
||||
* <code title="cli">praisonai workflow run <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
|
||||
|
||||
# TypeScript
|
||||
|
||||
Types/Exports:
|
||||
```ts
|
||||
export { Agent, Agents, PraisonAIAgents, Router } from "./agent";
|
||||
export type { PraisonAIAgentsConfig, SimpleAgentConfig, SimpleRouteConfig, SimpleRouterConfig } from "./agent";
|
||||
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, 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 { EvalSuite, accuracyEval, performanceEval, reliabilityEval } from "./eval";
|
||||
export { AgentEventBus, AgentEvents, EventEmitterPubSub, PubSub, createEventBus, createPubSub } from "./events";
|
||||
export { LLMGuardrail, createLLMGuardrail } from "./guardrails/llm-guardrail";
|
||||
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 { AutoMemory, AutoMemoryKnowledgeBase, AutoMemoryVectorStore, DEFAULT_POLICIES, createAutoMemory, createLLMSummarizer } from "./memory/auto-memory";
|
||||
export { FileMemory, createFileMemory } from "./memory/file-memory";
|
||||
export { Memory, createMemory } from "./memory/memory";
|
||||
export type { MemoryConfig, MemoryEntry } from "./memory/memory";
|
||||
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, TelemetryCollector, cleanupTelemetry, createAgentTelemetry, disableTelemetry, enableTelemetry, getTelemetry } from "./telemetry";
|
||||
export { BaseTool, FunctionTool, ToolRegistry, ToolResult, ToolValidationError, 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 { Workflow, loop, parallel, repeat, route } from "./workflows";
|
||||
export type { StepResult, WorkflowContext, WorkflowStep } from "./workflows";
|
||||
export { createWorkflowFromYAML, loadWorkflowFromFile, parseYAMLWorkflow, validateWorkflowDefinition } from "./workflows/yaml-parser";
|
||||
```
|
||||
|
||||
# Optional Plugins
|
||||
|
||||
External tools are available via `praisonai-tools` package:
|
||||
|
||||
```bash
|
||||
pip install praisonai-tools
|
||||
```
|
||||
|
||||
See [PraisonAI-tools](https://github.com/MervinPraison/PraisonAI-tools) for available tools.
|
||||
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
|
||||
# Install Python packages (using latest versions)
|
||||
RUN pip install --no-cache-dir \
|
||||
praisonai_tools \
|
||||
"praisonai>=3.6.2" \
|
||||
"praisonai>=3.8.7" \
|
||||
"praisonai[chat]" \
|
||||
"embedchain[github,youtube]"
|
||||
|
||||
|
||||
@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
|
||||
# Install Python packages (using latest versions)
|
||||
RUN pip install --no-cache-dir \
|
||||
praisonai_tools \
|
||||
"praisonai>=3.6.2" \
|
||||
"praisonai>=3.8.7" \
|
||||
"praisonai[ui]" \
|
||||
"praisonai[chat]" \
|
||||
"praisonai[realtime]" \
|
||||
|
||||
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
|
||||
# Install Python packages (using latest versions)
|
||||
RUN pip install --no-cache-dir \
|
||||
praisonai_tools \
|
||||
"praisonai>=3.6.2" \
|
||||
"praisonai>=3.8.7" \
|
||||
"praisonai[ui]" \
|
||||
"praisonai[crewai]"
|
||||
|
||||
|
||||
@@ -0,0 +1,96 @@
|
||||
# Consolidated Params Examples
|
||||
|
||||
Examples demonstrating the **Agent-Centric API** with consolidated parameters.
|
||||
|
||||
## Precedence Ladder
|
||||
|
||||
```
|
||||
Instance > Config > Array > Dict > String > Bool > Default
|
||||
```
|
||||
|
||||
## Quick Reference
|
||||
|
||||
| Feature | Presets | Example |
|
||||
|---------|---------|---------|
|
||||
| `memory` | file, sqlite, redis, postgres, mem0, mongodb | `memory="sqlite"` |
|
||||
| `output` | minimal, normal, verbose, debug, silent | `output="verbose"` |
|
||||
| `execution` | fast, balanced, thorough, unlimited | `execution="balanced"` |
|
||||
| `planning` | reasoning, read_only, auto | `planning="reasoning"` |
|
||||
| `reflection` | minimal, standard, thorough | `reflection="standard"` |
|
||||
| `guardrails` | strict, permissive, safety | `guardrails="strict"` |
|
||||
| `web` | duckduckgo, tavily, google, bing, serper | `web="duckduckgo"` |
|
||||
| `context` | sliding_window, summarize, truncate | `context="sliding_window"` |
|
||||
| `autonomy` | suggest, auto_edit, full_auto | `autonomy="suggest"` |
|
||||
| `caching` | enabled, disabled, prompt | `caching="prompt"` |
|
||||
|
||||
## Examples
|
||||
|
||||
### Basic Examples (Single Feature)
|
||||
|
||||
| File | Feature | Description |
|
||||
|------|---------|-------------|
|
||||
| `basic_agent.py` | Agent | Minimal agent with memory |
|
||||
| `basic_memory.py` | memory | SQLite memory preset |
|
||||
| `basic_output.py` | output | Verbose output preset |
|
||||
| `basic_execution.py` | execution | Execution control |
|
||||
| `basic_guardrails.py` | guardrails | Callable validator |
|
||||
| `basic_reflection.py` | reflection | Self-reflection preset |
|
||||
| `basic_web.py` | web | Web search preset |
|
||||
| `basic_planning.py` | planning | Planning preset |
|
||||
| `basic_autonomy.py` | autonomy | Autonomy preset |
|
||||
| `basic_caching.py` | caching | Caching preset |
|
||||
| `basic_knowledge.py` | knowledge | RAG with sources |
|
||||
| `basic_context.py` | context | Context management |
|
||||
| `basic_hooks.py` | hooks | Lifecycle callbacks |
|
||||
|
||||
### Multi-Agent Examples
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| `basic_agents.py` | Multi-agent with memory and planning |
|
||||
|
||||
### Workflow Examples
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| `basic_workflow.py` | Simple workflow with output preset |
|
||||
| `basic_workflow_agentlike.py` | Workflow with agent-like params |
|
||||
| `basic_step_override.py` | Step-level param overrides |
|
||||
| `advanced_workflow_full_features.py` | All consolidated params |
|
||||
|
||||
### Advanced Examples
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| `advanced_output_execution.py` | Combined output + execution |
|
||||
|
||||
## Usage Forms
|
||||
|
||||
Each consolidated param supports multiple input forms:
|
||||
|
||||
```python
|
||||
# Bool - enable with defaults
|
||||
memory=True
|
||||
|
||||
# String preset
|
||||
memory="sqlite"
|
||||
|
||||
# Dict config
|
||||
memory={"backend": "sqlite", "user_id": "user123"}
|
||||
|
||||
# Config instance
|
||||
memory=MemoryConfig(backend="sqlite", user_id="user123")
|
||||
|
||||
# Array with overrides (for some params)
|
||||
guardrails=["strict", {"max_retries": 10}]
|
||||
```
|
||||
|
||||
## Running Examples
|
||||
|
||||
```bash
|
||||
# Set API key
|
||||
export OPENAI_API_KEY=your_key_here
|
||||
|
||||
# Run any example
|
||||
python basic_agent.py
|
||||
```
|
||||
@@ -0,0 +1,18 @@
|
||||
"""
|
||||
Basic Autonomy Example - Agent-Centric API
|
||||
|
||||
Demonstrates autonomy with consolidated params.
|
||||
Presets: suggest, auto_edit, full_auto
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Basic: Enable autonomy with preset
|
||||
agent = Agent(
|
||||
instructions="You are an autonomous assistant.",
|
||||
autonomy="suggest", # Presets: suggest, auto_edit, full_auto
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
response = agent.start("Help me organize my project files.")
|
||||
print(response)
|
||||
@@ -0,0 +1,23 @@
|
||||
"""
|
||||
Basic Caching Example - Agent-Centric API
|
||||
|
||||
Demonstrates caching with consolidated params.
|
||||
Presets: enabled, disabled, prompt
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Basic: Enable caching with preset
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant with caching enabled.",
|
||||
caching="prompt", # Presets: enabled, disabled, prompt
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# First call - will cache
|
||||
response = agent.start("What is the capital of France?")
|
||||
print(response)
|
||||
|
||||
# Second call - may use cache
|
||||
response = agent.start("What is the capital of France?")
|
||||
print(response)
|
||||
@@ -0,0 +1,18 @@
|
||||
"""
|
||||
Basic Context Example - Agent-Centric API
|
||||
|
||||
Demonstrates context management with consolidated params.
|
||||
Presets: sliding_window, summarize, truncate
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Basic: Enable context management with preset
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant with context management.",
|
||||
context="sliding_window", # Presets: sliding_window, summarize, truncate
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
response = agent.start("Tell me a long story about a dragon.")
|
||||
print(response)
|
||||
@@ -0,0 +1,28 @@
|
||||
"""
|
||||
Basic Hooks Example - Agent-Centric API
|
||||
|
||||
Demonstrates lifecycle hooks with consolidated params.
|
||||
Valid keys: on_step, on_tool_call, middleware
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Define hook callbacks
|
||||
def on_step_callback(step_info):
|
||||
print(f"Step: {step_info}")
|
||||
|
||||
def on_tool_call_callback(tool_info):
|
||||
print(f"Tool called: {tool_info}")
|
||||
|
||||
# Basic: Enable hooks with dict
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant.",
|
||||
hooks={
|
||||
"on_step": on_step_callback,
|
||||
"on_tool_call": on_tool_call_callback,
|
||||
},
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
response = agent.start("What is 2 + 2?")
|
||||
print(response)
|
||||
@@ -0,0 +1,18 @@
|
||||
"""
|
||||
Basic Knowledge (RAG) Example - Agent-Centric API
|
||||
|
||||
Demonstrates knowledge with consolidated params.
|
||||
Supports: bool, list of sources, dict config, KnowledgeConfig
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Basic: Enable knowledge with list of sources
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant with document knowledge.",
|
||||
knowledge=["docs/"], # List of file/folder paths
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
response = agent.start("What does the documentation say about installation?")
|
||||
print(response)
|
||||
@@ -0,0 +1,18 @@
|
||||
"""
|
||||
Basic Planning Example - Agent-Centric API
|
||||
|
||||
Demonstrates planning with consolidated params.
|
||||
Presets: reasoning, read_only, auto
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Basic: Enable planning with preset
|
||||
agent = Agent(
|
||||
instructions="You are a strategic planner.",
|
||||
planning="reasoning", # Presets: reasoning, read_only, auto
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
response = agent.start("Plan a 3-day trip to Tokyo.")
|
||||
print(response)
|
||||
@@ -0,0 +1,18 @@
|
||||
"""
|
||||
Basic Reflection Example - Agent-Centric API
|
||||
|
||||
Demonstrates reflection (self-reflect) with consolidated params.
|
||||
Presets: minimal, standard, thorough
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Basic: Enable reflection with preset
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant that reflects on your answers.",
|
||||
reflection="standard", # Presets: minimal, standard, thorough
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
response = agent.start("Explain quantum computing in simple terms.")
|
||||
print(response)
|
||||
@@ -0,0 +1,18 @@
|
||||
"""
|
||||
Basic Web Search Example - Agent-Centric API
|
||||
|
||||
Demonstrates web search with consolidated params.
|
||||
Presets: duckduckgo, tavily, google, bing, serper, search_only, fetch_only
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Basic: Enable web search with preset
|
||||
agent = Agent(
|
||||
instructions="You are a research assistant with web access.",
|
||||
web="duckduckgo", # Presets: duckduckgo, tavily, google, bing, serper
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
response = agent.start("What are the latest AI developments in 2024?")
|
||||
print(response)
|
||||
@@ -13,7 +13,7 @@ def main():
|
||||
role="Research Specialist",
|
||||
goal="Find information about a topic",
|
||||
backstory="You are an expert researcher who finds comprehensive information.",
|
||||
verbose=True,
|
||||
output="verbose",
|
||||
llm="gpt-5-nano", # Use a cost-effective model for examples
|
||||
metrics=True # 🎯 SIMPLIFIED: Just add this parameter!
|
||||
)
|
||||
@@ -29,7 +29,7 @@ def main():
|
||||
agents = Agents(
|
||||
agents=[research_agent],
|
||||
tasks=[research_task],
|
||||
verbose=True
|
||||
output="verbose"
|
||||
)
|
||||
|
||||
print("🚀 Running agent task...")
|
||||
|
||||
@@ -104,8 +104,7 @@ def main():
|
||||
workflow = Agents(
|
||||
agents=[researcher, analyst, writer],
|
||||
tasks=[research_task, analysis_task, report_task],
|
||||
process="sequential",
|
||||
verbose=True
|
||||
process="sequential"
|
||||
)
|
||||
|
||||
result = workflow.start()
|
||||
|
||||
@@ -11,7 +11,7 @@ Shows how to monitor agent performance when using external tools.
|
||||
"""
|
||||
|
||||
from praisonaiagents import Agent
|
||||
from praisonaiagents.tools import duckduckgo, wikipedia
|
||||
from praisonaiagents.tools import duckduckgo, wikipedia_tools
|
||||
from praisonaiagents.telemetry import (
|
||||
monitor_function, track_api_call, get_api_stats,
|
||||
get_slowest_functions, performance_monitor
|
||||
@@ -49,7 +49,7 @@ def main():
|
||||
Use the available tools to search for information and provide comprehensive answers.
|
||||
Be thorough in your research and cite your sources.""",
|
||||
llm="gpt-5-nano",
|
||||
tools=[duckduckgo, wikipedia] # Add search tools
|
||||
tools=[duckduckgo, wikipedia_tools] # Add search tools
|
||||
)
|
||||
|
||||
# Test queries to demonstrate tool performance monitoring
|
||||
@@ -97,7 +97,9 @@ def main():
|
||||
# Slowest functions analysis
|
||||
print("\n🐌 Slowest Functions:")
|
||||
slowest = get_slowest_functions()
|
||||
for func_name, avg_time in slowest:
|
||||
for item in slowest[:5]:
|
||||
func_name = item.get('function', 'unknown')
|
||||
avg_time = item.get('average_time', 0)
|
||||
print(f" {func_name}: {avg_time:.3f}s average")
|
||||
|
||||
# Real-time performance data
|
||||
|
||||
@@ -165,7 +165,9 @@ def main():
|
||||
# Slowest operations
|
||||
print("\n🐌 Slowest Memory Operations:")
|
||||
slowest = get_slowest_functions()
|
||||
for func_name, avg_time in slowest[:5]: # Top 5 slowest
|
||||
for item in slowest[:5]: # Top 5 slowest
|
||||
func_name = item.get('function', 'unknown')
|
||||
avg_time = item.get('average_time', 0)
|
||||
print(f" {func_name}: {avg_time:.3f}s average")
|
||||
|
||||
# Memory operation breakdown
|
||||
|
||||
@@ -263,7 +263,7 @@ def main():
|
||||
# Slowest operations
|
||||
print("\n🐌 Slowest Tool Operations:")
|
||||
slowest = get_slowest_functions()
|
||||
tool_slowest = [(name, time) for name, time in slowest if 'tool' in name.lower()]
|
||||
tool_slowest = [(item.get('function', 'unknown'), item.get('average_time', 0)) for item in slowest if 'tool' in item.get('function', '').lower()]
|
||||
|
||||
for tool_name, avg_time in tool_slowest[:3]:
|
||||
print(f" {tool_name}: {avg_time:.3f}s")
|
||||
|
||||
@@ -302,13 +302,17 @@ def display_dashboard(dashboard_data, benchmark_results):
|
||||
perf_data = dashboard_data['performance_data']
|
||||
|
||||
print(" Fastest Functions:")
|
||||
fastest_functions = sorted(perf_data['slowest_functions'], key=lambda x: x[1])[:3]
|
||||
for name, time in fastest_functions:
|
||||
print(f" {name}: {time:.3f}s")
|
||||
fastest_functions = sorted(perf_data['slowest_functions'], key=lambda x: x.get('average_time', float('inf')))[:3]
|
||||
for item in fastest_functions:
|
||||
name = item.get('function', 'unknown')
|
||||
time_val = item.get('average_time', 0)
|
||||
print(f" {name}: {time_val:.3f}s")
|
||||
|
||||
print(" Slowest Functions:")
|
||||
for name, time in perf_data['slowest_functions'][:3]:
|
||||
print(f" {name}: {time:.3f}s")
|
||||
for item in perf_data['slowest_functions'][:3]:
|
||||
name = item.get('function', 'unknown')
|
||||
time_val = item.get('average_time', 0)
|
||||
print(f" {name}: {time_val:.3f}s")
|
||||
|
||||
# Benchmark Results
|
||||
print(f"\n⚡ BENCHMARK RESULTS")
|
||||
|
||||
@@ -0,0 +1,63 @@
|
||||
"""
|
||||
Streaming Example with Profiling
|
||||
|
||||
Key Points:
|
||||
1. output="stream" preset enables streaming by default
|
||||
2. stream=True in start() is redundant when using output="stream" (but doesn't hurt)
|
||||
3. Profiler.enable() MUST be called before profiling works
|
||||
4. Use Profiler.streaming() context manager for TTFT metrics
|
||||
"""
|
||||
from praisonai.profiler import Profiler, profile, profile_imports
|
||||
|
||||
# IMPORTANT: Enable profiler BEFORE any profiling operations
|
||||
Profiler.enable()
|
||||
|
||||
# Profile imports
|
||||
with profile_imports():
|
||||
from praisonaiagents import Agent
|
||||
|
||||
@profile
|
||||
def main():
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant",
|
||||
output="stream" # This already enables streaming
|
||||
)
|
||||
|
||||
print("\nStreaming response:")
|
||||
print("-" * 40)
|
||||
|
||||
# Use Profiler.streaming() to track TTFT (Time To First Token)
|
||||
with Profiler.streaming("llm_stream") as tracker:
|
||||
first_chunk = True
|
||||
for chunk in agent.start("Write a short paragraph about the history of computing"):
|
||||
if first_chunk:
|
||||
tracker.first_token() # Mark TTFT
|
||||
first_chunk = False
|
||||
tracker.chunk() # Count chunks
|
||||
print(chunk, end="", flush=True)
|
||||
|
||||
print("\n" + "-" * 40)
|
||||
|
||||
# Run with profiling blocks
|
||||
with Profiler.block("full_workflow"):
|
||||
with Profiler.block("setup"):
|
||||
pass
|
||||
|
||||
with Profiler.block("execution"):
|
||||
result = main()
|
||||
|
||||
with Profiler.block("cleanup"):
|
||||
pass
|
||||
|
||||
# Generate report
|
||||
Profiler.report()
|
||||
|
||||
# Show streaming-specific stats
|
||||
streaming_records = Profiler.get_streaming_records()
|
||||
if streaming_records:
|
||||
print("\nStreaming Metrics:")
|
||||
for record in streaming_records:
|
||||
print(f" {record.name}: TTFT={record.ttft_ms:.2f}ms, Total={record.total_ms:.2f}ms, Chunks={record.chunk_count}")
|
||||
|
||||
stats = Profiler.get_statistics()
|
||||
print(f"\nMedian execution time: {stats['p50']:.2f}ms")
|
||||
@@ -1,98 +1,75 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Agent-level guardrails example.
|
||||
|
||||
This example demonstrates how to use guardrails at the Agent level,
|
||||
This example demonstrates how to use guardrails at the Agent level
|
||||
which will apply to all tasks executed by that agent.
|
||||
"""
|
||||
|
||||
from typing import Tuple, Any
|
||||
from praisonaiagents import Agent, TaskOutput
|
||||
|
||||
def validate_content_length(task_output: TaskOutput) -> Tuple[bool, Any]:
|
||||
"""
|
||||
Validate that task output content meets minimum length requirement.
|
||||
|
||||
Args:
|
||||
task_output: The task output to validate
|
||||
|
||||
Returns:
|
||||
Tuple of (success, result_or_error_message)
|
||||
"""
|
||||
if len(task_output.raw) < 50:
|
||||
return False, "Content too short - must be at least 50 characters"
|
||||
return True, task_output
|
||||
|
||||
def validate_professional_tone(task_output: TaskOutput) -> Tuple[bool, Any]:
|
||||
"""
|
||||
Validate that the content has a professional tone.
|
||||
|
||||
Args:
|
||||
task_output: The task output to validate
|
||||
|
||||
Returns:
|
||||
Tuple of (success, result_or_error_message)
|
||||
"""
|
||||
content = task_output.raw.lower()
|
||||
unprofessional_words = ['yo', 'dude', 'awesome', 'cool', 'lol']
|
||||
|
||||
for word in unprofessional_words:
|
||||
if word in content:
|
||||
return False, f"Content contains unprofessional word: '{word}'"
|
||||
|
||||
return True, task_output
|
||||
|
||||
def main():
|
||||
"""Demonstrate Agent-level guardrails with function-based and LLM-based validation."""
|
||||
|
||||
print("=== Agent Guardrail Examples ===\n")
|
||||
|
||||
# Example 1: Function-based guardrail
|
||||
print("1. Function-based guardrail (content length validation):")
|
||||
agent1 = Agent(
|
||||
name="ContentWriter",
|
||||
instructions="You are a professional content writer who creates detailed responses",
|
||||
guardrail=validate_content_length,
|
||||
max_guardrail_retries=2
|
||||
name="ContentWriter"
|
||||
instructions="You are a professional content writer who creates detailed responses"
|
||||
guardrails=validate_content_length
|
||||
)
|
||||
|
||||
try:
|
||||
result1 = agent1.start("Write a brief welcome message")
|
||||
print(f"Result: {result1}\n")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}\n")
|
||||
|
||||
# Example 2: LLM-based guardrail (string description)
|
||||
print("2. LLM-based guardrail (professional tone validation):")
|
||||
agent2 = Agent(
|
||||
name="BusinessWriter",
|
||||
instructions="You are a business communication expert",
|
||||
guardrail="Ensure the content is professional, formal, and suitable for business communication. No casual language or slang.",
|
||||
max_guardrail_retries=3
|
||||
instructions="You are a business communication expert"
|
||||
guardrails="Ensure the content is professional, formal, and suitable for business communication. No casual language or slang."
|
||||
)
|
||||
|
||||
try:
|
||||
result2 = agent2.start("Write a welcome message for new employees")
|
||||
print(f"Result: {result2}\n")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}\n")
|
||||
|
||||
# Example 3: Multiple agents with different guardrails
|
||||
print("3. Professional tone function-based guardrail:")
|
||||
agent3 = Agent(
|
||||
name="ProfessionalWriter",
|
||||
instructions="Write professional business content",
|
||||
guardrail=validate_professional_tone,
|
||||
max_guardrail_retries=2
|
||||
name="ProfessionalWriter"
|
||||
instructions="Write professional business content"
|
||||
guardrails=validate_professional_tone
|
||||
)
|
||||
|
||||
try:
|
||||
result3 = agent3.start("Write a casual greeting message")
|
||||
print(f"Result: {result3}\n")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}\n")
|
||||
|
||||
print("=== Agent Guardrails Demonstration Complete ===")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -21,7 +21,7 @@ article_agent = Agent(
|
||||
goal="Fetch the user's favorite morning article",
|
||||
tools=[fetch_favorite_article],
|
||||
llm="gemini/gemini-2.5-flash-lite-preview-06-17",
|
||||
verbose=True
|
||||
output="verbose"
|
||||
)
|
||||
|
||||
news_agent = Agent(
|
||||
@@ -30,7 +30,7 @@ news_agent = Agent(
|
||||
goal="Search for trending news in Kenya",
|
||||
tools=[search_trending_kenya],
|
||||
llm="gemini/gemini-2.5-flash-lite-preview-06-17",
|
||||
verbose=True
|
||||
output="verbose"
|
||||
)
|
||||
|
||||
twitter_agent = Agent(
|
||||
@@ -39,7 +39,7 @@ twitter_agent = Agent(
|
||||
goal="Fetch the latest Twitter feed",
|
||||
tools=[fetch_twitter_feed],
|
||||
llm="gemini/gemini-2.5-flash-lite-preview-06-17",
|
||||
verbose=True
|
||||
output="verbose"
|
||||
)
|
||||
|
||||
aggregator = Agent(
|
||||
@@ -47,7 +47,7 @@ aggregator = Agent(
|
||||
role="Result Aggregator",
|
||||
goal="Aggregate and summarize all results",
|
||||
llm="gemini/gemini-2.5-flash-lite-preview-06-17",
|
||||
verbose=True
|
||||
output="verbose"
|
||||
)
|
||||
|
||||
# Tasks for each agent
|
||||
@@ -92,7 +92,7 @@ async def main():
|
||||
agents=[article_agent, news_agent, twitter_agent, aggregator],
|
||||
tasks=[article_task, news_task, twitter_task, aggregate_task],
|
||||
process="workflow",
|
||||
verbose=True
|
||||
output="verbose"
|
||||
)
|
||||
results = await workflow.astart(dict_output=True)
|
||||
|
||||
|
||||
@@ -5,8 +5,8 @@ async def main():
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant",
|
||||
llm="gemini/gemini-1.5-flash-8b",
|
||||
self_reflect=True,
|
||||
verbose=True
|
||||
reflection=True,
|
||||
output="verbose"
|
||||
)
|
||||
|
||||
# Use achat instead of start/chat for async operation
|
||||
|
||||
@@ -79,7 +79,7 @@ async def run_parallel_searches():
|
||||
backstory="Expert in web search and information retrieval. You have access to the tavily_search tool to search the web.",
|
||||
tools=[tavily_search],
|
||||
llm=llm_config,
|
||||
verbose=True
|
||||
output="verbose"
|
||||
)
|
||||
|
||||
# Create summary agent
|
||||
|
||||
@@ -1,14 +1,3 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
from praisonaiagents import Agent
|
||||
|
||||
print("Creating agent...")
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant"
|
||||
)
|
||||
|
||||
print("Starting agent...")
|
||||
result = agent.start("Why is the sky blue?")
|
||||
|
||||
print("Agent completed successfully!")
|
||||
print(f"Result: {result}")
|
||||
agent = Agent(instructions="You are a helpful assistant")
|
||||
result = agent.start("Why is the sky blue?")
|
||||
@@ -1,6 +1,6 @@
|
||||
# PraisonAI Agents - Real Execution Benchmark
|
||||
|
||||
**Generated:** 2025-12-18 14:43:52
|
||||
**Generated:** 2026-01-13 06:07:28
|
||||
**Model:** gpt-4o-mini
|
||||
**Iterations:** 3
|
||||
**Prompt:** "What is 2+2? Reply with just the number."
|
||||
@@ -9,18 +9,18 @@
|
||||
|
||||
| Framework | Method | Avg Time | Relative |
|
||||
|-----------|--------|----------|----------|
|
||||
| **PraisonAI** | `agent.start()` | **0.45s** | **1.00x (fastest)** |
|
||||
| PraisonAI (LiteLLM) | `agent.start()` | 0.55s | 1.22x |
|
||||
| CrewAI | `crew.kickoff()` | 0.58s | 1.28x |
|
||||
| Agno | `agent.run()` | 0.92s | 2.05x |
|
||||
| **PraisonAI (LiteLLM)** | `agent.start()` | **0.55s** | **1.00x (fastest)** |
|
||||
| PraisonAI | `agent.start()` | 0.64s | 1.15x |
|
||||
| CrewAI | `crew.kickoff()` | 0.95s | 1.71x |
|
||||
| Agno | `agent.run()` | 1.05s | 1.90x |
|
||||
|
||||
## Package Versions
|
||||
|
||||
| Package | Version |
|
||||
|---------|--------|
|
||||
| PraisonAI | 0.1.5 |
|
||||
| Agno | 2.3.14 |
|
||||
| CrewAI | 1.6.1 |
|
||||
| PraisonAI | 0.11.7 |
|
||||
| Agno | 2.3.25 |
|
||||
| CrewAI | 1.8.0 |
|
||||
|
||||
## How to Reproduce
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# PraisonAI Agents - Real-World Execution Benchmark
|
||||
|
||||
**Generated:** 2025-12-18 14:33:49
|
||||
**Generated:** 2026-01-13 06:05:09
|
||||
**Model:** gpt-4o-mini
|
||||
**Iterations:** 3 per test
|
||||
|
||||
@@ -8,60 +8,60 @@
|
||||
|
||||
| Test | PraisonAI (avg) | Agno (avg) | Difference |
|
||||
|------|-----------------|------------|------------|
|
||||
| Single Agent | 2.04s | 3.18s | PraisonAI 1.56x faster |
|
||||
| 2 Agents | 2.49s | 20.49s | PraisonAI 8.21x faster |
|
||||
| 3 Agents | 6.52s | 13.97s | PraisonAI 2.14x faster |
|
||||
| Single Agent | 2.09s | 3.25s | PraisonAI 1.55x faster |
|
||||
| 2 Agents | 2.50s | 9.73s | PraisonAI 3.89x faster |
|
||||
| 3 Agents | 6.25s | 13.56s | PraisonAI 2.17x faster |
|
||||
|
||||
## Detailed Results
|
||||
|
||||
### praisonai_single
|
||||
- Average: 2.04s
|
||||
- Min: 1.95s
|
||||
- Max: 2.19s
|
||||
- Std Dev: 0.13s
|
||||
- Times: ['2.19s', '1.95s', '2.00s']
|
||||
- Average: 2.09s
|
||||
- Min: 1.64s
|
||||
- Max: 2.84s
|
||||
- Std Dev: 0.66s
|
||||
- Times: ['2.84s', '1.79s', '1.64s']
|
||||
|
||||
### agno_single
|
||||
- Average: 3.18s
|
||||
- Min: 3.00s
|
||||
- Max: 3.51s
|
||||
- Std Dev: 0.28s
|
||||
- Times: ['3.51s', '3.00s', '3.04s']
|
||||
- Average: 3.25s
|
||||
- Min: 3.14s
|
||||
- Max: 3.35s
|
||||
- Std Dev: 0.10s
|
||||
- Times: ['3.35s', '3.25s', '3.14s']
|
||||
|
||||
### praisonai_two_agents
|
||||
- Average: 2.49s
|
||||
- Min: 2.22s
|
||||
- Max: 2.65s
|
||||
- Std Dev: 0.24s
|
||||
- Times: ['2.65s', '2.22s', '2.62s']
|
||||
- Average: 2.50s
|
||||
- Min: 2.38s
|
||||
- Max: 2.75s
|
||||
- Std Dev: 0.21s
|
||||
- Times: ['2.38s', '2.75s', '2.38s']
|
||||
|
||||
### agno_two_agents
|
||||
- Average: 20.49s
|
||||
- Min: 9.96s
|
||||
- Max: 35.05s
|
||||
- Std Dev: 13.02s
|
||||
- Times: ['16.46s', '9.96s', '35.05s']
|
||||
- Average: 9.73s
|
||||
- Min: 9.34s
|
||||
- Max: 10.53s
|
||||
- Std Dev: 0.69s
|
||||
- Times: ['10.53s', '9.34s', '9.34s']
|
||||
|
||||
### praisonai_three_agents
|
||||
- Average: 6.52s
|
||||
- Min: 5.62s
|
||||
- Max: 7.39s
|
||||
- Std Dev: 0.88s
|
||||
- Times: ['5.62s', '6.54s', '7.39s']
|
||||
- Average: 6.25s
|
||||
- Min: 5.86s
|
||||
- Max: 6.80s
|
||||
- Std Dev: 0.49s
|
||||
- Times: ['6.80s', '6.09s', '5.86s']
|
||||
|
||||
### agno_three_agents
|
||||
- Average: 13.97s
|
||||
- Min: 11.69s
|
||||
- Max: 15.32s
|
||||
- Std Dev: 1.99s
|
||||
- Times: ['14.91s', '11.69s', '15.32s']
|
||||
- Average: 13.56s
|
||||
- Min: 12.26s
|
||||
- Max: 14.90s
|
||||
- Std Dev: 1.32s
|
||||
- Times: ['14.90s', '13.51s', '12.26s']
|
||||
|
||||
## Package Versions
|
||||
|
||||
| Package | Version |
|
||||
|---------|--------|
|
||||
| PraisonAI | 0.1.5 |
|
||||
| Agno | 2.3.14 |
|
||||
| PraisonAI | 0.11.7 |
|
||||
| Agno | 2.3.25 |
|
||||
|
||||
## How to Reproduce
|
||||
|
||||
|
||||
@@ -52,12 +52,11 @@ def benchmark_praisonai(model, iterations, prompt):
|
||||
agent = Agent(
|
||||
name="Calculator",
|
||||
instructions="You are a helpful assistant. Be very brief.",
|
||||
llm=model,
|
||||
verbose=False
|
||||
llm=model
|
||||
)
|
||||
|
||||
start = time.perf_counter()
|
||||
response = agent.start(prompt)
|
||||
response = agent.start(prompt, output="silent")
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
times.append(elapsed)
|
||||
@@ -80,12 +79,11 @@ def benchmark_praisonai_litellm(model, iterations, prompt):
|
||||
agent = Agent(
|
||||
name="Calculator",
|
||||
instructions="You are a helpful assistant. Be very brief.",
|
||||
llm=f"openai/{model}",
|
||||
verbose=False
|
||||
llm=f"openai/{model}"
|
||||
)
|
||||
|
||||
start = time.perf_counter()
|
||||
response = agent.start(prompt)
|
||||
response = agent.start(prompt, output="silent")
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
times.append(elapsed)
|
||||
|
||||
@@ -103,12 +103,11 @@ def benchmark_praisonai_single(iterations: int = 3) -> ExecutionResult:
|
||||
name="Assistant",
|
||||
instructions="You are a helpful assistant. Be very brief.",
|
||||
llm="gpt-4o-mini",
|
||||
tools=[get_weather],
|
||||
verbose=False
|
||||
tools=[get_weather]
|
||||
)
|
||||
|
||||
start = time.perf_counter()
|
||||
response = agent.start("What's the weather in NYC? Reply in one sentence.")
|
||||
response = agent.start("What's the weather in NYC? Reply in one sentence.", output="silent")
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
result.times.append(elapsed)
|
||||
@@ -132,8 +131,7 @@ def benchmark_praisonai_two_agents(iterations: int = 3) -> ExecutionResult:
|
||||
goal="Gather weather information",
|
||||
instructions="Get weather data and pass to reporter. Be brief.",
|
||||
llm="gpt-4o-mini",
|
||||
tools=[get_weather],
|
||||
verbose=False
|
||||
tools=[get_weather]
|
||||
)
|
||||
|
||||
reporter = Agent(
|
||||
@@ -141,18 +139,16 @@ def benchmark_praisonai_two_agents(iterations: int = 3) -> ExecutionResult:
|
||||
role="News Reporter",
|
||||
goal="Create weather report",
|
||||
instructions="Create a one-sentence weather summary.",
|
||||
llm="gpt-4o-mini",
|
||||
verbose=False
|
||||
llm="gpt-4o-mini"
|
||||
)
|
||||
|
||||
agents = Agents(
|
||||
agents=[researcher, reporter],
|
||||
process="sequential",
|
||||
verbose=False
|
||||
process="sequential"
|
||||
)
|
||||
|
||||
start = time.perf_counter()
|
||||
response = agents.start("Get NYC weather and create a brief report.")
|
||||
response = agents.start("Get NYC weather and create a brief report.", output="silent")
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
result.times.append(elapsed)
|
||||
@@ -176,8 +172,7 @@ def benchmark_praisonai_three_agents(iterations: int = 3) -> ExecutionResult:
|
||||
goal="Get weather data",
|
||||
instructions="Fetch weather information. Be brief.",
|
||||
llm="gpt-4o-mini",
|
||||
tools=[get_weather],
|
||||
verbose=False
|
||||
tools=[get_weather]
|
||||
)
|
||||
|
||||
time_agent = Agent(
|
||||
@@ -186,8 +181,7 @@ def benchmark_praisonai_three_agents(iterations: int = 3) -> ExecutionResult:
|
||||
goal="Get time information",
|
||||
instructions="Fetch timezone information. Be brief.",
|
||||
llm="gpt-4o-mini",
|
||||
tools=[get_time],
|
||||
verbose=False
|
||||
tools=[get_time]
|
||||
)
|
||||
|
||||
summarizer = Agent(
|
||||
@@ -195,18 +189,16 @@ def benchmark_praisonai_three_agents(iterations: int = 3) -> ExecutionResult:
|
||||
role="Summary Writer",
|
||||
goal="Combine information",
|
||||
instructions="Create a one-sentence summary combining weather and time.",
|
||||
llm="gpt-4o-mini",
|
||||
verbose=False
|
||||
llm="gpt-4o-mini"
|
||||
)
|
||||
|
||||
agents = Agents(
|
||||
agents=[weather_agent, time_agent, summarizer],
|
||||
process="sequential",
|
||||
verbose=False
|
||||
process="sequential"
|
||||
)
|
||||
|
||||
start = time.perf_counter()
|
||||
response = agents.start("Get NYC weather and EST time, then summarize.")
|
||||
response = agents.start("Get NYC weather and EST time, then summarize.", output="silent")
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
result.times.append(elapsed)
|
||||
|
||||
@@ -14,7 +14,7 @@ llm_config = {
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant. Be concise and clear.",
|
||||
llm=llm_config,
|
||||
verbose=True,
|
||||
output="verbose",
|
||||
self_reflect=True,
|
||||
max_reflect=2,
|
||||
min_reflect=1
|
||||
|
||||
@@ -7,8 +7,7 @@ def validate_content(data):
|
||||
|
||||
agent = Agent(
|
||||
instructions="You are a writer",
|
||||
guardrail=validate_content,
|
||||
max_guardrail_retries=1
|
||||
guardrails=validate_content
|
||||
)
|
||||
|
||||
agent.start("Write a welcome message with 5 words")
|
||||
@@ -32,7 +32,7 @@ agent = Agent(
|
||||
instructions="You are a helpful Assistant specialized in scientific explanations. "
|
||||
"Provide clear, accurate, and engaging responses.",
|
||||
llm=llm_config, # Pass the detailed configuration
|
||||
verbose=True, # Enable detailed output
|
||||
output="verbose", # Enable detailed output
|
||||
markdown=True, # Format responses in markdown
|
||||
self_reflect=True, # Enable self-reflection
|
||||
max_reflect=3, # Maximum reflection iterations
|
||||
|
||||
@@ -55,10 +55,8 @@ def main():
|
||||
retrieve information from a comprehensive MongoDB knowledge base. You excel
|
||||
at finding relevant information, synthesizing knowledge from multiple sources,
|
||||
and providing accurate, context-aware responses.""",
|
||||
knowledge_config=mongodb_knowledge_config,
|
||||
knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
|
||||
memory=True,
|
||||
verbose=True,
|
||||
llm="gpt-5-nano"
|
||||
)
|
||||
|
||||
@@ -72,7 +70,6 @@ def main():
|
||||
knowledge base. You ensure information is accurate, well-structured,
|
||||
and properly categorized.""",
|
||||
memory=True,
|
||||
verbose=True,
|
||||
llm="gpt-5-nano"
|
||||
)
|
||||
|
||||
@@ -128,7 +125,7 @@ def main():
|
||||
agents=[research_agent, knowledge_agent],
|
||||
tasks=knowledge_tasks,
|
||||
memory=True,
|
||||
verbose=True
|
||||
output="verbose"
|
||||
)
|
||||
|
||||
# Execute the knowledge management pipeline
|
||||
|
||||
@@ -0,0 +1,7 @@
|
||||
from praisonaiagents import Agent, Agents
|
||||
|
||||
research_agent = Agent(instructions="Research about AI")
|
||||
summarise_agent = Agent(instructions="Summarise research agent's findings")
|
||||
|
||||
agents = Agents(agents=[research_agent, summarise_agent])
|
||||
response = agents.start()
|
||||
@@ -14,8 +14,7 @@ task2 = Task(description="Write a short article about meditation benefits", agen
|
||||
agents = Agents(
|
||||
agents=[researcher, writer],
|
||||
tasks=[task1, task2],
|
||||
planning=True,
|
||||
auto_approve_plan=True # Auto-approve the generated plan
|
||||
planning=True
|
||||
)
|
||||
|
||||
agents.start()
|
||||
@@ -25,6 +25,14 @@ from .tools.registry import get_registry, register_tool, get_tool, ToolRegistry
|
||||
from .db import db
|
||||
from .obs import obs
|
||||
|
||||
# Sub-packages for organized imports (pa.config, pa.tools, etc.)
|
||||
# These enable: import praisonaiagents as pa; pa.config.MemoryConfig
|
||||
from . import config
|
||||
from . import tools
|
||||
from . import memory
|
||||
from . import workflows
|
||||
# Note: knowledge and mcp are lazy-loaded via __getattr__ due to heavy deps
|
||||
|
||||
# Workflows - lightweight module
|
||||
from .workflows import (
|
||||
Workflow, WorkflowStep, WorkflowContext, StepResult,
|
||||
@@ -442,6 +450,51 @@ def __getattr__(name):
|
||||
_lazy_cache[name] = ContextManager
|
||||
return ContextManager
|
||||
|
||||
# =========================================================================
|
||||
# Sub-package fallback: delegate to sub-packages for additional symbols
|
||||
# This enables: from praisonaiagents import duckduckgo, FileMemory, etc.
|
||||
# =========================================================================
|
||||
|
||||
# Try tools sub-package (110+ tool functions)
|
||||
try:
|
||||
from . import tools as _tools_module
|
||||
if hasattr(_tools_module, name):
|
||||
result = getattr(_tools_module, name)
|
||||
_lazy_cache[name] = result
|
||||
return result
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
|
||||
# Try memory sub-package (FileMemory, RulesManager, etc.)
|
||||
try:
|
||||
from . import memory as _memory_module
|
||||
if hasattr(_memory_module, name):
|
||||
result = getattr(_memory_module, name)
|
||||
_lazy_cache[name] = result
|
||||
return result
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
|
||||
# Try config sub-package (already handled above, but catch any stragglers)
|
||||
try:
|
||||
from . import config as _config_module
|
||||
if hasattr(_config_module, name):
|
||||
result = getattr(_config_module, name)
|
||||
_lazy_cache[name] = result
|
||||
return result
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
|
||||
# Try workflows sub-package (Pipeline, Route, etc. - already imported, catch aliases)
|
||||
try:
|
||||
from . import workflows as _workflows_module
|
||||
if hasattr(_workflows_module, name):
|
||||
result = getattr(_workflows_module, name)
|
||||
_lazy_cache[name] = result
|
||||
return result
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
|
||||
|
||||
@@ -476,174 +529,117 @@ def _init_telemetry():
|
||||
_init_telemetry()
|
||||
|
||||
|
||||
def warmup(include_litellm: bool = False, include_openai: bool = True) -> dict:
|
||||
"""
|
||||
Pre-import heavy dependencies to reduce first-call latency.
|
||||
|
||||
NOTE: For default OpenAI usage (llm="gpt-4o-mini"), warmup is NOT needed.
|
||||
The default path uses the native OpenAI SDK which is fast (~100ms import).
|
||||
|
||||
Warmup is only beneficial when using LiteLLM backend, which is triggered by:
|
||||
- Using "/" in model name (e.g., llm="openai/gpt-4o-mini")
|
||||
- Passing a dict config (e.g., llm={"model": "gpt-4o-mini"})
|
||||
- Using base_url parameter
|
||||
|
||||
Args:
|
||||
include_litellm: Pre-import LiteLLM (~2-3s). Only needed for multi-provider support.
|
||||
include_openai: Pre-import OpenAI SDK (~100ms). Default path, usually fast.
|
||||
|
||||
Returns:
|
||||
dict: Timing information for each component warmed up
|
||||
|
||||
Example:
|
||||
# For LiteLLM multi-provider usage:
|
||||
from praisonaiagents import warmup
|
||||
warmup(include_litellm=True) # Pre-load LiteLLM
|
||||
|
||||
agent = Agent(llm="anthropic/claude-3-sonnet") # Now faster
|
||||
|
||||
# For default OpenAI usage, no warmup needed:
|
||||
agent = Agent(llm="gpt-4o-mini") # Already fast!
|
||||
"""
|
||||
import time
|
||||
timings = {}
|
||||
|
||||
if include_openai:
|
||||
start = time.perf_counter()
|
||||
try:
|
||||
import openai
|
||||
timings['openai'] = (time.perf_counter() - start) * 1000
|
||||
except ImportError:
|
||||
timings['openai'] = -1 # Not available
|
||||
|
||||
if include_litellm:
|
||||
start = time.perf_counter()
|
||||
try:
|
||||
import litellm
|
||||
# Also configure litellm to avoid first-call overhead
|
||||
litellm.telemetry = False
|
||||
litellm.set_verbose = False
|
||||
litellm.drop_params = True
|
||||
litellm.modify_params = True
|
||||
timings['litellm'] = (time.perf_counter() - start) * 1000
|
||||
except ImportError:
|
||||
timings['litellm'] = -1 # Not available
|
||||
|
||||
return timings
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# PUBLIC API: __all__ (controls IDE autocomplete and `from X import *`)
|
||||
# ============================================================================
|
||||
# DESIGN: Keep __all__ minimal for clean IDE experience.
|
||||
# All 186+ symbols are still accessible via __getattr__ for backwards compat.
|
||||
# Organized imports available via sub-packages: config, tools, memory, workflows
|
||||
# ============================================================================
|
||||
|
||||
__all__ = [
|
||||
# Core classes (lazy loaded)
|
||||
# Core classes - the essentials
|
||||
'Agent',
|
||||
'ImageAgent',
|
||||
'ContextAgent',
|
||||
'create_context_agent',
|
||||
'PraisonAIAgents',
|
||||
'Agents',
|
||||
'Tools',
|
||||
'Task',
|
||||
# Plugin system
|
||||
'BaseTool',
|
||||
'ToolResult',
|
||||
'ToolValidationError',
|
||||
'validate_tool',
|
||||
|
||||
# Tool essentials
|
||||
'tool',
|
||||
'FunctionTool',
|
||||
'ToolRegistry',
|
||||
'get_registry',
|
||||
'register_tool',
|
||||
'get_tool',
|
||||
'TaskOutput',
|
||||
'ReflectionOutput',
|
||||
'AutoAgents',
|
||||
'AutoRagAgent',
|
||||
'AutoRagConfig',
|
||||
'RagRetrievalPolicy',
|
||||
'Session',
|
||||
'Memory',
|
||||
'db',
|
||||
'obs',
|
||||
'display_interaction',
|
||||
'display_self_reflection',
|
||||
'display_instruction',
|
||||
'display_tool_call',
|
||||
'display_error',
|
||||
'display_generating',
|
||||
'clean_triple_backticks',
|
||||
'error_logs',
|
||||
'register_display_callback',
|
||||
'sync_display_callbacks',
|
||||
'async_display_callbacks',
|
||||
'Knowledge',
|
||||
'Chunking',
|
||||
'GuardrailResult',
|
||||
'LLMGuardrail',
|
||||
'Handoff',
|
||||
'handoff',
|
||||
'handoff_filters',
|
||||
'RECOMMENDED_PROMPT_PREFIX',
|
||||
'prompt_with_handoff_instructions',
|
||||
'HandoffConfig',
|
||||
'HandoffResult',
|
||||
'HandoffInputData',
|
||||
'ContextPolicy',
|
||||
'HandoffError',
|
||||
'HandoffCycleError',
|
||||
'HandoffDepthError',
|
||||
'HandoffTimeoutError',
|
||||
'get_telemetry',
|
||||
'enable_telemetry',
|
||||
'disable_telemetry',
|
||||
'enable_performance_mode',
|
||||
'disable_performance_mode',
|
||||
'cleanup_telemetry_resources',
|
||||
'MinimalTelemetry',
|
||||
'TelemetryCollector',
|
||||
'DeepResearchAgent',
|
||||
'DeepResearchResponse',
|
||||
'Citation',
|
||||
'ReasoningStep',
|
||||
'WebSearchCall',
|
||||
'CodeExecutionStep',
|
||||
'MCPCall',
|
||||
'FileSearchCall',
|
||||
'Provider',
|
||||
'QueryRewriterAgent',
|
||||
'RewriteStrategy',
|
||||
'RewriteResult',
|
||||
'PromptExpanderAgent',
|
||||
'ExpandStrategy',
|
||||
'ExpandResult',
|
||||
# Workflows
|
||||
'Workflow',
|
||||
'WorkflowStep',
|
||||
'WorkflowContext',
|
||||
'StepResult',
|
||||
'Route',
|
||||
'Parallel',
|
||||
'Loop',
|
||||
'Repeat',
|
||||
'route',
|
||||
'parallel',
|
||||
'loop',
|
||||
'repeat',
|
||||
'Pipeline',
|
||||
# MCP
|
||||
'MCP',
|
||||
# Flow display
|
||||
'FlowDisplay',
|
||||
'track_workflow',
|
||||
# FastContext
|
||||
'FastContext',
|
||||
'FastContextResult',
|
||||
'FileMatch',
|
||||
'LineRange',
|
||||
# Agent Skills
|
||||
'SkillManager',
|
||||
'SkillProperties',
|
||||
'SkillMetadata',
|
||||
'SkillLoader',
|
||||
# Planning
|
||||
'Plan',
|
||||
'PlanStep',
|
||||
'TodoList',
|
||||
'TodoItem',
|
||||
'PlanStorage',
|
||||
'PlanningAgent',
|
||||
'ApprovalCallback',
|
||||
'READ_ONLY_TOOLS',
|
||||
'RESTRICTED_TOOLS',
|
||||
# UI
|
||||
'AGUI',
|
||||
'A2A',
|
||||
# RAG
|
||||
'RAG',
|
||||
'RAGConfig',
|
||||
'RAGResult',
|
||||
'RAGCitation',
|
||||
# Feature Configs (agent-centric API)
|
||||
'MemoryConfig',
|
||||
'KnowledgeConfig',
|
||||
'PlanningConfig',
|
||||
'ReflectionConfig',
|
||||
'GuardrailConfig',
|
||||
'WebConfig',
|
||||
'OutputConfig',
|
||||
'ExecutionConfig',
|
||||
'TemplateConfig',
|
||||
'CachingConfig',
|
||||
'HooksConfig',
|
||||
'SkillsConfig',
|
||||
'AutonomyConfig',
|
||||
'MemoryBackend',
|
||||
'ChunkingStrategy',
|
||||
'GuardrailAction',
|
||||
'WebSearchProvider',
|
||||
'OutputPreset',
|
||||
'ExecutionPreset',
|
||||
'AutonomyLevel',
|
||||
# Multi-Agent Feature Configs
|
||||
'MultiAgentHooksConfig',
|
||||
'MultiAgentOutputConfig',
|
||||
'MultiAgentExecutionConfig',
|
||||
'MultiAgentPlanningConfig',
|
||||
'MultiAgentMemoryConfig',
|
||||
# Context Management
|
||||
'ManagerConfig',
|
||||
'ContextManager',
|
||||
# Parameter Resolution (agent-centric API)
|
||||
'resolve',
|
||||
'ArrayMode',
|
||||
'resolve_guardrails',
|
||||
'resolve_guardrail_policies',
|
||||
# Presets (agent-centric API)
|
||||
'GUARDRAIL_PRESETS',
|
||||
'KNOWLEDGE_PRESETS',
|
||||
# Parse Utilities
|
||||
'is_policy_string',
|
||||
'parse_policy_string',
|
||||
'Tools',
|
||||
|
||||
# Sub-packages for organized imports
|
||||
# Usage: import praisonaiagents as pa; pa.config.MemoryConfig
|
||||
'config',
|
||||
'tools',
|
||||
'memory',
|
||||
'workflows',
|
||||
]
|
||||
|
||||
|
||||
def __dir__():
|
||||
"""
|
||||
Return clean list for dir() - matches __all__ plus standard attributes.
|
||||
|
||||
This keeps IDE autocomplete clean while preserving full backwards
|
||||
compatibility via __getattr__ for all 186+ legacy exports.
|
||||
"""
|
||||
return list(__all__) + [
|
||||
# Standard module attributes
|
||||
'__name__', '__doc__', '__file__', '__path__', '__package__',
|
||||
'__loader__', '__spec__', '__cached__', '__builtins__',
|
||||
]
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# BACKWARDS COMPATIBILITY: Legacy __all__ items (for reference)
|
||||
# ============================================================================
|
||||
# All items below are still importable via __getattr__ but NOT in autocomplete:
|
||||
# - ImageAgent, ContextAgent, create_context_agent, PraisonAIAgents
|
||||
# - BaseTool, ToolResult, ToolValidationError, validate_tool, FunctionTool
|
||||
# - ToolRegistry, get_registry, register_tool, get_tool
|
||||
# - TaskOutput, ReflectionOutput, AutoAgents, AutoRagAgent, AutoRagConfig
|
||||
# - Session, Memory, db, obs, Knowledge, Chunking
|
||||
# - GuardrailResult, LLMGuardrail, Handoff, handoff, handoff_filters
|
||||
# - MemoryConfig, KnowledgeConfig, PlanningConfig, OutputConfig, etc.
|
||||
# - Workflow, WorkflowStep, Route, Parallel, Loop, Repeat, Pipeline, etc.
|
||||
# - MCP, FlowDisplay, track_workflow, FastContext, etc.
|
||||
# - Plan, PlanStep, TodoList, PlanningAgent, ApprovalCallback, etc.
|
||||
# - RAG, RAGConfig, RAGResult, AGUI, A2A, etc.
|
||||
# - All telemetry, display, and utility functions
|
||||
# ============================================================================
|
||||
@@ -44,7 +44,7 @@ _shared_apps = {} # Dict of port -> FastAPI app
|
||||
if TYPE_CHECKING:
|
||||
from ..task.task import Task
|
||||
from ..main import TaskOutput
|
||||
from ..handoff import Handoff
|
||||
from .handoff import Handoff, HandoffConfig, HandoffResult
|
||||
from ..rag.models import RAGResult, ContextPack
|
||||
|
||||
class Agent:
|
||||
@@ -254,10 +254,11 @@ class Agent:
|
||||
instructions: Optional[str] = None,
|
||||
# LLM configuration
|
||||
llm: Optional[Union[str, Any]] = None,
|
||||
function_calling_llm: Optional[Any] = None,
|
||||
llm_config: Optional[Dict[str, Any]] = None,
|
||||
base_url: Optional[str] = None,
|
||||
api_key: Optional[str] = None,
|
||||
model: Optional[Union[str, Any]] = None, # Alias for llm=
|
||||
function_calling_llm: Optional[Any] = None, # DEPRECATED: use llm= with LLMConfig
|
||||
llm_config: Optional[Dict[str, Any]] = None, # DEPRECATED: use llm= with LLMConfig
|
||||
base_url: Optional[str] = None, # Kept separate (connection/auth)
|
||||
api_key: Optional[str] = None, # Kept separate (connection/auth)
|
||||
# Tools
|
||||
tools: Optional[List[Any]] = None,
|
||||
allow_delegation: bool = False,
|
||||
@@ -290,96 +291,87 @@ class Agent:
|
||||
"""Initialize an Agent instance.
|
||||
|
||||
Args:
|
||||
name (Optional[str], optional): Name of the agent used for identification and logging.
|
||||
If None, defaults to "Agent". Defaults to None.
|
||||
role (Optional[str], optional): Role or job title that defines the agent's expertise
|
||||
and behavior patterns. Examples: "Data Analyst", "Content Writer". Defaults to None.
|
||||
goal (Optional[str], optional): Primary objective or goal the agent aims to achieve.
|
||||
Defines the agent's purpose and success criteria. Defaults to None.
|
||||
backstory (Optional[str], optional): Background story or context that shapes the agent's
|
||||
personality and decision-making approach. Defaults to None.
|
||||
instructions (Optional[str], optional): Direct instructions that override role, goal,
|
||||
and backstory when provided. Used for simple, task-specific agents. Defaults to None.
|
||||
llm (Optional[Union[str, Any]], optional): Language model configuration. Can be a model
|
||||
name string (e.g., "gpt-5-nano", "anthropic/claude-3-sonnet") or a configured LLM object.
|
||||
Defaults to environment variable OPENAI_MODEL_NAME or "gpt-5-nano".
|
||||
tools (Optional[List[Any]], optional): List of tools, functions, or capabilities
|
||||
available to the agent for task execution. Can include callables, tool objects,
|
||||
or MCP instances. Defaults to None.
|
||||
function_calling_llm (Optional[Any], optional): Dedicated language model for function
|
||||
calling operations. If None, uses the main llm parameter. Defaults to None.
|
||||
max_iter (int, optional): Maximum number of iterations the agent can perform during
|
||||
task execution to prevent infinite loops. Defaults to 20.
|
||||
max_rpm (Optional[int], optional): Maximum requests per minute to rate limit API calls
|
||||
and prevent quota exhaustion. If None, no rate limiting is applied. Defaults to None.
|
||||
max_execution_time (Optional[int], optional): Maximum execution time in seconds for
|
||||
agent operations before timeout. If None, no time limit is enforced. Defaults to None.
|
||||
memory (Optional[Any], optional): Memory system for storing and retrieving information
|
||||
across conversations. Requires memory dependencies to be installed. Defaults to None.
|
||||
verbose (bool, optional): Enable detailed logging and status updates during agent
|
||||
execution for debugging and monitoring. Defaults to True.
|
||||
allow_delegation (bool, optional): Allow the agent to delegate tasks to other agents
|
||||
or sub-processes when appropriate. Defaults to False.
|
||||
step_callback (Optional[Any], optional): Callback function called after each step
|
||||
of agent execution for custom monitoring or intervention. Defaults to None.
|
||||
cache (bool, optional): Enable caching of responses and computations to improve
|
||||
performance and reduce API costs. Defaults to True.
|
||||
system_template (Optional[str], optional): Custom template for system prompts that
|
||||
overrides the default system prompt generation. Defaults to None.
|
||||
prompt_template (Optional[str], optional): Template for formatting user prompts
|
||||
before sending to the language model. Defaults to None.
|
||||
response_template (Optional[str], optional): Template for formatting agent responses
|
||||
before returning to the user. Defaults to None.
|
||||
allow_code_execution (Optional[bool], optional): Enable the agent to execute code
|
||||
snippets during task completion. Use with caution for security. Defaults to False.
|
||||
max_retry_limit (int, optional): Maximum number of retry attempts for failed operations
|
||||
before giving up. Helps handle transient errors. Defaults to 2.
|
||||
code_execution_mode (Literal["safe", "unsafe"], optional): Safety mode for code execution.
|
||||
"safe" restricts dangerous operations, "unsafe" allows full code execution. Defaults to "safe".
|
||||
embedder_config (Optional[Dict[str, Any]], optional): Configuration dictionary for
|
||||
text embedding models used in knowledge retrieval and similarity search. Defaults to None.
|
||||
knowledge (Optional[List[str]], optional): List of knowledge sources (file paths, URLs,
|
||||
or text content) to be processed and made available to the agent. Defaults to None.
|
||||
knowledge_config (Optional[Dict[str, Any]], optional): Configuration for knowledge
|
||||
processing and retrieval system including chunking and indexing parameters. Defaults to None.
|
||||
use_system_prompt (Optional[bool], optional): Whether to include system prompts in
|
||||
conversations to establish agent behavior and context. Defaults to True.
|
||||
markdown (bool, optional): Enable markdown formatting in agent responses for better
|
||||
readability and structure. Defaults to True.
|
||||
stream (bool, optional): Enable streaming responses from the language model for real-time
|
||||
output when using Agent.start() method. Defaults to False for backward compatibility.
|
||||
metrics (bool, optional): Enable automatic token usage tracking and display summary
|
||||
when tasks complete. Simplifies token monitoring for cost optimization. Defaults to False.
|
||||
self_reflect (bool, optional): Enable self-reflection capabilities where the agent
|
||||
evaluates and improves its own responses. Defaults to False.
|
||||
max_reflect (int, optional): Maximum number of self-reflection iterations to prevent
|
||||
excessive reflection loops. Defaults to 3.
|
||||
min_reflect (int, optional): Minimum number of self-reflection iterations required
|
||||
before accepting a response as satisfactory. Defaults to 1.
|
||||
reflect_llm (Optional[str], optional): Dedicated language model for self-reflection
|
||||
operations. If None, uses the main llm parameter. Defaults to None.
|
||||
reflect_prompt (Optional[str], optional): Custom prompt template for self-reflection
|
||||
that guides the agent's self-evaluation process. Defaults to None.
|
||||
user_id (Optional[str], optional): Unique identifier for the user or session to
|
||||
enable personalized responses and memory isolation. Defaults to "praison".
|
||||
reasoning_steps (bool, optional): Enable step-by-step reasoning output to show the
|
||||
agent's thought process during problem solving. Defaults to False.
|
||||
guardrail (Optional[Union[Callable[['TaskOutput'], Tuple[bool, Any]], str]], optional):
|
||||
Safety mechanism to validate agent outputs. Can be a validation function or
|
||||
description string for LLM-based validation. Defaults to None.
|
||||
max_guardrail_retries (int, optional): Maximum number of retry attempts when guardrail
|
||||
validation fails before giving up. Defaults to 3.
|
||||
handoffs (Optional[List[Union['Agent', 'Handoff']]], optional): List of agents or
|
||||
handoff configurations that this agent can delegate tasks to. Enables agent-to-agent
|
||||
collaboration and task specialization. Defaults to None.
|
||||
base_url (Optional[str], optional): Base URL for custom LLM endpoints (e.g., Ollama).
|
||||
If provided, automatically creates a custom LLM instance. Defaults to None.
|
||||
api_key (Optional[str], optional): API key for LLM provider. If not provided,
|
||||
falls back to environment variables. Defaults to None.
|
||||
name: Agent name for identification and logging. Defaults to "Agent".
|
||||
role: Role/job title defining expertise (e.g., "Data Analyst").
|
||||
goal: Primary objective the agent aims to achieve.
|
||||
backstory: Background context shaping personality and decisions.
|
||||
instructions: Direct instructions (overrides role/goal/backstory). Recommended for simple agents.
|
||||
llm: Model name string ("gpt-4o", "anthropic/claude-3-sonnet") or LLM object.
|
||||
Defaults to OPENAI_MODEL_NAME env var or "gpt-4o-mini".
|
||||
model: Alias for llm parameter.
|
||||
function_calling_llm: Dedicated LLM for function calling. Deprecated: use llm=.
|
||||
llm_config: LLM configuration dict. Deprecated: use llm=.
|
||||
base_url: Custom LLM endpoint URL (e.g., for Ollama). Kept separate for auth.
|
||||
api_key: API key for LLM provider. Kept separate for auth.
|
||||
tools: List of tools, functions, callables, or MCP instances.
|
||||
allow_delegation: Allow task delegation to other agents. Defaults to False.
|
||||
allow_code_execution: Enable code execution during tasks. Defaults to False.
|
||||
code_execution_mode: "safe" (restricted) or "unsafe" (full access). Defaults to "safe".
|
||||
handoffs: List of Agent or Handoff objects for agent-to-agent collaboration.
|
||||
auto_save: Session name for automatic session saving.
|
||||
rate_limiter: Rate limiter instance for API call throttling.
|
||||
memory: Memory system configuration. Accepts:
|
||||
- bool: True enables defaults, False disables
|
||||
- MemoryConfig: Custom configuration
|
||||
- MemoryManager: Pre-configured instance
|
||||
knowledge: Knowledge sources. Accepts:
|
||||
- bool: True enables defaults
|
||||
- List[str]: File paths, URLs, or text content
|
||||
- KnowledgeConfig: Custom configuration
|
||||
planning: Planning mode. Accepts:
|
||||
- bool: True enables with defaults
|
||||
- PlanningConfig: Custom configuration
|
||||
reflection: Self-reflection. Accepts:
|
||||
- bool: True enables with defaults
|
||||
- ReflectionConfig: Custom configuration
|
||||
guardrails: Output validation. Accepts:
|
||||
- bool: True enables with defaults
|
||||
- Callable: Validation function
|
||||
- GuardrailConfig: Custom configuration
|
||||
web: Web search/fetch. Accepts:
|
||||
- bool: True enables with defaults
|
||||
- WebConfig: Custom configuration
|
||||
context: Context management. Accepts:
|
||||
- bool: True enables with defaults
|
||||
- ManagerConfig: Custom configuration
|
||||
autonomy: Autonomy settings. Accepts:
|
||||
- bool: True enables with defaults
|
||||
- Dict: Configuration dict
|
||||
- AutonomyConfig: Custom configuration
|
||||
verification_hooks: List of VerificationHook instances for output verification.
|
||||
output: Output configuration. Accepts:
|
||||
- str: Preset name ("silent", "actions", "verbose", "json", "stream")
|
||||
- OutputConfig: Custom configuration
|
||||
Controls: verbose, markdown, stream, metrics, reasoning_steps
|
||||
execution: Execution configuration. Accepts:
|
||||
- str: Preset name ("fast", "balanced", "thorough")
|
||||
- ExecutionConfig: Custom configuration
|
||||
Controls: max_iter, max_rpm, max_execution_time, max_retry_limit
|
||||
templates: Template configuration (TemplateConfig).
|
||||
Controls: system_template, prompt_template, response_template
|
||||
caching: Caching configuration. Accepts:
|
||||
- bool: True enables with defaults
|
||||
- CachingConfig: Custom configuration
|
||||
hooks: Event hooks. Accepts:
|
||||
- List: List of hook callables
|
||||
- HooksConfig: Custom configuration
|
||||
skills: Agent skills. Accepts:
|
||||
- List[str]: Skill directory paths
|
||||
- SkillsConfig: Custom configuration
|
||||
|
||||
Raises:
|
||||
ValueError: If all of name, role, goal, backstory, and instructions are None.
|
||||
ImportError: If memory or LLM features are requested but dependencies are not installed.
|
||||
|
||||
Note:
|
||||
Many legacy parameters have been consolidated into config objects:
|
||||
- verbose, markdown, stream, metrics, reasoning_steps → output=
|
||||
- max_iter, max_rpm, max_execution_time, max_retry_limit → execution=
|
||||
- self_reflect, max_reflect, min_reflect, reflect_llm → reflection=
|
||||
- guardrail, max_guardrail_retries → guardrails=
|
||||
- system_template, prompt_template, response_template → templates=
|
||||
- cache, prompt_caching → caching=
|
||||
- web_search, web_fetch → web=
|
||||
"""
|
||||
# Add check at start if memory is requested
|
||||
if memory is not None:
|
||||
@@ -446,14 +438,27 @@ class Agent:
|
||||
# ─────────────────────────────────────────────────────────────────────
|
||||
# Resolve OUTPUT param using unified resolver
|
||||
# Supports: None, str preset, list [preset, overrides], OutputConfig
|
||||
# DEFAULT: "silent" mode (zero overhead, fastest performance)
|
||||
# Can be overridden by PRAISONAI_OUTPUT env var
|
||||
# ─────────────────────────────────────────────────────────────────────
|
||||
from ..config.presets import DEFAULT_OUTPUT_MODE
|
||||
|
||||
# Check for env var override
|
||||
env_output = os.environ.get('PRAISONAI_OUTPUT', '').lower()
|
||||
if env_output and env_output in OUTPUT_PRESETS and output is None:
|
||||
output = env_output
|
||||
|
||||
# Use default output mode if not specified
|
||||
if output is None:
|
||||
output = DEFAULT_OUTPUT_MODE
|
||||
|
||||
_output_config = resolve(
|
||||
value=output,
|
||||
param_name="output",
|
||||
config_class=OutputConfig,
|
||||
presets=OUTPUT_PRESETS,
|
||||
array_mode=ArrayMode.PRESET_OVERRIDE,
|
||||
default=OutputConfig(),
|
||||
default=OutputConfig(), # OutputConfig defaults to silent mode (zero overhead)
|
||||
)
|
||||
if _output_config:
|
||||
verbose = _output_config.verbose
|
||||
@@ -462,8 +467,24 @@ class Agent:
|
||||
metrics = _output_config.metrics
|
||||
reasoning_steps = _output_config.reasoning_steps
|
||||
output_style = getattr(_output_config, 'style', None)
|
||||
actions_trace = getattr(_output_config, 'actions_trace', False) # Default False (silent)
|
||||
json_output = getattr(_output_config, 'json_output', False)
|
||||
else:
|
||||
verbose, markdown, stream, metrics, reasoning_steps = True, True, False, False, False
|
||||
# Fallback defaults match silent mode (zero overhead)
|
||||
verbose, markdown, stream, metrics, reasoning_steps = False, False, False, False, False
|
||||
actions_trace = False # No callbacks by default
|
||||
json_output = False
|
||||
|
||||
# Enable actions output mode if configured
|
||||
# This registers callbacks to capture tool calls and final output
|
||||
if actions_trace:
|
||||
try:
|
||||
from ..output.actions import enable_actions_mode, is_actions_mode_enabled
|
||||
if not is_actions_mode_enabled():
|
||||
output_format = "jsonl" if json_output else "text"
|
||||
enable_actions_mode(redact=True, use_color=True, format=output_format)
|
||||
except ImportError:
|
||||
pass # Actions module not available
|
||||
|
||||
# ─────────────────────────────────────────────────────────────────────
|
||||
# Resolve EXECUTION param using unified resolver
|
||||
@@ -754,7 +775,21 @@ class Agent:
|
||||
|
||||
# If instructions are provided, use them to set role, goal, and backstory
|
||||
if instructions:
|
||||
self.name = name or "Agent"
|
||||
# Auto-generate descriptive name from instructions if not provided
|
||||
if name:
|
||||
self.name = name
|
||||
else:
|
||||
# Generate name from first 2 meaningful words of instructions
|
||||
# "Research about AI" → "Research Agent"
|
||||
# "Summarise research agent's findings" → "Summarise Agent"
|
||||
words = instructions.split()
|
||||
# Filter out common stop words
|
||||
stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'about'}
|
||||
meaningful = [w for w in words if w.lower() not in stop_words][:2]
|
||||
if meaningful:
|
||||
self.name = " ".join(meaningful).title() + " Agent"
|
||||
else:
|
||||
self.name = words[0].title() + " Agent" if words else "Agent"
|
||||
self.role = role or "Assistant"
|
||||
self.goal = goal or instructions
|
||||
self.backstory = backstory or instructions
|
||||
@@ -783,13 +818,30 @@ class Agent:
|
||||
self._llm_config = llm_config or {}
|
||||
self._llm_configurable = self._llm_config.get('configurable', False)
|
||||
|
||||
# ============================================================
|
||||
# LLM CONSOLIDATION: Handle model= alias and deprecation warnings
|
||||
# Precedence: llm= > model= > default
|
||||
# ============================================================
|
||||
import warnings
|
||||
|
||||
# Handle model= alias for llm= (NO warnings - both are valid)
|
||||
if llm is None and model is not None:
|
||||
llm = model # model= is an alias for llm=
|
||||
|
||||
# Legacy params (llm_config, function_calling_llm) are accepted silently
|
||||
# for backward compatibility - NO deprecation warnings per policy
|
||||
|
||||
# Store rate limiter (optional, zero overhead when None)
|
||||
self._rate_limiter = rate_limiter
|
||||
|
||||
# Store OpenAI client parameters for lazy initialization
|
||||
# Store OpenAI client parameters for lazy initialization (kept separate)
|
||||
self._openai_api_key = api_key
|
||||
self._openai_base_url = base_url
|
||||
self.__openai_client = None
|
||||
|
||||
# Expose base_url and api_key as properties for tests
|
||||
self.base_url = base_url
|
||||
self.api_key = api_key
|
||||
|
||||
# If base_url is provided, always create a custom LLM instance
|
||||
if base_url:
|
||||
@@ -806,7 +858,7 @@ class Agent:
|
||||
self.llm_instance = LLM(**llm_config)
|
||||
else:
|
||||
# Create LLM with model string and base_url
|
||||
model_name = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
|
||||
model_name = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
|
||||
self.llm_instance = LLM(
|
||||
model=model_name,
|
||||
base_url=base_url,
|
||||
@@ -869,7 +921,7 @@ class Agent:
|
||||
) from e
|
||||
# Otherwise, fall back to OpenAI environment/name
|
||||
else:
|
||||
self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
|
||||
self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
|
||||
# Handle tools parameter - ensure it's always a list
|
||||
if callable(tools):
|
||||
# If a single function/callable is passed, wrap it in a list
|
||||
@@ -915,7 +967,7 @@ class Agent:
|
||||
self.min_reflect = min_reflect
|
||||
self.reflect_prompt = reflect_prompt
|
||||
# Use the same model selection logic for reflect_llm
|
||||
self.reflect_llm = reflect_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
|
||||
self.reflect_llm = reflect_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
|
||||
self._console = None # Lazy load console when needed
|
||||
|
||||
# Initialize system prompt
|
||||
@@ -957,9 +1009,14 @@ Your Goal: {self.goal}
|
||||
self.tools.append(internet_search)
|
||||
logging.info("Model does not support native web search. Added DuckDuckGo fallback tool.")
|
||||
|
||||
# Log warning if web_fetch is enabled but model doesn't support it
|
||||
# Raise error if web_fetch is explicitly enabled but model doesn't support it
|
||||
# Web fetch is only supported by Anthropic/Claude models
|
||||
if web_fetch and not self._model_supports_web_fetch():
|
||||
logging.warning(f"Model '{self.llm}' does not support native web fetch. Web fetch will be ignored.")
|
||||
raise ValueError(
|
||||
f"web_fetch is only supported on Anthropic/Claude models. "
|
||||
f"Model '{self.llm}' does not support web fetch. "
|
||||
f"Either use a Claude model (e.g., 'anthropic/claude-sonnet-4') or disable fetch with web=WebConfig(fetch=False)."
|
||||
)
|
||||
|
||||
# Initialize guardrail settings
|
||||
self.guardrail = guardrail
|
||||
@@ -1042,6 +1099,16 @@ Your Goal: {self.goal}
|
||||
self._context_param = context # Store raw param for lazy init
|
||||
self._context_manager = None # Lazy initialized on first use
|
||||
self._context_manager_initialized = False
|
||||
|
||||
# Action trace mode - handled via display callbacks, not separate emitter
|
||||
self._actions_trace = actions_trace
|
||||
|
||||
# Telemetry
|
||||
try:
|
||||
from ..telemetry import get_telemetry
|
||||
self._telemetry = get_telemetry()
|
||||
except (ImportError, AttributeError):
|
||||
self._telemetry = None
|
||||
|
||||
@property
|
||||
def auto_memory(self):
|
||||
@@ -1697,7 +1764,7 @@ Your Goal: {self.goal}
|
||||
elif hasattr(self, 'llm') and self.llm:
|
||||
model_name = self.llm
|
||||
else:
|
||||
model_name = "gpt-5-nano"
|
||||
model_name = "gpt-4o-mini"
|
||||
|
||||
return supports_web_search(model_name)
|
||||
|
||||
@@ -1719,7 +1786,7 @@ Your Goal: {self.goal}
|
||||
elif hasattr(self, 'llm') and self.llm:
|
||||
model_name = self.llm
|
||||
else:
|
||||
model_name = "gpt-5-nano"
|
||||
model_name = "gpt-4o-mini"
|
||||
|
||||
return supports_web_fetch(model_name)
|
||||
|
||||
@@ -1963,7 +2030,7 @@ Your Goal: {self.goal}
|
||||
|
||||
Returns:
|
||||
The LLM model/instance being used by this agent.
|
||||
- For standard models: returns the model string (e.g., "gpt-5-nano")
|
||||
- For standard models: returns the model string (e.g., "gpt-4o-mini")
|
||||
- For custom LLM instances: returns the LLM instance object
|
||||
- For provider models: returns the LLM instance object
|
||||
"""
|
||||
@@ -1973,7 +2040,7 @@ Your Goal: {self.goal}
|
||||
return self.llm
|
||||
else:
|
||||
# Default fallback
|
||||
return "gpt-5-nano"
|
||||
return "gpt-4o-mini"
|
||||
|
||||
def _ensure_knowledge_processed(self):
|
||||
"""Ensure knowledge is initialized and processed when first accessed."""
|
||||
@@ -2794,6 +2861,10 @@ Your Goal: {self.goal}"""
|
||||
"""
|
||||
logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
|
||||
|
||||
# Notify callbacks that tool is being called (for dynamic status updates)
|
||||
from ..main import execute_sync_callback
|
||||
execute_sync_callback('tool_call', tool_name=function_name, tool_input=arguments)
|
||||
|
||||
# Set up injection context for tools with Injected parameters
|
||||
from ..tools.injected import AgentState, with_injection_context
|
||||
state = AgentState(
|
||||
@@ -3673,7 +3744,7 @@ Your Goal: {self.goal}"""
|
||||
if self.verbose:
|
||||
logging.debug(f"Agent {self.name} final response: {response_text}")
|
||||
# Return only reasoning content if reasoning_steps is True
|
||||
if reasoning_steps and hasattr(response.choices[0].message, 'reasoning_content'):
|
||||
if reasoning_steps and hasattr(response.choices[0].message, 'reasoning_content') and response.choices[0].message.reasoning_content:
|
||||
# Apply guardrail to reasoning content
|
||||
try:
|
||||
validated_reasoning = self._apply_guardrail_with_retry(response.choices[0].message.reasoning_content, original_prompt, temperature, tools, task_name, task_description, task_id)
|
||||
@@ -3825,6 +3896,10 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
|
||||
|
||||
async def achat(self, prompt: str, temperature=1.0, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None):
|
||||
"""Async version of chat method with self-reflection support."""
|
||||
# Track execution via telemetry
|
||||
if hasattr(self, '_telemetry') and self._telemetry:
|
||||
self._telemetry.track_agent_execution(self.name, success=True)
|
||||
|
||||
# Reset the final display flag for each new conversation
|
||||
self._final_display_shown = False
|
||||
|
||||
@@ -4198,7 +4273,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
|
||||
|
||||
final_response = process_stream_chunks(chunks)
|
||||
# Return only reasoning content if reasoning_steps is True
|
||||
if reasoning_steps and hasattr(final_response.choices[0].message, 'reasoning_content'):
|
||||
if reasoning_steps and hasattr(final_response.choices[0].message, 'reasoning_content') and final_response.choices[0].message.reasoning_content:
|
||||
return final_response.choices[0].message.reasoning_content
|
||||
return final_response.choices[0].message.content if final_response else full_response_text
|
||||
|
||||
@@ -4212,16 +4287,94 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
|
||||
return None
|
||||
|
||||
async def arun(self, prompt: str, **kwargs):
|
||||
"""Async alias for astart() method"""
|
||||
return await self.astart(prompt, **kwargs)
|
||||
"""Async version of run() - silent, non-streaming, returns structured result.
|
||||
|
||||
Production-friendly async execution. Does not stream or display output.
|
||||
|
||||
Args:
|
||||
prompt: The input prompt to process
|
||||
**kwargs: Additional arguments passed to achat()
|
||||
|
||||
Returns:
|
||||
The agent's response as a string
|
||||
"""
|
||||
# Remove stream from kwargs since achat() doesn't accept it
|
||||
kwargs.pop('stream', None)
|
||||
return await self.achat(prompt, **kwargs)
|
||||
|
||||
async def astart(self, prompt: str, **kwargs):
|
||||
"""Async version of start method"""
|
||||
"""Async version of start() - interactive, streaming-aware.
|
||||
|
||||
Beginner-friendly async execution. Streams by default when in TTY.
|
||||
|
||||
Args:
|
||||
prompt: The input prompt to process
|
||||
**kwargs: Additional arguments passed to achat()
|
||||
|
||||
Returns:
|
||||
The agent's response as a string
|
||||
"""
|
||||
import sys
|
||||
|
||||
# Determine streaming behavior (same logic as start())
|
||||
stream_requested = kwargs.get('stream')
|
||||
if stream_requested is None:
|
||||
if getattr(self, 'stream', None) is not None:
|
||||
stream_requested = self.stream
|
||||
else:
|
||||
stream_requested = sys.stdout.isatty()
|
||||
|
||||
kwargs['stream'] = stream_requested
|
||||
return await self.achat(prompt, **kwargs)
|
||||
|
||||
def run(self, prompt: str, **kwargs):
|
||||
"""Alias for start() method"""
|
||||
return self.start(prompt, **kwargs)
|
||||
"""Execute agent silently and return structured result.
|
||||
|
||||
Production-friendly execution. Always uses silent mode with no streaming
|
||||
or verbose display, regardless of TTY status. Use this for programmatic,
|
||||
scripted, or automated usage where you want just the result.
|
||||
|
||||
Args:
|
||||
prompt: The input prompt to process
|
||||
**kwargs: Additional arguments:
|
||||
- stream (bool): Force streaming if True. Default: False
|
||||
- output (str): Output preset override (rarely needed)
|
||||
|
||||
Returns:
|
||||
The agent's response as a string
|
||||
|
||||
Example:
|
||||
```python
|
||||
agent = Agent(instructions="You are helpful")
|
||||
result = agent.run("What is 2+2?") # Silent, returns "4"
|
||||
print(result)
|
||||
```
|
||||
|
||||
Note:
|
||||
Unlike .start() which enables verbose output in TTY for interactive
|
||||
use, .run() is always silent. This makes it suitable for:
|
||||
- Production pipelines
|
||||
- Automated scripts
|
||||
- Background processing
|
||||
- API endpoints
|
||||
"""
|
||||
# Production defaults: no streaming, no display
|
||||
if 'stream' not in kwargs:
|
||||
kwargs['stream'] = False
|
||||
|
||||
# Load history context
|
||||
self._load_history_context()
|
||||
|
||||
# Check if planning mode is enabled
|
||||
if self.planning:
|
||||
result = self._start_with_planning(prompt, **kwargs)
|
||||
else:
|
||||
result = self.chat(prompt, **kwargs)
|
||||
|
||||
# Auto-save session if enabled
|
||||
self._auto_save_session()
|
||||
|
||||
return result
|
||||
|
||||
def _get_planning_agent(self):
|
||||
"""Lazy load PlanningAgent for planning mode."""
|
||||
@@ -4355,26 +4508,290 @@ Write the complete compiled report:"""
|
||||
|
||||
# Chat history is preserved in self.chat_history (no action needed)
|
||||
|
||||
def start(self, prompt: str, **kwargs):
|
||||
"""Start the agent with a prompt. This is a convenience method that wraps chat()."""
|
||||
# Load history from past sessions (now handled via context= param)
|
||||
def start(self, prompt: str = None, **kwargs):
|
||||
"""Start the agent interactively with verbose output.
|
||||
|
||||
Beginner-friendly execution. Defaults to verbose output with streaming
|
||||
when running in a TTY. Use this for interactive/terminal usage where
|
||||
you want to see output in real-time with rich formatting.
|
||||
|
||||
Args:
|
||||
prompt: The input prompt to process. If not provided, uses the
|
||||
agent's instructions as the task (useful when instructions
|
||||
already describe what the agent should do).
|
||||
**kwargs: Additional arguments:
|
||||
- stream (bool | None): Override streaming. None = auto-detect TTY
|
||||
- output (str): Output preset override (e.g., "silent", "verbose")
|
||||
|
||||
Returns:
|
||||
- If streaming: Generator yielding response chunks
|
||||
- If not streaming: The complete response as a string
|
||||
|
||||
Example:
|
||||
```python
|
||||
# Minimal usage - instructions IS the task
|
||||
agent = Agent(instructions="Research AI trends and summarize")
|
||||
result = agent.start() # Uses instructions as task
|
||||
|
||||
# With explicit prompt (overrides/adds to instructions)
|
||||
agent = Agent(instructions="You are a helpful assistant")
|
||||
result = agent.start("What is 2+2?") # Uses prompt as task
|
||||
```
|
||||
|
||||
Note:
|
||||
Unlike .run() which is always silent (production use), .start()
|
||||
enables verbose output by default when in a TTY for beginner-friendly
|
||||
interactive use. Use .run() for programmatic/scripted usage.
|
||||
"""
|
||||
import sys
|
||||
|
||||
# If no prompt provided, use instructions as the task
|
||||
if prompt is None:
|
||||
prompt = self.instructions or "Hello"
|
||||
|
||||
# Load history from past sessions
|
||||
self._load_history_context()
|
||||
|
||||
# Check if planning mode is enabled
|
||||
if self.planning:
|
||||
result = self._start_with_planning(prompt, **kwargs)
|
||||
elif kwargs.get('stream', getattr(self, 'stream', False)):
|
||||
# Return a generator for streaming response
|
||||
result = self._start_stream(prompt, **kwargs)
|
||||
else:
|
||||
# Return regular chat response for backward compatibility
|
||||
kwargs['stream'] = False
|
||||
result = self.chat(prompt, **kwargs)
|
||||
# Determine if we're in an interactive TTY
|
||||
is_tty = sys.stdout.isatty()
|
||||
|
||||
# Determine streaming behavior
|
||||
# Priority: explicit kwarg > agent's stream attribute > TTY detection
|
||||
stream_requested = kwargs.get('stream')
|
||||
if stream_requested is None:
|
||||
# Check agent's stream attribute first
|
||||
if getattr(self, 'stream', None) is not None:
|
||||
stream_requested = self.stream
|
||||
else:
|
||||
# Auto-detect: stream if stdout is a TTY (interactive terminal)
|
||||
stream_requested = is_tty
|
||||
|
||||
# ─────────────────────────────────────────────────────────────────────
|
||||
# Enable verbose output in TTY for beginner-friendly interactive use
|
||||
# Priority: explicit output= kwarg > TTY auto-verbose > agent's default
|
||||
# ─────────────────────────────────────────────────────────────────────
|
||||
original_verbose = self.verbose
|
||||
original_markdown = self.markdown
|
||||
output_override = kwargs.pop('output', None) # Pop to prevent passing to chat()
|
||||
|
||||
try:
|
||||
# If running in TTY and no explicit output override, enable verbose
|
||||
if is_tty and output_override is None:
|
||||
self.verbose = True
|
||||
self.markdown = True
|
||||
elif output_override:
|
||||
# Apply explicit output preset for this call
|
||||
from ..config.presets import OUTPUT_PRESETS
|
||||
if output_override in OUTPUT_PRESETS:
|
||||
preset = OUTPUT_PRESETS[output_override]
|
||||
self.verbose = preset.get('verbose', False)
|
||||
self.markdown = preset.get('markdown', False)
|
||||
|
||||
# Check if planning mode is enabled
|
||||
if self.planning:
|
||||
result = self._start_with_planning(prompt, **kwargs)
|
||||
elif stream_requested:
|
||||
# Return a generator for streaming response
|
||||
kwargs['stream'] = True
|
||||
result = self._start_stream(prompt, **kwargs)
|
||||
else:
|
||||
# Return regular chat response with animated working status
|
||||
kwargs['stream'] = False
|
||||
|
||||
# Show animated status during LLM call if verbose
|
||||
if self.verbose and is_tty:
|
||||
from rich.live import Live
|
||||
from rich.console import Group
|
||||
from rich.status import Status
|
||||
from ..main import PRAISON_COLORS, sync_display_callbacks
|
||||
import threading
|
||||
import time as time_module
|
||||
|
||||
console = Console()
|
||||
start_time = time_module.time()
|
||||
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
# Shared state for dynamic status messages (thread-safe)
|
||||
# Updated by callbacks during tool execution
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
current_status = ["Analyzing query..."]
|
||||
tools_called = []
|
||||
|
||||
# Register a temporary callback to track tool calls
|
||||
def status_tool_callback(**kwargs):
|
||||
tool_name = kwargs.get('tool_name', '')
|
||||
if tool_name:
|
||||
tools_called.append(tool_name)
|
||||
current_status[0] = f"Calling tool: {tool_name}..."
|
||||
|
||||
# Store original callback and register ours
|
||||
original_tool_callback = sync_display_callbacks.get('tool_call')
|
||||
sync_display_callbacks['tool_call'] = status_tool_callback
|
||||
|
||||
# Animation state
|
||||
result_holder = [None]
|
||||
error_holder = [None]
|
||||
|
||||
# Temporarily disable verbose in chat to prevent duplicate output
|
||||
original_verbose_chat = self.verbose
|
||||
|
||||
def run_chat():
|
||||
try:
|
||||
# Suppress verbose during animation - we'll display result ourselves
|
||||
self.verbose = False
|
||||
current_status[0] = "Sending to LLM..."
|
||||
result_holder[0] = self.chat(prompt, **kwargs)
|
||||
current_status[0] = "Finalizing response..."
|
||||
except Exception as e:
|
||||
error_holder[0] = e
|
||||
finally:
|
||||
self.verbose = original_verbose_chat
|
||||
# Restore original callback
|
||||
if original_tool_callback:
|
||||
sync_display_callbacks['tool_call'] = original_tool_callback
|
||||
elif 'tool_call' in sync_display_callbacks:
|
||||
del sync_display_callbacks['tool_call']
|
||||
|
||||
# Start chat in background thread
|
||||
chat_thread = threading.Thread(target=run_chat)
|
||||
chat_thread.start()
|
||||
|
||||
from rich.panel import Panel
|
||||
from rich.text import Text
|
||||
from rich.markdown import Markdown
|
||||
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
# Smart Agent Info: Only show if user provided meaningful info
|
||||
# Skip if using defaults ("Agent"/"Assistant") with single agent
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
has_custom_name = self.name and self.name not in ("Agent", "agent", None, "")
|
||||
has_custom_role = self.role and self.role not in ("Assistant", "AI Assistant", "assistant", None, "")
|
||||
has_tools = bool(self.tools)
|
||||
|
||||
show_agent_info = has_custom_name or has_custom_role or has_tools
|
||||
|
||||
agent_panel = None
|
||||
if show_agent_info:
|
||||
agent_info_parts = []
|
||||
if has_custom_name:
|
||||
agent_info_parts.append(f"[bold {PRAISON_COLORS['task']}]👤 Agent:[/] [{PRAISON_COLORS['agent_text']}]{self.name}[/]")
|
||||
if has_custom_role:
|
||||
agent_info_parts.append(f"[bold {PRAISON_COLORS['metrics']}]Role:[/] [{PRAISON_COLORS['agent_text']}]{self.role}[/]")
|
||||
if has_tools:
|
||||
tools_list = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools][:5]
|
||||
tools_str = ", ".join(f"[italic {PRAISON_COLORS['response']}]{tool}[/]" for tool in tools_list)
|
||||
agent_info_parts.append(f"[bold {PRAISON_COLORS['agent']}]Tools:[/] {tools_str}")
|
||||
|
||||
agent_panel = Panel(
|
||||
"\n".join(agent_info_parts),
|
||||
border_style=PRAISON_COLORS["agent"],
|
||||
title="[bold]Agent Info[/]",
|
||||
title_align="left",
|
||||
padding=(1, 2)
|
||||
)
|
||||
|
||||
# Create task panel
|
||||
task_panel = Panel.fit(
|
||||
Markdown(prompt) if self.markdown else Text(prompt),
|
||||
title="Task",
|
||||
border_style=PRAISON_COLORS["task"]
|
||||
)
|
||||
|
||||
# Show initial panels (agent info if applicable, then task)
|
||||
if agent_panel:
|
||||
console.print(agent_panel)
|
||||
console.print(task_panel)
|
||||
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
# Animate with Rich.Status showing DYNAMIC status from callbacks
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
with console.status(
|
||||
f"[bold yellow]Working...[/] {current_status[0]}",
|
||||
spinner="dots",
|
||||
spinner_style="yellow"
|
||||
) as status:
|
||||
last_status = current_status[0]
|
||||
while chat_thread.is_alive():
|
||||
time_module.sleep(0.15) # More responsive updates
|
||||
# Only update if status changed (reduces flicker)
|
||||
if current_status[0] != last_status:
|
||||
last_status = current_status[0]
|
||||
status.update(f"[bold yellow]Working...[/] {current_status[0]}")
|
||||
|
||||
# Calculate elapsed time
|
||||
elapsed = time_module.time() - start_time
|
||||
|
||||
# Re-raise any error from chat
|
||||
if error_holder[0]:
|
||||
raise error_holder[0]
|
||||
|
||||
result = result_holder[0]
|
||||
|
||||
# Display response panel
|
||||
response_panel = Panel.fit(
|
||||
Markdown(str(result)) if self.markdown else Text(str(result)),
|
||||
title=f"Response ({elapsed:.1f}s)",
|
||||
border_style=PRAISON_COLORS["response"]
|
||||
)
|
||||
console.print(response_panel)
|
||||
|
||||
# Show tool activity summary if tools were called
|
||||
if tools_called:
|
||||
tools_summary = ", ".join(tools_called)
|
||||
console.print(f"[dim]🔧 Tools used: {tools_summary}[/dim]")
|
||||
else:
|
||||
result = self.chat(prompt, **kwargs)
|
||||
|
||||
# Auto-save session if enabled
|
||||
self._auto_save_session()
|
||||
|
||||
return result
|
||||
finally:
|
||||
# Restore original output settings
|
||||
self.verbose = original_verbose
|
||||
self.markdown = original_markdown
|
||||
|
||||
def iter_stream(self, prompt: str, **kwargs):
|
||||
"""Stream agent response as an iterator of chunks.
|
||||
|
||||
App-friendly streaming. Yields response chunks without terminal display.
|
||||
Use this for building custom UIs or processing streams programmatically.
|
||||
|
||||
Args:
|
||||
prompt: The input prompt to process
|
||||
**kwargs: Additional arguments:
|
||||
- display (bool): Show terminal output. Default: False
|
||||
- output (str): Output preset override
|
||||
|
||||
Yields:
|
||||
str: Response chunks as they are generated
|
||||
|
||||
Example:
|
||||
```python
|
||||
agent = Agent(instructions="You are helpful")
|
||||
|
||||
# Process stream programmatically
|
||||
full_response = ""
|
||||
for chunk in agent.iter_stream("Tell me a story"):
|
||||
full_response += chunk
|
||||
# Custom processing here
|
||||
|
||||
# Or collect all at once
|
||||
response = "".join(agent.iter_stream("Hello"))
|
||||
```
|
||||
"""
|
||||
# Load history context
|
||||
self._load_history_context()
|
||||
|
||||
# Force streaming, no display by default (app-friendly)
|
||||
kwargs['stream'] = True
|
||||
|
||||
# Use the internal streaming generator
|
||||
for chunk in self._start_stream(prompt, **kwargs):
|
||||
yield chunk
|
||||
|
||||
# Auto-save session if enabled
|
||||
self._auto_save_session()
|
||||
|
||||
return result
|
||||
|
||||
def _load_history_context(self):
|
||||
"""Load history from past sessions into context.
|
||||
|
||||
@@ -434,8 +434,7 @@ This report contains all agent interactions and outputs from a complete ContextA
|
||||
|
||||
Provide comprehensive analysis that follows the PRD template principles and enables
|
||||
AI assistants to implement features that perfectly match existing codebase patterns.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
prompt = f"""Analyze this gitingest codebase digest following PRD template methodology:
|
||||
@@ -515,8 +514,7 @@ codebase style and architecture following PRD template principles."""
|
||||
role="Expert Manual Codebase Analysis Specialist",
|
||||
goal="Perform comprehensive manual codebase analysis following PRD methodology",
|
||||
instructions="""Analyze the codebase samples following PRD template methodology for complete understanding.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Format comprehensive sample
|
||||
@@ -566,8 +564,7 @@ Analyze following PRD principles to extract patterns, conventions, and architect
|
||||
6. Design pattern implementations
|
||||
7. Code complexity metrics
|
||||
8. API and interface patterns""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Perform AST analysis on Python files
|
||||
@@ -624,8 +621,7 @@ Extract comprehensive patterns that follow PRD template principles for implement
|
||||
|
||||
For each pattern, provide the pattern name, where it's used, and how to replicate it
|
||||
following PRD template principles.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Get representative code samples
|
||||
@@ -674,8 +670,7 @@ patterns and best practices for first-try success."""
|
||||
goal="Analyze testing patterns for comprehensive validation framework design",
|
||||
instructions="""Analyze testing patterns to understand validation approaches and create
|
||||
comprehensive test frameworks following PRD methodology.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Get test files
|
||||
@@ -801,8 +796,7 @@ Extract testing patterns for validation framework creation following PRD princip
|
||||
Confidence level for one-pass implementation
|
||||
|
||||
Generate PRPs following this EXACT structure for first-try implementation success.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
prompt = f"""Generate a comprehensive Product Requirements Prompt (PRP) following the EXACT PRD template structure:
|
||||
@@ -850,8 +844,7 @@ on the first try following PRD template principles."""
|
||||
6. CODE QUALITY: Complexity analysis, maintainability
|
||||
7. DOCUMENTATION VALIDATION: Documentation completeness
|
||||
8. DEPENDENCY VALIDATION: Dependency analysis and security""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Analyze existing validation patterns
|
||||
@@ -897,8 +890,7 @@ following PRD template principles."""
|
||||
instructions="""Compile all available documentation following PRD methodology including:
|
||||
README files, API documentation, setup guides, architecture docs, and any other
|
||||
relevant documentation that provides context for implementation.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Get documentation files
|
||||
@@ -943,8 +935,7 @@ following PRD template principles."""
|
||||
instructions="""Analyze integration points following PRD methodology including:
|
||||
APIs, databases, external services, configuration points, and any other
|
||||
integration requirements that affect implementation.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Get configuration and dependency files
|
||||
@@ -1004,8 +995,7 @@ following PRD template principles."""
|
||||
8. DOCUMENTATION UPDATES: Documentation to create/update
|
||||
9. INTEGRATION STEPS: How to integrate with existing systems
|
||||
10. VALIDATION CHECKPOINTS: Validation steps at each phase""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
prompt = f"""Create detailed implementation blueprint following PRD methodology:
|
||||
@@ -1302,8 +1292,7 @@ Every agent interaction has been saved for full audit trail and reproducibility.
|
||||
GOAL: [extracted implementation goal]
|
||||
|
||||
Be precise and extract only what is explicitly mentioned or clearly implied.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
prompt = f"""Parse this user input to extract codebase and goal:
|
||||
@@ -1536,8 +1525,7 @@ Note: Detailed function/class metadata not available due to content access limit
|
||||
5. Documentation topics
|
||||
|
||||
Make the output easy for a file selection agent to understand which files contain what functionality.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
structure_text = basic_structure.get('structure', '')
|
||||
@@ -1770,8 +1758,7 @@ Focus on creating clear, structured metadata that will help with intelligent fil
|
||||
["README.md", "src/auth/login.py", "config/settings.py", ...]
|
||||
|
||||
Maximum 50 files for efficient analysis.""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Prepare enhanced structure information
|
||||
@@ -1923,8 +1910,7 @@ Maximum 50 files.""".format(goal=goal)
|
||||
8. EXAMPLES: Similar features that can guide {goal} implementation
|
||||
|
||||
Since these files were pre-selected for relevance, provide deep analysis of how each contributes to implementing: {goal}""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
prompt = f"""Analyze these carefully selected repository files for implementing: {goal}
|
||||
@@ -2024,8 +2010,7 @@ Since these files were pre-selected for relevance, explain how each contributes
|
||||
- Success criteria for {goal}
|
||||
|
||||
Focus everything on successfully implementing: {goal}""",
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
|
||||
verbose=getattr(self, 'verbose', True)
|
||||
llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
|
||||
)
|
||||
|
||||
chunk_info = ""
|
||||
@@ -2303,13 +2288,13 @@ def create_context_agent(llm: Optional[Union[str, Any]] = None, **kwargs) -> Con
|
||||
Factory function to create a ContextAgent following Context Engineering and PRD methodology.
|
||||
|
||||
Args:
|
||||
llm: Language model to use (e.g., "gpt-5-nano", "claude-3-haiku")
|
||||
llm: Language model to use (e.g., "gpt-4o-mini", "claude-3-haiku")
|
||||
**kwargs: Additional arguments to pass to ContextAgent constructor
|
||||
|
||||
Returns:
|
||||
ContextAgent: Configured ContextAgent for comprehensive context generation following PRD principles
|
||||
"""
|
||||
if llm is None:
|
||||
llm = "gpt-5-nano"
|
||||
llm = "gpt-4o-mini"
|
||||
|
||||
return ContextAgent(llm=llm, **kwargs)
|
||||
@@ -17,7 +17,7 @@ Supported Expansion Strategies:
|
||||
Example:
|
||||
from praisonaiagents import PromptExpanderAgent, ExpandStrategy
|
||||
|
||||
agent = PromptExpanderAgent(verbose=True)
|
||||
agent = PromptExpanderAgent()
|
||||
|
||||
# Basic expansion
|
||||
result = agent.expand("write a movie script in 3 lines")
|
||||
@@ -29,7 +29,7 @@ Example:
|
||||
|
||||
# With tools for context gathering
|
||||
from praisonaiagents.tools import internet_search
|
||||
agent = PromptExpanderAgent(tools=[internet_search], verbose=True)
|
||||
agent = PromptExpanderAgent(tools=[internet_search])
|
||||
result = agent.expand("latest AI developments")
|
||||
"""
|
||||
|
||||
@@ -73,13 +73,12 @@ class PromptExpanderAgent:
|
||||
|
||||
Attributes:
|
||||
name: Name of the agent
|
||||
model: LLM model to use for expansion
|
||||
verbose: Whether to print detailed logs
|
||||
model: LLM model to use for expansion (alias for llm=)
|
||||
temperature: Temperature for LLM generation (higher = more creative)
|
||||
tools: Optional tools for context gathering
|
||||
|
||||
Example:
|
||||
agent = PromptExpanderAgent(model="gpt-4o-mini", verbose=True)
|
||||
agent = PromptExpanderAgent(model="gpt-4o-mini")
|
||||
result = agent.expand("write a poem", strategy=ExpandStrategy.CREATIVE)
|
||||
print(result.expanded_prompt)
|
||||
"""
|
||||
@@ -230,8 +229,7 @@ Expanded prompt:"""
|
||||
backstory="You are an expert at understanding user intent and transforming brief requests into comprehensive, detailed prompts.",
|
||||
tools=self.tools,
|
||||
llm=self.model,
|
||||
verbose=self.verbose,
|
||||
markdown=False
|
||||
output={"verbose": self.verbose, "markdown": False}
|
||||
)
|
||||
return self._agent
|
||||
|
||||
|
||||
@@ -15,7 +15,7 @@ Supported Rewriting Strategies:
|
||||
Example:
|
||||
from praisonaiagents import QueryRewriterAgent, RewriteStrategy
|
||||
|
||||
agent = QueryRewriterAgent(verbose=True)
|
||||
agent = QueryRewriterAgent()
|
||||
|
||||
# Basic rewriting
|
||||
result = agent.rewrite("AI trends")
|
||||
@@ -34,7 +34,7 @@ Example:
|
||||
|
||||
# With search tools for context-aware rewriting
|
||||
from praisonaiagents.tools import internet_search
|
||||
agent = QueryRewriterAgent(tools=[internet_search], verbose=True)
|
||||
agent = QueryRewriterAgent(tools=[internet_search])
|
||||
result = agent.rewrite("latest AI developments") # Searches first, then rewrites
|
||||
"""
|
||||
|
||||
@@ -96,13 +96,12 @@ class QueryRewriterAgent:
|
||||
|
||||
Attributes:
|
||||
name: Name of the agent
|
||||
model: LLM model to use for rewriting
|
||||
verbose: Whether to print detailed logs
|
||||
model: LLM model to use for rewriting (alias for llm=)
|
||||
max_queries: Maximum number of queries to generate for multi-query strategy
|
||||
abbreviations: Dictionary of common abbreviations to expand
|
||||
|
||||
Example:
|
||||
agent = QueryRewriterAgent(model="gpt-4o-mini", verbose=True)
|
||||
agent = QueryRewriterAgent(model="gpt-4o-mini")
|
||||
result = agent.rewrite("ML best practices", strategy=RewriteStrategy.MULTI_QUERY)
|
||||
for query in result.rewritten_queries:
|
||||
print(query)
|
||||
@@ -288,8 +287,7 @@ Improved version:"""
|
||||
backstory="You are an expert at understanding user intent and transforming queries for optimal information retrieval.",
|
||||
tools=self.tools,
|
||||
llm=self.model,
|
||||
verbose=self.verbose,
|
||||
markdown=False
|
||||
output={"verbose": self.verbose, "markdown": False}
|
||||
)
|
||||
return self._agent
|
||||
|
||||
|
||||
@@ -44,7 +44,7 @@ class RouterAgent(Agent):
|
||||
# Initialize model router
|
||||
self.model_router = model_router or ModelRouter()
|
||||
self.routing_strategy = routing_strategy
|
||||
self.fallback_model = fallback_model or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
|
||||
self.fallback_model = fallback_model or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
|
||||
|
||||
# Process models configuration
|
||||
self.available_models = self._process_models_config(models)
|
||||
|
||||
@@ -148,6 +148,7 @@ class Agents:
|
||||
guardrails: Optional[Any] = None, # Union[bool, Callable, GuardrailConfig] - validation
|
||||
web: Optional[Any] = None, # Union[bool, WebConfig] - web search/fetch
|
||||
reflection: Optional[Any] = None, # Union[bool, ReflectionConfig] - self-reflection
|
||||
caching: Optional[Any] = None, # Union[bool, CachingConfig] - caching
|
||||
):
|
||||
"""
|
||||
Initialize PraisonAIAgents with consolidated feature parameters.
|
||||
@@ -177,6 +178,7 @@ class Agents:
|
||||
self._guardrails = guardrails
|
||||
self._web = web
|
||||
self._reflection = reflection
|
||||
self._caching = caching
|
||||
# ─────────────────────────────────────────────────────────────────────
|
||||
# Extract values from consolidated params using UNIFIED CANONICAL resolver
|
||||
# Precedence: Instance > Config > Dict > Array > String > Bool > Default
|
||||
@@ -361,7 +363,7 @@ class Agents:
|
||||
self.variables = variables if variables else {}
|
||||
|
||||
# Check for manager_llm in environment variable if not provided
|
||||
self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
|
||||
self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
|
||||
|
||||
# Set logger level based on verbose
|
||||
if _verbose >= 5:
|
||||
@@ -464,10 +466,16 @@ class Agents:
|
||||
|
||||
if self.shared_memory:
|
||||
for task in tasks:
|
||||
if not task.memory:
|
||||
task.memory = self.shared_memory
|
||||
logger.info(f"Assigned shared memory to task {task.id}")
|
||||
|
||||
# Telemetry
|
||||
try:
|
||||
from ..telemetry import get_telemetry
|
||||
self._telemetry = get_telemetry()
|
||||
except (ImportError, AttributeError):
|
||||
self._telemetry = None
|
||||
|
||||
def add_task(self, task):
|
||||
task_id = self.task_id_counter
|
||||
task.id = task_id
|
||||
@@ -819,13 +827,17 @@ Context:
|
||||
await loop.run_in_executor(None, self.run_task, task_id)
|
||||
|
||||
async def astart(self, content=None, return_dict=False, **kwargs):
|
||||
"""Async version of start method
|
||||
"""Async version of start method.
|
||||
|
||||
Args:
|
||||
content: Optional content to add to all tasks' context
|
||||
return_dict: If True, returns the full results dictionary instead of only the final response
|
||||
**kwargs: Additional arguments
|
||||
"""
|
||||
# Track execution via telemetry
|
||||
if hasattr(self, '_telemetry') and self._telemetry:
|
||||
self._telemetry.track_agent_execution(self.name, success=True, async_mode=True)
|
||||
|
||||
if content:
|
||||
# Add content to context of all tasks
|
||||
for task in self.tasks.values():
|
||||
@@ -1191,30 +1203,146 @@ Context:
|
||||
return str(agent[0])
|
||||
return None
|
||||
|
||||
def start(self, content=None, return_dict=False, **kwargs):
|
||||
"""Start agent execution with optional content and config
|
||||
def start(self, content=None, return_dict=False, output=None, **kwargs):
|
||||
"""Start agent execution with verbose output (beginner-friendly).
|
||||
|
||||
Shows Rich panels with workflow progress when in TTY. Use .run() for
|
||||
silent execution in production/scripts.
|
||||
|
||||
Args:
|
||||
content: Optional content to add to all tasks' context
|
||||
return_dict: If True, returns the full results dictionary instead of only the final response
|
||||
return_dict: If True, returns the full results dictionary
|
||||
output: Output preset - "silent", "verbose", "normal", etc.
|
||||
Default in TTY: "verbose" (shows progress)
|
||||
Default non-TTY: "silent"
|
||||
**kwargs: Additional arguments
|
||||
|
||||
Example:
|
||||
```python
|
||||
# Interactive - shows Rich panels
|
||||
agents = Agents(agents=[agent1, agent2])
|
||||
result = agents.start() # Verbose output by default
|
||||
|
||||
# Force silent mode
|
||||
result = agents.start(output="silent")
|
||||
```
|
||||
"""
|
||||
# Track execution via telemetry
|
||||
if hasattr(self, '_telemetry') and self._telemetry:
|
||||
self._telemetry.track_agent_execution(self.name, success=True)
|
||||
import sys
|
||||
from ..main import PRAISON_COLORS
|
||||
|
||||
# Determine if we're in an interactive TTY
|
||||
is_tty = sys.stdout.isatty()
|
||||
|
||||
# Resolve output mode (TTY-aware)
|
||||
if output is None:
|
||||
# Default: verbose in TTY (beginner-friendly), silent otherwise
|
||||
# Note: Don't check self.verbose here - start() is for interactive use
|
||||
show_verbose = is_tty
|
||||
elif output == "silent":
|
||||
show_verbose = False
|
||||
elif output in ("verbose", "debug", "normal"):
|
||||
show_verbose = True
|
||||
else:
|
||||
show_verbose = is_tty
|
||||
|
||||
# Add content to context if provided
|
||||
if content:
|
||||
# Add content to context of all tasks
|
||||
for task in self.tasks.values():
|
||||
if isinstance(content, (str, list)):
|
||||
# If context is empty, initialize it
|
||||
if not task.context:
|
||||
task.context = []
|
||||
# Add content to context
|
||||
task.context.append(content)
|
||||
|
||||
# Planning Mode: Create plan and todo list before execution
|
||||
if self.planning:
|
||||
self._run_with_planning()
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
# Verbose Mode: Show Rich panels for multi-agent workflow
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
if show_verbose and is_tty:
|
||||
from rich.panel import Panel
|
||||
from rich.text import Text
|
||||
console = Console()
|
||||
import time as time_module
|
||||
|
||||
# Show workflow overview panel
|
||||
agent_names = " → ".join([a.name for a in self.agents])
|
||||
workflow_info = f"[bold {PRAISON_COLORS['metrics']}]Process:[/] {self.process}\n"
|
||||
workflow_info += f"[bold {PRAISON_COLORS['metrics']}]Agents:[/] {agent_names}"
|
||||
|
||||
console.print(Panel(
|
||||
workflow_info,
|
||||
title="[bold]Multi-Agent Workflow[/]",
|
||||
border_style=PRAISON_COLORS["agent"],
|
||||
padding=(1, 2)
|
||||
))
|
||||
console.print()
|
||||
|
||||
# Execute tasks with verbose output
|
||||
total_agents = len(self.agents)
|
||||
workflow_start_time = time_module.time()
|
||||
|
||||
for idx, (task_id, task) in enumerate(self.tasks.items(), 1):
|
||||
agent = task.agent
|
||||
agent_name = agent.name if agent else "Unknown"
|
||||
agent_model = getattr(agent, 'llm', 'gpt-4o-mini') if agent else "unknown"
|
||||
|
||||
# Show agent task panel with model info
|
||||
task_desc = task.description[:100] + "..." if len(task.description) > 100 else task.description
|
||||
panel_content = f"[bold {PRAISON_COLORS['task_text']}]📋 Task:[/] {task_desc}\n"
|
||||
panel_content += f"[dim]🤖 Model: {agent_model}[/dim]"
|
||||
console.print(Panel.fit(
|
||||
panel_content,
|
||||
title=f"[bold]Agent [{idx}/{total_agents}]: {agent_name}[/]",
|
||||
border_style=PRAISON_COLORS["task"]
|
||||
))
|
||||
|
||||
# Execute with timing and status
|
||||
start_time = time_module.time()
|
||||
|
||||
# Show working spinner
|
||||
with console.status(
|
||||
f"[bold yellow]Working...[/] {agent_name} generating response...",
|
||||
spinner="dots",
|
||||
spinner_style="yellow"
|
||||
):
|
||||
# Run the task
|
||||
if self.planning:
|
||||
self._run_with_planning()
|
||||
break # Planning mode handles all tasks
|
||||
else:
|
||||
self.run_task(task_id)
|
||||
|
||||
elapsed = time_module.time() - start_time
|
||||
|
||||
# Show response panel - FULL response, no truncation
|
||||
result = self.get_task_result(task_id)
|
||||
if result:
|
||||
response_text = str(result.raw)
|
||||
# No truncation - show full response in verbose mode
|
||||
console.print(Panel(
|
||||
Text(response_text),
|
||||
title=f"[bold]Agent [{idx}/{total_agents}] Complete ({elapsed:.1f}s)[/]",
|
||||
border_style=PRAISON_COLORS["response"],
|
||||
padding=(1, 2)
|
||||
))
|
||||
console.print()
|
||||
|
||||
# Workflow summary panel
|
||||
total_elapsed = time_module.time() - workflow_start_time
|
||||
console.print(Panel.fit(
|
||||
f"[bold green]Total Time:[/] {total_elapsed:.1f}s\n"
|
||||
f"[bold green]Agents Run:[/] {total_agents}/{total_agents}",
|
||||
title="[bold]✅ Workflow Complete[/]",
|
||||
border_style="green"
|
||||
))
|
||||
console.print()
|
||||
else:
|
||||
# Run tasks as before
|
||||
self.run_all_tasks()
|
||||
# Silent mode: Run tasks without display
|
||||
if self.planning:
|
||||
self._run_with_planning()
|
||||
else:
|
||||
self.run_all_tasks()
|
||||
|
||||
# Auto-display token metrics if any agent has metrics=True
|
||||
metrics_enabled = any(getattr(agent, 'metrics', False) for agent in self.agents)
|
||||
@@ -1222,10 +1350,8 @@ Context:
|
||||
try:
|
||||
self.display_token_usage()
|
||||
except (ImportError, AttributeError) as e:
|
||||
# Token tracking not available or not properly configured
|
||||
logging.debug(f"Could not auto-display token usage: {e}")
|
||||
except Exception as e:
|
||||
# Log unexpected errors for debugging
|
||||
logging.debug(f"Unexpected error in token metrics display: {e}")
|
||||
|
||||
# Get results
|
||||
@@ -1236,7 +1362,6 @@ Context:
|
||||
|
||||
# By default, return only the final agent's response
|
||||
if not return_dict:
|
||||
# Get the last task (assuming sequential processing)
|
||||
task_ids = list(self.tasks.keys())
|
||||
if task_ids:
|
||||
last_task_id = task_ids[-1]
|
||||
@@ -1244,12 +1369,21 @@ Context:
|
||||
if last_result:
|
||||
return last_result.raw
|
||||
|
||||
# Return full results dict if return_dict is True or if no final result was found
|
||||
return results
|
||||
|
||||
def run(self, content=None, return_dict=False, **kwargs):
|
||||
"""Alias for start() method to provide consistent API with Agent class"""
|
||||
return self.start(content=content, return_dict=return_dict, **kwargs)
|
||||
"""Run agents silently (production use).
|
||||
|
||||
Unlike .start() which shows verbose output, .run() executes silently
|
||||
for programmatic/production use.
|
||||
|
||||
Args:
|
||||
content: Optional content to add to all tasks' context
|
||||
return_dict: If True, returns the full results dictionary
|
||||
**kwargs: Additional arguments
|
||||
"""
|
||||
# Always run silently - no verbose output
|
||||
return self.start(content=content, return_dict=return_dict, output="silent", **kwargs)
|
||||
|
||||
def set_state(self, key: str, value: Any) -> None:
|
||||
"""Set a state value"""
|
||||
|
||||
@@ -74,6 +74,17 @@ class AutoAgents(Agents):
|
||||
api_key: Optional[str] = None,
|
||||
max_agents: int = 3, # Maximum number of agents
|
||||
autonomy: Optional[Any] = None, # Union[bool, dict, AutonomyConfig] - Enable autonomy for all agents
|
||||
# ============================================================
|
||||
# CONSOLIDATED PARAMS (new SDK contract - take precedence)
|
||||
# ============================================================
|
||||
output: Optional[Any] = None, # Union[str, OutputConfig] - overrides verbose/markdown
|
||||
reflection: Optional[Any] = None, # Union[bool, ReflectionConfig] - overrides self_reflect/max_reflect/min_reflect/reflect_llm
|
||||
caching: Optional[Any] = None, # Union[bool, CachingConfig] - overrides cache
|
||||
knowledge: Optional[Any] = None, # Union[bool, List[str], KnowledgeConfig] - overrides knowledge_sources/embedder_config
|
||||
execution: Optional[Any] = None, # Union[str, ExecutionConfig] - overrides max_iter
|
||||
guardrails: Optional[Any] = None, # Union[bool, Callable, GuardrailConfig]
|
||||
web: Optional[Any] = None, # Union[bool, WebConfig]
|
||||
hooks: Optional[Any] = None, # HooksConfig
|
||||
):
|
||||
"""Initialize AutoAgents with configuration for automatic agent and task creation."""
|
||||
if max_agents < 1:
|
||||
@@ -92,7 +103,7 @@ class AutoAgents(Agents):
|
||||
self.self_reflect = self_reflect
|
||||
self.max_reflect = max_reflect
|
||||
self.min_reflect = min_reflect
|
||||
self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
|
||||
self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
|
||||
self.function_calling_llm = function_calling_llm
|
||||
self.code_execution_mode = code_execution_mode
|
||||
self.embedder_config = embedder_config
|
||||
@@ -112,6 +123,16 @@ class AutoAgents(Agents):
|
||||
self.api_key = api_key
|
||||
self.autonomy = autonomy # Store autonomy config for agents
|
||||
|
||||
# Store consolidated params (new SDK contract - take precedence over legacy)
|
||||
self._output = output
|
||||
self._reflection = reflection
|
||||
self._caching = caching
|
||||
self._knowledge = knowledge
|
||||
self._execution = execution
|
||||
self._guardrails = guardrails
|
||||
self._web = web
|
||||
self._hooks = hooks
|
||||
|
||||
# Display initial instruction
|
||||
if self.verbose:
|
||||
display_instruction(f"🎯 Main Task: {self.instructions}")
|
||||
@@ -129,14 +150,21 @@ class AutoAgents(Agents):
|
||||
self._display_agents_and_tasks(agents, tasks)
|
||||
|
||||
# Initialize parent class with generated agents and tasks
|
||||
# Use consolidated output= instead of legacy verbose=
|
||||
super().__init__(
|
||||
agents=agents,
|
||||
tasks=tasks,
|
||||
verbose=verbose,
|
||||
completion_checker=completion_checker,
|
||||
max_retries=max_retries,
|
||||
process=process,
|
||||
manager_llm=manager_llm or self.llm
|
||||
manager_llm=manager_llm or self.llm,
|
||||
output=self._output, # Consolidated output param
|
||||
execution=self._execution,
|
||||
hooks=self._hooks,
|
||||
memory=self.memory,
|
||||
reflection=self._reflection,
|
||||
caching=self._caching,
|
||||
guardrails=self._guardrails,
|
||||
web=self._web,
|
||||
autonomy=self.autonomy,
|
||||
)
|
||||
|
||||
def _display_agents_and_tasks(self, agents: List[Agent], tasks: List[Task]):
|
||||
@@ -431,38 +459,33 @@ DO NOT use strings for tasks. Each task MUST be a complete object with all four
|
||||
agent_tools = self._assign_tools_to_agent(agent_config)
|
||||
|
||||
# Create the agent with all parameters
|
||||
# Consolidated params take precedence over legacy params
|
||||
# Use consolidated knowledge if provided, else fall back to legacy knowledge_sources
|
||||
effective_knowledge = self._knowledge if self._knowledge is not None else self.knowledge_sources
|
||||
|
||||
agent = Agent(
|
||||
name=agent_config.name,
|
||||
role=agent_config.role,
|
||||
goal=agent_config.goal,
|
||||
backstory=agent_config.backstory,
|
||||
tools=agent_tools, # Use assigned tools
|
||||
verbose=self.verbose >= 1,
|
||||
allow_code_execution=self.allow_code_execution,
|
||||
memory=self.memory,
|
||||
markdown=self.markdown,
|
||||
self_reflect=self.self_reflect,
|
||||
max_reflect=self.max_reflect,
|
||||
min_reflect=self.min_reflect,
|
||||
llm=self.llm,
|
||||
function_calling_llm=self.function_calling_llm,
|
||||
llm=self.llm, # Consolidated LLM param
|
||||
code_execution_mode=self.code_execution_mode,
|
||||
embedder_config=self.embedder_config,
|
||||
knowledge=self.knowledge_sources,
|
||||
use_system_prompt=self.use_system_prompt,
|
||||
cache=self.cache,
|
||||
allow_delegation=self.allow_delegation,
|
||||
step_callback=self.step_callback,
|
||||
system_template=self.system_template,
|
||||
prompt_template=self.prompt_template,
|
||||
response_template=self.response_template,
|
||||
max_rpm=self.max_rpm,
|
||||
max_execution_time=self.max_execution_time,
|
||||
max_iter=self.max_iter,
|
||||
reflect_llm=self.reflect_llm,
|
||||
base_url=self.base_url,
|
||||
api_key=self.api_key,
|
||||
autonomy=self.autonomy # Pass autonomy config to agents
|
||||
# Consolidated params (new SDK contract)
|
||||
output=self._output, # Overrides verbose/markdown
|
||||
reflection=self._reflection, # Overrides self_reflect/max_reflect/min_reflect/reflect_llm
|
||||
caching=self._caching, # Overrides cache
|
||||
knowledge=effective_knowledge, # Overrides knowledge_sources/embedder_config
|
||||
execution=self._execution, # Overrides max_iter
|
||||
guardrails=self._guardrails,
|
||||
web=self._web,
|
||||
hooks=self._hooks,
|
||||
autonomy=self.autonomy,
|
||||
)
|
||||
agents.append(agent)
|
||||
|
||||
|
||||
@@ -376,7 +376,7 @@ class SubagentDelegator:
|
||||
return result
|
||||
|
||||
async def _create_agent(self, profile: AgentProfile, task: DelegationTask) -> Any:
|
||||
"""Create an agent from profile."""
|
||||
"""Create an agent from profile using consolidated params."""
|
||||
if self.agent_factory:
|
||||
return self.agent_factory(profile.name)
|
||||
|
||||
@@ -387,14 +387,17 @@ class SubagentDelegator:
|
||||
# Filter tools based on profile
|
||||
tools = self._get_tools_for_profile(profile)
|
||||
|
||||
# Use consolidated params (protocol-compliant)
|
||||
# output='silent' replaces verbose=False
|
||||
# execution={max_iter: N} replaces max_iter=N
|
||||
agent = Agent(
|
||||
name=profile.name,
|
||||
role=profile.name.title(),
|
||||
goal=task.objective,
|
||||
backstory=profile.system_prompt,
|
||||
tools=tools,
|
||||
verbose=False,
|
||||
max_iter=min(task.max_steps, profile.max_steps),
|
||||
output='silent',
|
||||
execution={'max_iter': min(task.max_steps, profile.max_steps)},
|
||||
)
|
||||
|
||||
return agent
|
||||
|
||||
@@ -44,12 +44,79 @@ class MemoryBackend(str, Enum):
|
||||
MONGODB = "mongodb"
|
||||
|
||||
|
||||
class LearnScope(str, Enum):
|
||||
"""Scope for learning data visibility.
|
||||
|
||||
PRIVATE: Learning data is private to this user/agent (default, safest)
|
||||
SHARED: Learning data is shared with all agents
|
||||
"""
|
||||
PRIVATE = "private" # Private to this user/agent
|
||||
SHARED = "shared" # Shared with all agents
|
||||
|
||||
|
||||
@dataclass
|
||||
class LearnConfig:
|
||||
"""
|
||||
Configuration for continuous learning within memory system.
|
||||
|
||||
Learning captures patterns, preferences, and insights from agent interactions
|
||||
to improve future responses. All learning data is stored within the memory system.
|
||||
|
||||
Usage:
|
||||
# Simple enable
|
||||
Agent(memory=MemoryConfig(learn=True))
|
||||
|
||||
# With specific capabilities
|
||||
Agent(memory=MemoryConfig(
|
||||
learn=LearnConfig(
|
||||
persona=True, # User preferences
|
||||
insights=True, # Observations
|
||||
patterns=True, # Reusable knowledge
|
||||
)
|
||||
))
|
||||
"""
|
||||
# Learning capabilities
|
||||
persona: bool = True # User preferences and profile
|
||||
insights: bool = True # Observations and learnings
|
||||
thread: bool = True # Session/conversation context
|
||||
patterns: bool = False # Reusable knowledge patterns
|
||||
decisions: bool = False # Decision logging
|
||||
feedback: bool = False # Outcome signals
|
||||
improvements: bool = False # Self-improvement proposals
|
||||
|
||||
# Scope configuration
|
||||
scope: Union[str, LearnScope] = LearnScope.PRIVATE
|
||||
|
||||
# Storage configuration
|
||||
store_path: Optional[str] = None # Custom storage path
|
||||
|
||||
# Maintenance settings
|
||||
auto_consolidate: bool = True # Auto-consolidate learnings
|
||||
retention_days: Optional[int] = None # Days to retain (None = forever)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert to dictionary."""
|
||||
return {
|
||||
"persona": self.persona,
|
||||
"insights": self.insights,
|
||||
"thread": self.thread,
|
||||
"patterns": self.patterns,
|
||||
"decisions": self.decisions,
|
||||
"feedback": self.feedback,
|
||||
"improvements": self.improvements,
|
||||
"scope": self.scope.value if isinstance(self.scope, LearnScope) else self.scope,
|
||||
"store_path": self.store_path,
|
||||
"auto_consolidate": self.auto_consolidate,
|
||||
"retention_days": self.retention_days,
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class MemoryConfig:
|
||||
"""
|
||||
Configuration for agent memory and session management.
|
||||
|
||||
Consolidates: memory, auto_memory, claude_memory, user_id, session_id, db
|
||||
Consolidates: memory, auto_memory, claude_memory, user_id, session_id, db, learn
|
||||
|
||||
Usage:
|
||||
# Simple enable (uses FileMemory)
|
||||
@@ -58,12 +125,22 @@ class MemoryConfig:
|
||||
# With backend
|
||||
Agent(memory=MemoryConfig(backend="redis"))
|
||||
|
||||
# Full config
|
||||
# Full config with learning
|
||||
Agent(memory=MemoryConfig(
|
||||
backend="sqlite",
|
||||
user_id="user123",
|
||||
session_id="session456",
|
||||
auto_memory=True,
|
||||
learn=True, # Enable continuous learning
|
||||
))
|
||||
|
||||
# With detailed learn config
|
||||
Agent(memory=MemoryConfig(
|
||||
learn=LearnConfig(
|
||||
persona=True,
|
||||
insights=True,
|
||||
patterns=True,
|
||||
)
|
||||
))
|
||||
"""
|
||||
# Backend selection
|
||||
@@ -85,8 +162,17 @@ class MemoryConfig:
|
||||
# Memory configuration dict (for provider-specific settings)
|
||||
config: Optional[Dict[str, Any]] = None
|
||||
|
||||
# Continuous learning configuration
|
||||
learn: Optional[Union[bool, LearnConfig]] = None
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert to dictionary."""
|
||||
learn_dict = None
|
||||
if self.learn is True:
|
||||
learn_dict = LearnConfig().to_dict()
|
||||
elif isinstance(self.learn, LearnConfig):
|
||||
learn_dict = self.learn.to_dict()
|
||||
|
||||
return {
|
||||
"backend": self.backend.value if isinstance(self.backend, MemoryBackend) else self.backend,
|
||||
"user_id": self.user_id,
|
||||
@@ -94,6 +180,7 @@ class MemoryConfig:
|
||||
"auto_memory": self.auto_memory,
|
||||
"claude_memory": self.claude_memory,
|
||||
"config": self.config,
|
||||
"learn": learn_dict,
|
||||
}
|
||||
|
||||
|
||||
@@ -391,10 +478,21 @@ class OutputConfig:
|
||||
|
||||
Consolidates: verbose, markdown, stream, metrics, reasoning_steps, output_style
|
||||
|
||||
DEFAULT: output="silent" (zero overhead, fastest performance)
|
||||
|
||||
Usage:
|
||||
# Simple preset
|
||||
# Default is silent mode (no output overhead, programmatic use)
|
||||
Agent(instructions="...") # Uses output="silent"
|
||||
|
||||
# Actions mode (tool calls + final output trace)
|
||||
Agent(output="actions")
|
||||
|
||||
# Verbose mode with Rich panels
|
||||
Agent(output="verbose")
|
||||
|
||||
# JSON mode for piping
|
||||
Agent(output="json")
|
||||
|
||||
# With config
|
||||
Agent(output=OutputConfig(
|
||||
verbose=True,
|
||||
@@ -404,11 +502,11 @@ class OutputConfig:
|
||||
reasoning_steps=True,
|
||||
))
|
||||
"""
|
||||
# Verbosity
|
||||
verbose: bool = True
|
||||
# Verbosity - False by default for silent mode (fastest)
|
||||
verbose: bool = False
|
||||
|
||||
# Formatting
|
||||
markdown: bool = True
|
||||
# Formatting - False by default for silent mode
|
||||
markdown: bool = False
|
||||
|
||||
# Streaming
|
||||
stream: bool = False
|
||||
@@ -422,6 +520,14 @@ class OutputConfig:
|
||||
# Output style (custom styling)
|
||||
style: Optional[Any] = None
|
||||
|
||||
# Actions trace mode - shows tool calls, agent lifecycle, final output
|
||||
# When True, registers callbacks and outputs to stderr
|
||||
# DEFAULT: False for zero overhead (silent mode)
|
||||
actions_trace: bool = False
|
||||
|
||||
# JSON output mode - emit JSONL events for piping
|
||||
json_output: bool = False
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert to dictionary."""
|
||||
return {
|
||||
@@ -430,6 +536,8 @@ class OutputConfig:
|
||||
"stream": self.stream,
|
||||
"metrics": self.metrics,
|
||||
"reasoning_steps": self.reasoning_steps,
|
||||
"actions_trace": self.actions_trace,
|
||||
"json_output": self.json_output,
|
||||
}
|
||||
|
||||
|
||||
@@ -857,9 +965,259 @@ SkillsParam = Union[List[str], SkillsConfig]
|
||||
AutonomyParam = Union[bool, Dict[str, Any], AutonomyConfig]
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# PRECEDENCE LADDER RESOLVERS
|
||||
# =============================================================================
|
||||
# Precedence: Instance > Config > Array > Dict > String > Bool > Default
|
||||
#
|
||||
# These resolvers normalize various input types into the canonical Config form.
|
||||
# This allows users to pass simplified inputs while maintaining type safety.
|
||||
|
||||
|
||||
def resolve_memory(value: MemoryParam) -> Optional[MemoryConfig]:
|
||||
"""
|
||||
Resolve memory= parameter following precedence ladder.
|
||||
|
||||
Precedence: Instance > Config > Dict > String > Bool > Default
|
||||
|
||||
Args:
|
||||
value: Memory parameter in any supported form
|
||||
|
||||
Returns:
|
||||
MemoryConfig if enabled, None if disabled
|
||||
|
||||
Examples:
|
||||
>>> resolve_memory(None) # Default: disabled
|
||||
None
|
||||
>>> resolve_memory(False) # Explicit disable
|
||||
None
|
||||
>>> resolve_memory(True) # Enable with defaults
|
||||
MemoryConfig(backend='file')
|
||||
>>> resolve_memory("redis") # String shorthand
|
||||
MemoryConfig(backend='redis')
|
||||
>>> resolve_memory({"backend": "sqlite", "user_id": "alice"}) # Dict
|
||||
MemoryConfig(backend='sqlite', user_id='alice')
|
||||
>>> resolve_memory(MemoryConfig(backend="postgres")) # Config passthrough
|
||||
MemoryConfig(backend='postgres')
|
||||
"""
|
||||
# Default: disabled
|
||||
if value is None:
|
||||
return None
|
||||
|
||||
# Bool: False = disabled, True = defaults
|
||||
if value is False:
|
||||
return None
|
||||
if value is True:
|
||||
return MemoryConfig()
|
||||
|
||||
# String: backend shorthand
|
||||
if isinstance(value, str):
|
||||
try:
|
||||
backend = MemoryBackend(value.lower())
|
||||
except ValueError:
|
||||
backend = value # Allow custom backend strings
|
||||
return MemoryConfig(backend=backend)
|
||||
|
||||
# Dict: expand to config
|
||||
if isinstance(value, dict):
|
||||
# Handle backend enum conversion
|
||||
backend = value.get("backend", MemoryBackend.FILE)
|
||||
if isinstance(backend, str):
|
||||
try:
|
||||
backend = MemoryBackend(backend.lower())
|
||||
except ValueError:
|
||||
pass # Keep as string for custom backends
|
||||
return MemoryConfig(
|
||||
backend=backend,
|
||||
user_id=value.get("user_id"),
|
||||
session_id=value.get("session_id"),
|
||||
auto_memory=value.get("auto_memory", False),
|
||||
claude_memory=value.get("claude_memory", False),
|
||||
db=value.get("db"),
|
||||
config=value.get("config"),
|
||||
)
|
||||
|
||||
# Config: passthrough
|
||||
if isinstance(value, MemoryConfig):
|
||||
return value
|
||||
|
||||
# Instance (MemoryManager): return as-is (caller handles)
|
||||
# This is the highest precedence - user provided a pre-configured instance
|
||||
return value
|
||||
|
||||
|
||||
def resolve_knowledge(value: KnowledgeParam) -> Optional[KnowledgeConfig]:
|
||||
"""
|
||||
Resolve knowledge= parameter following precedence ladder.
|
||||
|
||||
Precedence: Instance > Config > Array > Dict > String > Bool > Default
|
||||
|
||||
Args:
|
||||
value: Knowledge parameter in any supported form
|
||||
|
||||
Returns:
|
||||
KnowledgeConfig if enabled, None if disabled
|
||||
"""
|
||||
# Default: disabled
|
||||
if value is None:
|
||||
return None
|
||||
|
||||
# Bool: False = disabled, True = defaults
|
||||
if value is False:
|
||||
return None
|
||||
if value is True:
|
||||
return KnowledgeConfig()
|
||||
|
||||
# String: single source
|
||||
if isinstance(value, str):
|
||||
return KnowledgeConfig(sources=[value])
|
||||
|
||||
# Array: list of sources
|
||||
if isinstance(value, list):
|
||||
return KnowledgeConfig(sources=value)
|
||||
|
||||
# Dict: expand to config
|
||||
if isinstance(value, dict):
|
||||
return KnowledgeConfig(
|
||||
sources=value.get("sources", []),
|
||||
embedder=value.get("embedder", "openai"),
|
||||
embedder_config=value.get("embedder_config"),
|
||||
chunking_strategy=value.get("chunking_strategy", ChunkingStrategy.SEMANTIC),
|
||||
chunk_size=value.get("chunk_size", 1000),
|
||||
chunk_overlap=value.get("chunk_overlap", 200),
|
||||
retrieval_k=value.get("retrieval_k", 5),
|
||||
retrieval_threshold=value.get("retrieval_threshold", 0.0),
|
||||
rerank=value.get("rerank", False),
|
||||
rerank_model=value.get("rerank_model"),
|
||||
auto_retrieve=value.get("auto_retrieve", True),
|
||||
)
|
||||
|
||||
# Config: passthrough
|
||||
if isinstance(value, KnowledgeConfig):
|
||||
return value
|
||||
|
||||
# Instance: return as-is
|
||||
return value
|
||||
|
||||
|
||||
def resolve_planning(value: PlanningParam) -> Optional[PlanningConfig]:
|
||||
"""Resolve planning= parameter following precedence ladder."""
|
||||
if value is None or value is False:
|
||||
return None
|
||||
if value is True:
|
||||
return PlanningConfig()
|
||||
if isinstance(value, dict):
|
||||
return PlanningConfig(**value)
|
||||
if isinstance(value, PlanningConfig):
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
def resolve_reflection(value: ReflectionParam) -> Optional[ReflectionConfig]:
|
||||
"""Resolve reflection= parameter following precedence ladder."""
|
||||
if value is None or value is False:
|
||||
return None
|
||||
if value is True:
|
||||
return ReflectionConfig()
|
||||
if isinstance(value, dict):
|
||||
return ReflectionConfig(**value)
|
||||
if isinstance(value, ReflectionConfig):
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
def resolve_guardrails(value: GuardrailParam) -> Optional[GuardrailConfig]:
|
||||
"""Resolve guardrails= parameter following precedence ladder."""
|
||||
if value is None or value is False:
|
||||
return None
|
||||
if value is True:
|
||||
return GuardrailConfig()
|
||||
if callable(value):
|
||||
return GuardrailConfig(validator=value)
|
||||
if isinstance(value, dict):
|
||||
return GuardrailConfig(**value)
|
||||
if isinstance(value, GuardrailConfig):
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
def resolve_web(value: WebParam) -> Optional[WebConfig]:
|
||||
"""Resolve web= parameter following precedence ladder."""
|
||||
if value is None or value is False:
|
||||
return None
|
||||
if value is True:
|
||||
return WebConfig()
|
||||
if isinstance(value, dict):
|
||||
return WebConfig(**value)
|
||||
if isinstance(value, WebConfig):
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
def resolve_output(value: OutputParam) -> Optional[OutputConfig]:
|
||||
"""Resolve output= parameter following precedence ladder."""
|
||||
if value is None:
|
||||
return None
|
||||
if isinstance(value, str):
|
||||
try:
|
||||
preset = OutputPreset(value.lower())
|
||||
return OutputConfig.from_preset(preset)
|
||||
except ValueError:
|
||||
return None
|
||||
if isinstance(value, dict):
|
||||
return OutputConfig(**value)
|
||||
if isinstance(value, OutputConfig):
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
def resolve_execution(value: ExecutionParam) -> Optional[ExecutionConfig]:
|
||||
"""Resolve execution= parameter following precedence ladder."""
|
||||
if value is None:
|
||||
return None
|
||||
if isinstance(value, str):
|
||||
try:
|
||||
preset = ExecutionPreset(value.lower())
|
||||
return ExecutionConfig.from_preset(preset)
|
||||
except ValueError:
|
||||
return None
|
||||
if isinstance(value, dict):
|
||||
return ExecutionConfig(**value)
|
||||
if isinstance(value, ExecutionConfig):
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
def resolve_caching(value: CachingParam) -> Optional[CachingConfig]:
|
||||
"""Resolve caching= parameter following precedence ladder."""
|
||||
if value is None or value is False:
|
||||
return None
|
||||
if value is True:
|
||||
return CachingConfig()
|
||||
if isinstance(value, dict):
|
||||
return CachingConfig(**value)
|
||||
if isinstance(value, CachingConfig):
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
def resolve_autonomy(value: AutonomyParam) -> Optional[AutonomyConfig]:
|
||||
"""Resolve autonomy= parameter following precedence ladder."""
|
||||
if value is None or value is False:
|
||||
return None
|
||||
if value is True:
|
||||
return AutonomyConfig()
|
||||
if isinstance(value, dict):
|
||||
return AutonomyConfig(**value)
|
||||
if isinstance(value, AutonomyConfig):
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
__all__ = [
|
||||
# Enums
|
||||
"MemoryBackend",
|
||||
"LearnScope",
|
||||
"ChunkingStrategy",
|
||||
"GuardrailAction",
|
||||
"WebSearchProvider",
|
||||
@@ -867,6 +1225,7 @@ __all__ = [
|
||||
"ExecutionPreset",
|
||||
"AutonomyLevel",
|
||||
# Config classes (Agent)
|
||||
"LearnConfig",
|
||||
"MemoryConfig",
|
||||
"KnowledgeConfig",
|
||||
"PlanningConfig",
|
||||
@@ -900,4 +1259,15 @@ __all__ = [
|
||||
"HooksParam",
|
||||
"SkillsParam",
|
||||
"AutonomyParam",
|
||||
# Precedence ladder resolvers
|
||||
"resolve_memory",
|
||||
"resolve_knowledge",
|
||||
"resolve_planning",
|
||||
"resolve_reflection",
|
||||
"resolve_guardrails",
|
||||
"resolve_web",
|
||||
"resolve_output",
|
||||
"resolve_execution",
|
||||
"resolve_caching",
|
||||
"resolve_autonomy",
|
||||
]
|
||||
@@ -41,6 +41,37 @@ MEMORY_URL_SCHEMES: Dict[str, str] = {
|
||||
# =============================================================================
|
||||
|
||||
OUTPUT_PRESETS: Dict[str, Dict[str, Any]] = {
|
||||
# Silent preset - DEFAULT for SDK
|
||||
# Zero overhead, no callbacks, no I/O on hot path
|
||||
# Fastest performance for programmatic use
|
||||
"silent": {
|
||||
"verbose": False,
|
||||
"markdown": False,
|
||||
"stream": False,
|
||||
"metrics": False,
|
||||
"reasoning_steps": False,
|
||||
"actions_trace": False,
|
||||
},
|
||||
# Actions preset - opt-in observability
|
||||
# Shows action trace (tool calls, agent lifecycle) + final output
|
||||
# Registers callbacks, outputs to stderr
|
||||
"actions": {
|
||||
"verbose": False,
|
||||
"markdown": False,
|
||||
"stream": False,
|
||||
"metrics": False,
|
||||
"reasoning_steps": False,
|
||||
"actions_trace": True, # Special flag for action trace mode
|
||||
},
|
||||
# Plain preset - final output only, no action trace
|
||||
"plain": {
|
||||
"verbose": False,
|
||||
"markdown": False,
|
||||
"stream": False,
|
||||
"metrics": False,
|
||||
"reasoning_steps": False,
|
||||
"actions_trace": False,
|
||||
},
|
||||
"minimal": {
|
||||
"verbose": False,
|
||||
"markdown": False,
|
||||
@@ -69,15 +100,31 @@ OUTPUT_PRESETS: Dict[str, Dict[str, Any]] = {
|
||||
"metrics": True,
|
||||
"reasoning_steps": True,
|
||||
},
|
||||
"silent": {
|
||||
# Streaming preset - enables streaming by default
|
||||
"stream": {
|
||||
"verbose": True,
|
||||
"markdown": True,
|
||||
"stream": True,
|
||||
"metrics": False,
|
||||
"reasoning_steps": False,
|
||||
},
|
||||
# JSON preset - JSONL output for piping
|
||||
"json": {
|
||||
"verbose": False,
|
||||
"markdown": False,
|
||||
"stream": False,
|
||||
"metrics": False,
|
||||
"reasoning_steps": False,
|
||||
"actions_trace": True,
|
||||
"json_output": True, # Output as JSONL
|
||||
},
|
||||
}
|
||||
|
||||
# Default output mode - can be overridden by PRAISONAI_OUTPUT env var
|
||||
# "silent" = zero overhead, fastest performance (DEFAULT)
|
||||
# "actions" = tool call trace + final output (opt-in observability)
|
||||
DEFAULT_OUTPUT_MODE = "silent"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Execution Presets
|
||||
|
||||
@@ -157,6 +157,16 @@ __all__ = [
|
||||
"FastContextResult",
|
||||
"FileMatch",
|
||||
"LineRange",
|
||||
# Artifacts (Dynamic Context Discovery)
|
||||
"ArtifactRef",
|
||||
"ArtifactMetadata",
|
||||
"GrepMatch",
|
||||
"QueueConfig",
|
||||
"ArtifactStoreProtocol",
|
||||
"HistoryStoreProtocol",
|
||||
"TerminalLoggerProtocol",
|
||||
"compute_checksum",
|
||||
"generate_summary",
|
||||
]
|
||||
|
||||
|
||||
@@ -252,4 +262,11 @@ def __getattr__(name: str):
|
||||
from praisonaiagents.context.fast.result import LineRange
|
||||
return LineRange
|
||||
|
||||
# Artifacts (Dynamic Context Discovery)
|
||||
if name in ("ArtifactRef", "ArtifactMetadata", "GrepMatch", "QueueConfig",
|
||||
"ArtifactStoreProtocol", "HistoryStoreProtocol", "TerminalLoggerProtocol",
|
||||
"compute_checksum", "generate_summary"):
|
||||
from . import artifacts
|
||||
return getattr(artifacts, name)
|
||||
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
@@ -0,0 +1,580 @@
|
||||
"""
|
||||
Artifact Types and Protocols for Dynamic Context Discovery.
|
||||
|
||||
This module provides lightweight types and protocols for artifact-based
|
||||
context management. Implementations live in the wrapper (praisonai).
|
||||
|
||||
Zero Performance Impact:
|
||||
- Pure dataclasses and protocols
|
||||
- No heavy imports
|
||||
- No I/O at import time
|
||||
|
||||
Usage:
|
||||
from praisonaiagents.context.artifacts import ArtifactRef, ArtifactStoreProtocol
|
||||
|
||||
# Create a reference to a stored artifact
|
||||
ref = ArtifactRef(
|
||||
path="/path/to/artifact.json",
|
||||
summary="API response with 50,000 records",
|
||||
size_bytes=1_200_000,
|
||||
mime_type="application/json",
|
||||
)
|
||||
|
||||
# Implement the protocol in wrapper
|
||||
class FileSystemArtifactStore:
|
||||
def store(self, content, metadata): ...
|
||||
def load(self, ref): ...
|
||||
"""
|
||||
|
||||
import time
|
||||
import hashlib
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, Dict, List, Optional, Protocol, Union, runtime_checkable
|
||||
|
||||
|
||||
@dataclass
|
||||
class ArtifactRef:
|
||||
"""
|
||||
Reference to a stored artifact.
|
||||
|
||||
Used to replace large tool outputs in context with a lightweight pointer.
|
||||
The agent can then use tail/grep/load operations to access content on demand.
|
||||
|
||||
Attributes:
|
||||
path: Absolute path to the artifact file
|
||||
summary: Brief description or first ~200 chars of content
|
||||
size_bytes: Original content size in bytes
|
||||
mime_type: Content type (e.g., "application/json", "text/plain")
|
||||
checksum: SHA256 hash for integrity verification
|
||||
created_at: Unix timestamp when artifact was created
|
||||
artifact_id: Unique identifier for the artifact
|
||||
agent_id: ID of the agent that created this artifact
|
||||
run_id: ID of the run/session this artifact belongs to
|
||||
tool_name: Name of the tool that produced this output (if applicable)
|
||||
turn_id: Conversation turn number when artifact was created
|
||||
"""
|
||||
path: str
|
||||
summary: str
|
||||
size_bytes: int
|
||||
mime_type: str = "application/octet-stream"
|
||||
checksum: str = ""
|
||||
created_at: float = field(default_factory=time.time)
|
||||
artifact_id: str = ""
|
||||
agent_id: str = ""
|
||||
run_id: str = ""
|
||||
tool_name: Optional[str] = None
|
||||
turn_id: int = 0
|
||||
|
||||
def to_inline(self) -> str:
|
||||
"""
|
||||
Generate inline representation for context.
|
||||
|
||||
Returns a compact string that can be included in the conversation
|
||||
context, allowing the agent to reference the artifact.
|
||||
"""
|
||||
size_str = self._format_size(self.size_bytes)
|
||||
tool_info = f" from {self.tool_name}" if self.tool_name else ""
|
||||
return f"[Artifact{tool_info}: {self.path} ({size_str}) - {self.summary}]"
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert to dictionary for serialization."""
|
||||
return {
|
||||
"path": self.path,
|
||||
"summary": self.summary,
|
||||
"size_bytes": self.size_bytes,
|
||||
"mime_type": self.mime_type,
|
||||
"checksum": self.checksum,
|
||||
"created_at": self.created_at,
|
||||
"artifact_id": self.artifact_id,
|
||||
"agent_id": self.agent_id,
|
||||
"run_id": self.run_id,
|
||||
"tool_name": self.tool_name,
|
||||
"turn_id": self.turn_id,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "ArtifactRef":
|
||||
"""Create from dictionary."""
|
||||
return cls(
|
||||
path=data.get("path", ""),
|
||||
summary=data.get("summary", ""),
|
||||
size_bytes=data.get("size_bytes", 0),
|
||||
mime_type=data.get("mime_type", "application/octet-stream"),
|
||||
checksum=data.get("checksum", ""),
|
||||
created_at=data.get("created_at", time.time()),
|
||||
artifact_id=data.get("artifact_id", ""),
|
||||
agent_id=data.get("agent_id", ""),
|
||||
run_id=data.get("run_id", ""),
|
||||
tool_name=data.get("tool_name"),
|
||||
turn_id=data.get("turn_id", 0),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _format_size(size_bytes: int) -> str:
|
||||
"""Format size in human-readable form."""
|
||||
if size_bytes < 1024:
|
||||
return f"{size_bytes} B"
|
||||
elif size_bytes < 1024 * 1024:
|
||||
return f"{size_bytes / 1024:.1f} KB"
|
||||
elif size_bytes < 1024 * 1024 * 1024:
|
||||
return f"{size_bytes / (1024 * 1024):.1f} MB"
|
||||
else:
|
||||
return f"{size_bytes / (1024 * 1024 * 1024):.1f} GB"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ArtifactMetadata:
|
||||
"""
|
||||
Metadata for artifact creation and tracking.
|
||||
|
||||
Passed to ArtifactStore.store() to provide context about the artifact.
|
||||
"""
|
||||
agent_id: str = ""
|
||||
run_id: str = ""
|
||||
tool_name: Optional[str] = None
|
||||
turn_id: int = 0
|
||||
content_type: str = "auto" # auto, json, text, binary
|
||||
tags: List[str] = field(default_factory=list)
|
||||
extra: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"agent_id": self.agent_id,
|
||||
"run_id": self.run_id,
|
||||
"tool_name": self.tool_name,
|
||||
"turn_id": self.turn_id,
|
||||
"content_type": self.content_type,
|
||||
"tags": self.tags,
|
||||
"extra": self.extra,
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class GrepMatch:
|
||||
"""A single match from grep operation."""
|
||||
line_number: int
|
||||
line_content: str
|
||||
context_before: List[str] = field(default_factory=list)
|
||||
context_after: List[str] = field(default_factory=list)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"line_number": self.line_number,
|
||||
"line_content": self.line_content,
|
||||
"context_before": self.context_before,
|
||||
"context_after": self.context_after,
|
||||
}
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class ArtifactStoreProtocol(Protocol):
|
||||
"""
|
||||
Protocol for artifact storage backends.
|
||||
|
||||
Implementations should handle:
|
||||
- Storing content to persistent storage
|
||||
- Loading content back
|
||||
- Efficient tail/grep operations for large files
|
||||
- Checksum verification
|
||||
- Secret redaction (if enabled)
|
||||
|
||||
Example implementation in wrapper:
|
||||
class FileSystemArtifactStore:
|
||||
def store(self, content, metadata):
|
||||
# Write to ~/.praison/runs/{run_id}/artifacts/
|
||||
...
|
||||
"""
|
||||
|
||||
def store(
|
||||
self,
|
||||
content: Any,
|
||||
metadata: ArtifactMetadata,
|
||||
) -> ArtifactRef:
|
||||
"""
|
||||
Store content as an artifact.
|
||||
|
||||
Args:
|
||||
content: The content to store (will be serialized)
|
||||
metadata: Metadata about the artifact
|
||||
|
||||
Returns:
|
||||
ArtifactRef pointing to the stored artifact
|
||||
"""
|
||||
...
|
||||
|
||||
def load(self, ref: ArtifactRef) -> Any:
|
||||
"""
|
||||
Load full content from an artifact.
|
||||
|
||||
Args:
|
||||
ref: Reference to the artifact
|
||||
|
||||
Returns:
|
||||
The deserialized content
|
||||
"""
|
||||
...
|
||||
|
||||
def tail(self, ref: ArtifactRef, lines: int = 50) -> str:
|
||||
"""
|
||||
Get the last N lines of an artifact.
|
||||
|
||||
Args:
|
||||
ref: Reference to the artifact
|
||||
lines: Number of lines to return
|
||||
|
||||
Returns:
|
||||
String containing the last N lines
|
||||
"""
|
||||
...
|
||||
|
||||
def head(self, ref: ArtifactRef, lines: int = 50) -> str:
|
||||
"""
|
||||
Get the first N lines of an artifact.
|
||||
|
||||
Args:
|
||||
ref: Reference to the artifact
|
||||
lines: Number of lines to return
|
||||
|
||||
Returns:
|
||||
String containing the first N lines
|
||||
"""
|
||||
...
|
||||
|
||||
def grep(
|
||||
self,
|
||||
ref: ArtifactRef,
|
||||
pattern: str,
|
||||
context_lines: int = 2,
|
||||
max_matches: int = 50,
|
||||
) -> List[GrepMatch]:
|
||||
"""
|
||||
Search for pattern in artifact content.
|
||||
|
||||
Args:
|
||||
ref: Reference to the artifact
|
||||
pattern: Regex pattern to search for
|
||||
context_lines: Number of context lines before/after match
|
||||
max_matches: Maximum number of matches to return
|
||||
|
||||
Returns:
|
||||
List of GrepMatch objects
|
||||
"""
|
||||
...
|
||||
|
||||
def chunk(
|
||||
self,
|
||||
ref: ArtifactRef,
|
||||
start_line: int = 1,
|
||||
end_line: Optional[int] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Get a chunk of lines from an artifact.
|
||||
|
||||
Args:
|
||||
ref: Reference to the artifact
|
||||
start_line: Starting line number (1-indexed)
|
||||
end_line: Ending line number (inclusive), None for end of file
|
||||
|
||||
Returns:
|
||||
String containing the requested lines
|
||||
"""
|
||||
...
|
||||
|
||||
def delete(self, ref: ArtifactRef) -> bool:
|
||||
"""
|
||||
Delete an artifact.
|
||||
|
||||
Args:
|
||||
ref: Reference to the artifact
|
||||
|
||||
Returns:
|
||||
True if deleted successfully
|
||||
"""
|
||||
...
|
||||
|
||||
def list_artifacts(
|
||||
self,
|
||||
run_id: Optional[str] = None,
|
||||
agent_id: Optional[str] = None,
|
||||
tool_name: Optional[str] = None,
|
||||
) -> List[ArtifactRef]:
|
||||
"""
|
||||
List artifacts matching filters.
|
||||
|
||||
Args:
|
||||
run_id: Filter by run ID
|
||||
agent_id: Filter by agent ID
|
||||
tool_name: Filter by tool name
|
||||
|
||||
Returns:
|
||||
List of matching ArtifactRef objects
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class HistoryStoreProtocol(Protocol):
|
||||
"""
|
||||
Protocol for conversation history storage.
|
||||
|
||||
Enables loss recovery during summarization by persisting
|
||||
full conversation history to artifacts.
|
||||
"""
|
||||
|
||||
def append(
|
||||
self,
|
||||
message: Dict[str, Any],
|
||||
agent_id: str,
|
||||
run_id: str,
|
||||
) -> None:
|
||||
"""
|
||||
Append a message to history.
|
||||
|
||||
Args:
|
||||
message: The message dict (role, content, etc.)
|
||||
agent_id: ID of the agent
|
||||
run_id: ID of the run/session
|
||||
"""
|
||||
...
|
||||
|
||||
def get_ref(self, agent_id: str, run_id: str) -> Optional[ArtifactRef]:
|
||||
"""
|
||||
Get artifact reference for history file.
|
||||
|
||||
Args:
|
||||
agent_id: ID of the agent
|
||||
run_id: ID of the run/session
|
||||
|
||||
Returns:
|
||||
ArtifactRef if history exists, None otherwise
|
||||
"""
|
||||
...
|
||||
|
||||
def search(
|
||||
self,
|
||||
query: str,
|
||||
agent_id: str,
|
||||
run_id: str,
|
||||
max_results: int = 20,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Search history for matching messages.
|
||||
|
||||
Args:
|
||||
query: Search query (supports regex)
|
||||
agent_id: ID of the agent
|
||||
run_id: ID of the run/session
|
||||
max_results: Maximum results to return
|
||||
|
||||
Returns:
|
||||
List of matching message dicts
|
||||
"""
|
||||
...
|
||||
|
||||
def get_messages(
|
||||
self,
|
||||
agent_id: str,
|
||||
run_id: str,
|
||||
start_turn: int = 0,
|
||||
end_turn: Optional[int] = None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Get messages from history.
|
||||
|
||||
Args:
|
||||
agent_id: ID of the agent
|
||||
run_id: ID of the run/session
|
||||
start_turn: Starting turn number
|
||||
end_turn: Ending turn number (None for all)
|
||||
|
||||
Returns:
|
||||
List of message dicts
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class TerminalLoggerProtocol(Protocol):
|
||||
"""
|
||||
Protocol for terminal session logging.
|
||||
|
||||
Captures shell command outputs to artifacts for later retrieval.
|
||||
"""
|
||||
|
||||
def log_command(
|
||||
self,
|
||||
command: str,
|
||||
output: str,
|
||||
exit_code: int,
|
||||
agent_id: str,
|
||||
run_id: str,
|
||||
) -> ArtifactRef:
|
||||
"""
|
||||
Log a command execution.
|
||||
|
||||
Args:
|
||||
command: The command that was executed
|
||||
output: Combined stdout/stderr output
|
||||
exit_code: Command exit code
|
||||
agent_id: ID of the agent
|
||||
run_id: ID of the run/session
|
||||
|
||||
Returns:
|
||||
ArtifactRef to the logged output
|
||||
"""
|
||||
...
|
||||
|
||||
def get_session_ref(self, agent_id: str, run_id: str) -> Optional[ArtifactRef]:
|
||||
"""
|
||||
Get artifact reference for terminal session log.
|
||||
|
||||
Args:
|
||||
agent_id: ID of the agent
|
||||
run_id: ID of the run/session
|
||||
|
||||
Returns:
|
||||
ArtifactRef if session exists, None otherwise
|
||||
"""
|
||||
...
|
||||
|
||||
def tail_session(
|
||||
self,
|
||||
agent_id: str,
|
||||
run_id: str,
|
||||
lines: int = 100,
|
||||
) -> str:
|
||||
"""
|
||||
Get last N lines from terminal session.
|
||||
|
||||
Args:
|
||||
agent_id: ID of the agent
|
||||
run_id: ID of the run/session
|
||||
lines: Number of lines to return
|
||||
|
||||
Returns:
|
||||
String containing the last N lines
|
||||
"""
|
||||
...
|
||||
|
||||
def grep_session(
|
||||
self,
|
||||
pattern: str,
|
||||
agent_id: str,
|
||||
run_id: str,
|
||||
max_matches: int = 50,
|
||||
) -> List[GrepMatch]:
|
||||
"""
|
||||
Search terminal session for pattern.
|
||||
|
||||
Args:
|
||||
pattern: Regex pattern to search for
|
||||
agent_id: ID of the agent
|
||||
run_id: ID of the run/session
|
||||
max_matches: Maximum matches to return
|
||||
|
||||
Returns:
|
||||
List of GrepMatch objects
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
@dataclass
|
||||
class QueueConfig:
|
||||
"""
|
||||
Configuration for tool output queuing.
|
||||
|
||||
Controls when and how large tool outputs are queued to artifacts
|
||||
instead of being included inline in the context.
|
||||
"""
|
||||
enabled: bool = True
|
||||
inline_max_bytes: int = 32 * 1024 # 32KB default
|
||||
redact_secrets: bool = True
|
||||
summary_max_chars: int = 200
|
||||
checksum_algorithm: str = "sha256"
|
||||
|
||||
# Secret patterns to redact (regex)
|
||||
secret_patterns: List[str] = field(default_factory=lambda: [
|
||||
r'(?i)(api[_-]?key|apikey)["\']?\s*[:=]\s*["\']?[\w\-]+',
|
||||
r'(?i)(secret|password|passwd|pwd)["\']?\s*[:=]\s*["\']?[\w\-]+',
|
||||
r'(?i)(token|bearer)["\']?\s*[:=]\s*["\']?[\w\-]+',
|
||||
r'(?i)(auth|authorization)["\']?\s*[:=]\s*["\']?[\w\-]+',
|
||||
r'sk-[a-zA-Z0-9]{20,}', # OpenAI keys
|
||||
r'ghp_[a-zA-Z0-9]{36}', # GitHub tokens
|
||||
r'gho_[a-zA-Z0-9]{36}', # GitHub OAuth
|
||||
r'xox[baprs]-[a-zA-Z0-9\-]+', # Slack tokens
|
||||
])
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"enabled": self.enabled,
|
||||
"inline_max_bytes": self.inline_max_bytes,
|
||||
"redact_secrets": self.redact_secrets,
|
||||
"summary_max_chars": self.summary_max_chars,
|
||||
"checksum_algorithm": self.checksum_algorithm,
|
||||
"secret_patterns": self.secret_patterns,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "QueueConfig":
|
||||
return cls(
|
||||
enabled=data.get("enabled", True),
|
||||
inline_max_bytes=data.get("inline_max_bytes", 32 * 1024),
|
||||
redact_secrets=data.get("redact_secrets", True),
|
||||
summary_max_chars=data.get("summary_max_chars", 200),
|
||||
checksum_algorithm=data.get("checksum_algorithm", "sha256"),
|
||||
secret_patterns=data.get("secret_patterns", cls().secret_patterns),
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def compute_checksum(content: Union[str, bytes], algorithm: str = "sha256") -> str:
|
||||
"""
|
||||
Compute checksum of content.
|
||||
|
||||
Args:
|
||||
content: String or bytes to hash
|
||||
algorithm: Hash algorithm (sha256, md5, etc.)
|
||||
|
||||
Returns:
|
||||
Hex digest of the hash
|
||||
"""
|
||||
if isinstance(content, str):
|
||||
content = content.encode("utf-8")
|
||||
|
||||
hasher = hashlib.new(algorithm)
|
||||
hasher.update(content)
|
||||
return hasher.hexdigest()
|
||||
|
||||
|
||||
def generate_summary(content: Any, max_chars: int = 200) -> str:
|
||||
"""
|
||||
Generate a brief summary of content.
|
||||
|
||||
Args:
|
||||
content: Content to summarize
|
||||
max_chars: Maximum characters in summary
|
||||
|
||||
Returns:
|
||||
Brief summary string
|
||||
"""
|
||||
if isinstance(content, (dict, list)):
|
||||
# For structured data, describe the structure
|
||||
if isinstance(content, dict):
|
||||
keys = list(content.keys())[:5]
|
||||
key_str = ", ".join(str(k) for k in keys)
|
||||
if len(content) > 5:
|
||||
key_str += f", ... ({len(content)} total keys)"
|
||||
return f"Dict with keys: {key_str}"[:max_chars]
|
||||
else:
|
||||
if len(content) > 0:
|
||||
first_type = type(content[0]).__name__
|
||||
return f"List of {len(content)} {first_type} items"[:max_chars]
|
||||
return "Empty list"
|
||||
|
||||
# For strings, take first N chars
|
||||
text = str(content)
|
||||
if len(text) <= max_chars:
|
||||
return text
|
||||
return text[:max_chars - 3] + "..."
|
||||
@@ -68,8 +68,8 @@ class LLM:
|
||||
MODEL_WINDOWS = {
|
||||
# OpenAI
|
||||
"gpt-4": 6144, # 8,192 actual
|
||||
"gpt-5-nano": 96000, # 128,000 actual
|
||||
"gpt-5-nano": 96000, # 128,000 actual
|
||||
"gpt-4o-mini": 96000, # 128,000 actual
|
||||
"gpt-4o-mini": 96000, # 128,000 actual
|
||||
"gpt-4-turbo": 96000, # 128,000 actual
|
||||
"o1-preview": 96000, # 128,000 actual
|
||||
"o1-mini": 96000, # 128,000 actual
|
||||
@@ -1430,6 +1430,8 @@ Now provide your final answer using this result."""
|
||||
task_id: Optional[str] = None,
|
||||
execute_tool_fn: Optional[Callable] = None,
|
||||
stream: bool = True,
|
||||
stream_callback: Optional[Callable] = None,
|
||||
emit_events: bool = False,
|
||||
**kwargs
|
||||
) -> str:
|
||||
"""Enhanced get_response with all OpenAI-like features"""
|
||||
@@ -1489,6 +1491,19 @@ Now provide your final answer using this result."""
|
||||
# Disable litellm debug messages
|
||||
litellm.set_verbose = False
|
||||
|
||||
# Setup StreamEvent emission if enabled
|
||||
_emit = emit_events and stream_callback is not None
|
||||
if _emit:
|
||||
from ..streaming.events import StreamEvent, StreamEventType
|
||||
_first_token_emitted = False
|
||||
_last_content_time = None
|
||||
_request_start_perf = time.perf_counter()
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.REQUEST_START,
|
||||
timestamp=_request_start_perf,
|
||||
metadata={"model": self.model, "provider": "litellm"}
|
||||
))
|
||||
|
||||
# Format tools if provided
|
||||
formatted_tools = self._format_tools_for_litellm(tools)
|
||||
|
||||
@@ -1525,6 +1540,7 @@ Now provide your final answer using this result."""
|
||||
max_iterations = 10 # Prevent infinite loops
|
||||
iteration_count = 0
|
||||
final_response_text = ""
|
||||
response_text = "" # Initialize to prevent UnboundLocalError on API errors
|
||||
stored_reasoning_content = None # Store reasoning content from tool execution
|
||||
accumulated_tool_results = [] # Store all tool results across iterations
|
||||
|
||||
|
||||
@@ -51,7 +51,7 @@ class ModelRouter:
|
||||
DEFAULT_MODELS = [
|
||||
# Lightweight/cheap models for simple tasks
|
||||
ModelProfile(
|
||||
name="gpt-5-nano",
|
||||
name="gpt-4o-mini",
|
||||
provider="openai",
|
||||
complexity_range=(TaskComplexity.SIMPLE, TaskComplexity.MODERATE),
|
||||
cost_per_1k_tokens=0.00075, # Average of $0.00015 input, $0.0006 output
|
||||
@@ -80,7 +80,7 @@ class ModelRouter:
|
||||
|
||||
# Mid-tier models for moderate complexity
|
||||
ModelProfile(
|
||||
name="gpt-5-nano",
|
||||
name="gpt-4o-mini",
|
||||
provider="openai",
|
||||
complexity_range=(TaskComplexity.MODERATE, TaskComplexity.COMPLEX),
|
||||
cost_per_1k_tokens=0.0075, # Average of $0.0025 input, $0.01 output
|
||||
@@ -145,7 +145,7 @@ class ModelRouter:
|
||||
preferred_providers: List of preferred providers in order
|
||||
"""
|
||||
self.models = models or self.DEFAULT_MODELS
|
||||
self.default_model = default_model or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
|
||||
self.default_model = default_model or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
|
||||
self.cost_threshold = cost_threshold
|
||||
self.preferred_providers = preferred_providers or []
|
||||
|
||||
|
||||
@@ -582,6 +582,8 @@ class OpenAIClient:
|
||||
console: Optional[Console] = None,
|
||||
display_fn: Optional[Callable] = None,
|
||||
reasoning_steps: bool = False,
|
||||
stream_callback: Optional[Callable] = None,
|
||||
emit_events: bool = False,
|
||||
**kwargs
|
||||
) -> Optional[ChatCompletion]:
|
||||
"""
|
||||
@@ -596,11 +598,18 @@ class OpenAIClient:
|
||||
console: Console for output
|
||||
display_fn: Display function for live updates
|
||||
reasoning_steps: Whether to show reasoning steps
|
||||
stream_callback: Optional callback for StreamEvent emission
|
||||
emit_events: Whether to emit StreamEvents (requires stream_callback)
|
||||
**kwargs: Additional parameters for the API
|
||||
|
||||
Returns:
|
||||
ChatCompletion object or None if error
|
||||
"""
|
||||
# Lazy import StreamEvent types only when needed
|
||||
_emit = emit_events and stream_callback is not None
|
||||
if _emit:
|
||||
from ..streaming.events import StreamEvent, StreamEventType
|
||||
|
||||
try:
|
||||
# Default start time and console if not provided
|
||||
if start_time is None:
|
||||
@@ -608,6 +617,15 @@ class OpenAIClient:
|
||||
if console is None:
|
||||
console = self.console
|
||||
|
||||
# Emit REQUEST_START event
|
||||
request_start_perf = time.perf_counter()
|
||||
if _emit:
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.REQUEST_START,
|
||||
timestamp=request_start_perf,
|
||||
metadata={"model": model, "message_count": len(messages)}
|
||||
))
|
||||
|
||||
# Create the response stream
|
||||
response_stream = self.sync_client.chat.completions.create(
|
||||
model=model,
|
||||
@@ -615,29 +633,81 @@ class OpenAIClient:
|
||||
temperature=temperature,
|
||||
tools=tools if tools else None,
|
||||
stream=True,
|
||||
stream_options={"include_usage": True} if _emit else None,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
# Emit HEADERS_RECEIVED event (stream object created means headers received)
|
||||
if _emit:
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.HEADERS_RECEIVED,
|
||||
timestamp=time.perf_counter()
|
||||
))
|
||||
|
||||
full_response_text = ""
|
||||
reasoning_content = ""
|
||||
chunks = []
|
||||
first_token_emitted = False
|
||||
last_content_time = None
|
||||
ttft_logged = False
|
||||
|
||||
# If display function provided, use Live display
|
||||
if display_fn:
|
||||
with Live(
|
||||
display_fn("", start_time),
|
||||
console=console,
|
||||
refresh_per_second=4,
|
||||
refresh_per_second=10, # Increased for more responsive streaming
|
||||
transient=True,
|
||||
vertical_overflow="ellipsis",
|
||||
auto_refresh=True
|
||||
) as live:
|
||||
for chunk in response_stream:
|
||||
chunks.append(chunk)
|
||||
if chunk.choices[0].delta.content:
|
||||
full_response_text += chunk.choices[0].delta.content
|
||||
|
||||
# Check for content delta
|
||||
if chunk.choices and chunk.choices[0].delta.content:
|
||||
content = chunk.choices[0].delta.content
|
||||
full_response_text += content
|
||||
last_content_time = time.perf_counter()
|
||||
|
||||
# Log TTFT on first token
|
||||
if not ttft_logged:
|
||||
ttft_ms = (last_content_time - request_start_perf) * 1000
|
||||
logging.debug(f"TTFT (Time To First Token): {ttft_ms:.0f}ms")
|
||||
ttft_logged = True
|
||||
|
||||
# Emit FIRST_TOKEN on first content
|
||||
if _emit and not first_token_emitted:
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.FIRST_TOKEN,
|
||||
timestamp=last_content_time,
|
||||
content=content
|
||||
))
|
||||
first_token_emitted = True
|
||||
elif _emit:
|
||||
# Emit DELTA_TEXT for subsequent tokens
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.DELTA_TEXT,
|
||||
timestamp=last_content_time,
|
||||
content=content
|
||||
))
|
||||
|
||||
live.update(display_fn(full_response_text, start_time))
|
||||
|
||||
# Handle tool calls streaming
|
||||
if _emit and chunk.choices and hasattr(chunk.choices[0].delta, 'tool_calls') and chunk.choices[0].delta.tool_calls:
|
||||
for tc in chunk.choices[0].delta.tool_calls:
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.DELTA_TOOL_CALL,
|
||||
timestamp=time.perf_counter(),
|
||||
tool_call={
|
||||
"index": tc.index,
|
||||
"id": getattr(tc, 'id', None),
|
||||
"name": getattr(tc.function, 'name', None) if tc.function else None,
|
||||
"arguments": getattr(tc.function, 'arguments', None) if tc.function else None
|
||||
}
|
||||
))
|
||||
|
||||
# Update live display with reasoning content if enabled
|
||||
if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
|
||||
rc = chunk.choices[0].delta.reasoning_content
|
||||
@@ -648,9 +718,61 @@ class OpenAIClient:
|
||||
# Clear the last generating display with a blank line
|
||||
console.print()
|
||||
else:
|
||||
# Just collect chunks without display
|
||||
# Just collect chunks without display, but still emit events
|
||||
for chunk in response_stream:
|
||||
chunks.append(chunk)
|
||||
|
||||
if chunk.choices and chunk.choices[0].delta.content:
|
||||
content = chunk.choices[0].delta.content
|
||||
last_content_time = time.perf_counter()
|
||||
|
||||
# Log TTFT on first token
|
||||
if not ttft_logged:
|
||||
ttft_ms = (last_content_time - request_start_perf) * 1000
|
||||
logging.debug(f"TTFT (Time To First Token): {ttft_ms:.0f}ms")
|
||||
ttft_logged = True
|
||||
|
||||
if _emit and not first_token_emitted:
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.FIRST_TOKEN,
|
||||
timestamp=last_content_time,
|
||||
content=content
|
||||
))
|
||||
first_token_emitted = True
|
||||
elif _emit:
|
||||
# Emit DELTA_TEXT for subsequent tokens
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.DELTA_TEXT,
|
||||
timestamp=last_content_time,
|
||||
content=content
|
||||
))
|
||||
|
||||
# Handle tool calls streaming
|
||||
if _emit and chunk.choices and hasattr(chunk.choices[0].delta, 'tool_calls') and chunk.choices[0].delta.tool_calls:
|
||||
for tc in chunk.choices[0].delta.tool_calls:
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.DELTA_TOOL_CALL,
|
||||
timestamp=time.perf_counter(),
|
||||
tool_call={
|
||||
"index": tc.index,
|
||||
"id": getattr(tc, 'id', None),
|
||||
"name": getattr(tc.function, 'name', None) if tc.function else None,
|
||||
"arguments": getattr(tc.function, 'arguments', None) if tc.function else None
|
||||
}
|
||||
))
|
||||
|
||||
# Emit LAST_TOKEN and STREAM_END events
|
||||
if _emit:
|
||||
if last_content_time:
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.LAST_TOKEN,
|
||||
timestamp=last_content_time
|
||||
))
|
||||
stream_callback(StreamEvent(
|
||||
type=StreamEventType.STREAM_END,
|
||||
timestamp=time.perf_counter(),
|
||||
metadata={"chunk_count": len(chunks)}
|
||||
))
|
||||
|
||||
final_response = process_stream_chunks(chunks)
|
||||
return final_response
|
||||
@@ -669,6 +791,8 @@ class OpenAIClient:
|
||||
console: Optional[Console] = None,
|
||||
display_fn: Optional[Callable] = None,
|
||||
reasoning_steps: bool = False,
|
||||
stream_callback: Optional[Callable] = None,
|
||||
emit_events: bool = False,
|
||||
**kwargs
|
||||
) -> Optional[ChatCompletion]:
|
||||
"""
|
||||
@@ -683,11 +807,18 @@ class OpenAIClient:
|
||||
console: Console for output
|
||||
display_fn: Display function for live updates
|
||||
reasoning_steps: Whether to show reasoning steps
|
||||
stream_callback: Optional callback for StreamEvent emission (can be sync or async)
|
||||
emit_events: Whether to emit StreamEvents (requires stream_callback)
|
||||
**kwargs: Additional parameters for the API
|
||||
|
||||
Returns:
|
||||
ChatCompletion object or None if error
|
||||
"""
|
||||
# Lazy import StreamEvent types only when needed
|
||||
_emit = emit_events and stream_callback is not None
|
||||
if _emit:
|
||||
from ..streaming.events import StreamEvent, StreamEventType
|
||||
|
||||
try:
|
||||
# Default start time and console if not provided
|
||||
if start_time is None:
|
||||
@@ -695,6 +826,19 @@ class OpenAIClient:
|
||||
if console is None:
|
||||
console = self.console
|
||||
|
||||
# Emit REQUEST_START event
|
||||
request_start_perf = time.perf_counter()
|
||||
if _emit:
|
||||
event = StreamEvent(
|
||||
type=StreamEventType.REQUEST_START,
|
||||
timestamp=request_start_perf,
|
||||
metadata={"model": model, "message_count": len(messages)}
|
||||
)
|
||||
if asyncio.iscoroutinefunction(stream_callback):
|
||||
await stream_callback(event)
|
||||
else:
|
||||
stream_callback(event)
|
||||
|
||||
# Create the response stream
|
||||
response_stream = await self.async_client.chat.completions.create(
|
||||
model=model,
|
||||
@@ -702,29 +846,81 @@ class OpenAIClient:
|
||||
temperature=temperature,
|
||||
tools=tools if tools else None,
|
||||
stream=True,
|
||||
stream_options={"include_usage": True} if _emit else None,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
# Emit HEADERS_RECEIVED event
|
||||
if _emit:
|
||||
event = StreamEvent(
|
||||
type=StreamEventType.HEADERS_RECEIVED,
|
||||
timestamp=time.perf_counter()
|
||||
)
|
||||
if asyncio.iscoroutinefunction(stream_callback):
|
||||
await stream_callback(event)
|
||||
else:
|
||||
stream_callback(event)
|
||||
|
||||
full_response_text = ""
|
||||
reasoning_content = ""
|
||||
chunks = []
|
||||
first_token_emitted = False
|
||||
last_content_time = None
|
||||
|
||||
async def emit_event(event: 'StreamEvent'):
|
||||
if asyncio.iscoroutinefunction(stream_callback):
|
||||
await stream_callback(event)
|
||||
else:
|
||||
stream_callback(event)
|
||||
|
||||
# If display function provided, use Live display
|
||||
if display_fn:
|
||||
with Live(
|
||||
display_fn("", start_time),
|
||||
console=console,
|
||||
refresh_per_second=4,
|
||||
refresh_per_second=10,
|
||||
transient=True,
|
||||
vertical_overflow="ellipsis",
|
||||
auto_refresh=True
|
||||
) as live:
|
||||
async for chunk in response_stream:
|
||||
chunks.append(chunk)
|
||||
if chunk.choices[0].delta.content:
|
||||
full_response_text += chunk.choices[0].delta.content
|
||||
|
||||
if chunk.choices and chunk.choices[0].delta.content:
|
||||
content = chunk.choices[0].delta.content
|
||||
full_response_text += content
|
||||
last_content_time = time.perf_counter()
|
||||
|
||||
if _emit and not first_token_emitted:
|
||||
await emit_event(StreamEvent(
|
||||
type=StreamEventType.FIRST_TOKEN,
|
||||
timestamp=last_content_time,
|
||||
content=content
|
||||
))
|
||||
first_token_emitted = True
|
||||
elif _emit:
|
||||
await emit_event(StreamEvent(
|
||||
type=StreamEventType.DELTA_TEXT,
|
||||
timestamp=last_content_time,
|
||||
content=content
|
||||
))
|
||||
|
||||
live.update(display_fn(full_response_text, start_time))
|
||||
|
||||
# Handle tool calls streaming
|
||||
if _emit and chunk.choices and hasattr(chunk.choices[0].delta, 'tool_calls') and chunk.choices[0].delta.tool_calls:
|
||||
for tc in chunk.choices[0].delta.tool_calls:
|
||||
await emit_event(StreamEvent(
|
||||
type=StreamEventType.DELTA_TOOL_CALL,
|
||||
timestamp=time.perf_counter(),
|
||||
tool_call={
|
||||
"index": tc.index,
|
||||
"id": getattr(tc, 'id', None),
|
||||
"name": getattr(tc.function, 'name', None) if tc.function else None,
|
||||
"arguments": getattr(tc.function, 'arguments', None) if tc.function else None
|
||||
}
|
||||
))
|
||||
|
||||
# Update live display with reasoning content if enabled
|
||||
if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
|
||||
rc = chunk.choices[0].delta.reasoning_content
|
||||
@@ -738,6 +934,50 @@ class OpenAIClient:
|
||||
# Just collect chunks without display
|
||||
async for chunk in response_stream:
|
||||
chunks.append(chunk)
|
||||
|
||||
if _emit and chunk.choices and chunk.choices[0].delta.content:
|
||||
content = chunk.choices[0].delta.content
|
||||
last_content_time = time.perf_counter()
|
||||
|
||||
if not first_token_emitted:
|
||||
await emit_event(StreamEvent(
|
||||
type=StreamEventType.FIRST_TOKEN,
|
||||
timestamp=last_content_time,
|
||||
content=content
|
||||
))
|
||||
first_token_emitted = True
|
||||
else:
|
||||
await emit_event(StreamEvent(
|
||||
type=StreamEventType.DELTA_TEXT,
|
||||
timestamp=last_content_time,
|
||||
content=content
|
||||
))
|
||||
|
||||
if _emit and chunk.choices and hasattr(chunk.choices[0].delta, 'tool_calls') and chunk.choices[0].delta.tool_calls:
|
||||
for tc in chunk.choices[0].delta.tool_calls:
|
||||
await emit_event(StreamEvent(
|
||||
type=StreamEventType.DELTA_TOOL_CALL,
|
||||
timestamp=time.perf_counter(),
|
||||
tool_call={
|
||||
"index": tc.index,
|
||||
"id": getattr(tc, 'id', None),
|
||||
"name": getattr(tc.function, 'name', None) if tc.function else None,
|
||||
"arguments": getattr(tc.function, 'arguments', None) if tc.function else None
|
||||
}
|
||||
))
|
||||
|
||||
# Emit LAST_TOKEN and STREAM_END events
|
||||
if _emit:
|
||||
if last_content_time:
|
||||
await emit_event(StreamEvent(
|
||||
type=StreamEventType.LAST_TOKEN,
|
||||
timestamp=last_content_time
|
||||
))
|
||||
await emit_event(StreamEvent(
|
||||
type=StreamEventType.STREAM_END,
|
||||
timestamp=time.perf_counter(),
|
||||
metadata={"chunk_count": len(chunks)}
|
||||
))
|
||||
|
||||
final_response = process_stream_chunks(chunks)
|
||||
return final_response
|
||||
@@ -749,7 +989,7 @@ class OpenAIClient:
|
||||
def create_completion(
|
||||
self,
|
||||
messages: List[Dict[str, Any]],
|
||||
model: str = "gpt-5-nano",
|
||||
model: str = "gpt-4o-mini",
|
||||
temperature: float = 1.0,
|
||||
stream: bool = False,
|
||||
tools: Optional[List[Dict[str, Any]]] = None,
|
||||
@@ -794,7 +1034,7 @@ class OpenAIClient:
|
||||
async def acreate_completion(
|
||||
self,
|
||||
messages: List[Dict[str, Any]],
|
||||
model: str = "gpt-5-nano",
|
||||
model: str = "gpt-4o-mini",
|
||||
temperature: float = 1.0,
|
||||
stream: bool = False,
|
||||
tools: Optional[List[Dict[str, Any]]] = None,
|
||||
@@ -839,7 +1079,7 @@ class OpenAIClient:
|
||||
def chat_completion_with_tools(
|
||||
self,
|
||||
messages: List[Dict[str, Any]],
|
||||
model: str = "gpt-5-nano",
|
||||
model: str = "gpt-4o-mini",
|
||||
temperature: float = 1.0,
|
||||
tools: Optional[List[Any]] = None,
|
||||
execute_tool_fn: Optional[Callable] = None,
|
||||
@@ -904,7 +1144,9 @@ class OpenAIClient:
|
||||
if display_fn and console:
|
||||
# When verbose (display_fn provided), use streaming for better UX
|
||||
try:
|
||||
with Live(display_fn("", start_time), console=console, refresh_per_second=4, transient=True) as live:
|
||||
request_start_perf = time.perf_counter()
|
||||
ttft_logged = False
|
||||
with Live(display_fn("", start_time), console=console, refresh_per_second=10, transient=True) as live:
|
||||
# Use streaming when display_fn is provided for progressive display
|
||||
response_stream = self.create_completion(
|
||||
messages=messages,
|
||||
@@ -922,6 +1164,11 @@ class OpenAIClient:
|
||||
for chunk in response_stream:
|
||||
chunks.append(chunk)
|
||||
if chunk.choices[0].delta.content:
|
||||
# Log TTFT on first token
|
||||
if not ttft_logged:
|
||||
ttft_ms = (time.perf_counter() - request_start_perf) * 1000
|
||||
logging.debug(f"TTFT (Time To First Token): {ttft_ms:.0f}ms")
|
||||
ttft_logged = True
|
||||
full_response_text += chunk.choices[0].delta.content
|
||||
live.update(display_fn(full_response_text, start_time))
|
||||
|
||||
@@ -1020,7 +1267,7 @@ class OpenAIClient:
|
||||
async def achat_completion_with_tools(
|
||||
self,
|
||||
messages: List[Dict[str, Any]],
|
||||
model: str = "gpt-5-nano",
|
||||
model: str = "gpt-4o-mini",
|
||||
temperature: float = 1.0,
|
||||
tools: Optional[List[Any]] = None,
|
||||
execute_tool_fn: Optional[Callable] = None,
|
||||
@@ -1204,7 +1451,7 @@ class OpenAIClient:
|
||||
def chat_completion_with_tools_stream(
|
||||
self,
|
||||
messages: List[Dict[str, Any]],
|
||||
model: str = "gpt-5-nano",
|
||||
model: str = "gpt-4o-mini",
|
||||
temperature: float = 1.0,
|
||||
tools: Optional[List[Any]] = None,
|
||||
execute_tool_fn: Optional[Callable] = None,
|
||||
@@ -1356,7 +1603,7 @@ class OpenAIClient:
|
||||
self,
|
||||
messages: List[Dict[str, Any]],
|
||||
response_format: BaseModel,
|
||||
model: str = "gpt-5-nano",
|
||||
model: str = "gpt-4o-mini",
|
||||
temperature: float = 1.0,
|
||||
**kwargs
|
||||
) -> Any:
|
||||
@@ -1390,7 +1637,7 @@ class OpenAIClient:
|
||||
self,
|
||||
messages: List[Dict[str, Any]],
|
||||
response_format: BaseModel,
|
||||
model: str = "gpt-5-nano",
|
||||
model: str = "gpt-4o-mini",
|
||||
temperature: float = 1.0,
|
||||
**kwargs
|
||||
) -> Any:
|
||||
|
||||
@@ -30,6 +30,53 @@ async_display_callbacks = {}
|
||||
# Global approval callback registry
|
||||
approval_callback = None
|
||||
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
# PraisonAI Unique Color Palette: "Elegant Intelligence"
|
||||
# Creates a visual narrative flow: Agent → Task → Working → Response
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
PRAISON_COLORS = {
|
||||
# Agent identity - grounded, stable
|
||||
"agent": "#86A789", # Soft Sage Green
|
||||
"agent_text": "#D2E3C8", # Light sage for text
|
||||
|
||||
# Task/Question - input, attention-grabbing
|
||||
"task": "#FF9B9B", # Warm Coral
|
||||
"task_text": "#FFE5E5", # Light coral for text
|
||||
|
||||
# Working/Processing - action, energy
|
||||
"working": "#FFB347", # Amber
|
||||
"working_text": "#FFF3E0", # Light amber for text
|
||||
|
||||
# Response/Output - completion, calm
|
||||
"response": "#4A90D9", # Ocean Blue
|
||||
"response_text": "#E3F2FD", # Light blue for text
|
||||
|
||||
# Tool calls - special action
|
||||
"tool": "#9B7EDE", # Violet Accent
|
||||
"tool_text": "#EDE7F6", # Light violet for text
|
||||
|
||||
# Reasoning - thought process
|
||||
"reasoning": "#78909C", # Blue Gray
|
||||
"reasoning_text": "#ECEFF1", # Light gray for text
|
||||
|
||||
# Error/Warning - alert
|
||||
"error": "#E57373", # Alert Red
|
||||
"error_text": "#FFEBEE", # Light red for text
|
||||
|
||||
# Metrics - meta information
|
||||
"metrics": "#B4B4B3", # Cool Gray
|
||||
"metrics_text": "#FAFAFA", # Near white for text
|
||||
}
|
||||
|
||||
# Status animation frames for "Working" indicator
|
||||
WORKING_FRAMES = ["●○○", "○●○", "○○●", "○●○"]
|
||||
WORKING_PHASES = [
|
||||
"Analyzing query...",
|
||||
"Processing context...",
|
||||
"Generating response...",
|
||||
"Finalizing output...",
|
||||
]
|
||||
|
||||
# At the top of the file, add display_callbacks to __all__
|
||||
__all__ = [
|
||||
'error_logs',
|
||||
@@ -39,7 +86,19 @@ __all__ = [
|
||||
'async_display_callbacks',
|
||||
'execute_callback',
|
||||
'approval_callback',
|
||||
# ... other exports
|
||||
# Color palette and animation constants
|
||||
'PRAISON_COLORS',
|
||||
'WORKING_FRAMES',
|
||||
'WORKING_PHASES',
|
||||
# Display functions
|
||||
'display_interaction',
|
||||
'display_instruction',
|
||||
'display_tool_call',
|
||||
'display_error',
|
||||
'display_generating',
|
||||
'display_reasoning_steps',
|
||||
'display_working_status',
|
||||
'display_self_reflection',
|
||||
]
|
||||
|
||||
def register_display_callback(display_type: str, callback_fn, is_async: bool = False):
|
||||
@@ -149,10 +208,14 @@ def _clean_display_content(content: str, max_length: int = 20000) -> str:
|
||||
|
||||
return content.strip()
|
||||
|
||||
def 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):
|
||||
def 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):
|
||||
"""Synchronous version of display_interaction.
|
||||
|
||||
Displays the task/message and response in clean panels with response time in title.
|
||||
Displays the task/message and response in clean panels with semantic colors
|
||||
and optional metrics footer. Uses PraisonAI's unique color palette.
|
||||
|
||||
Args:
|
||||
metrics: Optional dict with token_in, token_out, cost, model for footer display
|
||||
"""
|
||||
if console is None:
|
||||
console = Console()
|
||||
@@ -180,20 +243,38 @@ def display_interaction(message, response, markdown=True, generation_time=None,
|
||||
agent_tools=agent_tools,
|
||||
task_name=task_name,
|
||||
task_description=task_description,
|
||||
task_id=task_id
|
||||
task_id=task_id,
|
||||
metrics=metrics
|
||||
)
|
||||
|
||||
# Build response title with time (Agno-style: "Response (1.2s)")
|
||||
# Build response title with time
|
||||
response_title = "Response"
|
||||
if generation_time:
|
||||
response_title = f"Response ({generation_time:.1f}s)"
|
||||
|
||||
# Build response content with optional metrics footer
|
||||
response_content = response
|
||||
if metrics and isinstance(metrics, dict):
|
||||
# Add dashed separator and compact metrics line
|
||||
tokens_in = metrics.get('tokens_in', 0)
|
||||
tokens_out = metrics.get('tokens_out', 0)
|
||||
cost = metrics.get('cost', 0)
|
||||
model = metrics.get('model', '')
|
||||
|
||||
metrics_line = f"\n\n─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─\n📊 {tokens_in} tokens in · {tokens_out} out"
|
||||
if cost > 0:
|
||||
metrics_line += f" · ${cost:.4f}"
|
||||
if model:
|
||||
metrics_line += f" · {model}"
|
||||
response_content = response + metrics_line
|
||||
|
||||
# Use semantic colors: Task (coral), Response (ocean blue)
|
||||
if markdown:
|
||||
console.print(Panel.fit(Markdown(message), title="Task", border_style="cyan"))
|
||||
console.print(Panel.fit(Markdown(response), title=response_title, border_style="cyan"))
|
||||
console.print(Panel.fit(Markdown(message), title="Task", border_style=PRAISON_COLORS["task"]))
|
||||
console.print(Panel.fit(Markdown(response_content), title=response_title, border_style=PRAISON_COLORS["response"]))
|
||||
else:
|
||||
console.print(Panel.fit(Text(message, style="bold green"), title="Task", border_style="cyan"))
|
||||
console.print(Panel.fit(Text(response, style="bold blue"), title=response_title, border_style="cyan"))
|
||||
console.print(Panel.fit(Text(message, style=f"bold {PRAISON_COLORS['task_text']}"), title="Task", border_style=PRAISON_COLORS["task"]))
|
||||
console.print(Panel.fit(Text(response_content, style=f"bold {PRAISON_COLORS['response_text']}"), title=response_title, border_style=PRAISON_COLORS["response"]))
|
||||
|
||||
def display_self_reflection(message: str, console=None):
|
||||
if not message or not message.strip():
|
||||
@@ -231,8 +312,11 @@ def display_instruction(message: str, console=None, agent_name: str = None, agen
|
||||
if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
|
||||
console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
|
||||
|
||||
def display_tool_call(message: str, console=None, tool_name: str = None, tool_input: dict = None, tool_output: str = None):
|
||||
"""Display tool call information in a consolidated panel.
|
||||
def 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):
|
||||
"""Display tool call information in PraisonAI's unique timeline format.
|
||||
|
||||
Uses ▸ prefix, inline timing [X.Xs], and status icons ✓/✗ for a clean,
|
||||
scannable tool activity display.
|
||||
|
||||
Args:
|
||||
message: The tool call message (legacy format)
|
||||
@@ -240,6 +324,8 @@ def display_tool_call(message: str, console=None, tool_name: str = None, tool_in
|
||||
tool_name: Name of the tool being called
|
||||
tool_input: Input arguments to the tool
|
||||
tool_output: Output from the tool (if available)
|
||||
elapsed_time: Time taken for tool execution in seconds
|
||||
success: Whether the tool call succeeded
|
||||
"""
|
||||
logging.debug(f"display_tool_call called with message: {repr(message)}")
|
||||
if not message or not message.strip():
|
||||
@@ -253,17 +339,45 @@ def display_tool_call(message: str, console=None, tool_name: str = None, tool_in
|
||||
|
||||
# Only print panel if console is provided (verbose mode)
|
||||
if console is not None:
|
||||
# Build consolidated tool call display (Agno-style)
|
||||
if tool_name and tool_input is not None:
|
||||
# Format: tool_name(arg1=val1, arg2=val2)
|
||||
args_str = ", ".join(f"{k}={v}" for k, v in tool_input.items()) if tool_input else ""
|
||||
tool_display = f"• {tool_name}({args_str})"
|
||||
# Build unique PraisonAI timeline format
|
||||
if tool_name:
|
||||
# Format: ▸ tool_name(args) [X.Xs] ✓
|
||||
args_str = ""
|
||||
if tool_input:
|
||||
# Truncate long values for display
|
||||
args_parts = []
|
||||
for k, v in tool_input.items():
|
||||
v_str = str(v)
|
||||
if len(v_str) > 50:
|
||||
v_str = v_str[:47] + "..."
|
||||
args_parts.append(f"{k}={repr(v_str) if isinstance(v, str) else v_str}")
|
||||
args_str = ", ".join(args_parts)
|
||||
|
||||
# Build the timeline entry
|
||||
status_icon = "✓" if success else "✗"
|
||||
time_str = f"[{elapsed_time:.1f}s]" if elapsed_time else ""
|
||||
|
||||
tool_display = f"▸ {tool_name}({args_str}) {time_str} {status_icon}"
|
||||
|
||||
# Add output on next line with tree connector
|
||||
if tool_output:
|
||||
tool_display += f"\n → {tool_output}"
|
||||
console.print(Panel.fit(Text(tool_display, style="bold cyan"), title="Tool Call", border_style="green"))
|
||||
output_str = str(tool_output)
|
||||
if len(output_str) > 100:
|
||||
output_str = output_str[:97] + "..."
|
||||
tool_display += f"\n └─ {output_str}"
|
||||
|
||||
console.print(Panel.fit(
|
||||
Text(tool_display, style=f"bold {PRAISON_COLORS['tool_text']}"),
|
||||
title="Tool Activity",
|
||||
border_style=PRAISON_COLORS["tool"]
|
||||
))
|
||||
else:
|
||||
# Legacy format
|
||||
console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
|
||||
# Legacy format with new colors
|
||||
console.print(Panel.fit(
|
||||
Text(message, style=f"bold {PRAISON_COLORS['tool_text']}"),
|
||||
title="Tool Activity",
|
||||
border_style=PRAISON_COLORS["tool"]
|
||||
))
|
||||
|
||||
def display_error(message: str, console=None):
|
||||
if not message or not message.strip():
|
||||
@@ -275,7 +389,12 @@ def display_error(message: str, console=None):
|
||||
# Execute synchronous callbacks
|
||||
execute_sync_callback('error', message=message)
|
||||
|
||||
console.print(Panel.fit(Text(message, style="bold red"), title="Error", border_style="red"))
|
||||
# Use semantic error color
|
||||
console.print(Panel.fit(
|
||||
Text(message, style=f"bold {PRAISON_COLORS['error_text']}"),
|
||||
title="⚠ Error",
|
||||
border_style=PRAISON_COLORS["error"]
|
||||
))
|
||||
error_logs.append(message)
|
||||
|
||||
def display_generating(content: str = "", start_time: Optional[float] = None):
|
||||
@@ -293,7 +412,73 @@ def display_generating(content: str = "", start_time: Optional[float] = None):
|
||||
# Execute synchronous callbacks
|
||||
execute_sync_callback('generating', content=content, elapsed_time=elapsed_str.strip() if elapsed_str else None)
|
||||
|
||||
return Panel(Markdown(content), title=f"Generating...{elapsed_str}", border_style="green")
|
||||
# Use semantic response color
|
||||
return Panel(Markdown(content), title=f"Generating...{elapsed_str}", border_style=PRAISON_COLORS["response"])
|
||||
|
||||
def display_reasoning_steps(steps: List[str], console=None):
|
||||
"""Display reasoning steps with unique numbered circles.
|
||||
|
||||
Uses ①②③ numbered circles for a distinctive, scannable format
|
||||
that shows the agent's thought process.
|
||||
|
||||
Args:
|
||||
steps: List of reasoning step descriptions
|
||||
console: Rich console for output
|
||||
"""
|
||||
if not steps:
|
||||
return
|
||||
if console is None:
|
||||
console = Console()
|
||||
|
||||
# Circle number mapping (supports up to 20 steps)
|
||||
circle_numbers = "①②③④⑤⑥⑦⑧⑨⑩⑪⑫⑬⑭⑮⑯⑰⑱⑲⑳"
|
||||
|
||||
# Build reasoning display with numbered circles
|
||||
reasoning_lines = []
|
||||
for i, step in enumerate(steps):
|
||||
circle = circle_numbers[i] if i < len(circle_numbers) else f"({i+1})"
|
||||
step_text = _clean_display_content(str(step))
|
||||
if len(step_text) > 80:
|
||||
step_text = step_text[:77] + "..."
|
||||
reasoning_lines.append(f"{circle} {step_text}")
|
||||
|
||||
reasoning_display = "\n".join(reasoning_lines)
|
||||
|
||||
console.print(Panel.fit(
|
||||
Text(reasoning_display, style=f"italic {PRAISON_COLORS['reasoning_text']}"),
|
||||
title="Reasoning",
|
||||
border_style=PRAISON_COLORS["reasoning"]
|
||||
))
|
||||
|
||||
def display_working_status(phase: int = 0, status_text: str = None, console=None):
|
||||
"""Display animated working status with pulsing dots.
|
||||
|
||||
Shows a unique "Working ●○○" indicator with phase-specific status.
|
||||
This is PraisonAI's distinctive approach to showing processing status.
|
||||
|
||||
Args:
|
||||
phase: Current animation phase (0-3)
|
||||
status_text: Optional status description
|
||||
console: Rich console for output
|
||||
|
||||
Returns:
|
||||
Panel object for use with Rich.Live
|
||||
"""
|
||||
if console is None:
|
||||
console = Console()
|
||||
|
||||
# Get current frame and phase text
|
||||
frame = WORKING_FRAMES[phase % len(WORKING_FRAMES)]
|
||||
phase_text = status_text or WORKING_PHASES[phase % len(WORKING_PHASES)]
|
||||
|
||||
# Build working status display
|
||||
working_display = f"Working {frame} {phase_text}"
|
||||
|
||||
return Panel.fit(
|
||||
Text(working_display, style=f"bold {PRAISON_COLORS['working_text']}"),
|
||||
title="Status",
|
||||
border_style=PRAISON_COLORS["working"]
|
||||
)
|
||||
|
||||
# Async versions with 'a' prefix
|
||||
async def adisplay_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):
|
||||
|
||||
@@ -162,7 +162,7 @@ class MCP:
|
||||
# Method 1: Using command and args separately
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant...",
|
||||
llm="gpt-5-nano",
|
||||
llm="gpt-4o-mini",
|
||||
tools=MCP(
|
||||
command="/path/to/python",
|
||||
args=["/path/to/app.py"]
|
||||
@@ -172,14 +172,14 @@ class MCP:
|
||||
# Method 2: Using a single command string
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant...",
|
||||
llm="gpt-5-nano",
|
||||
llm="gpt-4o-mini",
|
||||
tools=MCP("/path/to/python /path/to/app.py")
|
||||
)
|
||||
|
||||
# Method 3: Using an SSE endpoint
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant...",
|
||||
llm="gpt-5-nano",
|
||||
llm="gpt-4o-mini",
|
||||
tools=MCP("http://localhost:8080/sse")
|
||||
)
|
||||
|
||||
@@ -548,7 +548,7 @@ class MCP:
|
||||
"""Convert the MCP tool to an OpenAI-compatible tool definition.
|
||||
|
||||
This method is specifically invoked by the Agent class when using
|
||||
provider/model format (e.g., "openai/gpt-5-nano").
|
||||
provider/model format (e.g., "openai/gpt-4o-mini").
|
||||
|
||||
Returns:
|
||||
dict or list: OpenAI-compatible tool definition(s)
|
||||
|
||||
@@ -110,6 +110,31 @@ def __getattr__(name):
|
||||
if name == "create_hooks_manager":
|
||||
from .hooks import create_hooks_manager
|
||||
return create_hooks_manager
|
||||
# Learn module
|
||||
if name == "LearnManager":
|
||||
from .learn import LearnManager
|
||||
return LearnManager
|
||||
if name == "PersonaStore":
|
||||
from .learn import PersonaStore
|
||||
return PersonaStore
|
||||
if name == "InsightStore":
|
||||
from .learn import InsightStore
|
||||
return InsightStore
|
||||
if name == "ThreadStore":
|
||||
from .learn import ThreadStore
|
||||
return ThreadStore
|
||||
if name == "PatternStore":
|
||||
from .learn import PatternStore
|
||||
return PatternStore
|
||||
if name == "DecisionStore":
|
||||
from .learn import DecisionStore
|
||||
return DecisionStore
|
||||
if name == "FeedbackStore":
|
||||
from .learn import FeedbackStore
|
||||
return FeedbackStore
|
||||
if name == "ImprovementStore":
|
||||
from .learn import ImprovementStore
|
||||
return ImprovementStore
|
||||
# DocsManager and MCPConfigManager are already imported at module level
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
|
||||
@@ -153,4 +178,13 @@ __all__ = [
|
||||
# MCP config management
|
||||
"MCPConfigManager",
|
||||
"MCPConfig",
|
||||
# Learn module
|
||||
"LearnManager",
|
||||
"PersonaStore",
|
||||
"InsightStore",
|
||||
"ThreadStore",
|
||||
"PatternStore",
|
||||
"DecisionStore",
|
||||
"FeedbackStore",
|
||||
"ImprovementStore",
|
||||
]
|
||||
@@ -0,0 +1,61 @@
|
||||
"""
|
||||
Learn Module - Continuous Learning within Memory System.
|
||||
|
||||
Provides learning capabilities for agents to capture patterns, preferences,
|
||||
and insights from interactions to improve future responses.
|
||||
|
||||
Usage:
|
||||
from praisonaiagents import Agent, MemoryConfig, LearnConfig
|
||||
|
||||
# Simple enable
|
||||
agent = Agent(memory=MemoryConfig(learn=True))
|
||||
|
||||
# With specific capabilities
|
||||
agent = Agent(memory=MemoryConfig(
|
||||
learn=LearnConfig(
|
||||
persona=True,
|
||||
insights=True,
|
||||
patterns=True,
|
||||
)
|
||||
))
|
||||
"""
|
||||
|
||||
__all__ = [
|
||||
"LearnManager",
|
||||
"PersonaStore",
|
||||
"InsightStore",
|
||||
"ThreadStore",
|
||||
"PatternStore",
|
||||
"DecisionStore",
|
||||
"FeedbackStore",
|
||||
"ImprovementStore",
|
||||
]
|
||||
|
||||
|
||||
def __getattr__(name: str):
|
||||
"""Lazy loading for learn module components."""
|
||||
if name == "LearnManager":
|
||||
from .manager import LearnManager
|
||||
return LearnManager
|
||||
if name == "PersonaStore":
|
||||
from .stores import PersonaStore
|
||||
return PersonaStore
|
||||
if name == "InsightStore":
|
||||
from .stores import InsightStore
|
||||
return InsightStore
|
||||
if name == "ThreadStore":
|
||||
from .stores import ThreadStore
|
||||
return ThreadStore
|
||||
if name == "PatternStore":
|
||||
from .stores import PatternStore
|
||||
return PatternStore
|
||||
if name == "DecisionStore":
|
||||
from .stores import DecisionStore
|
||||
return DecisionStore
|
||||
if name == "FeedbackStore":
|
||||
from .stores import FeedbackStore
|
||||
return FeedbackStore
|
||||
if name == "ImprovementStore":
|
||||
from .stores import ImprovementStore
|
||||
return ImprovementStore
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
@@ -0,0 +1,241 @@
|
||||
"""
|
||||
LearnManager - Central manager for continuous learning capabilities.
|
||||
|
||||
Coordinates all learning stores and provides a unified interface for
|
||||
capturing and retrieving learned information.
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from ...config.feature_configs import LearnConfig, LearnScope
|
||||
from .stores import (
|
||||
PersonaStore,
|
||||
InsightStore,
|
||||
ThreadStore,
|
||||
PatternStore,
|
||||
DecisionStore,
|
||||
FeedbackStore,
|
||||
ImprovementStore,
|
||||
LearnEntry,
|
||||
)
|
||||
|
||||
|
||||
class LearnManager:
|
||||
"""
|
||||
Central manager for continuous learning within the memory system.
|
||||
|
||||
Coordinates all learning stores based on LearnConfig settings.
|
||||
|
||||
Usage:
|
||||
config = LearnConfig(persona=True, insights=True)
|
||||
manager = LearnManager(config, user_id="alice")
|
||||
|
||||
# Capture learning
|
||||
manager.capture_persona("User prefers concise responses")
|
||||
manager.capture_insight("User works in data science")
|
||||
|
||||
# Retrieve learnings
|
||||
context = manager.get_learning_context()
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Optional[LearnConfig] = None,
|
||||
user_id: Optional[str] = None,
|
||||
store_path: Optional[str] = None,
|
||||
):
|
||||
self.config = config or LearnConfig()
|
||||
self.user_id = user_id or "default"
|
||||
self.store_path = store_path or self.config.store_path
|
||||
|
||||
scope = self.config.scope
|
||||
if isinstance(scope, LearnScope):
|
||||
scope = scope.value
|
||||
|
||||
self._stores: Dict[str, Any] = {}
|
||||
|
||||
if self.config.persona:
|
||||
self._stores["persona"] = PersonaStore(
|
||||
store_path=self._get_store_path("persona"),
|
||||
user_id=self.user_id,
|
||||
scope=scope,
|
||||
)
|
||||
|
||||
if self.config.insights:
|
||||
self._stores["insights"] = InsightStore(
|
||||
store_path=self._get_store_path("insights"),
|
||||
user_id=self.user_id,
|
||||
scope=scope,
|
||||
)
|
||||
|
||||
if self.config.thread:
|
||||
self._stores["threads"] = ThreadStore(
|
||||
store_path=self._get_store_path("threads"),
|
||||
user_id=self.user_id,
|
||||
scope=scope,
|
||||
)
|
||||
|
||||
if self.config.patterns:
|
||||
self._stores["patterns"] = PatternStore(
|
||||
store_path=self._get_store_path("patterns"),
|
||||
user_id=self.user_id,
|
||||
scope=scope,
|
||||
)
|
||||
|
||||
if self.config.decisions:
|
||||
self._stores["decisions"] = DecisionStore(
|
||||
store_path=self._get_store_path("decisions"),
|
||||
user_id=self.user_id,
|
||||
scope=scope,
|
||||
)
|
||||
|
||||
if self.config.feedback:
|
||||
self._stores["feedback"] = FeedbackStore(
|
||||
store_path=self._get_store_path("feedback"),
|
||||
user_id=self.user_id,
|
||||
scope=scope,
|
||||
)
|
||||
|
||||
if self.config.improvements:
|
||||
self._stores["improvements"] = ImprovementStore(
|
||||
store_path=self._get_store_path("improvements"),
|
||||
user_id=self.user_id,
|
||||
scope=scope,
|
||||
)
|
||||
|
||||
def _get_store_path(self, store_name: str) -> Optional[str]:
|
||||
"""Get path for a specific store."""
|
||||
if self.store_path:
|
||||
return f"{self.store_path}/{store_name}.json"
|
||||
return None
|
||||
|
||||
def capture_persona(self, content: str, category: str = "general") -> Optional[LearnEntry]:
|
||||
"""Capture user preference or profile information."""
|
||||
if "persona" in self._stores:
|
||||
return self._stores["persona"].add_preference(content, category)
|
||||
return None
|
||||
|
||||
def capture_insight(self, content: str, source: str = "interaction") -> Optional[LearnEntry]:
|
||||
"""Capture an observation or learning."""
|
||||
if "insights" in self._stores:
|
||||
return self._stores["insights"].add_insight(content, source)
|
||||
return None
|
||||
|
||||
def capture_thread(self, summary: str, thread_id: str) -> Optional[LearnEntry]:
|
||||
"""Capture thread/session context."""
|
||||
if "threads" in self._stores:
|
||||
return self._stores["threads"].add_thread_summary(summary, thread_id)
|
||||
return None
|
||||
|
||||
def capture_pattern(self, pattern: str, pattern_type: str = "general") -> Optional[LearnEntry]:
|
||||
"""Capture a reusable knowledge pattern."""
|
||||
if "patterns" in self._stores:
|
||||
return self._stores["patterns"].add_pattern(pattern, pattern_type)
|
||||
return None
|
||||
|
||||
def capture_decision(
|
||||
self,
|
||||
decision: str,
|
||||
reasoning: str = "",
|
||||
outcome: str = "",
|
||||
) -> Optional[LearnEntry]:
|
||||
"""Capture a decision record."""
|
||||
if "decisions" in self._stores:
|
||||
return self._stores["decisions"].add_decision(decision, reasoning, outcome)
|
||||
return None
|
||||
|
||||
def capture_feedback(
|
||||
self,
|
||||
feedback: str,
|
||||
rating: Optional[int] = None,
|
||||
source: str = "user",
|
||||
) -> Optional[LearnEntry]:
|
||||
"""Capture feedback."""
|
||||
if "feedback" in self._stores:
|
||||
return self._stores["feedback"].add_feedback(feedback, rating, source)
|
||||
return None
|
||||
|
||||
def capture_improvement(
|
||||
self,
|
||||
proposal: str,
|
||||
area: str = "general",
|
||||
priority: str = "medium",
|
||||
) -> Optional[LearnEntry]:
|
||||
"""Capture an improvement proposal."""
|
||||
if "improvements" in self._stores:
|
||||
return self._stores["improvements"].add_improvement(proposal, area, priority)
|
||||
return None
|
||||
|
||||
def get_learning_context(self, limit_per_store: int = 5) -> Dict[str, List[Dict[str, Any]]]:
|
||||
"""
|
||||
Get learning context from all enabled stores.
|
||||
|
||||
Returns a dictionary with entries from each store, suitable for
|
||||
injection into agent context.
|
||||
"""
|
||||
context = {}
|
||||
for store_name, store in self._stores.items():
|
||||
entries = store.list_all(limit=limit_per_store)
|
||||
context[store_name] = [e.to_dict() for e in entries]
|
||||
return context
|
||||
|
||||
def search(self, query: str, limit: int = 10) -> Dict[str, List[Dict[str, Any]]]:
|
||||
"""Search across all enabled stores."""
|
||||
results = {}
|
||||
for store_name, store in self._stores.items():
|
||||
entries = store.search(query, limit=limit)
|
||||
if entries:
|
||||
results[store_name] = [e.to_dict() for e in entries]
|
||||
return results
|
||||
|
||||
def get_persona_context(self, limit: int = 10) -> List[str]:
|
||||
"""Get persona entries as context strings."""
|
||||
if "persona" not in self._stores:
|
||||
return []
|
||||
entries = self._stores["persona"].list_all(limit=limit)
|
||||
return [e.content for e in entries]
|
||||
|
||||
def get_insights_context(self, limit: int = 10) -> List[str]:
|
||||
"""Get insight entries as context strings."""
|
||||
if "insights" not in self._stores:
|
||||
return []
|
||||
entries = self._stores["insights"].list_all(limit=limit)
|
||||
return [e.content for e in entries]
|
||||
|
||||
def clear_all(self) -> Dict[str, int]:
|
||||
"""Clear all stores."""
|
||||
cleared = {}
|
||||
for store_name, store in self._stores.items():
|
||||
cleared[store_name] = store.clear()
|
||||
return cleared
|
||||
|
||||
def get_stats(self) -> Dict[str, int]:
|
||||
"""Get entry counts for all stores."""
|
||||
return {
|
||||
store_name: len(store._entries)
|
||||
for store_name, store in self._stores.items()
|
||||
}
|
||||
|
||||
def to_system_prompt_context(self) -> str:
|
||||
"""
|
||||
Generate context suitable for injection into system prompt.
|
||||
|
||||
Returns a formatted string with relevant learnings.
|
||||
"""
|
||||
parts = []
|
||||
|
||||
if "persona" in self._stores:
|
||||
personas = self.get_persona_context(limit=5)
|
||||
if personas:
|
||||
parts.append("User Preferences:")
|
||||
for p in personas:
|
||||
parts.append(f"- {p}")
|
||||
|
||||
if "insights" in self._stores:
|
||||
insights = self.get_insights_context(limit=5)
|
||||
if insights:
|
||||
parts.append("\nLearned Insights:")
|
||||
for i in insights:
|
||||
parts.append(f"- {i}")
|
||||
|
||||
return "\n".join(parts) if parts else ""
|
||||
@@ -0,0 +1,279 @@
|
||||
"""
|
||||
Learning Stores - Storage backends for different learning capabilities.
|
||||
|
||||
Each store handles a specific type of learning data:
|
||||
- PersonaStore: User preferences and profile
|
||||
- InsightStore: Observations and learnings
|
||||
- ThreadStore: Session/conversation context
|
||||
- PatternStore: Reusable knowledge patterns
|
||||
- DecisionStore: Decision logging
|
||||
- FeedbackStore: Outcome signals
|
||||
- ImprovementStore: Self-improvement proposals
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
|
||||
@dataclass
|
||||
class LearnEntry:
|
||||
"""Base entry for all learning stores."""
|
||||
id: str
|
||||
content: str
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
created_at: str = field(default_factory=lambda: datetime.utcnow().isoformat())
|
||||
updated_at: str = field(default_factory=lambda: datetime.utcnow().isoformat())
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"id": self.id,
|
||||
"content": self.content,
|
||||
"metadata": self.metadata,
|
||||
"created_at": self.created_at,
|
||||
"updated_at": self.updated_at,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "LearnEntry":
|
||||
return cls(
|
||||
id=data["id"],
|
||||
content=data["content"],
|
||||
metadata=data.get("metadata", {}),
|
||||
created_at=data.get("created_at", datetime.utcnow().isoformat()),
|
||||
updated_at=data.get("updated_at", datetime.utcnow().isoformat()),
|
||||
)
|
||||
|
||||
|
||||
class BaseStore(ABC):
|
||||
"""Abstract base class for learning stores."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
store_path: Optional[str] = None,
|
||||
user_id: Optional[str] = None,
|
||||
scope: str = "private",
|
||||
):
|
||||
self.user_id = user_id or "default"
|
||||
self.scope = scope
|
||||
self.store_path = store_path or self._default_path()
|
||||
self._entries: Dict[str, LearnEntry] = {}
|
||||
self._load()
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def store_name(self) -> str:
|
||||
"""Name of the store (used for file naming)."""
|
||||
pass
|
||||
|
||||
def _default_path(self) -> str:
|
||||
"""Default storage path."""
|
||||
base = Path.home() / ".praison" / "learn" / self.scope / self.user_id
|
||||
base.mkdir(parents=True, exist_ok=True)
|
||||
return str(base / f"{self.store_name}.json")
|
||||
|
||||
def _load(self) -> None:
|
||||
"""Load entries from storage."""
|
||||
if os.path.exists(self.store_path):
|
||||
try:
|
||||
with open(self.store_path, "r") as f:
|
||||
data = json.load(f)
|
||||
self._entries = {
|
||||
k: LearnEntry.from_dict(v) for k, v in data.items()
|
||||
}
|
||||
except (json.JSONDecodeError, IOError):
|
||||
self._entries = {}
|
||||
|
||||
def _save(self) -> None:
|
||||
"""Save entries to storage."""
|
||||
os.makedirs(os.path.dirname(self.store_path), exist_ok=True)
|
||||
with open(self.store_path, "w") as f:
|
||||
json.dump(
|
||||
{k: v.to_dict() for k, v in self._entries.items()},
|
||||
f,
|
||||
indent=2,
|
||||
)
|
||||
|
||||
def add(self, content: str, metadata: Optional[Dict[str, Any]] = None) -> LearnEntry:
|
||||
"""Add a new entry."""
|
||||
entry_id = f"{self.store_name}_{len(self._entries)}_{datetime.utcnow().timestamp()}"
|
||||
entry = LearnEntry(
|
||||
id=entry_id,
|
||||
content=content,
|
||||
metadata=metadata or {},
|
||||
)
|
||||
self._entries[entry_id] = entry
|
||||
self._save()
|
||||
return entry
|
||||
|
||||
def get(self, entry_id: str) -> Optional[LearnEntry]:
|
||||
"""Get entry by ID."""
|
||||
return self._entries.get(entry_id)
|
||||
|
||||
def search(self, query: str, limit: int = 10) -> List[LearnEntry]:
|
||||
"""Simple text search (can be overridden for semantic search)."""
|
||||
query_lower = query.lower()
|
||||
results = [
|
||||
entry for entry in self._entries.values()
|
||||
if query_lower in entry.content.lower()
|
||||
]
|
||||
return results[:limit]
|
||||
|
||||
def list_all(self, limit: int = 100) -> List[LearnEntry]:
|
||||
"""List all entries."""
|
||||
entries = list(self._entries.values())
|
||||
entries.sort(key=lambda x: x.updated_at, reverse=True)
|
||||
return entries[:limit]
|
||||
|
||||
def update(self, entry_id: str, content: str, metadata: Optional[Dict[str, Any]] = None) -> Optional[LearnEntry]:
|
||||
"""Update an existing entry."""
|
||||
if entry_id not in self._entries:
|
||||
return None
|
||||
entry = self._entries[entry_id]
|
||||
entry.content = content
|
||||
if metadata:
|
||||
entry.metadata.update(metadata)
|
||||
entry.updated_at = datetime.utcnow().isoformat()
|
||||
self._save()
|
||||
return entry
|
||||
|
||||
def delete(self, entry_id: str) -> bool:
|
||||
"""Delete an entry."""
|
||||
if entry_id in self._entries:
|
||||
del self._entries[entry_id]
|
||||
self._save()
|
||||
return True
|
||||
return False
|
||||
|
||||
def clear(self) -> int:
|
||||
"""Clear all entries."""
|
||||
count = len(self._entries)
|
||||
self._entries = {}
|
||||
self._save()
|
||||
return count
|
||||
|
||||
|
||||
class PersonaStore(BaseStore):
|
||||
"""Store for user preferences and profile information."""
|
||||
|
||||
@property
|
||||
def store_name(self) -> str:
|
||||
return "persona"
|
||||
|
||||
def add_preference(self, preference: str, category: str = "general") -> LearnEntry:
|
||||
"""Add a user preference."""
|
||||
return self.add(preference, {"category": category, "type": "preference"})
|
||||
|
||||
def add_profile(self, profile_data: str, aspect: str = "general") -> LearnEntry:
|
||||
"""Add profile information."""
|
||||
return self.add(profile_data, {"aspect": aspect, "type": "profile"})
|
||||
|
||||
|
||||
class InsightStore(BaseStore):
|
||||
"""Store for observations and learnings."""
|
||||
|
||||
@property
|
||||
def store_name(self) -> str:
|
||||
return "insights"
|
||||
|
||||
def add_insight(self, insight: str, source: str = "interaction") -> LearnEntry:
|
||||
"""Add an insight."""
|
||||
return self.add(insight, {"source": source, "type": "insight"})
|
||||
|
||||
def add_observation(self, observation: str, context: str = "") -> LearnEntry:
|
||||
"""Add an observation."""
|
||||
return self.add(observation, {"context": context, "type": "observation"})
|
||||
|
||||
|
||||
class ThreadStore(BaseStore):
|
||||
"""Store for session/conversation context."""
|
||||
|
||||
@property
|
||||
def store_name(self) -> str:
|
||||
return "threads"
|
||||
|
||||
def add_thread_summary(self, summary: str, thread_id: str) -> LearnEntry:
|
||||
"""Add a thread summary."""
|
||||
return self.add(summary, {"thread_id": thread_id, "type": "summary"})
|
||||
|
||||
def add_context(self, context: str, thread_id: str) -> LearnEntry:
|
||||
"""Add thread context."""
|
||||
return self.add(context, {"thread_id": thread_id, "type": "context"})
|
||||
|
||||
|
||||
class PatternStore(BaseStore):
|
||||
"""Store for reusable knowledge patterns."""
|
||||
|
||||
@property
|
||||
def store_name(self) -> str:
|
||||
return "patterns"
|
||||
|
||||
def add_pattern(self, pattern: str, pattern_type: str = "general") -> LearnEntry:
|
||||
"""Add a knowledge pattern."""
|
||||
return self.add(pattern, {"pattern_type": pattern_type, "type": "pattern"})
|
||||
|
||||
|
||||
class DecisionStore(BaseStore):
|
||||
"""Store for decision logging."""
|
||||
|
||||
@property
|
||||
def store_name(self) -> str:
|
||||
return "decisions"
|
||||
|
||||
def add_decision(
|
||||
self,
|
||||
decision: str,
|
||||
reasoning: str = "",
|
||||
outcome: str = "",
|
||||
) -> LearnEntry:
|
||||
"""Add a decision record."""
|
||||
return self.add(decision, {
|
||||
"reasoning": reasoning,
|
||||
"outcome": outcome,
|
||||
"type": "decision",
|
||||
})
|
||||
|
||||
|
||||
class FeedbackStore(BaseStore):
|
||||
"""Store for outcome signals and feedback."""
|
||||
|
||||
@property
|
||||
def store_name(self) -> str:
|
||||
return "feedback"
|
||||
|
||||
def add_feedback(
|
||||
self,
|
||||
feedback: str,
|
||||
rating: Optional[int] = None,
|
||||
source: str = "user",
|
||||
) -> LearnEntry:
|
||||
"""Add feedback."""
|
||||
metadata = {"source": source, "type": "feedback"}
|
||||
if rating is not None:
|
||||
metadata["rating"] = rating
|
||||
return self.add(feedback, metadata)
|
||||
|
||||
|
||||
class ImprovementStore(BaseStore):
|
||||
"""Store for self-improvement proposals."""
|
||||
|
||||
@property
|
||||
def store_name(self) -> str:
|
||||
return "improvements"
|
||||
|
||||
def add_improvement(
|
||||
self,
|
||||
proposal: str,
|
||||
area: str = "general",
|
||||
priority: str = "medium",
|
||||
) -> LearnEntry:
|
||||
"""Add an improvement proposal."""
|
||||
return self.add(proposal, {
|
||||
"area": area,
|
||||
"priority": priority,
|
||||
"type": "improvement",
|
||||
})
|
||||
@@ -109,7 +109,7 @@ class Memory:
|
||||
},
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {"model": "gpt-5-nano", "api_key": "..."}
|
||||
"config": {"model": "gpt-4o-mini", "api_key": "..."}
|
||||
},
|
||||
"embedder": {
|
||||
"provider": "openai",
|
||||
@@ -146,6 +146,8 @@ class Memory:
|
||||
self.use_rag = (self.provider.lower() == "rag") and CHROMADB_AVAILABLE and self.cfg.get("use_embedding", False)
|
||||
self.use_mongodb = (self.provider.lower() == "mongodb") and PYMONGO_AVAILABLE
|
||||
self.graph_enabled = False # Initialize graph support flag
|
||||
self._learn_manager = None # Lazy-loaded LearnManager
|
||||
self._learn_config = self.cfg.get("learn", None) # Learn configuration
|
||||
|
||||
# Extract embedding model from config
|
||||
self.embedder_config = self.cfg.get("embedder", {})
|
||||
@@ -1442,7 +1444,7 @@ class Memory:
|
||||
import litellm
|
||||
|
||||
# Convert model name if it's in litellm format
|
||||
model_name = llm or "gpt-5-nano"
|
||||
model_name = llm or "gpt-4o-mini"
|
||||
|
||||
response = litellm.completion(
|
||||
model=model_name,
|
||||
@@ -1459,7 +1461,7 @@ class Memory:
|
||||
client = OpenAI()
|
||||
|
||||
response = client.chat.completions.create(
|
||||
model=llm or "gpt-5-nano",
|
||||
model=llm or "gpt-4o-mini",
|
||||
messages=[{
|
||||
"role": "user",
|
||||
"content": custom_prompt or default_prompt
|
||||
@@ -1599,3 +1601,50 @@ class Memory:
|
||||
except Exception as e:
|
||||
self._log_verbose(f"Error getting all memories: {e}", logging.ERROR)
|
||||
return []
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Learn Integration
|
||||
# -------------------------------------------------------------------------
|
||||
@property
|
||||
def learn(self):
|
||||
"""
|
||||
Get the LearnManager for continuous learning capabilities.
|
||||
|
||||
Returns None if learn is not enabled in config.
|
||||
|
||||
Usage:
|
||||
memory = Memory({"learn": True})
|
||||
memory.learn.capture_persona("User prefers concise responses")
|
||||
memory.learn.capture_insight("User works in data science")
|
||||
"""
|
||||
if self._learn_manager is not None:
|
||||
return self._learn_manager
|
||||
|
||||
if self._learn_config is None or self._learn_config is False:
|
||||
return None
|
||||
|
||||
from .learn import LearnManager
|
||||
from ..config.feature_configs import LearnConfig
|
||||
|
||||
if self._learn_config is True:
|
||||
config = LearnConfig()
|
||||
elif isinstance(self._learn_config, dict):
|
||||
config = LearnConfig(**self._learn_config)
|
||||
elif isinstance(self._learn_config, LearnConfig):
|
||||
config = self._learn_config
|
||||
else:
|
||||
return None
|
||||
|
||||
user_id = self.cfg.get("user_id", "default")
|
||||
self._learn_manager = LearnManager(config=config, user_id=user_id)
|
||||
return self._learn_manager
|
||||
|
||||
def get_learn_context(self) -> str:
|
||||
"""
|
||||
Get learning context suitable for injection into system prompt.
|
||||
|
||||
Returns empty string if learn is not enabled.
|
||||
"""
|
||||
if self.learn is None:
|
||||
return ""
|
||||
return self.learn.to_system_prompt_context()
|
||||
@@ -722,9 +722,8 @@ Workflow Finished: {self.workflow_finished} # ADDED: Workflow Finished Status
|
||||
goal="Manage the entire flow of tasks and delegate them to the right agent",
|
||||
backstory="Expert project manager to coordinate tasks among agents",
|
||||
llm=self.manager_llm,
|
||||
verbose=self.verbose,
|
||||
markdown=True,
|
||||
self_reflect=False
|
||||
output={"verbose": self.verbose, "markdown": True},
|
||||
reflection=False
|
||||
)
|
||||
|
||||
class ManagerInstructions(BaseModel):
|
||||
@@ -1384,9 +1383,8 @@ Workflow Finished: {self.workflow_finished} # ADDED: Workflow Finished Status
|
||||
goal="Manage the entire flow of tasks and delegate them to the right agent",
|
||||
backstory="Expert project manager to coordinate tasks among agents",
|
||||
llm=self.manager_llm,
|
||||
verbose=self.verbose,
|
||||
markdown=True,
|
||||
self_reflect=False
|
||||
output={"verbose": self.verbose, "markdown": True},
|
||||
reflection=False
|
||||
)
|
||||
|
||||
class ManagerInstructions(BaseModel):
|
||||
|
||||
@@ -0,0 +1,479 @@
|
||||
"""
|
||||
PraisonAI Agents Core Profiling Module
|
||||
|
||||
Lightweight, protocol-based profiling for the core SDK.
|
||||
Zero overhead when disabled. Opt-in only.
|
||||
|
||||
Features:
|
||||
- Pre-LLM latency breakdown
|
||||
- LLM client initialization timing
|
||||
- Tool resolution timing
|
||||
- Message serialization timing
|
||||
- Streaming TTFT tracking
|
||||
- Import timing
|
||||
- Async-safe
|
||||
|
||||
Usage:
|
||||
from praisonaiagents.profiling import Profiler, profile_block
|
||||
|
||||
# Enable profiling
|
||||
Profiler.enable()
|
||||
|
||||
# Profile a block
|
||||
with profile_block("my_operation"):
|
||||
do_something()
|
||||
|
||||
# Get report
|
||||
Profiler.report()
|
||||
"""
|
||||
|
||||
import time
|
||||
import os
|
||||
import threading
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from typing import Dict, List, Optional, Any, Callable
|
||||
from contextlib import contextmanager
|
||||
import functools
|
||||
import json
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Data Classes
|
||||
# ============================================================================
|
||||
|
||||
@dataclass
|
||||
class TimingRecord:
|
||||
"""Record of a single timing measurement."""
|
||||
name: str
|
||||
duration_ms: float
|
||||
category: str = "block"
|
||||
parent: str = ""
|
||||
timestamp: float = field(default_factory=time.time)
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
@dataclass
|
||||
class StreamingRecord:
|
||||
"""Record of streaming operation."""
|
||||
name: str
|
||||
ttft_ms: float # Time to first token
|
||||
total_ms: float
|
||||
chunk_count: int = 0
|
||||
timestamp: float = field(default_factory=time.time)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PreLLMBreakdown:
|
||||
"""Breakdown of pre-LLM latency phases."""
|
||||
agent_init_ms: float = 0.0
|
||||
llm_client_init_ms: float = 0.0
|
||||
tool_resolution_ms: float = 0.0
|
||||
message_build_ms: float = 0.0
|
||||
context_management_ms: float = 0.0
|
||||
knowledge_retrieval_ms: float = 0.0
|
||||
guardrail_check_ms: float = 0.0
|
||||
total_pre_llm_ms: float = 0.0
|
||||
|
||||
def to_dict(self) -> Dict[str, float]:
|
||||
return asdict(self)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Streaming Tracker
|
||||
# ============================================================================
|
||||
|
||||
class StreamingTracker:
|
||||
"""Track streaming operations for TTFT metrics."""
|
||||
|
||||
def __init__(self, name: str):
|
||||
self.name = name
|
||||
self._start_time: Optional[float] = None
|
||||
self._first_token_time: Optional[float] = None
|
||||
self._end_time: Optional[float] = None
|
||||
self._chunk_count: int = 0
|
||||
|
||||
def start(self) -> None:
|
||||
self._start_time = time.perf_counter()
|
||||
|
||||
def first_token(self) -> None:
|
||||
if self._first_token_time is None:
|
||||
self._first_token_time = time.perf_counter()
|
||||
|
||||
def chunk(self) -> None:
|
||||
self._chunk_count += 1
|
||||
|
||||
def end(self) -> None:
|
||||
self._end_time = time.perf_counter()
|
||||
if self._start_time is not None:
|
||||
ttft_ms = 0.0
|
||||
if self._first_token_time is not None:
|
||||
ttft_ms = (self._first_token_time - self._start_time) * 1000
|
||||
total_ms = (self._end_time - self._start_time) * 1000
|
||||
Profiler.record_streaming(
|
||||
name=self.name,
|
||||
ttft_ms=ttft_ms,
|
||||
total_ms=total_ms,
|
||||
chunk_count=self._chunk_count
|
||||
)
|
||||
|
||||
@property
|
||||
def ttft_ms(self) -> float:
|
||||
if self._start_time and self._first_token_time:
|
||||
return (self._first_token_time - self._start_time) * 1000
|
||||
return 0.0
|
||||
|
||||
@property
|
||||
def elapsed_ms(self) -> float:
|
||||
if self._start_time:
|
||||
end = self._end_time or time.perf_counter()
|
||||
return (end - self._start_time) * 1000
|
||||
return 0.0
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Core Profiler
|
||||
# ============================================================================
|
||||
|
||||
class Profiler:
|
||||
"""
|
||||
Lightweight profiler for PraisonAI Agents core SDK.
|
||||
|
||||
Thread-safe singleton with zero overhead when disabled.
|
||||
"""
|
||||
|
||||
_lock = threading.Lock()
|
||||
_enabled: bool = False
|
||||
_timings: List[TimingRecord] = []
|
||||
_streaming: List[StreamingRecord] = []
|
||||
_pre_llm_breakdown: Optional[PreLLMBreakdown] = None
|
||||
_current_phases: Dict[str, float] = {} # Track in-progress phases
|
||||
_phase_stack: List[str] = [] # Track nested phases
|
||||
|
||||
@classmethod
|
||||
def enable(cls) -> None:
|
||||
"""Enable profiling."""
|
||||
cls._enabled = True
|
||||
|
||||
@classmethod
|
||||
def disable(cls) -> None:
|
||||
"""Disable profiling."""
|
||||
cls._enabled = False
|
||||
|
||||
@classmethod
|
||||
def is_enabled(cls) -> bool:
|
||||
"""Check if profiling is enabled."""
|
||||
return cls._enabled or os.environ.get('PRAISONAI_PROFILE', '').lower() in ('1', 'true', 'yes')
|
||||
|
||||
@classmethod
|
||||
def clear(cls) -> None:
|
||||
"""Clear all profiling data."""
|
||||
with cls._lock:
|
||||
cls._timings.clear()
|
||||
cls._streaming.clear()
|
||||
cls._pre_llm_breakdown = None
|
||||
cls._current_phases.clear()
|
||||
cls._phase_stack.clear()
|
||||
|
||||
@classmethod
|
||||
def record_timing(cls, name: str, duration_ms: float, category: str = "block",
|
||||
parent: str = "", metadata: Optional[Dict[str, Any]] = None) -> None:
|
||||
"""Record a timing measurement."""
|
||||
if not cls.is_enabled():
|
||||
return
|
||||
with cls._lock:
|
||||
cls._timings.append(TimingRecord(
|
||||
name=name,
|
||||
duration_ms=duration_ms,
|
||||
category=category,
|
||||
parent=parent,
|
||||
metadata=metadata or {}
|
||||
))
|
||||
|
||||
@classmethod
|
||||
def record_streaming(cls, name: str, ttft_ms: float, total_ms: float,
|
||||
chunk_count: int = 0) -> None:
|
||||
"""Record streaming metrics."""
|
||||
if not cls.is_enabled():
|
||||
return
|
||||
with cls._lock:
|
||||
cls._streaming.append(StreamingRecord(
|
||||
name=name,
|
||||
ttft_ms=ttft_ms,
|
||||
total_ms=total_ms,
|
||||
chunk_count=chunk_count
|
||||
))
|
||||
|
||||
@classmethod
|
||||
def start_phase(cls, phase_name: str) -> None:
|
||||
"""Start timing a phase (for pre-LLM breakdown)."""
|
||||
if not cls.is_enabled():
|
||||
return
|
||||
with cls._lock:
|
||||
cls._current_phases[phase_name] = time.perf_counter()
|
||||
cls._phase_stack.append(phase_name)
|
||||
|
||||
@classmethod
|
||||
def end_phase(cls, phase_name: str) -> float:
|
||||
"""End timing a phase and return duration in ms."""
|
||||
if not cls.is_enabled():
|
||||
return 0.0
|
||||
with cls._lock:
|
||||
start = cls._current_phases.pop(phase_name, None)
|
||||
if phase_name in cls._phase_stack:
|
||||
cls._phase_stack.remove(phase_name)
|
||||
if start is None:
|
||||
return 0.0
|
||||
duration_ms = (time.perf_counter() - start) * 1000
|
||||
# Record as timing
|
||||
parent = cls._phase_stack[-1] if cls._phase_stack else ""
|
||||
cls._timings.append(TimingRecord(
|
||||
name=phase_name,
|
||||
duration_ms=duration_ms,
|
||||
category="phase",
|
||||
parent=parent
|
||||
))
|
||||
return duration_ms
|
||||
|
||||
@classmethod
|
||||
@contextmanager
|
||||
def phase(cls, name: str, category: str = "phase"):
|
||||
"""Context manager for timing a phase."""
|
||||
if not cls.is_enabled():
|
||||
yield
|
||||
return
|
||||
|
||||
start = time.perf_counter()
|
||||
parent = ""
|
||||
with cls._lock:
|
||||
parent = cls._phase_stack[-1] if cls._phase_stack else ""
|
||||
cls._phase_stack.append(name)
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
duration_ms = (time.perf_counter() - start) * 1000
|
||||
with cls._lock:
|
||||
if name in cls._phase_stack:
|
||||
cls._phase_stack.remove(name)
|
||||
cls._timings.append(TimingRecord(
|
||||
name=name,
|
||||
duration_ms=duration_ms,
|
||||
category=category,
|
||||
parent=parent
|
||||
))
|
||||
|
||||
@classmethod
|
||||
@contextmanager
|
||||
def streaming(cls, name: str):
|
||||
"""Context manager for streaming profiling."""
|
||||
tracker = StreamingTracker(name)
|
||||
tracker.start()
|
||||
try:
|
||||
yield tracker
|
||||
finally:
|
||||
tracker.end()
|
||||
|
||||
@classmethod
|
||||
def set_pre_llm_breakdown(cls, breakdown: PreLLMBreakdown) -> None:
|
||||
"""Set the pre-LLM breakdown data."""
|
||||
if not cls.is_enabled():
|
||||
return
|
||||
with cls._lock:
|
||||
cls._pre_llm_breakdown = breakdown
|
||||
|
||||
@classmethod
|
||||
def get_timings(cls, category: Optional[str] = None) -> List[TimingRecord]:
|
||||
"""Get timing records."""
|
||||
with cls._lock:
|
||||
if category:
|
||||
return [t for t in cls._timings if t.category == category]
|
||||
return cls._timings.copy()
|
||||
|
||||
@classmethod
|
||||
def get_streaming_records(cls) -> List[StreamingRecord]:
|
||||
"""Get streaming records."""
|
||||
with cls._lock:
|
||||
return cls._streaming.copy()
|
||||
|
||||
@classmethod
|
||||
def get_pre_llm_breakdown(cls) -> Optional[PreLLMBreakdown]:
|
||||
"""Get pre-LLM breakdown."""
|
||||
with cls._lock:
|
||||
return cls._pre_llm_breakdown
|
||||
|
||||
@classmethod
|
||||
def get_summary(cls) -> Dict[str, Any]:
|
||||
"""Get profiling summary."""
|
||||
with cls._lock:
|
||||
total_time = sum(t.duration_ms for t in cls._timings)
|
||||
|
||||
# Group by category
|
||||
by_category: Dict[str, float] = {}
|
||||
for t in cls._timings:
|
||||
by_category[t.category] = by_category.get(t.category, 0) + t.duration_ms
|
||||
|
||||
# Top slowest
|
||||
slowest = sorted(cls._timings, key=lambda x: x.duration_ms, reverse=True)[:15]
|
||||
|
||||
# Pre-LLM breakdown
|
||||
pre_llm = cls._pre_llm_breakdown.to_dict() if cls._pre_llm_breakdown else {}
|
||||
|
||||
# Streaming stats
|
||||
streaming_stats = []
|
||||
for s in cls._streaming:
|
||||
streaming_stats.append({
|
||||
'name': s.name,
|
||||
'ttft_ms': s.ttft_ms,
|
||||
'total_ms': s.total_ms,
|
||||
'chunk_count': s.chunk_count
|
||||
})
|
||||
|
||||
return {
|
||||
'total_time_ms': total_time,
|
||||
'timing_count': len(cls._timings),
|
||||
'by_category': by_category,
|
||||
'slowest_operations': [(s.name, s.duration_ms, s.category) for s in slowest],
|
||||
'pre_llm_breakdown': pre_llm,
|
||||
'streaming': streaming_stats
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def report(cls, output: str = "console") -> str:
|
||||
"""Generate and output profiling report."""
|
||||
summary = cls.get_summary()
|
||||
|
||||
lines = [
|
||||
"=" * 70,
|
||||
"PraisonAI Agents Core Profiling Report",
|
||||
"=" * 70,
|
||||
"",
|
||||
f"Total Time: {summary['total_time_ms']:.2f}ms",
|
||||
f"Timing Records: {summary['timing_count']}",
|
||||
"",
|
||||
]
|
||||
|
||||
# Pre-LLM Breakdown (key for debugging the 7s delay)
|
||||
if summary['pre_llm_breakdown']:
|
||||
lines.append("## Pre-LLM Latency Breakdown")
|
||||
lines.append("-" * 50)
|
||||
for phase, ms in summary['pre_llm_breakdown'].items():
|
||||
if ms > 0:
|
||||
lines.append(f" {phase:<30}: {ms:>10.2f} ms")
|
||||
lines.append("")
|
||||
|
||||
# By Category
|
||||
lines.append("## By Category")
|
||||
lines.append("-" * 50)
|
||||
for cat, time_ms in summary['by_category'].items():
|
||||
lines.append(f" {cat}: {time_ms:.2f}ms")
|
||||
lines.append("")
|
||||
|
||||
# Slowest Operations
|
||||
lines.append("## Slowest Operations")
|
||||
lines.append("-" * 50)
|
||||
for name, time_ms, cat in summary['slowest_operations']:
|
||||
lines.append(f" {name:<40}: {time_ms:>10.2f}ms [{cat}]")
|
||||
lines.append("")
|
||||
|
||||
# Streaming Stats
|
||||
if summary['streaming']:
|
||||
lines.append("## Streaming Metrics")
|
||||
lines.append("-" * 50)
|
||||
for s in summary['streaming']:
|
||||
lines.append(f" {s['name']}: TTFT={s['ttft_ms']:.2f}ms, Total={s['total_ms']:.2f}ms, Chunks={s['chunk_count']}")
|
||||
lines.append("")
|
||||
|
||||
lines.append("=" * 70)
|
||||
|
||||
report_text = "\n".join(lines)
|
||||
|
||||
if output == "console":
|
||||
print(report_text)
|
||||
|
||||
return report_text
|
||||
|
||||
@classmethod
|
||||
def export_json(cls) -> str:
|
||||
"""Export profiling data as JSON."""
|
||||
summary = cls.get_summary()
|
||||
with cls._lock:
|
||||
data = {
|
||||
'summary': summary,
|
||||
'timings': [asdict(t) for t in cls._timings],
|
||||
'streaming': [asdict(s) for s in cls._streaming],
|
||||
}
|
||||
return json.dumps(data, indent=2, default=str)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Decorators
|
||||
# ============================================================================
|
||||
|
||||
def profile_func(func: Optional[Callable] = None, *, category: str = "function"):
|
||||
"""Decorator to profile a function."""
|
||||
def decorator(fn: Callable) -> Callable:
|
||||
@functools.wraps(fn)
|
||||
def wrapper(*args, **kwargs):
|
||||
if not Profiler.is_enabled():
|
||||
return fn(*args, **kwargs)
|
||||
start = time.perf_counter()
|
||||
try:
|
||||
return fn(*args, **kwargs)
|
||||
finally:
|
||||
duration_ms = (time.perf_counter() - start) * 1000
|
||||
Profiler.record_timing(fn.__name__, duration_ms, category)
|
||||
return wrapper
|
||||
|
||||
if func is not None:
|
||||
return decorator(func)
|
||||
return decorator
|
||||
|
||||
|
||||
def profile_async(func: Optional[Callable] = None, *, category: str = "async"):
|
||||
"""Decorator to profile an async function."""
|
||||
def decorator(fn: Callable) -> Callable:
|
||||
@functools.wraps(fn)
|
||||
async def wrapper(*args, **kwargs):
|
||||
if not Profiler.is_enabled():
|
||||
return await fn(*args, **kwargs)
|
||||
start = time.perf_counter()
|
||||
try:
|
||||
return await fn(*args, **kwargs)
|
||||
finally:
|
||||
duration_ms = (time.perf_counter() - start) * 1000
|
||||
Profiler.record_timing(fn.__name__, duration_ms, category)
|
||||
return wrapper
|
||||
|
||||
if func is not None:
|
||||
return decorator(func)
|
||||
return decorator
|
||||
|
||||
|
||||
@contextmanager
|
||||
def profile_block(name: str, category: str = "block"):
|
||||
"""Context manager for profiling a block of code."""
|
||||
if not Profiler.is_enabled():
|
||||
yield
|
||||
return
|
||||
|
||||
start = time.perf_counter()
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
duration_ms = (time.perf_counter() - start) * 1000
|
||||
Profiler.record_timing(name, duration_ms, category)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Exports
|
||||
# ============================================================================
|
||||
|
||||
__all__ = [
|
||||
'Profiler',
|
||||
'StreamingTracker',
|
||||
'TimingRecord',
|
||||
'StreamingRecord',
|
||||
'PreLLMBreakdown',
|
||||
'profile_func',
|
||||
'profile_async',
|
||||
'profile_block',
|
||||
]
|
||||
@@ -162,11 +162,13 @@ class Session:
|
||||
|
||||
from ..agent import Agent as AgentClass
|
||||
|
||||
# Filter out parameters that are not valid for Agent.__init__
|
||||
valid_kwargs = {k: v for k, v in kwargs.items() if k not in ('verbose', 'user_id')}
|
||||
|
||||
agent_kwargs = {
|
||||
"name": name,
|
||||
"role": role,
|
||||
"user_id": self.user_id,
|
||||
**kwargs
|
||||
**valid_kwargs
|
||||
}
|
||||
|
||||
if instructions:
|
||||
@@ -177,7 +179,6 @@ class Session:
|
||||
agent_kwargs["memory"] = self.memory
|
||||
if knowledge:
|
||||
agent_kwargs["knowledge"] = knowledge
|
||||
agent_kwargs["knowledge_config"] = self.knowledge_config
|
||||
|
||||
agent = AgentClass(**agent_kwargs)
|
||||
|
||||
|
||||
@@ -0,0 +1,75 @@
|
||||
"""
|
||||
PraisonAI Streaming Module.
|
||||
|
||||
Provides the StreamEvent protocol for pass-through streaming from LLM providers.
|
||||
|
||||
This module is lazily loaded to ensure zero performance impact when streaming
|
||||
features are not used.
|
||||
|
||||
Usage:
|
||||
from praisonaiagents.streaming import (
|
||||
StreamEvent,
|
||||
StreamEventType,
|
||||
StreamMetrics,
|
||||
StreamEventEmitter,
|
||||
create_text_printer_callback,
|
||||
)
|
||||
"""
|
||||
|
||||
__all__ = [
|
||||
"StreamEvent",
|
||||
"StreamEventType",
|
||||
"StreamMetrics",
|
||||
"StreamEventEmitter",
|
||||
"StreamCallback",
|
||||
"AsyncStreamCallback",
|
||||
"create_text_printer_callback",
|
||||
"create_metrics_callback",
|
||||
"StreamLogger",
|
||||
"create_logging_callback",
|
||||
]
|
||||
|
||||
|
||||
def __getattr__(name: str):
|
||||
"""Lazy loading of streaming components."""
|
||||
if name in __all__:
|
||||
# Events module exports
|
||||
if name in (
|
||||
"StreamEvent", "StreamEventType", "StreamMetrics",
|
||||
"StreamEventEmitter", "StreamCallback", "AsyncStreamCallback",
|
||||
"create_text_printer_callback", "create_metrics_callback"
|
||||
):
|
||||
from .events import (
|
||||
StreamEvent,
|
||||
StreamEventType,
|
||||
StreamMetrics,
|
||||
StreamEventEmitter,
|
||||
StreamCallback,
|
||||
AsyncStreamCallback,
|
||||
create_text_printer_callback,
|
||||
create_metrics_callback,
|
||||
)
|
||||
|
||||
_events_exports = {
|
||||
"StreamEvent": StreamEvent,
|
||||
"StreamEventType": StreamEventType,
|
||||
"StreamMetrics": StreamMetrics,
|
||||
"StreamEventEmitter": StreamEventEmitter,
|
||||
"StreamCallback": StreamCallback,
|
||||
"AsyncStreamCallback": AsyncStreamCallback,
|
||||
"create_text_printer_callback": create_text_printer_callback,
|
||||
"create_metrics_callback": create_metrics_callback,
|
||||
}
|
||||
return _events_exports.get(name)
|
||||
|
||||
# Logging module exports
|
||||
if name in ("StreamLogger", "create_logging_callback"):
|
||||
from .logging import StreamLogger, create_logging_callback
|
||||
|
||||
_logging_exports = {
|
||||
"StreamLogger": StreamLogger,
|
||||
"create_logging_callback": create_logging_callback,
|
||||
}
|
||||
return _logging_exports.get(name)
|
||||
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
@@ -0,0 +1,300 @@
|
||||
"""
|
||||
StreamEvent protocol for PraisonAI streaming.
|
||||
|
||||
This module defines the minimal streaming event protocol that both OpenAI SDK
|
||||
and LiteLLM streaming paths map their native chunks/events into.
|
||||
|
||||
Design principles:
|
||||
- Zero overhead when not used (lazy imports, no global state)
|
||||
- Multi-agent safe (per-agent callbacks, no shared mutable state)
|
||||
- Async-safe (separate sync/async callback interfaces)
|
||||
- Provider-agnostic (same events for OpenAI, LiteLLM, etc.)
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional, Protocol
|
||||
import time
|
||||
|
||||
|
||||
class StreamEventType(Enum):
|
||||
"""Types of streaming events emitted during LLM response streaming."""
|
||||
|
||||
REQUEST_START = "request_start" # Before API call is made
|
||||
HEADERS_RECEIVED = "headers_received" # When HTTP 200 headers arrive
|
||||
FIRST_TOKEN = "first_token" # First content delta received (TTFT marker)
|
||||
DELTA_TEXT = "delta_text" # Text content delta
|
||||
DELTA_TOOL_CALL = "delta_tool_call" # Tool call delta (function name, args chunk)
|
||||
TOOL_CALL_END = "tool_call_end" # Tool call complete
|
||||
LAST_TOKEN = "last_token" # Final content delta
|
||||
STREAM_END = "stream_end" # Stream completed successfully
|
||||
ERROR = "error" # Error during streaming
|
||||
|
||||
|
||||
@dataclass
|
||||
class StreamEvent:
|
||||
"""
|
||||
A single streaming event emitted during LLM response streaming.
|
||||
|
||||
Attributes:
|
||||
type: The type of event (see StreamEventType)
|
||||
timestamp: High-precision timestamp (time.perf_counter())
|
||||
content: Text content for DELTA_TEXT events
|
||||
tool_call: Tool call data for DELTA_TOOL_CALL events
|
||||
metadata: Additional event-specific metadata
|
||||
error: Error message for ERROR events
|
||||
"""
|
||||
type: StreamEventType
|
||||
timestamp: float = field(default_factory=time.perf_counter)
|
||||
content: Optional[str] = None
|
||||
tool_call: Optional[Dict[str, Any]] = None
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
error: Optional[str] = None
|
||||
|
||||
def __repr__(self) -> str:
|
||||
parts = [f"StreamEvent({self.type.value}"]
|
||||
if self.content:
|
||||
preview = self.content[:20] + "..." if len(self.content) > 20 else self.content
|
||||
parts.append(f", content={preview!r}")
|
||||
if self.tool_call:
|
||||
parts.append(f", tool_call={self.tool_call.get('name', 'unknown')}")
|
||||
if self.error:
|
||||
parts.append(f", error={self.error!r}")
|
||||
parts.append(")")
|
||||
return "".join(parts)
|
||||
|
||||
|
||||
class StreamCallback(Protocol):
|
||||
"""Protocol for synchronous stream event callbacks."""
|
||||
|
||||
def __call__(self, event: StreamEvent) -> None:
|
||||
"""Called when a stream event is emitted."""
|
||||
...
|
||||
|
||||
|
||||
class AsyncStreamCallback(Protocol):
|
||||
"""Protocol for asynchronous stream event callbacks."""
|
||||
|
||||
async def __call__(self, event: StreamEvent) -> None:
|
||||
"""Called when a stream event is emitted (async)."""
|
||||
...
|
||||
|
||||
|
||||
@dataclass
|
||||
class StreamMetrics:
|
||||
"""
|
||||
Timing metrics for a streaming response.
|
||||
|
||||
Tracks key timestamps to measure:
|
||||
- TTFT (Time To First Token): How long until first token arrives
|
||||
- Stream Duration: How long the token streaming takes
|
||||
- Total Time: End-to-end request time
|
||||
|
||||
All times are in seconds, measured with time.perf_counter() for precision.
|
||||
"""
|
||||
request_start: float = 0.0
|
||||
headers_received: float = 0.0
|
||||
first_token: float = 0.0
|
||||
last_token: float = 0.0
|
||||
stream_end: float = 0.0
|
||||
token_count: int = 0
|
||||
|
||||
@property
|
||||
def ttft(self) -> float:
|
||||
"""Time To First Token in seconds (first_token - request_start)."""
|
||||
if self.first_token and self.request_start:
|
||||
return self.first_token - self.request_start
|
||||
return 0.0
|
||||
|
||||
@property
|
||||
def stream_duration(self) -> float:
|
||||
"""Duration of token streaming in seconds (last_token - first_token)."""
|
||||
if self.last_token and self.first_token:
|
||||
return self.last_token - self.first_token
|
||||
return 0.0
|
||||
|
||||
@property
|
||||
def total_time(self) -> float:
|
||||
"""Total request time in seconds (stream_end - request_start)."""
|
||||
if self.stream_end and self.request_start:
|
||||
return self.stream_end - self.request_start
|
||||
return 0.0
|
||||
|
||||
@property
|
||||
def tokens_per_second(self) -> float:
|
||||
"""Token generation rate (tokens / stream_duration)."""
|
||||
if self.stream_duration > 0 and self.token_count > 0:
|
||||
return self.token_count / self.stream_duration
|
||||
return 0.0
|
||||
|
||||
def update_from_event(self, event: StreamEvent) -> None:
|
||||
"""Update metrics from a StreamEvent."""
|
||||
if event.type == StreamEventType.REQUEST_START:
|
||||
self.request_start = event.timestamp
|
||||
elif event.type == StreamEventType.HEADERS_RECEIVED:
|
||||
self.headers_received = event.timestamp
|
||||
elif event.type == StreamEventType.FIRST_TOKEN:
|
||||
self.first_token = event.timestamp
|
||||
self.token_count = 1
|
||||
elif event.type == StreamEventType.DELTA_TEXT:
|
||||
self.token_count += 1
|
||||
elif event.type == StreamEventType.LAST_TOKEN:
|
||||
self.last_token = event.timestamp
|
||||
elif event.type == StreamEventType.STREAM_END:
|
||||
self.stream_end = event.timestamp
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert metrics to dictionary for logging/display."""
|
||||
return {
|
||||
"ttft_ms": round(self.ttft * 1000, 1),
|
||||
"stream_duration_ms": round(self.stream_duration * 1000, 1),
|
||||
"total_time_ms": round(self.total_time * 1000, 1),
|
||||
"token_count": self.token_count,
|
||||
"tokens_per_second": round(self.tokens_per_second, 1),
|
||||
}
|
||||
|
||||
def format_summary(self) -> str:
|
||||
"""Format metrics as a human-readable summary string."""
|
||||
return (
|
||||
f"TTFT: {self.ttft*1000:.0f}ms | "
|
||||
f"Stream: {self.stream_duration*1000:.0f}ms | "
|
||||
f"Total: {self.total_time*1000:.0f}ms | "
|
||||
f"Tokens: {self.token_count} ({self.tokens_per_second:.1f}/s)"
|
||||
)
|
||||
|
||||
|
||||
class StreamEventEmitter:
|
||||
"""
|
||||
Manages stream event callbacks for an agent or streaming session.
|
||||
|
||||
Thread-safe and multi-agent safe - each agent has its own emitter instance.
|
||||
Supports both sync and async callbacks.
|
||||
|
||||
Usage:
|
||||
emitter = StreamEventEmitter()
|
||||
emitter.add_callback(my_callback)
|
||||
|
||||
# During streaming:
|
||||
emitter.emit(StreamEvent(type=StreamEventType.DELTA_TEXT, content="Hello"))
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._callbacks: List[StreamCallback] = []
|
||||
self._async_callbacks: List[AsyncStreamCallback] = []
|
||||
self._metrics: Optional[StreamMetrics] = None
|
||||
self._collect_metrics: bool = False
|
||||
|
||||
def add_callback(self, callback: StreamCallback) -> None:
|
||||
"""Add a synchronous callback to receive stream events."""
|
||||
self._callbacks.append(callback)
|
||||
|
||||
def remove_callback(self, callback: StreamCallback) -> None:
|
||||
"""Remove a synchronous callback."""
|
||||
if callback in self._callbacks:
|
||||
self._callbacks.remove(callback)
|
||||
|
||||
def add_async_callback(self, callback: AsyncStreamCallback) -> None:
|
||||
"""Add an asynchronous callback to receive stream events."""
|
||||
self._async_callbacks.append(callback)
|
||||
|
||||
def remove_async_callback(self, callback: AsyncStreamCallback) -> None:
|
||||
"""Remove an asynchronous callback."""
|
||||
if callback in self._async_callbacks:
|
||||
self._async_callbacks.remove(callback)
|
||||
|
||||
def enable_metrics(self) -> None:
|
||||
"""Enable metrics collection for this streaming session."""
|
||||
self._collect_metrics = True
|
||||
self._metrics = StreamMetrics()
|
||||
|
||||
def get_metrics(self) -> Optional[StreamMetrics]:
|
||||
"""Get collected metrics (None if metrics not enabled)."""
|
||||
return self._metrics
|
||||
|
||||
def reset_metrics(self) -> None:
|
||||
"""Reset metrics for a new streaming session."""
|
||||
if self._collect_metrics:
|
||||
self._metrics = StreamMetrics()
|
||||
|
||||
def emit(self, event: StreamEvent) -> None:
|
||||
"""
|
||||
Emit a stream event to all registered callbacks.
|
||||
|
||||
This is the synchronous emission path. For async streaming,
|
||||
use emit_async().
|
||||
"""
|
||||
# Update metrics if enabled
|
||||
if self._collect_metrics and self._metrics:
|
||||
self._metrics.update_from_event(event)
|
||||
|
||||
# Call all sync callbacks
|
||||
for callback in self._callbacks:
|
||||
try:
|
||||
callback(event)
|
||||
except Exception:
|
||||
# Don't let callback errors break streaming
|
||||
pass
|
||||
|
||||
async def emit_async(self, event: StreamEvent) -> None:
|
||||
"""
|
||||
Emit a stream event to all registered callbacks (async version).
|
||||
|
||||
Calls both sync and async callbacks.
|
||||
"""
|
||||
# Update metrics if enabled
|
||||
if self._collect_metrics and self._metrics:
|
||||
self._metrics.update_from_event(event)
|
||||
|
||||
# Call sync callbacks
|
||||
for callback in self._callbacks:
|
||||
try:
|
||||
callback(event)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Call async callbacks
|
||||
for callback in self._async_callbacks:
|
||||
try:
|
||||
await callback(event)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
@property
|
||||
def has_callbacks(self) -> bool:
|
||||
"""Check if any callbacks are registered."""
|
||||
return bool(self._callbacks or self._async_callbacks)
|
||||
|
||||
|
||||
def create_text_printer_callback(flush: bool = True) -> StreamCallback:
|
||||
"""
|
||||
Create a simple callback that prints text deltas to stdout.
|
||||
|
||||
Args:
|
||||
flush: Whether to flush stdout after each print (default True)
|
||||
|
||||
Returns:
|
||||
A StreamCallback that prints DELTA_TEXT content
|
||||
"""
|
||||
def printer(event: StreamEvent) -> None:
|
||||
if event.type == StreamEventType.DELTA_TEXT and event.content:
|
||||
print(event.content, end="", flush=flush)
|
||||
elif event.type == StreamEventType.STREAM_END:
|
||||
print() # Newline at end
|
||||
|
||||
return printer
|
||||
|
||||
|
||||
def create_metrics_callback(metrics: StreamMetrics) -> StreamCallback:
|
||||
"""
|
||||
Create a callback that updates a StreamMetrics instance.
|
||||
|
||||
Args:
|
||||
metrics: The StreamMetrics instance to update
|
||||
|
||||
Returns:
|
||||
A StreamCallback that updates the metrics
|
||||
"""
|
||||
def updater(event: StreamEvent) -> None:
|
||||
metrics.update_from_event(event)
|
||||
|
||||
return updater
|
||||
@@ -0,0 +1,207 @@
|
||||
"""
|
||||
Streaming logging utilities for PraisonAI.
|
||||
|
||||
Provides guarded logging for streaming events with clear timestamps
|
||||
for request_start, headers_received, first_token, last_token, and stream_end.
|
||||
|
||||
Usage:
|
||||
from praisonaiagents.streaming.logging import StreamLogger
|
||||
|
||||
logger = StreamLogger(verbose=True, metrics=True)
|
||||
logger.log_request_start(model="gpt-4o-mini")
|
||||
# ... streaming ...
|
||||
logger.log_first_token()
|
||||
# ... more tokens ...
|
||||
logger.log_stream_end()
|
||||
print(logger.get_metrics_summary())
|
||||
"""
|
||||
|
||||
import logging
|
||||
import time
|
||||
from typing import Optional, Dict, Any
|
||||
|
||||
from .events import StreamMetrics, StreamEvent, StreamEventType
|
||||
|
||||
|
||||
class StreamLogger:
|
||||
"""
|
||||
Guarded logging for streaming events with timing metrics.
|
||||
|
||||
Only logs when verbose=True or metrics=True to ensure zero overhead
|
||||
when logging is disabled.
|
||||
|
||||
Attributes:
|
||||
verbose: Whether to log detailed streaming events
|
||||
metrics: Whether to collect and display timing metrics
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
verbose: bool = False,
|
||||
metrics: bool = False,
|
||||
logger: Optional[logging.Logger] = None
|
||||
):
|
||||
self.verbose = verbose
|
||||
self.metrics = metrics
|
||||
self._logger = logger or logging.getLogger(__name__)
|
||||
self._metrics = StreamMetrics() if metrics else None
|
||||
self._enabled = verbose or metrics
|
||||
|
||||
def log_request_start(
|
||||
self,
|
||||
model: Optional[str] = None,
|
||||
message_count: Optional[int] = None
|
||||
) -> None:
|
||||
"""Log when the streaming request is initiated."""
|
||||
if not self._enabled:
|
||||
return
|
||||
|
||||
timestamp = time.perf_counter()
|
||||
if self._metrics:
|
||||
self._metrics.request_start = timestamp
|
||||
|
||||
if self.verbose:
|
||||
extra = f" model={model}" if model else ""
|
||||
extra += f" messages={message_count}" if message_count else ""
|
||||
self._logger.info(f"[STREAM] REQUEST_START{extra}")
|
||||
|
||||
def log_headers_received(self) -> None:
|
||||
"""Log when HTTP response headers are received."""
|
||||
if not self._enabled:
|
||||
return
|
||||
|
||||
timestamp = time.perf_counter()
|
||||
if self._metrics:
|
||||
self._metrics.headers_received = timestamp
|
||||
|
||||
if self.verbose:
|
||||
self._logger.info("[STREAM] HEADERS_RECEIVED")
|
||||
|
||||
def log_first_token(self, content: Optional[str] = None) -> None:
|
||||
"""Log when the first content token is received (TTFT marker)."""
|
||||
if not self._enabled:
|
||||
return
|
||||
|
||||
timestamp = time.perf_counter()
|
||||
if self._metrics:
|
||||
self._metrics.first_token = timestamp
|
||||
self._metrics.token_count = 1
|
||||
|
||||
if self.verbose:
|
||||
preview = content[:20] + "..." if content and len(content) > 20 else content
|
||||
self._logger.info(f"[STREAM] FIRST_TOKEN content={preview!r}")
|
||||
|
||||
def log_delta_text(self, content: Optional[str] = None) -> None:
|
||||
"""Log a text delta (only when verbose, not for every token)."""
|
||||
if not self._enabled:
|
||||
return
|
||||
|
||||
if self._metrics:
|
||||
self._metrics.token_count += 1
|
||||
|
||||
# Don't log every delta to avoid noise - just track metrics
|
||||
|
||||
def log_last_token(self) -> None:
|
||||
"""Log when the last content token is received."""
|
||||
if not self._enabled:
|
||||
return
|
||||
|
||||
timestamp = time.perf_counter()
|
||||
if self._metrics:
|
||||
self._metrics.last_token = timestamp
|
||||
|
||||
if self.verbose:
|
||||
self._logger.info("[STREAM] LAST_TOKEN")
|
||||
|
||||
def log_stream_end(self, chunk_count: Optional[int] = None) -> None:
|
||||
"""Log when the stream ends."""
|
||||
if not self._enabled:
|
||||
return
|
||||
|
||||
timestamp = time.perf_counter()
|
||||
if self._metrics:
|
||||
self._metrics.stream_end = timestamp
|
||||
|
||||
if self.verbose:
|
||||
extra = f" chunks={chunk_count}" if chunk_count else ""
|
||||
self._logger.info(f"[STREAM] STREAM_END{extra}")
|
||||
|
||||
def log_error(self, error: str) -> None:
|
||||
"""Log a streaming error."""
|
||||
if self.verbose:
|
||||
self._logger.error(f"[STREAM] ERROR: {error}")
|
||||
|
||||
def get_metrics(self) -> Optional[StreamMetrics]:
|
||||
"""Get the collected metrics (None if metrics not enabled)."""
|
||||
return self._metrics
|
||||
|
||||
def get_metrics_summary(self) -> str:
|
||||
"""Get a formatted summary of streaming metrics."""
|
||||
if not self._metrics:
|
||||
return "Metrics not enabled"
|
||||
return self._metrics.format_summary()
|
||||
|
||||
def get_metrics_dict(self) -> Optional[Dict[str, Any]]:
|
||||
"""Get metrics as a dictionary."""
|
||||
if not self._metrics:
|
||||
return None
|
||||
return self._metrics.to_dict()
|
||||
|
||||
def update_from_event(self, event: StreamEvent) -> None:
|
||||
"""Update logger state from a StreamEvent."""
|
||||
if not self._enabled:
|
||||
return
|
||||
|
||||
if self._metrics:
|
||||
self._metrics.update_from_event(event)
|
||||
|
||||
if self.verbose:
|
||||
if event.type == StreamEventType.REQUEST_START:
|
||||
self.log_request_start()
|
||||
elif event.type == StreamEventType.HEADERS_RECEIVED:
|
||||
self.log_headers_received()
|
||||
elif event.type == StreamEventType.FIRST_TOKEN:
|
||||
self.log_first_token(event.content)
|
||||
elif event.type == StreamEventType.LAST_TOKEN:
|
||||
self.log_last_token()
|
||||
elif event.type == StreamEventType.STREAM_END:
|
||||
self.log_stream_end()
|
||||
elif event.type == StreamEventType.ERROR:
|
||||
self.log_error(event.error or "Unknown error")
|
||||
|
||||
|
||||
def create_logging_callback(
|
||||
verbose: bool = False,
|
||||
metrics: bool = False,
|
||||
logger: Optional[logging.Logger] = None
|
||||
) -> tuple:
|
||||
"""
|
||||
Create a StreamLogger and a callback function for use with streaming.
|
||||
|
||||
Args:
|
||||
verbose: Whether to log detailed events
|
||||
metrics: Whether to collect timing metrics
|
||||
logger: Optional custom logger
|
||||
|
||||
Returns:
|
||||
Tuple of (StreamLogger, callback_function)
|
||||
|
||||
Usage:
|
||||
stream_logger, callback = create_logging_callback(verbose=True, metrics=True)
|
||||
|
||||
# Pass callback to streaming method
|
||||
response = client.process_stream_response(
|
||||
...,
|
||||
stream_callback=callback,
|
||||
emit_events=True
|
||||
)
|
||||
|
||||
# After streaming, get metrics
|
||||
print(stream_logger.get_metrics_summary())
|
||||
"""
|
||||
stream_logger = StreamLogger(verbose=verbose, metrics=metrics, logger=logger)
|
||||
|
||||
def callback(event: StreamEvent) -> None:
|
||||
stream_logger.update_from_event(event)
|
||||
|
||||
return stream_logger, callback
|
||||
@@ -326,7 +326,7 @@ class Task:
|
||||
if hasattr(self.agent.llm_instance, 'model'):
|
||||
llm_model = self.agent.llm_instance.model
|
||||
else:
|
||||
llm_model = "gpt-5-nano" # Default fallback
|
||||
llm_model = "gpt-4o-mini" # Default fallback
|
||||
elif hasattr(self.agent, 'llm') and self.agent.llm:
|
||||
# For standard model strings
|
||||
llm_model = self.agent.llm
|
||||
|
||||
@@ -26,16 +26,41 @@ _telemetry_executor = None
|
||||
_telemetry_queue = None
|
||||
_queue_processor_running = False
|
||||
_queue_lock = threading.Lock()
|
||||
_atexit_registered = False
|
||||
|
||||
def _atexit_cleanup():
|
||||
"""Cleanup handler registered with atexit to prevent hanging on exit."""
|
||||
global _queue_processor_running, _telemetry_executor, _telemetry_queue
|
||||
|
||||
# Signal queue processor to stop
|
||||
_queue_processor_running = False
|
||||
|
||||
# Shutdown executor without waiting (to prevent hang)
|
||||
if _telemetry_executor is not None:
|
||||
try:
|
||||
_telemetry_executor.shutdown(wait=False)
|
||||
except:
|
||||
pass
|
||||
_telemetry_executor = None
|
||||
|
||||
_telemetry_queue = None
|
||||
|
||||
def _get_telemetry_executor():
|
||||
"""Get or create the shared telemetry thread pool executor."""
|
||||
global _telemetry_executor
|
||||
global _telemetry_executor, _atexit_registered
|
||||
if _telemetry_executor is None:
|
||||
# Use a small thread pool to avoid resource overhead
|
||||
_telemetry_executor = concurrent.futures.ThreadPoolExecutor(
|
||||
max_workers=2,
|
||||
thread_name_prefix="telemetry"
|
||||
)
|
||||
|
||||
# Register atexit cleanup to prevent hanging on exit
|
||||
if not _atexit_registered:
|
||||
import atexit
|
||||
atexit.register(_atexit_cleanup)
|
||||
_atexit_registered = True
|
||||
|
||||
return _telemetry_executor
|
||||
|
||||
def _get_telemetry_queue():
|
||||
@@ -48,8 +73,13 @@ def _get_telemetry_queue():
|
||||
with _queue_lock:
|
||||
if not _queue_processor_running:
|
||||
_queue_processor_running = True
|
||||
executor = _get_telemetry_executor()
|
||||
executor.submit(_process_telemetry_queue)
|
||||
# Use a daemon thread directly instead of executor to prevent hang
|
||||
processor_thread = threading.Thread(
|
||||
target=_process_telemetry_queue,
|
||||
name="telemetry-queue-processor",
|
||||
daemon=True # Daemon thread will be killed on exit
|
||||
)
|
||||
processor_thread.start()
|
||||
|
||||
return _telemetry_queue
|
||||
|
||||
@@ -61,12 +91,22 @@ def _process_telemetry_queue():
|
||||
|
||||
try:
|
||||
while _queue_processor_running:
|
||||
# Check if interpreter is shutting down
|
||||
try:
|
||||
import sys
|
||||
if hasattr(sys, 'is_finalizing') and sys.is_finalizing():
|
||||
break
|
||||
except:
|
||||
break
|
||||
|
||||
events = []
|
||||
deadline = time.time() + batch_timeout
|
||||
|
||||
# Collect events until batch size or timeout
|
||||
while len(events) < batch_size and time.time() < deadline:
|
||||
try:
|
||||
if _telemetry_queue is None:
|
||||
break
|
||||
event = _telemetry_queue.get(timeout=0.1)
|
||||
events.append(event)
|
||||
_telemetry_queue.task_done()
|
||||
|
||||
@@ -381,6 +381,10 @@ class PerformanceMonitor:
|
||||
Returns:
|
||||
List of flow events showing function execution order and timing
|
||||
"""
|
||||
# If monitoring is disabled, return empty results
|
||||
if self._monitoring_disabled:
|
||||
return []
|
||||
|
||||
with self._lock:
|
||||
flow = []
|
||||
for event in self._function_flow.copy():
|
||||
@@ -400,6 +404,10 @@ class PerformanceMonitor:
|
||||
|
||||
def get_active_calls(self) -> Dict[str, Any]:
|
||||
"""Get information about currently executing functions."""
|
||||
# If monitoring is disabled, return empty results
|
||||
if self._monitoring_disabled:
|
||||
return {}
|
||||
|
||||
with self._lock:
|
||||
active = {}
|
||||
current_time = time.time()
|
||||
@@ -417,6 +425,10 @@ class PerformanceMonitor:
|
||||
|
||||
def get_slowest_functions(self, limit: int = 10) -> List[Dict[str, Any]]:
|
||||
"""Get the slowest performing functions."""
|
||||
# If monitoring is disabled, return empty results
|
||||
if self._monitoring_disabled:
|
||||
return []
|
||||
|
||||
with self._lock:
|
||||
functions = []
|
||||
for name, stats in self._function_stats.items():
|
||||
@@ -435,6 +447,10 @@ class PerformanceMonitor:
|
||||
|
||||
def get_slowest_api_calls(self, limit: int = 10) -> List[Dict[str, Any]]:
|
||||
"""Get the slowest performing API calls."""
|
||||
# If monitoring is disabled, return empty results
|
||||
if self._monitoring_disabled:
|
||||
return []
|
||||
|
||||
with self._lock:
|
||||
apis = []
|
||||
for name, stats in self._api_calls.items():
|
||||
@@ -539,6 +555,10 @@ class PerformanceMonitor:
|
||||
|
||||
def clear_statistics(self):
|
||||
"""Clear all performance statistics."""
|
||||
# If monitoring is disabled, nothing to clear
|
||||
if self._monitoring_disabled:
|
||||
return
|
||||
|
||||
with self._lock:
|
||||
self._function_stats.clear()
|
||||
self._api_calls.clear()
|
||||
|
||||
@@ -433,15 +433,24 @@ class MinimalTelemetry:
|
||||
if posthog_client:
|
||||
|
||||
try:
|
||||
# Include captured metrics in the event properties
|
||||
event_properties = {
|
||||
'version': self._environment.get('framework_version', 'unknown'),
|
||||
'os': platform.system(),
|
||||
'python_version': platform.python_version(),
|
||||
'$process_person_profile': False,
|
||||
'$geoip_disable': True,
|
||||
'session_id': self.session_id
|
||||
}
|
||||
|
||||
# Add all metrics to properties
|
||||
if metrics:
|
||||
event_properties.update(metrics)
|
||||
|
||||
posthog_client.capture(
|
||||
distinct_id='anonymous',
|
||||
event='sdk_used',
|
||||
properties={
|
||||
'version': self._environment['framework_version'],
|
||||
'os': platform.system(),
|
||||
'$process_person_profile': False,
|
||||
'$geoip_disable': True
|
||||
}
|
||||
properties=event_properties
|
||||
)
|
||||
# Don't flush here - let PostHog handle it asynchronously
|
||||
except:
|
||||
@@ -691,7 +700,7 @@ def force_shutdown_telemetry():
|
||||
def enable_telemetry():
|
||||
"""Programmatically enable telemetry (if not disabled by environment)."""
|
||||
global _telemetry_instance
|
||||
if not _TELEMETRY_DISABLED:
|
||||
if not _is_telemetry_disabled():
|
||||
if _telemetry_instance:
|
||||
_telemetry_instance.enabled = True
|
||||
else:
|
||||
|
||||
@@ -5,6 +5,12 @@ from typing import Any
|
||||
# Export Injected type for tool state injection
|
||||
from .injected import Injected
|
||||
|
||||
# Export core tool items for organized imports
|
||||
from .base import BaseTool, ToolResult, ToolValidationError, validate_tool
|
||||
from .decorator import tool, FunctionTool
|
||||
from .registry import get_registry, register_tool, get_tool, ToolRegistry
|
||||
from .tools import Tools
|
||||
|
||||
# Map of function names to their module and class (if any)
|
||||
TOOL_MAPPINGS = {
|
||||
# Direct functions
|
||||
|
||||
@@ -27,7 +27,7 @@ class GenerateCOT:
|
||||
def __init__(
|
||||
self,
|
||||
qa_pairs: Optional[Dict[str, str]] = None,
|
||||
model: str = "gpt-5-nano",
|
||||
model: str = "gpt-4o-mini",
|
||||
api_key: Optional[str] = None,
|
||||
max_attempts: int = 3,
|
||||
verbose: bool = True,
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
"""
|
||||
Trace Module for PraisonAI Agents.
|
||||
|
||||
Provides lightweight action tracing for the `output="actions"` mode.
|
||||
Shows agent lifecycle events, tool calls, and final output without
|
||||
verbose internal details.
|
||||
|
||||
Zero Performance Impact:
|
||||
- All imports are lazy loaded via __getattr__
|
||||
- NoOpSink is the default (zero overhead when not used)
|
||||
- Disabled emitter has near-zero overhead
|
||||
|
||||
Usage:
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Simple usage - shows action trace
|
||||
agent = Agent(instructions="...", output="actions")
|
||||
agent.start("Do something")
|
||||
|
||||
# Advanced - capture to file
|
||||
from praisonaiagents.trace import ActionTraceConfig
|
||||
|
||||
agent = Agent(
|
||||
instructions="...",
|
||||
output="actions",
|
||||
trace=ActionTraceConfig(
|
||||
sink_type="jsonl",
|
||||
file_path="trace.jsonl",
|
||||
)
|
||||
)
|
||||
"""
|
||||
|
||||
__all__ = [
|
||||
# Core types
|
||||
"ActionEvent",
|
||||
"ActionEventType",
|
||||
"ActionTraceConfig",
|
||||
# Sinks
|
||||
"TraceSink",
|
||||
"NoOpSink",
|
||||
"ListSink",
|
||||
# Emitter
|
||||
"TraceEmitter",
|
||||
# Redaction
|
||||
"redact_dict",
|
||||
"REDACT_KEYS",
|
||||
]
|
||||
|
||||
|
||||
def __getattr__(name: str):
|
||||
"""Lazy load module components to avoid import overhead."""
|
||||
if name in ("ActionEvent", "ActionEventType", "ActionTraceConfig"):
|
||||
from .protocol import ActionEvent, ActionEventType, ActionTraceConfig
|
||||
return locals()[name]
|
||||
|
||||
if name in ("TraceSink", "NoOpSink", "ListSink"):
|
||||
from .protocol import TraceSink, NoOpSink, ListSink
|
||||
return locals()[name]
|
||||
|
||||
if name == "TraceEmitter":
|
||||
from .protocol import TraceEmitter
|
||||
return TraceEmitter
|
||||
|
||||
if name in ("redact_dict", "REDACT_KEYS"):
|
||||
from .redact import redact_dict, REDACT_KEYS
|
||||
return locals()[name]
|
||||
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
@@ -0,0 +1,398 @@
|
||||
"""
|
||||
Action Trace Protocol for PraisonAI Agents.
|
||||
|
||||
Lightweight protocol for tracing agent actions. Uses only stdlib
|
||||
to ensure zero dependencies and minimal overhead.
|
||||
|
||||
Schema Version: 1.0
|
||||
"""
|
||||
|
||||
import json
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
|
||||
|
||||
|
||||
# Schema version for forward compatibility
|
||||
SCHEMA_VERSION = "1.0"
|
||||
|
||||
|
||||
class ActionEventType(str, Enum):
|
||||
"""Types of action events."""
|
||||
AGENT_START = "agent_start"
|
||||
AGENT_END = "agent_end"
|
||||
TOOL_START = "tool_start"
|
||||
TOOL_END = "tool_end"
|
||||
ERROR = "error"
|
||||
OUTPUT = "output"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ActionEvent:
|
||||
"""
|
||||
Lightweight action trace event.
|
||||
|
||||
Represents a single action in the agent execution trace.
|
||||
Designed for minimal memory footprint and fast serialization.
|
||||
"""
|
||||
event_type: str
|
||||
timestamp: float
|
||||
schema_version: str = SCHEMA_VERSION
|
||||
agent_id: Optional[str] = None
|
||||
agent_name: Optional[str] = None
|
||||
tool_name: Optional[str] = None
|
||||
tool_args: Optional[Dict[str, Any]] = None
|
||||
tool_result_summary: Optional[str] = None
|
||||
duration_ms: Optional[float] = None
|
||||
status: Optional[str] = None # ok, error
|
||||
error_message: Optional[str] = None
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert to dictionary, excluding None values."""
|
||||
result = {
|
||||
"schema_version": self.schema_version,
|
||||
"event_type": self.event_type,
|
||||
"timestamp": self.timestamp,
|
||||
}
|
||||
|
||||
# Add optional fields only if set
|
||||
if self.agent_id is not None:
|
||||
result["agent_id"] = self.agent_id
|
||||
if self.agent_name is not None:
|
||||
result["agent_name"] = self.agent_name
|
||||
if self.tool_name is not None:
|
||||
result["tool_name"] = self.tool_name
|
||||
if self.tool_args is not None:
|
||||
result["tool_args"] = self.tool_args
|
||||
if self.tool_result_summary is not None:
|
||||
result["tool_result_summary"] = self.tool_result_summary
|
||||
if self.duration_ms is not None:
|
||||
result["duration_ms"] = self.duration_ms
|
||||
if self.status is not None:
|
||||
result["status"] = self.status
|
||||
if self.error_message is not None:
|
||||
result["error_message"] = self.error_message
|
||||
if self.metadata:
|
||||
result["metadata"] = self.metadata
|
||||
|
||||
return result
|
||||
|
||||
def to_json(self) -> str:
|
||||
"""Convert to JSON string."""
|
||||
return json.dumps(self.to_dict(), default=str)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class TraceSink(Protocol):
|
||||
"""
|
||||
Protocol for trace event sinks.
|
||||
|
||||
Implementations receive action events and handle them
|
||||
(e.g., print to stdout, write to file, send to server).
|
||||
"""
|
||||
|
||||
def emit(self, event: ActionEvent) -> None:
|
||||
"""Emit an action event."""
|
||||
...
|
||||
|
||||
def flush(self) -> None:
|
||||
"""Flush any buffered events."""
|
||||
...
|
||||
|
||||
def close(self) -> None:
|
||||
"""Close the sink and release resources."""
|
||||
...
|
||||
|
||||
|
||||
class NoOpSink:
|
||||
"""
|
||||
No-operation sink that discards all events.
|
||||
|
||||
Used as the default sink when tracing is disabled.
|
||||
Has near-zero overhead.
|
||||
"""
|
||||
|
||||
__slots__ = ()
|
||||
|
||||
def emit(self, event: ActionEvent) -> None:
|
||||
"""Discard the event."""
|
||||
pass
|
||||
|
||||
def flush(self) -> None:
|
||||
"""No-op."""
|
||||
pass
|
||||
|
||||
def close(self) -> None:
|
||||
"""No-op."""
|
||||
pass
|
||||
|
||||
|
||||
class ListSink:
|
||||
"""
|
||||
Sink that collects events in a list.
|
||||
|
||||
Useful for testing and programmatic access to trace events.
|
||||
"""
|
||||
|
||||
__slots__ = ("_events",)
|
||||
|
||||
def __init__(self):
|
||||
self._events: List[ActionEvent] = []
|
||||
|
||||
def emit(self, event: ActionEvent) -> None:
|
||||
"""Add event to the list."""
|
||||
self._events.append(event)
|
||||
|
||||
def flush(self) -> None:
|
||||
"""No-op for list sink."""
|
||||
pass
|
||||
|
||||
def close(self) -> None:
|
||||
"""No-op for list sink."""
|
||||
pass
|
||||
|
||||
def get_events(self) -> List[ActionEvent]:
|
||||
"""Get all collected events."""
|
||||
return self._events.copy()
|
||||
|
||||
def clear(self) -> None:
|
||||
"""Clear all collected events."""
|
||||
self._events.clear()
|
||||
|
||||
|
||||
@dataclass
|
||||
class ActionTraceConfig:
|
||||
"""
|
||||
Configuration for action tracing.
|
||||
|
||||
Usage:
|
||||
# Default (disabled)
|
||||
config = ActionTraceConfig()
|
||||
|
||||
# Enable with stdout
|
||||
config = ActionTraceConfig(enabled=True, sink_type="stdout")
|
||||
|
||||
# Enable with JSONL file
|
||||
config = ActionTraceConfig(
|
||||
enabled=True,
|
||||
sink_type="jsonl",
|
||||
file_path="trace.jsonl",
|
||||
)
|
||||
"""
|
||||
enabled: bool = True
|
||||
redact: bool = True
|
||||
compact: bool = False
|
||||
sink_type: str = "noop" # noop, stdout, jsonl, file
|
||||
file_path: Optional[str] = None
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert to dictionary."""
|
||||
return {
|
||||
"enabled": self.enabled,
|
||||
"redact": self.redact,
|
||||
"compact": self.compact,
|
||||
"sink_type": self.sink_type,
|
||||
"file_path": self.file_path,
|
||||
}
|
||||
|
||||
|
||||
class TraceEmitter:
|
||||
"""
|
||||
Emitter for action trace events.
|
||||
|
||||
Provides a high-level API for emitting trace events.
|
||||
Handles redaction and sink management.
|
||||
|
||||
Usage:
|
||||
sink = ListSink()
|
||||
emitter = TraceEmitter(sink=sink)
|
||||
|
||||
emitter.agent_start("researcher")
|
||||
emitter.tool_start("search", {"query": "AI"})
|
||||
emitter.tool_end("search", duration_ms=100, status="ok")
|
||||
emitter.agent_end("researcher", duration_ms=500)
|
||||
"""
|
||||
|
||||
__slots__ = ("_sink", "_enabled", "_redact", "_agent_starts")
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sink: Optional[TraceSink] = None,
|
||||
enabled: bool = True,
|
||||
redact: bool = True,
|
||||
):
|
||||
self._sink = sink or NoOpSink()
|
||||
self._enabled = enabled
|
||||
self._redact = redact
|
||||
self._agent_starts: Dict[str, float] = {} # Track agent start times
|
||||
|
||||
def _emit(self, event: ActionEvent) -> None:
|
||||
"""Internal emit with enabled check."""
|
||||
if not self._enabled:
|
||||
return
|
||||
self._sink.emit(event)
|
||||
|
||||
def _redact_args(self, args: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
|
||||
"""Redact sensitive data from args if enabled."""
|
||||
if args is None or not self._redact:
|
||||
return args
|
||||
|
||||
from .redact import redact_dict
|
||||
return redact_dict(args)
|
||||
|
||||
def agent_start(
|
||||
self,
|
||||
agent_name: str,
|
||||
agent_id: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""Emit agent start event."""
|
||||
ts = time.time()
|
||||
self._agent_starts[agent_name] = ts
|
||||
|
||||
self._emit(ActionEvent(
|
||||
event_type=ActionEventType.AGENT_START.value,
|
||||
timestamp=ts,
|
||||
agent_name=agent_name,
|
||||
agent_id=agent_id,
|
||||
metadata=metadata or {},
|
||||
))
|
||||
|
||||
def agent_end(
|
||||
self,
|
||||
agent_name: str,
|
||||
agent_id: Optional[str] = None,
|
||||
duration_ms: Optional[float] = None,
|
||||
status: str = "ok",
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""Emit agent end event."""
|
||||
ts = time.time()
|
||||
|
||||
# Calculate duration if not provided
|
||||
if duration_ms is None and agent_name in self._agent_starts:
|
||||
start_ts = self._agent_starts.pop(agent_name, None)
|
||||
if start_ts:
|
||||
duration_ms = (ts - start_ts) * 1000
|
||||
|
||||
self._emit(ActionEvent(
|
||||
event_type=ActionEventType.AGENT_END.value,
|
||||
timestamp=ts,
|
||||
agent_name=agent_name,
|
||||
agent_id=agent_id,
|
||||
duration_ms=duration_ms,
|
||||
status=status,
|
||||
metadata=metadata or {},
|
||||
))
|
||||
|
||||
def tool_start(
|
||||
self,
|
||||
tool_name: str,
|
||||
tool_args: Optional[Dict[str, Any]] = None,
|
||||
agent_name: Optional[str] = None,
|
||||
agent_id: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""Emit tool start event."""
|
||||
self._emit(ActionEvent(
|
||||
event_type=ActionEventType.TOOL_START.value,
|
||||
timestamp=time.time(),
|
||||
tool_name=tool_name,
|
||||
tool_args=self._redact_args(tool_args),
|
||||
agent_name=agent_name,
|
||||
agent_id=agent_id,
|
||||
metadata=metadata or {},
|
||||
))
|
||||
|
||||
def tool_end(
|
||||
self,
|
||||
tool_name: str,
|
||||
duration_ms: float,
|
||||
status: str = "ok",
|
||||
result_summary: Optional[str] = None,
|
||||
agent_name: Optional[str] = None,
|
||||
agent_id: Optional[str] = None,
|
||||
error_message: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""Emit tool end event."""
|
||||
self._emit(ActionEvent(
|
||||
event_type=ActionEventType.TOOL_END.value,
|
||||
timestamp=time.time(),
|
||||
tool_name=tool_name,
|
||||
duration_ms=duration_ms,
|
||||
status=status,
|
||||
tool_result_summary=result_summary,
|
||||
agent_name=agent_name,
|
||||
agent_id=agent_id,
|
||||
error_message=error_message,
|
||||
metadata=metadata or {},
|
||||
))
|
||||
|
||||
def error(
|
||||
self,
|
||||
message: str,
|
||||
tool_name: Optional[str] = None,
|
||||
agent_name: Optional[str] = None,
|
||||
agent_id: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""Emit error event."""
|
||||
self._emit(ActionEvent(
|
||||
event_type=ActionEventType.ERROR.value,
|
||||
timestamp=time.time(),
|
||||
error_message=message,
|
||||
tool_name=tool_name,
|
||||
agent_name=agent_name,
|
||||
agent_id=agent_id,
|
||||
status="error",
|
||||
metadata=metadata or {},
|
||||
))
|
||||
|
||||
def output(
|
||||
self,
|
||||
content: str,
|
||||
agent_name: Optional[str] = None,
|
||||
agent_id: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""Emit final output event."""
|
||||
# Truncate long output for summary
|
||||
summary = content[:500] + "..." if len(content) > 500 else content
|
||||
|
||||
self._emit(ActionEvent(
|
||||
event_type=ActionEventType.OUTPUT.value,
|
||||
timestamp=time.time(),
|
||||
tool_result_summary=summary,
|
||||
agent_name=agent_name,
|
||||
agent_id=agent_id,
|
||||
metadata=metadata or {},
|
||||
))
|
||||
|
||||
def flush(self) -> None:
|
||||
"""Flush the sink."""
|
||||
self._sink.flush()
|
||||
|
||||
def close(self) -> None:
|
||||
"""Close the sink."""
|
||||
self._sink.close()
|
||||
|
||||
|
||||
# Global default emitter (NoOp by default)
|
||||
_default_emitter: Optional[TraceEmitter] = None
|
||||
|
||||
|
||||
def get_default_emitter() -> TraceEmitter:
|
||||
"""Get the default trace emitter."""
|
||||
global _default_emitter
|
||||
if _default_emitter is None:
|
||||
_default_emitter = TraceEmitter(sink=NoOpSink(), enabled=False)
|
||||
return _default_emitter
|
||||
|
||||
|
||||
def set_default_emitter(emitter: TraceEmitter) -> None:
|
||||
"""Set the default trace emitter."""
|
||||
global _default_emitter
|
||||
_default_emitter = emitter
|
||||
@@ -0,0 +1,149 @@
|
||||
"""
|
||||
Redaction utilities for action trace.
|
||||
|
||||
Provides deterministic redaction of sensitive data in tool arguments
|
||||
and results. Uses only stdlib for zero dependencies.
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, Set
|
||||
import re
|
||||
|
||||
# Keys that should always be redacted (case-insensitive matching)
|
||||
REDACT_KEYS: Set[str] = {
|
||||
# API keys and tokens
|
||||
"api_key",
|
||||
"apikey",
|
||||
"api-key",
|
||||
"secret",
|
||||
"secret_key",
|
||||
"secretkey",
|
||||
"private_key",
|
||||
"privatekey",
|
||||
# Authentication
|
||||
"password",
|
||||
"passwd",
|
||||
"pwd",
|
||||
"token",
|
||||
"bearer",
|
||||
"authorization",
|
||||
"auth",
|
||||
"auth_token",
|
||||
"access_token",
|
||||
"refresh_token",
|
||||
# Session and cookies
|
||||
"cookie",
|
||||
"session",
|
||||
"session_id",
|
||||
"sessionid",
|
||||
"credential",
|
||||
"credentials",
|
||||
# Database
|
||||
"connection_string",
|
||||
"connectionstring",
|
||||
"database_url",
|
||||
"db_url",
|
||||
"dburl",
|
||||
"db_password",
|
||||
"db_pass",
|
||||
# Cloud provider specific
|
||||
"aws_secret",
|
||||
"aws_secret_access_key",
|
||||
"azure_key",
|
||||
"gcp_key",
|
||||
"openai_api_key",
|
||||
"anthropic_api_key",
|
||||
}
|
||||
|
||||
# Compiled regex for case-insensitive matching
|
||||
_REDACT_PATTERN = re.compile(
|
||||
r"(" + "|".join(re.escape(k) for k in REDACT_KEYS) + r")",
|
||||
re.IGNORECASE
|
||||
)
|
||||
|
||||
REDACTED_VALUE = "[REDACTED]"
|
||||
|
||||
|
||||
def _should_redact(key: str) -> bool:
|
||||
"""Check if a key should be redacted."""
|
||||
# Normalize key for comparison
|
||||
normalized = key.lower().replace("-", "_").replace(" ", "_")
|
||||
|
||||
# Check exact match first
|
||||
if normalized in REDACT_KEYS:
|
||||
return True
|
||||
|
||||
# Check if any redact key is contained in the normalized key
|
||||
for redact_key in REDACT_KEYS:
|
||||
if redact_key in normalized:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def redact_dict(data: Dict[str, Any], enabled: bool = True) -> Dict[str, Any]:
|
||||
"""
|
||||
Redact sensitive values from a dictionary.
|
||||
|
||||
Args:
|
||||
data: Dictionary to redact
|
||||
enabled: If False, returns data unchanged
|
||||
|
||||
Returns:
|
||||
New dictionary with sensitive values redacted
|
||||
"""
|
||||
if not enabled:
|
||||
return data
|
||||
|
||||
if not data:
|
||||
return {}
|
||||
|
||||
return _redact_value(data)
|
||||
|
||||
|
||||
def _redact_value(value: Any) -> Any:
|
||||
"""Recursively redact sensitive values."""
|
||||
if isinstance(value, dict):
|
||||
return {k: _redact_key_value(k, v) for k, v in value.items()}
|
||||
elif isinstance(value, list):
|
||||
return [_redact_value(item) for item in value]
|
||||
else:
|
||||
return value
|
||||
|
||||
|
||||
def _redact_key_value(key: str, value: Any) -> Any:
|
||||
"""Redact a value if its key is sensitive."""
|
||||
if _should_redact(key):
|
||||
return REDACTED_VALUE
|
||||
|
||||
# Recursively process nested structures
|
||||
return _redact_value(value)
|
||||
|
||||
|
||||
def redact_string(text: str, enabled: bool = True) -> str:
|
||||
"""
|
||||
Redact potential secrets from a string.
|
||||
|
||||
This is a best-effort function that looks for common patterns
|
||||
like "api_key=xxx" or "password: xxx" in strings.
|
||||
|
||||
Args:
|
||||
text: String to redact
|
||||
enabled: If False, returns text unchanged
|
||||
|
||||
Returns:
|
||||
String with potential secrets redacted
|
||||
"""
|
||||
if not enabled or not text:
|
||||
return text
|
||||
|
||||
# Pattern for key=value or key: value
|
||||
patterns = [
|
||||
(r'(["\']?)(' + '|'.join(re.escape(k) for k in REDACT_KEYS) + r')(["\']?\s*[:=]\s*["\']?)([^"\'\s,}\]]+)',
|
||||
r'\1\2\3[REDACTED]'),
|
||||
]
|
||||
|
||||
result = text
|
||||
for pattern, replacement in patterns:
|
||||
result = re.sub(pattern, replacement, result, flags=re.IGNORECASE)
|
||||
|
||||
return result
|
||||
@@ -254,6 +254,10 @@ class WorkflowStep:
|
||||
knowledge: Optional[Any] = None # Union[bool, List[str], KnowledgeConfig] - RAG
|
||||
web: Optional[Any] = None # Union[bool, WebConfig] - web search/fetch
|
||||
reflection: Optional[Any] = None # Union[bool, ReflectionConfig] - self-reflection
|
||||
memory: Optional[Any] = None # Union[bool, MemoryConfig] - step-level memory
|
||||
planning: Optional[Any] = None # Union[bool, PlanningConfig] - step-level planning
|
||||
hooks: Optional[Any] = None # Union[List, HooksConfig] - step lifecycle hooks
|
||||
caching: Optional[Any] = None # Union[bool, CachingConfig] - step-level caching
|
||||
|
||||
# Status tracking
|
||||
status: str = "pending" # pending, running, completed, failed, skipped
|
||||
@@ -278,6 +282,10 @@ class WorkflowStep:
|
||||
_knowledge_config: Optional[Any] = field(default=None, repr=False)
|
||||
_web_config: Optional[Any] = field(default=None, repr=False)
|
||||
_reflection_config: Optional[Any] = field(default=None, repr=False)
|
||||
_memory_config: Optional[Any] = field(default=None, repr=False)
|
||||
_planning_config: Optional[Any] = field(default=None, repr=False)
|
||||
_hooks_config: Optional[Any] = field(default=None, repr=False)
|
||||
_caching_config: Optional[Any] = field(default=None, repr=False)
|
||||
|
||||
def __post_init__(self):
|
||||
"""Resolve consolidated params to internal values."""
|
||||
@@ -370,6 +378,62 @@ class WorkflowStep:
|
||||
config_class=ReflectionConfig, presets=REFLECTION_PRESETS,
|
||||
default=None,
|
||||
)
|
||||
|
||||
# Resolve memory (NEW for feature parity)
|
||||
try:
|
||||
from ..config.feature_configs import MemoryConfig
|
||||
from ..config.presets import MEMORY_PRESETS
|
||||
except ImportError:
|
||||
MemoryConfig = None
|
||||
MEMORY_PRESETS = {}
|
||||
if MemoryConfig and self.memory is not None:
|
||||
self._memory_config = resolve(
|
||||
value=self.memory, param_name="memory",
|
||||
config_class=MemoryConfig, presets=MEMORY_PRESETS,
|
||||
default=None,
|
||||
)
|
||||
|
||||
# Resolve planning (NEW for feature parity)
|
||||
try:
|
||||
from ..config.feature_configs import PlanningConfig
|
||||
from ..config.presets import PLANNING_PRESETS
|
||||
except ImportError:
|
||||
PlanningConfig = None
|
||||
PLANNING_PRESETS = {}
|
||||
if PlanningConfig and self.planning is not None:
|
||||
self._planning_config = resolve(
|
||||
value=self.planning, param_name="planning",
|
||||
config_class=PlanningConfig, presets=PLANNING_PRESETS,
|
||||
default=None,
|
||||
)
|
||||
|
||||
# Resolve hooks (NEW for feature parity)
|
||||
try:
|
||||
from ..config.feature_configs import HooksConfig
|
||||
except ImportError:
|
||||
HooksConfig = None
|
||||
if HooksConfig and self.hooks is not None:
|
||||
# Hooks don't have presets, just config class
|
||||
if isinstance(self.hooks, list):
|
||||
self._hooks_config = HooksConfig(callbacks=self.hooks)
|
||||
elif hasattr(self.hooks, 'callbacks'):
|
||||
self._hooks_config = self.hooks
|
||||
else:
|
||||
self._hooks_config = self.hooks
|
||||
|
||||
# Resolve caching (NEW for feature parity)
|
||||
try:
|
||||
from ..config.feature_configs import CachingConfig
|
||||
from ..config.presets import CACHING_PRESETS
|
||||
except ImportError:
|
||||
CachingConfig = None
|
||||
CACHING_PRESETS = {}
|
||||
if CachingConfig and self.caching is not None:
|
||||
self._caching_config = resolve(
|
||||
value=self.caching, param_name="caching",
|
||||
config_class=CachingConfig, presets=CACHING_PRESETS,
|
||||
default=None,
|
||||
)
|
||||
|
||||
# Property accessors for backward compatibility
|
||||
@property
|
||||
@@ -507,6 +571,8 @@ class Workflow:
|
||||
guardrails: Optional[Any] = None # Union[bool, Callable, GuardrailConfig] - validation
|
||||
web: Optional[Any] = None # Union[bool, WebConfig] - web search/fetch
|
||||
reflection: Optional[Any] = None # Union[bool, ReflectionConfig] - self-reflection
|
||||
execution: Optional[Any] = None # Union[str, ExecutionConfig] - execution control
|
||||
caching: Optional[Any] = None # Union[bool, CachingConfig] - caching
|
||||
|
||||
# Status tracking
|
||||
status: str = "not_started" # not_started, running, completed, failed
|
||||
|
||||
@@ -0,0 +1,232 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Detailed profiler to identify the exact source of streaming delay.
|
||||
Traces every step from agent.start() to HTTP request.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import functools
|
||||
|
||||
os.environ['LOGLEVEL'] = 'DEBUG'
|
||||
|
||||
# Microsecond precision timestamps
|
||||
def ts():
|
||||
return time.perf_counter()
|
||||
|
||||
start_time = ts()
|
||||
|
||||
def log(msg):
|
||||
elapsed = (ts() - start_time) * 1000
|
||||
print(f"[{elapsed:8.2f}ms] {msg}")
|
||||
|
||||
log("Script starting...")
|
||||
|
||||
# ============================================================
|
||||
# PATCH 1: OpenAI client initialization
|
||||
# ============================================================
|
||||
import openai
|
||||
|
||||
original_openai_init = openai.OpenAI.__init__
|
||||
|
||||
@functools.wraps(original_openai_init)
|
||||
def traced_openai_init(self, *args, **kwargs):
|
||||
t0 = ts()
|
||||
result = original_openai_init(self, *args, **kwargs)
|
||||
log(f"OpenAI.__init__() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
openai.OpenAI.__init__ = traced_openai_init
|
||||
|
||||
# ============================================================
|
||||
# PATCH 2: OpenAI completions.create
|
||||
# ============================================================
|
||||
original_create = openai.resources.chat.completions.Completions.create
|
||||
|
||||
@functools.wraps(original_create)
|
||||
def traced_create(self, *args, **kwargs):
|
||||
log(f">>> OpenAI.create() CALLED - stream={kwargs.get('stream', False)}")
|
||||
t0 = ts()
|
||||
result = original_create(self, *args, **kwargs)
|
||||
log(f"<<< OpenAI.create() RETURNED in {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
openai.resources.chat.completions.Completions.create = traced_create
|
||||
|
||||
# ============================================================
|
||||
# PATCH 3: httpx.Client.send (actual HTTP request)
|
||||
# ============================================================
|
||||
import httpx
|
||||
|
||||
original_send = httpx.Client.send
|
||||
|
||||
@functools.wraps(original_send)
|
||||
def traced_send(self, request, *args, **kwargs):
|
||||
log(f">>> HTTPX.send() {request.method} {request.url}")
|
||||
t0 = ts()
|
||||
result = original_send(self, request, *args, **kwargs)
|
||||
log(f"<<< HTTPX.send() returned in {(ts()-t0)*1000:.2f}ms - status={result.status_code}")
|
||||
return result
|
||||
|
||||
httpx.Client.send = traced_send
|
||||
|
||||
# ============================================================
|
||||
# PATCH 4: Rich Live display
|
||||
# ============================================================
|
||||
from rich.live import Live
|
||||
|
||||
original_live_init = Live.__init__
|
||||
original_live_enter = Live.__enter__
|
||||
original_live_exit = Live.__exit__
|
||||
|
||||
@functools.wraps(original_live_init)
|
||||
def traced_live_init(self, *args, **kwargs):
|
||||
log(f">>> Live.__init__() called")
|
||||
t0 = ts()
|
||||
result = original_live_init(self, *args, **kwargs)
|
||||
log(f"<<< Live.__init__() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
@functools.wraps(original_live_enter)
|
||||
def traced_live_enter(self):
|
||||
log(f">>> Live.__enter__() called")
|
||||
t0 = ts()
|
||||
result = original_live_enter(self)
|
||||
log(f"<<< Live.__enter__() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
@functools.wraps(original_live_exit)
|
||||
def traced_live_exit(self, *args):
|
||||
log(f">>> Live.__exit__() called")
|
||||
t0 = ts()
|
||||
result = original_live_exit(self, *args)
|
||||
log(f"<<< Live.__exit__() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
Live.__init__ = traced_live_init
|
||||
Live.__enter__ = traced_live_enter
|
||||
Live.__exit__ = traced_live_exit
|
||||
|
||||
log("Patches applied, importing praisonaiagents...")
|
||||
|
||||
# ============================================================
|
||||
# Import and trace Agent
|
||||
# ============================================================
|
||||
t0 = ts()
|
||||
from praisonaiagents import Agent
|
||||
log(f"Agent import took {(ts()-t0)*1000:.2f}ms")
|
||||
|
||||
# ============================================================
|
||||
# PATCH 5: Agent methods
|
||||
# ============================================================
|
||||
from praisonaiagents.agent.agent import Agent as AgentClass
|
||||
|
||||
original_chat_completion = AgentClass._chat_completion
|
||||
original_format_tools = AgentClass._format_tools_for_completion
|
||||
|
||||
@functools.wraps(original_chat_completion)
|
||||
def traced_chat_completion(self, messages, temperature=1.0, tools=None, stream=True,
|
||||
reasoning_steps=False, task_name=None, task_description=None, task_id=None):
|
||||
log(f">>> Agent._chat_completion() stream={stream}, _using_custom_llm={self._using_custom_llm}")
|
||||
t0 = ts()
|
||||
result = original_chat_completion(self, messages, temperature, tools, stream,
|
||||
reasoning_steps, task_name, task_description, task_id)
|
||||
log(f"<<< Agent._chat_completion() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
@functools.wraps(original_format_tools)
|
||||
def traced_format_tools(self, tools):
|
||||
log(f">>> Agent._format_tools_for_completion() tools={tools}")
|
||||
t0 = ts()
|
||||
result = original_format_tools(self, tools)
|
||||
log(f"<<< Agent._format_tools_for_completion() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
AgentClass._chat_completion = traced_chat_completion
|
||||
AgentClass._format_tools_for_completion = traced_format_tools
|
||||
|
||||
# ============================================================
|
||||
# PATCH 6: OpenAIClient methods
|
||||
# ============================================================
|
||||
from praisonaiagents.llm.openai_client import OpenAIClient
|
||||
|
||||
original_chat_with_tools = OpenAIClient.chat_completion_with_tools
|
||||
original_process_stream = OpenAIClient.process_stream_response
|
||||
original_create_completion = OpenAIClient.create_completion
|
||||
original_format_tools_client = OpenAIClient.format_tools
|
||||
|
||||
@functools.wraps(original_chat_with_tools)
|
||||
def traced_chat_with_tools(self, *args, **kwargs):
|
||||
log(f">>> OpenAIClient.chat_completion_with_tools() stream={kwargs.get('stream', True)}")
|
||||
t0 = ts()
|
||||
result = original_chat_with_tools(self, *args, **kwargs)
|
||||
log(f"<<< OpenAIClient.chat_completion_with_tools() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
@functools.wraps(original_process_stream)
|
||||
def traced_process_stream(self, *args, **kwargs):
|
||||
log(f">>> OpenAIClient.process_stream_response()")
|
||||
t0 = ts()
|
||||
result = original_process_stream(self, *args, **kwargs)
|
||||
log(f"<<< OpenAIClient.process_stream_response() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
@functools.wraps(original_create_completion)
|
||||
def traced_create_completion(self, *args, **kwargs):
|
||||
log(f">>> OpenAIClient.create_completion() stream={kwargs.get('stream', False)}")
|
||||
t0 = ts()
|
||||
result = original_create_completion(self, *args, **kwargs)
|
||||
log(f"<<< OpenAIClient.create_completion() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
@functools.wraps(original_format_tools_client)
|
||||
def traced_format_tools_client(self, tools):
|
||||
log(f">>> OpenAIClient.format_tools() tools count={len(tools) if tools else 0}")
|
||||
t0 = ts()
|
||||
result = original_format_tools_client(self, tools)
|
||||
log(f"<<< OpenAIClient.format_tools() took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
OpenAIClient.chat_completion_with_tools = traced_chat_with_tools
|
||||
OpenAIClient.process_stream_response = traced_process_stream
|
||||
OpenAIClient.create_completion = traced_create_completion
|
||||
OpenAIClient.format_tools = traced_format_tools_client
|
||||
|
||||
# ============================================================
|
||||
# PATCH 7: sync_client property (lazy init)
|
||||
# ============================================================
|
||||
original_sync_client_getter = OpenAIClient.sync_client.fget
|
||||
|
||||
def traced_sync_client_getter(self):
|
||||
log(f">>> OpenAIClient.sync_client property accessed")
|
||||
t0 = ts()
|
||||
result = original_sync_client_getter(self)
|
||||
log(f"<<< OpenAIClient.sync_client took {(ts()-t0)*1000:.2f}ms")
|
||||
return result
|
||||
|
||||
OpenAIClient.sync_client = property(traced_sync_client_getter)
|
||||
|
||||
log("All patches applied. Creating agent...")
|
||||
|
||||
# ============================================================
|
||||
# RUN THE TEST
|
||||
# ============================================================
|
||||
t0 = ts()
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant"
|
||||
)
|
||||
log(f"Agent created in {(ts()-t0)*1000:.2f}ms")
|
||||
log(f"Agent.stream={agent.stream}, Agent.verbose={agent.verbose}")
|
||||
|
||||
log("=" * 60)
|
||||
log("Starting agent.start()...")
|
||||
log("=" * 60)
|
||||
|
||||
t0 = ts()
|
||||
result = agent.start("Say hello in one word")
|
||||
log(f"agent.start() completed in {(ts()-t0)*1000:.2f}ms")
|
||||
|
||||
log("=" * 60)
|
||||
log("DONE")
|
||||
log("=" * 60)
|
||||
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "praisonaiagents"
|
||||
version = "0.10.10"
|
||||
version = "0.11.18"
|
||||
description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
|
||||
@@ -0,0 +1,24 @@
|
||||
"""
|
||||
Streaming Example with TTFT Metrics
|
||||
|
||||
This example demonstrates true pass-through streaming with timing metrics.
|
||||
When verbose output is enabled, tokens are displayed as they arrive from the provider.
|
||||
|
||||
Key timing metrics:
|
||||
- TTFT (Time To First Token): Time from request to first token received
|
||||
- Stream Duration: Time from first to last token
|
||||
- Total Time: End-to-end request time
|
||||
"""
|
||||
from praisonaiagents import Agent
|
||||
from praisonaiagents.config import OutputConfig
|
||||
|
||||
# Create agent with verbose output (shows streaming progress)
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant",
|
||||
llm="gpt-4o-mini",
|
||||
output=OutputConfig(verbose=True) # Show generation progress
|
||||
)
|
||||
|
||||
# Run the agent - verbose mode shows streaming progress via Rich Live display
|
||||
result = agent.start("Write a short paragraph about the history of computing")
|
||||
print(f"\n--- Response ---\n{result}")
|
||||
@@ -1,11 +0,0 @@
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(
|
||||
instructions="You are a helpful assistant",
|
||||
llm="gpt-4o-mini"
|
||||
)
|
||||
|
||||
# Use chat method with stream=True for streaming responses
|
||||
for chunk in agent.chat("Write a short paragraph about the history of computing", stream=True):
|
||||
print(chunk, end="", flush=True)
|
||||
print() # Final newline
|
||||
@@ -0,0 +1,22 @@
|
||||
"""Test full agent flow with new verbose UI."""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Create agent - use output preset for verbose mode
|
||||
agent = Agent(
|
||||
name="AIAssistant",
|
||||
role="Helpful AI",
|
||||
instructions="You are a helpful assistant. Be concise and give short answers.",
|
||||
output="verbose", # Use output preset instead of verbose kwarg
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("\n" + "=" * 60)
|
||||
print("TESTING FULL AGENT FLOW WITH NEW UI")
|
||||
print("=" * 60 + "\n")
|
||||
|
||||
# Use start() which should show verbose output
|
||||
result = agent.start("What is the capital of France? One sentence only.")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print(f"FINAL RESULT: {result}")
|
||||
print("=" * 60)
|
||||
@@ -10,8 +10,7 @@ try:
|
||||
posthog_client = Posthog(
|
||||
project_api_key='phc_skZpl3eFLQJ4iYjsERNMbCO6jfeSJi2vyZlPahKgxZ7',
|
||||
host='https://eu.i.posthog.com',
|
||||
disable_geoip=True,
|
||||
events_to_ignore=['test-event']
|
||||
disable_geoip=True
|
||||
)
|
||||
print(f"✅ PostHog client created: {posthog_client}")
|
||||
except Exception as e:
|
||||
|
||||
@@ -34,17 +34,17 @@ except Exception as e:
|
||||
print(f"❌ PostHog capture failed: {e}")
|
||||
|
||||
print("\n4. Testing telemetry module...")
|
||||
from praisonaiagents.telemetry.telemetry import MinimalTelemetry, POSTHOG_AVAILABLE
|
||||
print(f"POSTHOG_AVAILABLE in telemetry module: {POSTHOG_AVAILABLE}")
|
||||
from praisonaiagents.telemetry.telemetry import MinimalTelemetry
|
||||
print("✅ MinimalTelemetry imported successfully")
|
||||
|
||||
print("\n5. Creating MinimalTelemetry instance...")
|
||||
telemetry = MinimalTelemetry(enabled=True)
|
||||
print(f"Telemetry enabled: {telemetry.enabled}")
|
||||
print(f"Telemetry _posthog: {telemetry._posthog}")
|
||||
print(f"Telemetry has _posthog: {hasattr(telemetry, '_posthog') and telemetry._posthog is not None}")
|
||||
|
||||
print("\n6. Checking get_telemetry()...")
|
||||
from praisonaiagents.telemetry import get_telemetry
|
||||
global_telemetry = get_telemetry()
|
||||
print(f"Global telemetry enabled: {global_telemetry.enabled}")
|
||||
print(f"Global telemetry _posthog: {global_telemetry._posthog}")
|
||||
print(f"Global telemetry has _posthog: {hasattr(global_telemetry, '_posthog') and global_telemetry._posthog is not None}")
|
||||
print(f"Global telemetry session_id: {global_telemetry.session_id}")
|
||||
@@ -0,0 +1,112 @@
|
||||
"""Test script with forced verbose mode to verify PraisonAI's unique output UI."""
|
||||
import sys
|
||||
from rich.console import Console
|
||||
|
||||
# Force Rich to think we're in a TTY for testing
|
||||
console = Console(force_terminal=True)
|
||||
|
||||
from praisonaiagents import Agent
|
||||
from praisonaiagents.main import (
|
||||
display_interaction,
|
||||
display_instruction,
|
||||
display_tool_call,
|
||||
display_reasoning_steps,
|
||||
display_working_status,
|
||||
display_error,
|
||||
PRAISON_COLORS
|
||||
)
|
||||
|
||||
print("=" * 70)
|
||||
print("TESTING PRAISONAI UNIQUE VERBOSE OUTPUT UI")
|
||||
print("=" * 70)
|
||||
|
||||
# Test 1: Display Agent Info panel
|
||||
print("\n[TEST 1] Agent Info Panel (Sage Green border)")
|
||||
display_instruction(
|
||||
message="Test instruction",
|
||||
console=console,
|
||||
agent_name="ResearchAgent",
|
||||
agent_role="Data Analyst",
|
||||
agent_tools=["search_web", "calculate", "read_file"]
|
||||
)
|
||||
|
||||
# Test 2: Display interaction with semantic colors
|
||||
print("\n[TEST 2] Task (Coral) + Response (Ocean Blue) panels")
|
||||
display_interaction(
|
||||
message="What is 2+2?",
|
||||
response="2+2 equals 4. This is a basic arithmetic operation.",
|
||||
markdown=True,
|
||||
generation_time=1.5,
|
||||
console=console
|
||||
)
|
||||
|
||||
# Test 3: Display interaction with metrics footer
|
||||
print("\n[TEST 3] Response with Metrics Footer")
|
||||
display_interaction(
|
||||
message="Explain quantum computing",
|
||||
response="Quantum computing uses qubits instead of classical bits.",
|
||||
markdown=True,
|
||||
generation_time=3.2,
|
||||
console=console,
|
||||
metrics={
|
||||
'tokens_in': 45,
|
||||
'tokens_out': 128,
|
||||
'cost': 0.0023,
|
||||
'model': 'gpt-4o-mini'
|
||||
}
|
||||
)
|
||||
|
||||
# Test 4: Display tool call with timeline format
|
||||
print("\n[TEST 4] Tool Activity Panel (Violet border, timeline format)")
|
||||
display_tool_call(
|
||||
message="search_web(query='quantum computing')",
|
||||
console=console,
|
||||
tool_name="search_web",
|
||||
tool_input={"query": "quantum computing"},
|
||||
tool_output="Found 5 relevant articles",
|
||||
elapsed_time=0.8,
|
||||
success=True
|
||||
)
|
||||
|
||||
# Test 5: Display tool call failure
|
||||
print("\n[TEST 5] Tool Activity Panel (Failed call)")
|
||||
display_tool_call(
|
||||
message="api_call failed",
|
||||
console=console,
|
||||
tool_name="api_call",
|
||||
tool_input={"endpoint": "/users", "method": "GET"},
|
||||
tool_output="Connection timeout",
|
||||
elapsed_time=5.0,
|
||||
success=False
|
||||
)
|
||||
|
||||
# Test 6: Display reasoning steps with numbered circles
|
||||
print("\n[TEST 6] Reasoning Steps (Numbered circles ①②③)")
|
||||
display_reasoning_steps(
|
||||
steps=[
|
||||
"Identified query about arithmetic",
|
||||
"Retrieved basic math rules",
|
||||
"Calculated result: 2+2=4",
|
||||
"Formulated concise response"
|
||||
],
|
||||
console=console
|
||||
)
|
||||
|
||||
# Test 7: Display working status (static, shows format)
|
||||
print("\n[TEST 7] Working Status (Amber border, pulsing dots)")
|
||||
for phase in range(4):
|
||||
panel = display_working_status(phase=phase, console=console)
|
||||
console.print(panel)
|
||||
|
||||
# Test 8: Display error
|
||||
print("\n[TEST 8] Error Panel (Red border)")
|
||||
display_error(
|
||||
message="API rate limit exceeded. Please try again in 60 seconds.",
|
||||
console=console
|
||||
)
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("COLORS BEING USED:")
|
||||
for name, color in PRAISON_COLORS.items():
|
||||
console.print(f" {name}: [bold {color}]████████[/] {color}")
|
||||
print("=" * 70)
|
||||
@@ -0,0 +1,207 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Micro-benchmark for output mode overhead comparison.
|
||||
|
||||
Measures:
|
||||
1. Import-time cost (cold import)
|
||||
2. Agent initialization overhead per output mode
|
||||
3. Hot-path overhead (simulated callback execution)
|
||||
|
||||
Usage:
|
||||
python tests/benchmarks/output_mode_benchmark.py
|
||||
|
||||
All stdlib - no external dependencies required.
|
||||
"""
|
||||
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from statistics import mean, stdev
|
||||
|
||||
|
||||
def measure_import_time(mode: str, iterations: int = 5) -> dict:
|
||||
"""Measure cold import time for a specific output mode."""
|
||||
times = []
|
||||
|
||||
# Script that imports and creates an agent with specific output mode
|
||||
script = f'''
|
||||
import time
|
||||
start = time.perf_counter()
|
||||
import os
|
||||
os.environ['PRAISONAI_OUTPUT'] = '{mode}'
|
||||
from praisonaiagents import Agent
|
||||
agent = Agent(name="BenchAgent", instructions="Test", output="{mode}")
|
||||
end = time.perf_counter()
|
||||
print(f"{{(end - start) * 1000:.3f}}")
|
||||
'''
|
||||
|
||||
for _ in range(iterations):
|
||||
result = subprocess.run(
|
||||
[sys.executable, '-c', script],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
env={**dict(__import__('os').environ), 'PYTHONDONTWRITEBYTECODE': '1'}
|
||||
)
|
||||
if result.returncode == 0:
|
||||
try:
|
||||
times.append(float(result.stdout.strip()))
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
if not times:
|
||||
return {"mode": mode, "mean_ms": -1, "stdev_ms": -1, "samples": 0}
|
||||
|
||||
return {
|
||||
"mode": mode,
|
||||
"mean_ms": mean(times),
|
||||
"stdev_ms": stdev(times) if len(times) > 1 else 0,
|
||||
"samples": len(times),
|
||||
}
|
||||
|
||||
|
||||
def measure_callback_overhead(iterations: int = 1000) -> dict:
|
||||
"""Measure hot-path overhead of callback execution vs no callbacks."""
|
||||
results = {}
|
||||
|
||||
# Silent mode - no callbacks
|
||||
silent_times = []
|
||||
for _ in range(iterations):
|
||||
start = time.perf_counter()
|
||||
# Simulate what happens in silent mode: nothing
|
||||
pass
|
||||
end = time.perf_counter()
|
||||
silent_times.append((end - start) * 1_000_000) # microseconds
|
||||
|
||||
results["silent"] = {
|
||||
"mean_us": mean(silent_times),
|
||||
"stdev_us": stdev(silent_times) if len(silent_times) > 1 else 0,
|
||||
}
|
||||
|
||||
# Actions mode - simulate callback overhead
|
||||
actions_times = []
|
||||
|
||||
# Simulate the work done by actions mode per tool call
|
||||
def simulate_actions_callback():
|
||||
import time as t
|
||||
# Simulate: time.time(), dict creation, string formatting, stderr write
|
||||
_ = t.time()
|
||||
event = {
|
||||
"type": "tool_call",
|
||||
"tool": "test_tool",
|
||||
"args": {"arg1": "value1", "arg2": "value2"},
|
||||
"timestamp": t.time(),
|
||||
}
|
||||
formatted = f"[tool_call] {event['tool']}({event['args']})"
|
||||
# Don't actually write to avoid polluting output
|
||||
_ = formatted.encode()
|
||||
|
||||
for _ in range(iterations):
|
||||
start = time.perf_counter()
|
||||
simulate_actions_callback()
|
||||
end = time.perf_counter()
|
||||
actions_times.append((end - start) * 1_000_000) # microseconds
|
||||
|
||||
results["actions"] = {
|
||||
"mean_us": mean(actions_times),
|
||||
"stdev_us": stdev(actions_times) if len(actions_times) > 1 else 0,
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def measure_agent_init_overhead() -> dict:
|
||||
"""Measure Agent.__init__ overhead for different output modes."""
|
||||
import os
|
||||
|
||||
# Clear any env var
|
||||
os.environ.pop('PRAISONAI_OUTPUT', None)
|
||||
|
||||
results = {}
|
||||
iterations = 10
|
||||
|
||||
for mode in ["silent", "actions", "verbose"]:
|
||||
times = []
|
||||
for _ in range(iterations):
|
||||
# Import fresh each time to avoid caching effects
|
||||
from praisonaiagents import Agent
|
||||
|
||||
start = time.perf_counter()
|
||||
agent = Agent(
|
||||
name="BenchAgent",
|
||||
instructions="Test agent",
|
||||
output=mode,
|
||||
)
|
||||
end = time.perf_counter()
|
||||
times.append((end - start) * 1000) # milliseconds
|
||||
del agent
|
||||
|
||||
results[mode] = {
|
||||
"mean_ms": mean(times),
|
||||
"stdev_ms": stdev(times) if len(times) > 1 else 0,
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print("OUTPUT MODE MICRO-BENCHMARK")
|
||||
print("=" * 60)
|
||||
print()
|
||||
|
||||
# 1. Import time comparison
|
||||
print("1. IMPORT + INIT TIME (cold start, subprocess)")
|
||||
print("-" * 40)
|
||||
|
||||
modes = ["silent", "actions", "verbose"]
|
||||
import_results = []
|
||||
|
||||
for mode in modes:
|
||||
print(f" Measuring {mode}...", end=" ", flush=True)
|
||||
result = measure_import_time(mode, iterations=3)
|
||||
import_results.append(result)
|
||||
print(f"{result['mean_ms']:.1f}ms (±{result['stdev_ms']:.1f})")
|
||||
|
||||
print()
|
||||
|
||||
# 2. Agent init overhead (warm)
|
||||
print("2. AGENT INIT OVERHEAD (warm, same process)")
|
||||
print("-" * 40)
|
||||
|
||||
init_results = measure_agent_init_overhead()
|
||||
for mode, data in init_results.items():
|
||||
print(f" {mode:12s}: {data['mean_ms']:.2f}ms (±{data['stdev_ms']:.2f})")
|
||||
|
||||
print()
|
||||
|
||||
# 3. Hot-path callback overhead
|
||||
print("3. HOT-PATH CALLBACK OVERHEAD (per call)")
|
||||
print("-" * 40)
|
||||
|
||||
callback_results = measure_callback_overhead(iterations=10000)
|
||||
for mode, data in callback_results.items():
|
||||
print(f" {mode:12s}: {data['mean_us']:.3f}µs (±{data['stdev_us']:.3f})")
|
||||
|
||||
print()
|
||||
|
||||
# Summary
|
||||
print("=" * 60)
|
||||
print("SUMMARY")
|
||||
print("=" * 60)
|
||||
|
||||
silent_init = init_results.get("silent", {}).get("mean_ms", 0)
|
||||
actions_init = init_results.get("actions", {}).get("mean_ms", 0)
|
||||
|
||||
silent_callback = callback_results.get("silent", {}).get("mean_us", 0)
|
||||
actions_callback = callback_results.get("actions", {}).get("mean_us", 0)
|
||||
|
||||
print(f" Init overhead (actions vs silent): +{actions_init - silent_init:.2f}ms")
|
||||
print(f" Per-call overhead (actions vs silent): +{actions_callback - silent_callback:.3f}µs")
|
||||
print()
|
||||
print(" DEFAULT MODE: silent (zero overhead)")
|
||||
print(" OPT-IN: output='actions' for observability")
|
||||
print()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -179,25 +179,27 @@ class TestAgentWebFetchParameter:
|
||||
"""Test the web_fetch parameter in Agent class."""
|
||||
|
||||
def test_agent_init_with_web_fetch_true(self):
|
||||
"""Test Agent initialization with web_fetch=True."""
|
||||
"""Test Agent initialization with web=WebConfig(fetch=True)."""
|
||||
from praisonaiagents.agent.agent import Agent
|
||||
from praisonaiagents.config.feature_configs import WebConfig
|
||||
agent = Agent(
|
||||
name="Test Agent",
|
||||
llm="anthropic/claude-3-5-sonnet-latest",
|
||||
web_fetch=True
|
||||
web=WebConfig(fetch=True)
|
||||
)
|
||||
assert agent.web_fetch is True
|
||||
|
||||
def test_agent_init_with_web_fetch_dict(self):
|
||||
"""Test Agent initialization with web_fetch as dict."""
|
||||
"""Test Agent initialization with web=WebConfig(fetch=True)."""
|
||||
from praisonaiagents.agent.agent import Agent
|
||||
web_fetch_config = {"max_uses": 10, "allowed_domains": ["example.com"]}
|
||||
from praisonaiagents.config.feature_configs import WebConfig
|
||||
# Now using consolidated web= param with WebConfig
|
||||
agent = Agent(
|
||||
name="Test Agent",
|
||||
llm="anthropic/claude-3-5-sonnet-latest",
|
||||
web_fetch=web_fetch_config
|
||||
web=WebConfig(fetch=True)
|
||||
)
|
||||
assert agent.web_fetch == web_fetch_config
|
||||
assert agent.web_fetch is True
|
||||
|
||||
def test_agent_init_without_web_fetch(self):
|
||||
"""Test Agent initialization without web_fetch."""
|
||||
@@ -208,10 +210,11 @@ class TestAgentWebFetchParameter:
|
||||
def test_agent_passes_web_fetch_to_llm(self):
|
||||
"""Test that Agent passes web_fetch to LLM instance."""
|
||||
from praisonaiagents.agent.agent import Agent
|
||||
from praisonaiagents.config.feature_configs import WebConfig
|
||||
agent = Agent(
|
||||
name="Test Agent",
|
||||
llm="anthropic/claude-3-5-sonnet-latest",
|
||||
web_fetch=True
|
||||
web=WebConfig(fetch=True)
|
||||
)
|
||||
assert agent.llm_instance.web_fetch is True
|
||||
|
||||
@@ -270,12 +273,13 @@ class TestWebFetchIntegration:
|
||||
def test_web_fetch_with_anthropic(self):
|
||||
"""Test actual web fetch with Anthropic API."""
|
||||
from praisonaiagents.agent.agent import Agent
|
||||
from praisonaiagents.config.feature_configs import WebConfig
|
||||
|
||||
agent = Agent(
|
||||
name="Web Fetch Agent",
|
||||
instructions="You are a helpful assistant that can fetch web content.",
|
||||
llm="anthropic/claude-3-5-sonnet-latest",
|
||||
web_fetch=True
|
||||
web=WebConfig(fetch=True)
|
||||
)
|
||||
|
||||
# This would make an actual API call
|
||||
|
||||
@@ -0,0 +1,257 @@
|
||||
"""
|
||||
Tests for Agent execution API semantics: start(), run(), stream()
|
||||
|
||||
TDD tests for the new execution API:
|
||||
- start() = interactive, streams by default in TTY
|
||||
- run() = production, silent by default
|
||||
- stream() = app-friendly iterator
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import sys
|
||||
from unittest.mock import patch
|
||||
|
||||
|
||||
class TestAgentStartMethod:
|
||||
"""Tests for Agent.start() - interactive, streaming by default in TTY"""
|
||||
|
||||
def test_start_streams_by_default_in_tty(self):
|
||||
"""start() should stream by default when stdout is a TTY"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Create agent without stream attribute set (None)
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
# Ensure stream attribute is None so TTY detection kicks in
|
||||
agent.stream = None
|
||||
|
||||
# Mock TTY detection
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
# Mock chat to avoid actual LLM call
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
mock_stream.return_value = iter(["Hello", " World"])
|
||||
with patch.object(agent, 'chat') as mock_chat:
|
||||
agent.start("Hello")
|
||||
# Should call _start_stream when TTY
|
||||
mock_stream.assert_called_once()
|
||||
mock_chat.assert_not_called()
|
||||
|
||||
def test_start_does_not_stream_when_not_tty(self):
|
||||
"""start() should not stream when stdout is not a TTY"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
# Mock non-TTY
|
||||
with patch.object(sys.stdout, 'isatty', return_value=False):
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
with patch.object(agent, 'chat', return_value="Hello World") as mock_chat:
|
||||
result = agent.start("Hello")
|
||||
# Should call chat, not _start_stream
|
||||
mock_chat.assert_called_once()
|
||||
mock_stream.assert_not_called()
|
||||
|
||||
def test_start_respects_explicit_stream_true(self):
|
||||
"""start(stream=True) should always stream"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
# Even when not TTY, explicit stream=True should stream
|
||||
with patch.object(sys.stdout, 'isatty', return_value=False):
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
mock_stream.return_value = iter(["Hello"])
|
||||
result = agent.start("Hello", stream=True)
|
||||
mock_stream.assert_called_once()
|
||||
|
||||
def test_start_respects_explicit_stream_false(self):
|
||||
"""start(stream=False) should never stream"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
# Even when TTY, explicit stream=False should not stream
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
with patch.object(agent, 'chat', return_value="Hello") as mock_chat:
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
result = agent.start("Hello", stream=False)
|
||||
mock_chat.assert_called_once()
|
||||
mock_stream.assert_not_called()
|
||||
|
||||
def test_start_respects_agent_stream_attribute(self):
|
||||
"""start() should respect agent's stream attribute over TTY detection"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
# Agent with stream=True should always stream
|
||||
agent = Agent(name="Test", instructions="You are helpful", output="stream")
|
||||
|
||||
with patch.object(sys.stdout, 'isatty', return_value=False):
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
mock_stream.return_value = iter(["Hello"])
|
||||
# Agent's stream attribute should take precedence
|
||||
if agent.stream:
|
||||
result = agent.start("Hello")
|
||||
mock_stream.assert_called()
|
||||
|
||||
|
||||
class TestAgentRunMethod:
|
||||
"""Tests for Agent.run() - production, silent by default"""
|
||||
|
||||
def test_run_does_not_stream_by_default(self):
|
||||
"""run() should not stream by default"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
with patch.object(agent, 'chat', return_value="Hello World") as mock_chat:
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
result = agent.run("Hello")
|
||||
# Should call chat with stream=False
|
||||
mock_chat.assert_called_once()
|
||||
call_kwargs = mock_chat.call_args[1]
|
||||
assert call_kwargs.get('stream') == False
|
||||
mock_stream.assert_not_called()
|
||||
|
||||
def test_run_returns_result_directly(self):
|
||||
"""run() should return the result directly, not a generator"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
with patch.object(agent, 'chat', return_value="Hello World") as mock_chat:
|
||||
result = agent.run("Hello")
|
||||
assert result == "Hello World"
|
||||
assert not hasattr(result, '__iter__') or isinstance(result, str)
|
||||
|
||||
def test_run_ignores_tty_detection(self):
|
||||
"""run() should ignore TTY detection and never stream by default"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
# Even in TTY, run() should not stream
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
with patch.object(agent, 'chat', return_value="Hello") as mock_chat:
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
result = agent.run("Hello")
|
||||
mock_chat.assert_called_once()
|
||||
mock_stream.assert_not_called()
|
||||
|
||||
def test_run_can_be_forced_to_stream(self):
|
||||
"""run(stream=True) should stream when explicitly requested"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
with patch.object(agent, 'chat', return_value="Hello") as mock_chat:
|
||||
result = agent.run("Hello", stream=True)
|
||||
# When stream=True is passed, it should be passed to chat
|
||||
call_kwargs = mock_chat.call_args[1]
|
||||
assert call_kwargs.get('stream') == True
|
||||
|
||||
|
||||
class TestAgentIterStreamMethod:
|
||||
"""Tests for Agent.iter_stream() - app-friendly iterator"""
|
||||
|
||||
def test_iter_stream_returns_iterator(self):
|
||||
"""iter_stream() should return an iterator"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
mock_stream.return_value = iter(["Hello", " ", "World"])
|
||||
result = agent.iter_stream("Hello")
|
||||
# Should be an iterator/generator
|
||||
assert hasattr(result, '__iter__')
|
||||
assert hasattr(result, '__next__')
|
||||
|
||||
def test_iter_stream_yields_chunks(self):
|
||||
"""iter_stream() should yield response chunks"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
mock_stream.return_value = iter(["Hello", " ", "World"])
|
||||
chunks = list(agent.iter_stream("Hello"))
|
||||
assert chunks == ["Hello", " ", "World"]
|
||||
|
||||
def test_iter_stream_always_streams(self):
|
||||
"""iter_stream() should always stream regardless of TTY"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
# Even when not TTY
|
||||
with patch.object(sys.stdout, 'isatty', return_value=False):
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
mock_stream.return_value = iter(["Hello"])
|
||||
result = agent.iter_stream("Hello")
|
||||
# Consume the generator
|
||||
list(result)
|
||||
mock_stream.assert_called_once()
|
||||
|
||||
|
||||
class TestAsyncMethods:
|
||||
"""Tests for async versions: arun(), astart()"""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_arun_does_not_stream_by_default(self):
|
||||
"""arun() should not stream by default"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
|
||||
with patch.object(agent, 'achat', return_value="Hello World") as mock_achat:
|
||||
result = await agent.arun("Hello")
|
||||
mock_achat.assert_called_once()
|
||||
call_kwargs = mock_achat.call_args[1]
|
||||
assert call_kwargs.get('stream') == False
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_astart_streams_in_tty(self):
|
||||
"""astart() should stream when in TTY"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
# Ensure stream attribute is None so TTY detection kicks in
|
||||
agent.stream = None
|
||||
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
with patch.object(agent, 'achat', return_value="Hello") as mock_achat:
|
||||
await agent.astart("Hello")
|
||||
call_kwargs = mock_achat.call_args[1]
|
||||
assert call_kwargs.get('stream')
|
||||
|
||||
|
||||
class TestMethodSemanticDistinction:
|
||||
"""Tests to verify start() and run() have distinct semantics"""
|
||||
|
||||
def test_start_and_run_are_different(self):
|
||||
"""start() and run() should have different default behaviors"""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent = Agent(name="Test", instructions="You are helpful")
|
||||
# Ensure stream attribute is None so TTY detection kicks in
|
||||
agent.stream = None
|
||||
|
||||
# In TTY context
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
with patch.object(agent, 'chat', return_value="Hello") as mock_chat:
|
||||
with patch.object(agent, '_start_stream') as mock_stream:
|
||||
mock_stream.return_value = iter(["Hello"])
|
||||
|
||||
# start() should stream
|
||||
agent.start("Hello")
|
||||
start_streamed = mock_stream.called
|
||||
|
||||
mock_stream.reset_mock()
|
||||
mock_chat.reset_mock()
|
||||
|
||||
# run() should not stream
|
||||
agent.run("Hello")
|
||||
run_streamed = mock_stream.called
|
||||
|
||||
# They should behave differently
|
||||
assert start_streamed
|
||||
assert not run_streamed
|
||||
@@ -0,0 +1,156 @@
|
||||
"""
|
||||
Unit tests for Agent output parameter with verbose preset.
|
||||
|
||||
Tests the fix for the bug where output="verbose" would return None
|
||||
because reasoning_steps=True in the verbose preset was causing the code
|
||||
to try to return reasoning_content, but this attribute exists as None
|
||||
for non-reasoning models.
|
||||
"""
|
||||
import pytest
|
||||
from unittest.mock import Mock
|
||||
|
||||
|
||||
class TestReasoningContentLogic:
|
||||
"""Test the reasoning_content check logic directly."""
|
||||
|
||||
def test_condition_with_none_reasoning_content(self):
|
||||
"""Verify the fix: condition should be False when reasoning_content is None."""
|
||||
mock_message = Mock()
|
||||
mock_message.reasoning_content = None
|
||||
|
||||
reasoning_steps = True
|
||||
|
||||
# Old buggy condition:
|
||||
# if reasoning_steps and hasattr(mock_message, 'reasoning_content'):
|
||||
old_condition = reasoning_steps and hasattr(mock_message, 'reasoning_content')
|
||||
assert old_condition == True, "Old condition would be True (the bug)"
|
||||
|
||||
# Fixed condition:
|
||||
# if reasoning_steps and hasattr(mock_message, 'reasoning_content') and mock_message.reasoning_content:
|
||||
# The truthiness of the whole expression should be False
|
||||
fixed_condition = reasoning_steps and hasattr(mock_message, 'reasoning_content') and mock_message.reasoning_content
|
||||
assert not fixed_condition, "Fixed condition should be falsy when reasoning_content is None"
|
||||
|
||||
def test_condition_with_empty_string_reasoning_content(self):
|
||||
"""Verify the fix: condition should be False when reasoning_content is empty string."""
|
||||
mock_message = Mock()
|
||||
mock_message.reasoning_content = ""
|
||||
|
||||
reasoning_steps = True
|
||||
|
||||
# Old condition would True (bug)
|
||||
old_condition = reasoning_steps and hasattr(mock_message, 'reasoning_content')
|
||||
assert old_condition == True
|
||||
|
||||
# Fixed condition should be falsy
|
||||
fixed_condition = reasoning_steps and hasattr(mock_message, 'reasoning_content') and mock_message.reasoning_content
|
||||
assert not fixed_condition, "Fixed condition should be falsy when reasoning_content is empty string"
|
||||
|
||||
def test_condition_with_actual_reasoning_content(self):
|
||||
"""Verify the fix: condition should be True when reasoning_content has content."""
|
||||
mock_message = Mock()
|
||||
mock_message.reasoning_content = "This is my reasoning process..."
|
||||
|
||||
reasoning_steps = True
|
||||
|
||||
# Both old and new condition should be truthy
|
||||
old_condition = reasoning_steps and hasattr(mock_message, 'reasoning_content')
|
||||
assert old_condition
|
||||
|
||||
fixed_condition = reasoning_steps and hasattr(mock_message, 'reasoning_content') and mock_message.reasoning_content
|
||||
assert fixed_condition, "Fixed condition should be truthy when reasoning_content has content"
|
||||
|
||||
def test_condition_when_reasoning_steps_false(self):
|
||||
"""Verify that when reasoning_steps is False, condition is always False."""
|
||||
mock_message = Mock()
|
||||
mock_message.reasoning_content = "This is my reasoning process..."
|
||||
|
||||
reasoning_steps = False
|
||||
|
||||
fixed_condition = reasoning_steps and hasattr(mock_message, 'reasoning_content') and mock_message.reasoning_content
|
||||
assert not fixed_condition, "Condition should be falsy when reasoning_steps is False"
|
||||
|
||||
def test_if_statement_behavior_with_none(self):
|
||||
"""Verify that an if statement using the condition skips when reasoning_content is None."""
|
||||
mock_message = Mock()
|
||||
mock_message.reasoning_content = None
|
||||
|
||||
reasoning_steps = True
|
||||
execution_path = "not_taken"
|
||||
|
||||
# This is how the fixed code behaves
|
||||
if reasoning_steps and hasattr(mock_message, 'reasoning_content') and mock_message.reasoning_content:
|
||||
execution_path = "reasoning_path"
|
||||
else:
|
||||
execution_path = "regular_path"
|
||||
|
||||
assert execution_path == "regular_path", "Should take regular path when reasoning_content is None"
|
||||
|
||||
def test_if_statement_behavior_with_reasoning(self):
|
||||
"""Verify that an if statement using the condition enters when reasoning_content has value."""
|
||||
mock_message = Mock()
|
||||
mock_message.reasoning_content = "Reasoning steps here..."
|
||||
|
||||
reasoning_steps = True
|
||||
execution_path = "not_taken"
|
||||
|
||||
if reasoning_steps and hasattr(mock_message, 'reasoning_content') and mock_message.reasoning_content:
|
||||
execution_path = "reasoning_path"
|
||||
else:
|
||||
execution_path = "regular_path"
|
||||
|
||||
assert execution_path == "reasoning_path", "Should take reasoning path when reasoning_content has value"
|
||||
|
||||
|
||||
class TestVerbosePresetConfig:
|
||||
"""Test that the verbose preset has the expected configuration."""
|
||||
|
||||
def test_verbose_preset_has_reasoning_steps_true(self):
|
||||
"""Verify that the verbose preset enables reasoning_steps."""
|
||||
from praisonaiagents.config.presets import OUTPUT_PRESETS
|
||||
|
||||
verbose_preset = OUTPUT_PRESETS.get("verbose")
|
||||
assert verbose_preset is not None, "verbose preset should exist"
|
||||
assert verbose_preset.get("reasoning_steps") == True, "verbose preset should have reasoning_steps=True"
|
||||
|
||||
def test_verbose_preset_has_verbose_true(self):
|
||||
"""Verify that the verbose preset enables verbose output."""
|
||||
from praisonaiagents.config.presets import OUTPUT_PRESETS
|
||||
|
||||
verbose_preset = OUTPUT_PRESETS.get("verbose")
|
||||
assert verbose_preset.get("verbose") == True, "verbose preset should have verbose=True"
|
||||
|
||||
def test_verbose_preset_has_stream_false(self):
|
||||
"""Verify that the verbose preset does not enable streaming by default."""
|
||||
from praisonaiagents.config.presets import OUTPUT_PRESETS
|
||||
|
||||
verbose_preset = OUTPUT_PRESETS.get("verbose")
|
||||
assert verbose_preset.get("stream") == False, "verbose preset should have stream=False"
|
||||
|
||||
|
||||
class TestChatCompletionMessageDataclass:
|
||||
"""Test the ChatCompletionMessage dataclass has reasoning_content attribute."""
|
||||
|
||||
def test_chat_completion_message_has_reasoning_content(self):
|
||||
"""Verify ChatCompletionMessage has reasoning_content attribute that defaults to None."""
|
||||
from praisonaiagents.llm.openai_client import ChatCompletionMessage
|
||||
|
||||
# Create without reasoning_content
|
||||
message = ChatCompletionMessage(content="Test", role="assistant")
|
||||
|
||||
# Should have the attribute
|
||||
assert hasattr(message, 'reasoning_content')
|
||||
# Should default to None
|
||||
assert message.reasoning_content is None
|
||||
|
||||
def test_chat_completion_message_with_reasoning(self):
|
||||
"""Verify ChatCompletionMessage can have reasoning_content set."""
|
||||
from praisonaiagents.llm.openai_client import ChatCompletionMessage
|
||||
|
||||
message = ChatCompletionMessage(
|
||||
content="Test",
|
||||
role="assistant",
|
||||
reasoning_content="This is my reasoning..."
|
||||
)
|
||||
|
||||
assert message.reasoning_content == "This is my reasoning..."
|
||||
@@ -0,0 +1,182 @@
|
||||
"""
|
||||
Unit tests for Agent.start() method defaulting to verbose output in TTY.
|
||||
|
||||
Tests the behavior that:
|
||||
- .start() defaults to verbose output when running in a TTY
|
||||
- .start() uses agent's default (silent) when NOT in TTY
|
||||
- .run() always uses the agent's default (silent)
|
||||
- Explicit output= kwarg overrides TTY detection
|
||||
|
||||
This implements Option B: Make .start() default to verbose output in TTY.
|
||||
"""
|
||||
import pytest
|
||||
from unittest.mock import Mock, patch
|
||||
import sys
|
||||
|
||||
|
||||
class TestStartVerboseInTTY:
|
||||
"""Test that .start() defaults to verbose output in TTY."""
|
||||
|
||||
def test_start_sets_verbose_in_tty(self):
|
||||
"""Verify .start() sets verbose=True when running in TTY."""
|
||||
# Mock isatty to return True
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
# Create agent with default (silent) output
|
||||
from praisonaiagents import Agent
|
||||
agent = Agent(instructions="Test agent")
|
||||
|
||||
# Verify default is silent
|
||||
assert agent.verbose == False, "Default should be silent (verbose=False)"
|
||||
|
||||
# Track what happens in start
|
||||
original_chat = agent.chat
|
||||
captured_verbose = []
|
||||
|
||||
def mock_chat(*args, **kwargs):
|
||||
captured_verbose.append(agent.verbose)
|
||||
return "mock response"
|
||||
|
||||
agent.chat = mock_chat
|
||||
agent._load_history_context = Mock()
|
||||
agent._auto_save_session = Mock()
|
||||
|
||||
# Call start
|
||||
result = agent.start("Hello")
|
||||
|
||||
# Verbose should have been True during the call
|
||||
assert len(captured_verbose) == 1, "chat() should have been called"
|
||||
assert captured_verbose[0] == True, ".start() should set verbose=True in TTY"
|
||||
|
||||
# Verbose should be restored after
|
||||
assert agent.verbose == False, "verbose should be restored after .start()"
|
||||
|
||||
def test_start_respects_explicit_output_kwarg(self):
|
||||
"""Verify .start() respects explicit output='silent' even in TTY."""
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
from praisonaiagents import Agent
|
||||
agent = Agent(instructions="Test agent")
|
||||
|
||||
captured_verbose = []
|
||||
|
||||
def mock_chat(*args, **kwargs):
|
||||
captured_verbose.append(agent.verbose)
|
||||
return "mock response"
|
||||
|
||||
agent.chat = mock_chat
|
||||
agent._load_history_context = Mock()
|
||||
agent._auto_save_session = Mock()
|
||||
|
||||
# Call start with explicit output='silent'
|
||||
result = agent.start("Hello", output="silent")
|
||||
|
||||
# Should respect explicit silent
|
||||
assert captured_verbose[0] == False, "explicit output='silent' should be respected"
|
||||
|
||||
def test_start_silent_when_not_tty(self):
|
||||
"""Verify .start() stays silent when not in TTY."""
|
||||
with patch.object(sys.stdout, 'isatty', return_value=False):
|
||||
from praisonaiagents import Agent
|
||||
agent = Agent(instructions="Test agent")
|
||||
|
||||
captured_verbose = []
|
||||
|
||||
def mock_chat(*args, **kwargs):
|
||||
captured_verbose.append(agent.verbose)
|
||||
return "mock response"
|
||||
|
||||
agent.chat = mock_chat
|
||||
agent._load_history_context = Mock()
|
||||
agent._auto_save_session = Mock()
|
||||
|
||||
# Call start in non-TTY
|
||||
result = agent.start("Hello")
|
||||
|
||||
# Should remain silent
|
||||
assert captured_verbose[0] == False, ".start() should stay silent when not TTY"
|
||||
|
||||
def test_run_always_silent(self):
|
||||
"""Verify .run() is always silent regardless of TTY."""
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
from praisonaiagents import Agent
|
||||
agent = Agent(instructions="Test agent")
|
||||
|
||||
captured_verbose = []
|
||||
|
||||
def mock_chat(*args, **kwargs):
|
||||
captured_verbose.append(agent.verbose)
|
||||
return "mock response"
|
||||
|
||||
agent.chat = mock_chat
|
||||
agent._load_history_context = Mock()
|
||||
agent._auto_save_session = Mock()
|
||||
|
||||
# Call run (should stay silent even in TTY)
|
||||
result = agent.run("Hello")
|
||||
|
||||
# Should be silent
|
||||
assert captured_verbose[0] == False, ".run() should always be silent"
|
||||
|
||||
|
||||
class TestStartVerbosePresets:
|
||||
"""Test the verbose preset values applied by .start()."""
|
||||
|
||||
def test_start_enables_markdown_in_tty(self):
|
||||
"""Verify .start() also enables markdown=True in TTY."""
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
from praisonaiagents import Agent
|
||||
agent = Agent(instructions="Test agent")
|
||||
|
||||
# Verify default is no markdown
|
||||
assert agent.markdown == False, "Default should be no markdown"
|
||||
|
||||
captured_markdown = []
|
||||
|
||||
def mock_chat(*args, **kwargs):
|
||||
captured_markdown.append(agent.markdown)
|
||||
return "mock response"
|
||||
|
||||
agent.chat = mock_chat
|
||||
agent._load_history_context = Mock()
|
||||
agent._auto_save_session = Mock()
|
||||
|
||||
# Call start
|
||||
result = agent.start("Hello")
|
||||
|
||||
# Markdown should have been True during the call
|
||||
assert captured_markdown[0] == True, ".start() should set markdown=True in TTY"
|
||||
|
||||
# Should be restored after
|
||||
assert agent.markdown == False, "markdown should be restored after .start()"
|
||||
|
||||
|
||||
class TestAgentWithExplicitVerbose:
|
||||
"""Test that agents created with output='verbose' work correctly."""
|
||||
|
||||
def test_agent_with_verbose_output(self):
|
||||
"""Agent created with output='verbose' should have verbose=True."""
|
||||
from praisonaiagents import Agent
|
||||
agent = Agent(instructions="Test", output="verbose")
|
||||
|
||||
assert agent.verbose == True, "output='verbose' should set verbose=True"
|
||||
assert agent.markdown == True, "output='verbose' should set markdown=True"
|
||||
|
||||
def test_start_with_already_verbose_agent(self):
|
||||
"""Start on already-verbose agent should stay verbose."""
|
||||
with patch.object(sys.stdout, 'isatty', return_value=True):
|
||||
from praisonaiagents import Agent
|
||||
agent = Agent(instructions="Test", output="verbose")
|
||||
|
||||
captured_verbose = []
|
||||
|
||||
def mock_chat(*args, **kwargs):
|
||||
captured_verbose.append(agent.verbose)
|
||||
return "mock response"
|
||||
|
||||
agent.chat = mock_chat
|
||||
agent._load_history_context = Mock()
|
||||
agent._auto_save_session = Mock()
|
||||
|
||||
result = agent.start("Hello")
|
||||
|
||||
assert captured_verbose[0] == True
|
||||
assert agent.verbose == True, "Already-verbose agent should stay verbose"
|
||||
@@ -0,0 +1,123 @@
|
||||
"""
|
||||
Tests for wrapper agent modernization - ensuring legacy args are mapped to consolidated params.
|
||||
|
||||
TDD: These tests verify that wrapper classes (PromptExpanderAgent, QueryRewriterAgent)
|
||||
correctly use consolidated params (output=) instead of legacy args (verbose=, markdown=).
|
||||
"""
|
||||
|
||||
|
||||
class TestPromptExpanderAgentModernization:
|
||||
"""Test PromptExpanderAgent uses consolidated output param."""
|
||||
|
||||
def test_agent_uses_output_config_not_legacy_args(self):
|
||||
"""PromptExpanderAgent should pass output= to Agent, not verbose=/markdown=."""
|
||||
from praisonaiagents.agent.prompt_expander_agent import PromptExpanderAgent
|
||||
|
||||
# Create wrapper with verbose=True
|
||||
expander = PromptExpanderAgent(verbose=True)
|
||||
|
||||
# Access the lazy-initialized agent
|
||||
agent = expander.agent
|
||||
|
||||
# Verify: Agent should have output config set, not legacy verbose/markdown as direct args
|
||||
# The Agent stores resolved output in self.verbose and self.markdown after resolution
|
||||
# But the key is that the wrapper passed output= dict, not verbose=/markdown= directly
|
||||
assert hasattr(agent, 'verbose')
|
||||
assert hasattr(agent, 'markdown')
|
||||
# verbose should be True (from output config)
|
||||
assert agent.verbose == True
|
||||
# markdown should be False (from output config)
|
||||
assert agent.markdown == False
|
||||
|
||||
def test_agent_verbose_false_propagates(self):
|
||||
"""PromptExpanderAgent with verbose=False should propagate correctly."""
|
||||
from praisonaiagents.agent.prompt_expander_agent import PromptExpanderAgent
|
||||
|
||||
expander = PromptExpanderAgent(verbose=False)
|
||||
agent = expander.agent
|
||||
|
||||
assert agent.verbose == False
|
||||
assert agent.markdown == False
|
||||
|
||||
|
||||
class TestQueryRewriterAgentModernization:
|
||||
"""Test QueryRewriterAgent uses consolidated output param."""
|
||||
|
||||
def test_agent_uses_output_config_not_legacy_args(self):
|
||||
"""QueryRewriterAgent should pass output= to Agent, not verbose=/markdown=."""
|
||||
from praisonaiagents.agent.query_rewriter_agent import QueryRewriterAgent
|
||||
|
||||
# Create wrapper with verbose=True
|
||||
rewriter = QueryRewriterAgent(verbose=True)
|
||||
|
||||
# Access the lazy-initialized agent
|
||||
agent = rewriter.agent
|
||||
|
||||
# Verify output config was applied
|
||||
assert agent.verbose == True
|
||||
assert agent.markdown == False
|
||||
|
||||
def test_agent_verbose_false_propagates(self):
|
||||
"""QueryRewriterAgent with verbose=False should propagate correctly."""
|
||||
from praisonaiagents.agent.query_rewriter_agent import QueryRewriterAgent
|
||||
|
||||
rewriter = QueryRewriterAgent(verbose=False)
|
||||
agent = rewriter.agent
|
||||
|
||||
assert agent.verbose == False
|
||||
assert agent.markdown == False
|
||||
|
||||
|
||||
class TestProcessManagerAgentModernization:
|
||||
"""Test Process class manager agents use consolidated params."""
|
||||
|
||||
def test_manager_agent_creation_uses_consolidated_params(self):
|
||||
"""Process manager agent should use output= and reflection=, not legacy args."""
|
||||
# This is a structural test - we verify the code pattern
|
||||
import inspect
|
||||
from praisonaiagents.process.process import Process
|
||||
|
||||
# Get source of hierarchical method (sync version)
|
||||
source = inspect.getsource(Process.hierarchical)
|
||||
|
||||
# After fix: should contain output= and reflection=
|
||||
assert 'output=' in source, "Process.hierarchical should use output= consolidated param"
|
||||
assert 'reflection=' in source, "Process.hierarchical should use reflection= consolidated param"
|
||||
|
||||
# Should NOT contain legacy args passed to Agent
|
||||
assert 'self_reflect=' not in source, "Process.hierarchical should not use legacy self_reflect="
|
||||
|
||||
|
||||
class TestAutoAgentsConsolidatedParams:
|
||||
"""Test AutoAgents already uses consolidated params correctly."""
|
||||
|
||||
def test_autoagents_has_consolidated_params(self):
|
||||
"""AutoAgents should accept consolidated params."""
|
||||
import inspect
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
|
||||
# Should have consolidated params
|
||||
assert 'output' in params
|
||||
assert 'reflection' in params
|
||||
assert 'caching' in params
|
||||
assert 'knowledge' in params
|
||||
assert 'execution' in params
|
||||
assert 'guardrails' in params
|
||||
assert 'web' in params
|
||||
assert 'hooks' in params
|
||||
|
||||
def test_autoagents_consolidated_takes_precedence(self):
|
||||
"""AutoAgents consolidated params should override legacy params."""
|
||||
import inspect
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
|
||||
# Get source of _create_agents_and_tasks
|
||||
source = inspect.getsource(AutoAgents._create_agents_and_tasks)
|
||||
|
||||
# Should pass consolidated params to Agent
|
||||
assert 'output=self._output' in source
|
||||
assert 'reflection=self._reflection' in source
|
||||
assert 'caching=self._caching' in source
|
||||
@@ -0,0 +1,113 @@
|
||||
"""
|
||||
TDD Tests for AutoAgents SDK Parity.
|
||||
|
||||
Tests that AutoAgents correctly maps legacy params to consolidated params
|
||||
and passes them through to Agent instances.
|
||||
"""
|
||||
import pytest
|
||||
from unittest.mock import patch, MagicMock
|
||||
|
||||
|
||||
class TestAutoAgentsLegacyParamMapping:
|
||||
"""Test that AutoAgents maps legacy params to consolidated params."""
|
||||
|
||||
def test_autoagents_accepts_legacy_verbose_param(self):
|
||||
"""AutoAgents should accept verbose param (legacy)."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
assert 'verbose' in params
|
||||
|
||||
def test_autoagents_accepts_legacy_markdown_param(self):
|
||||
"""AutoAgents should accept markdown param (legacy)."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
assert 'markdown' in params
|
||||
|
||||
def test_autoagents_accepts_legacy_self_reflect_param(self):
|
||||
"""AutoAgents should accept self_reflect param (legacy)."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
assert 'self_reflect' in params
|
||||
|
||||
def test_autoagents_accepts_legacy_cache_param(self):
|
||||
"""AutoAgents should accept cache param (legacy)."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
assert 'cache' in params
|
||||
|
||||
def test_autoagents_accepts_consolidated_output_param(self):
|
||||
"""AutoAgents should accept consolidated output param."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
# This test will FAIL initially - AutoAgents doesn't have output param yet
|
||||
assert 'output' in params, "AutoAgents should accept consolidated 'output' param"
|
||||
|
||||
def test_autoagents_accepts_consolidated_reflection_param(self):
|
||||
"""AutoAgents should accept consolidated reflection param."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
# This test will FAIL initially - AutoAgents doesn't have reflection param yet
|
||||
assert 'reflection' in params, "AutoAgents should accept consolidated 'reflection' param"
|
||||
|
||||
def test_autoagents_accepts_consolidated_caching_param(self):
|
||||
"""AutoAgents should accept consolidated caching param."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
# This test will FAIL initially - AutoAgents doesn't have caching param yet
|
||||
assert 'caching' in params, "AutoAgents should accept consolidated 'caching' param"
|
||||
|
||||
def test_autoagents_accepts_consolidated_knowledge_param(self):
|
||||
"""AutoAgents should accept consolidated knowledge param."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
sig = inspect.signature(AutoAgents.__init__)
|
||||
params = list(sig.parameters.keys())
|
||||
# This test will FAIL initially - AutoAgents doesn't have knowledge param yet
|
||||
assert 'knowledge' in params, "AutoAgents should accept consolidated 'knowledge' param"
|
||||
|
||||
|
||||
class TestAutoAgentsParamPrecedence:
|
||||
"""Test that consolidated params take precedence over legacy params."""
|
||||
|
||||
def test_consolidated_output_overrides_legacy_verbose(self):
|
||||
"""Consolidated output param should override legacy verbose."""
|
||||
# This tests the mapping logic once implemented
|
||||
pass # Placeholder - will be implemented after AutoAgents is updated
|
||||
|
||||
def test_consolidated_reflection_overrides_legacy_self_reflect(self):
|
||||
"""Consolidated reflection param should override legacy self_reflect."""
|
||||
pass # Placeholder
|
||||
|
||||
def test_consolidated_caching_overrides_legacy_cache(self):
|
||||
"""Consolidated caching param should override legacy cache."""
|
||||
pass # Placeholder
|
||||
|
||||
|
||||
class TestAutoAgentsAgentCreation:
|
||||
"""Test that AutoAgents creates Agent instances with correct params."""
|
||||
|
||||
@patch('praisonaiagents.agents.autoagents.AutoAgents._generate_config')
|
||||
@patch('praisonaiagents.agents.autoagents.AutoAgents._create_agents_and_tasks')
|
||||
def test_agent_receives_consolidated_params(self, mock_create, mock_config):
|
||||
"""Agents created by AutoAgents should receive consolidated params."""
|
||||
# Setup mocks to avoid LLM calls
|
||||
mock_config.return_value = MagicMock()
|
||||
mock_create.return_value = ([], [])
|
||||
|
||||
# This test verifies the param passing logic
|
||||
# Will be more detailed once implementation is done
|
||||
pass
|
||||
@@ -0,0 +1,124 @@
|
||||
"""
|
||||
TDD tests for delegator.py protocol compliance.
|
||||
|
||||
Tests that SubagentDelegator creates agents using consolidated params
|
||||
instead of legacy params like verbose=, max_iter=.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import Mock, patch
|
||||
|
||||
|
||||
class TestDelegatorProtocolCompliance:
|
||||
"""Test that delegator uses consolidated params when creating agents."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_create_agent_uses_output_not_verbose(self):
|
||||
"""Delegator should use output='silent' instead of verbose=False."""
|
||||
from praisonaiagents.agents.delegator import SubagentDelegator, DelegationTask
|
||||
from praisonaiagents.agents.profiles import AgentProfile
|
||||
|
||||
# Create a delegator
|
||||
delegator = SubagentDelegator()
|
||||
|
||||
# Create a profile and task
|
||||
profile = AgentProfile(
|
||||
name="test_agent",
|
||||
system_prompt="Test prompt",
|
||||
max_steps=10,
|
||||
)
|
||||
task = DelegationTask(
|
||||
task_id="test-1",
|
||||
agent_name="test_agent",
|
||||
objective="Test task",
|
||||
max_steps=5,
|
||||
)
|
||||
|
||||
# Mock Agent to capture how it's called (Agent is imported inside _create_agent)
|
||||
with patch('praisonaiagents.agent.agent.Agent') as MockAgent:
|
||||
MockAgent.return_value = Mock()
|
||||
|
||||
await delegator._create_agent(profile, task)
|
||||
|
||||
# Verify Agent was called
|
||||
MockAgent.assert_called_once()
|
||||
call_kwargs = MockAgent.call_args[1]
|
||||
|
||||
# Should NOT have verbose= param
|
||||
assert 'verbose' not in call_kwargs, "Should not use legacy verbose= param"
|
||||
|
||||
# Should have output= param with 'silent' preset
|
||||
assert 'output' in call_kwargs, "Should use consolidated output= param"
|
||||
assert call_kwargs['output'] == 'silent', "Should use output='silent' for quiet mode"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_create_agent_uses_execution_not_max_iter(self):
|
||||
"""Delegator should use execution= instead of max_iter=."""
|
||||
from praisonaiagents.agents.delegator import SubagentDelegator, DelegationTask
|
||||
from praisonaiagents.agents.profiles import AgentProfile
|
||||
|
||||
delegator = SubagentDelegator()
|
||||
|
||||
profile = AgentProfile(
|
||||
name="test_agent",
|
||||
system_prompt="Test prompt",
|
||||
max_steps=10,
|
||||
)
|
||||
task = DelegationTask(
|
||||
task_id="test-2",
|
||||
agent_name="test_agent",
|
||||
objective="Test task",
|
||||
max_steps=5,
|
||||
)
|
||||
|
||||
with patch('praisonaiagents.agent.agent.Agent') as MockAgent:
|
||||
MockAgent.return_value = Mock()
|
||||
|
||||
await delegator._create_agent(profile, task)
|
||||
|
||||
call_kwargs = MockAgent.call_args[1]
|
||||
|
||||
# Should NOT have max_iter= param
|
||||
assert 'max_iter' not in call_kwargs, "Should not use legacy max_iter= param"
|
||||
|
||||
# Should have execution= param with max_iter in config
|
||||
assert 'execution' in call_kwargs, "Should use consolidated execution= param"
|
||||
exec_config = call_kwargs['execution']
|
||||
assert isinstance(exec_config, dict), "execution should be a dict config"
|
||||
assert 'max_iter' in exec_config, "execution config should contain max_iter"
|
||||
assert exec_config['max_iter'] == 5, "max_iter should be min(task.max_steps, profile.max_steps)"
|
||||
|
||||
|
||||
class TestDelegatorBackwardCompatibility:
|
||||
"""Test that delegator still works correctly after modernization."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_create_agent_returns_valid_agent(self):
|
||||
"""Created agent should be usable."""
|
||||
from praisonaiagents.agents.delegator import SubagentDelegator, DelegationTask
|
||||
from praisonaiagents.agents.profiles import AgentProfile
|
||||
|
||||
delegator = SubagentDelegator()
|
||||
|
||||
profile = AgentProfile(
|
||||
name="test_agent",
|
||||
system_prompt="You are a helpful assistant.",
|
||||
max_steps=10,
|
||||
)
|
||||
task = DelegationTask(
|
||||
task_id="test-3",
|
||||
agent_name="test_agent",
|
||||
objective="Say hello",
|
||||
max_steps=5,
|
||||
)
|
||||
|
||||
with patch('praisonaiagents.agent.agent.Agent') as MockAgent:
|
||||
mock_agent = Mock()
|
||||
mock_agent.chat = Mock(return_value="Hello!")
|
||||
MockAgent.return_value = mock_agent
|
||||
|
||||
agent = await delegator._create_agent(profile, task)
|
||||
|
||||
# Agent should be returned
|
||||
assert agent is not None
|
||||
assert agent == mock_agent
|
||||
@@ -0,0 +1,104 @@
|
||||
"""
|
||||
Tests for LearnScope enum - verifying PRIVATE/SHARED values only.
|
||||
|
||||
These tests verify that:
|
||||
1. LearnScope only has PRIVATE and SHARED values
|
||||
2. TEAM and ORG values do NOT exist
|
||||
3. LearnConfig defaults to PRIVATE scope
|
||||
4. BaseStore defaults to "private" scope
|
||||
"""
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
class TestLearnScopeValues:
|
||||
"""Test LearnScope enum has exactly the right values."""
|
||||
|
||||
def test_learn_scope_has_private(self):
|
||||
"""LearnScope should have PRIVATE value."""
|
||||
from praisonaiagents.config.feature_configs import LearnScope
|
||||
|
||||
assert hasattr(LearnScope, 'PRIVATE')
|
||||
assert LearnScope.PRIVATE.value == "private"
|
||||
|
||||
def test_learn_scope_has_shared(self):
|
||||
"""LearnScope should have SHARED value."""
|
||||
from praisonaiagents.config.feature_configs import LearnScope
|
||||
|
||||
assert hasattr(LearnScope, 'SHARED')
|
||||
assert LearnScope.SHARED.value == "shared"
|
||||
|
||||
def test_learn_scope_does_not_have_team(self):
|
||||
"""LearnScope should NOT have TEAM value."""
|
||||
from praisonaiagents.config.feature_configs import LearnScope
|
||||
|
||||
assert not hasattr(LearnScope, 'TEAM'), "TEAM should be removed from LearnScope"
|
||||
|
||||
def test_learn_scope_does_not_have_org(self):
|
||||
"""LearnScope should NOT have ORG value."""
|
||||
from praisonaiagents.config.feature_configs import LearnScope
|
||||
|
||||
assert not hasattr(LearnScope, 'ORG'), "ORG should be removed from LearnScope"
|
||||
|
||||
def test_learn_scope_does_not_have_user(self):
|
||||
"""LearnScope should NOT have USER value (replaced by PRIVATE)."""
|
||||
from praisonaiagents.config.feature_configs import LearnScope
|
||||
|
||||
assert not hasattr(LearnScope, 'USER'), "USER should be replaced by PRIVATE"
|
||||
|
||||
def test_learn_scope_does_not_have_global(self):
|
||||
"""LearnScope should NOT have GLOBAL value (replaced by SHARED)."""
|
||||
from praisonaiagents.config.feature_configs import LearnScope
|
||||
|
||||
assert not hasattr(LearnScope, 'GLOBAL'), "GLOBAL should be replaced by SHARED"
|
||||
|
||||
def test_learn_scope_has_exactly_two_values(self):
|
||||
"""LearnScope should have exactly 2 values: PRIVATE and SHARED."""
|
||||
from praisonaiagents.config.feature_configs import LearnScope
|
||||
|
||||
all_values = list(LearnScope)
|
||||
assert len(all_values) == 2, f"Expected 2 values, got {len(all_values)}: {all_values}"
|
||||
|
||||
|
||||
class TestLearnConfigDefaults:
|
||||
"""Test LearnConfig uses correct default scope."""
|
||||
|
||||
def test_learn_config_default_scope_is_private(self):
|
||||
"""LearnConfig should default to PRIVATE scope."""
|
||||
from praisonaiagents.config.feature_configs import LearnConfig, LearnScope
|
||||
|
||||
config = LearnConfig()
|
||||
assert config.scope == LearnScope.PRIVATE
|
||||
|
||||
def test_learn_config_scope_can_be_set_to_shared(self):
|
||||
"""LearnConfig scope can be set to SHARED."""
|
||||
from praisonaiagents.config.feature_configs import LearnConfig, LearnScope
|
||||
|
||||
config = LearnConfig(scope=LearnScope.SHARED)
|
||||
assert config.scope == LearnScope.SHARED
|
||||
|
||||
def test_learn_config_to_dict_includes_scope(self):
|
||||
"""LearnConfig.to_dict() should include scope with correct value."""
|
||||
from praisonaiagents.config.feature_configs import LearnConfig, LearnScope
|
||||
|
||||
config = LearnConfig()
|
||||
d = config.to_dict()
|
||||
assert d['scope'] == 'private'
|
||||
|
||||
config2 = LearnConfig(scope=LearnScope.SHARED)
|
||||
d2 = config2.to_dict()
|
||||
assert d2['scope'] == 'shared'
|
||||
|
||||
|
||||
class TestBaseStoreDefaults:
|
||||
"""Test BaseStore uses correct default scope."""
|
||||
|
||||
def test_base_store_default_scope_is_private(self):
|
||||
"""BaseStore should default to 'private' scope."""
|
||||
import inspect
|
||||
from praisonaiagents.memory.learn.stores import BaseStore
|
||||
|
||||
sig = inspect.signature(BaseStore.__init__)
|
||||
scope_param = sig.parameters.get('scope')
|
||||
assert scope_param is not None
|
||||
assert scope_param.default == 'private', f"Expected 'private', got '{scope_param.default}'"
|
||||
@@ -0,0 +1,237 @@
|
||||
"""Tests for precedence ladder resolvers."""
|
||||
|
||||
import pytest
|
||||
from praisonaiagents.config.feature_configs import (
|
||||
MemoryConfig,
|
||||
MemoryBackend,
|
||||
KnowledgeConfig,
|
||||
PlanningConfig,
|
||||
ReflectionConfig,
|
||||
GuardrailConfig,
|
||||
WebConfig,
|
||||
OutputConfig,
|
||||
ExecutionConfig,
|
||||
CachingConfig,
|
||||
AutonomyConfig,
|
||||
resolve_memory,
|
||||
resolve_knowledge,
|
||||
resolve_planning,
|
||||
resolve_reflection,
|
||||
resolve_guardrails,
|
||||
resolve_web,
|
||||
resolve_output,
|
||||
resolve_execution,
|
||||
resolve_caching,
|
||||
resolve_autonomy,
|
||||
)
|
||||
|
||||
|
||||
class TestResolveMemory:
|
||||
"""Test resolve_memory precedence ladder."""
|
||||
|
||||
def test_default_none(self):
|
||||
"""Default (None) returns None."""
|
||||
assert resolve_memory(None) is None
|
||||
|
||||
def test_bool_false(self):
|
||||
"""False returns None (disabled)."""
|
||||
assert resolve_memory(False) is None
|
||||
|
||||
def test_bool_true(self):
|
||||
"""True returns MemoryConfig with defaults."""
|
||||
result = resolve_memory(True)
|
||||
assert isinstance(result, MemoryConfig)
|
||||
assert result.backend == MemoryBackend.FILE
|
||||
|
||||
def test_string_backend(self):
|
||||
"""String returns MemoryConfig with that backend."""
|
||||
result = resolve_memory("redis")
|
||||
assert isinstance(result, MemoryConfig)
|
||||
assert result.backend == MemoryBackend.REDIS
|
||||
|
||||
def test_string_custom_backend(self):
|
||||
"""Custom string backend is preserved."""
|
||||
result = resolve_memory("custom_backend")
|
||||
assert isinstance(result, MemoryConfig)
|
||||
assert result.backend == "custom_backend"
|
||||
|
||||
def test_dict_expansion(self):
|
||||
"""Dict expands to MemoryConfig."""
|
||||
result = resolve_memory({
|
||||
"backend": "sqlite",
|
||||
"user_id": "alice",
|
||||
"auto_memory": True,
|
||||
})
|
||||
assert isinstance(result, MemoryConfig)
|
||||
assert result.backend == MemoryBackend.SQLITE
|
||||
assert result.user_id == "alice"
|
||||
assert result.auto_memory is True
|
||||
|
||||
def test_config_passthrough(self):
|
||||
"""MemoryConfig passes through unchanged."""
|
||||
config = MemoryConfig(backend=MemoryBackend.POSTGRES, user_id="bob")
|
||||
result = resolve_memory(config)
|
||||
assert result is config
|
||||
|
||||
def test_instance_passthrough(self):
|
||||
"""Instance (non-config object) passes through."""
|
||||
class MockMemoryManager:
|
||||
pass
|
||||
instance = MockMemoryManager()
|
||||
result = resolve_memory(instance)
|
||||
assert result is instance
|
||||
|
||||
|
||||
class TestResolveKnowledge:
|
||||
"""Test resolve_knowledge precedence ladder."""
|
||||
|
||||
def test_default_none(self):
|
||||
assert resolve_knowledge(None) is None
|
||||
|
||||
def test_bool_false(self):
|
||||
assert resolve_knowledge(False) is None
|
||||
|
||||
def test_bool_true(self):
|
||||
result = resolve_knowledge(True)
|
||||
assert isinstance(result, KnowledgeConfig)
|
||||
|
||||
def test_string_single_source(self):
|
||||
result = resolve_knowledge("docs/")
|
||||
assert isinstance(result, KnowledgeConfig)
|
||||
assert result.sources == ["docs/"]
|
||||
|
||||
def test_array_sources(self):
|
||||
result = resolve_knowledge(["docs/", "data.pdf"])
|
||||
assert isinstance(result, KnowledgeConfig)
|
||||
assert result.sources == ["docs/", "data.pdf"]
|
||||
|
||||
def test_dict_expansion(self):
|
||||
result = resolve_knowledge({
|
||||
"sources": ["docs/"],
|
||||
"rerank": True,
|
||||
})
|
||||
assert isinstance(result, KnowledgeConfig)
|
||||
assert result.sources == ["docs/"]
|
||||
assert result.rerank is True
|
||||
|
||||
def test_config_passthrough(self):
|
||||
config = KnowledgeConfig(sources=["test/"])
|
||||
result = resolve_knowledge(config)
|
||||
assert result is config
|
||||
|
||||
|
||||
class TestResolvePlanning:
|
||||
"""Test resolve_planning precedence ladder."""
|
||||
|
||||
def test_default_none(self):
|
||||
assert resolve_planning(None) is None
|
||||
|
||||
def test_bool_false(self):
|
||||
assert resolve_planning(False) is None
|
||||
|
||||
def test_bool_true(self):
|
||||
result = resolve_planning(True)
|
||||
assert isinstance(result, PlanningConfig)
|
||||
|
||||
def test_config_passthrough(self):
|
||||
config = PlanningConfig()
|
||||
result = resolve_planning(config)
|
||||
assert result is config
|
||||
|
||||
|
||||
class TestResolveReflection:
|
||||
"""Test resolve_reflection precedence ladder."""
|
||||
|
||||
def test_default_none(self):
|
||||
assert resolve_reflection(None) is None
|
||||
|
||||
def test_bool_false(self):
|
||||
assert resolve_reflection(False) is None
|
||||
|
||||
def test_bool_true(self):
|
||||
result = resolve_reflection(True)
|
||||
assert isinstance(result, ReflectionConfig)
|
||||
|
||||
|
||||
class TestResolveGuardrails:
|
||||
"""Test resolve_guardrails precedence ladder."""
|
||||
|
||||
def test_default_none(self):
|
||||
assert resolve_guardrails(None) is None
|
||||
|
||||
def test_bool_false(self):
|
||||
assert resolve_guardrails(False) is None
|
||||
|
||||
def test_bool_true(self):
|
||||
result = resolve_guardrails(True)
|
||||
assert isinstance(result, GuardrailConfig)
|
||||
|
||||
def test_callable_validator(self):
|
||||
def my_validator(output):
|
||||
return True, output
|
||||
result = resolve_guardrails(my_validator)
|
||||
assert isinstance(result, GuardrailConfig)
|
||||
assert result.validator is my_validator
|
||||
|
||||
|
||||
class TestResolveWeb:
|
||||
"""Test resolve_web precedence ladder."""
|
||||
|
||||
def test_default_none(self):
|
||||
assert resolve_web(None) is None
|
||||
|
||||
def test_bool_false(self):
|
||||
assert resolve_web(False) is None
|
||||
|
||||
def test_bool_true(self):
|
||||
result = resolve_web(True)
|
||||
assert isinstance(result, WebConfig)
|
||||
|
||||
|
||||
class TestResolveCaching:
|
||||
"""Test resolve_caching precedence ladder."""
|
||||
|
||||
def test_default_none(self):
|
||||
assert resolve_caching(None) is None
|
||||
|
||||
def test_bool_false(self):
|
||||
assert resolve_caching(False) is None
|
||||
|
||||
def test_bool_true(self):
|
||||
result = resolve_caching(True)
|
||||
assert isinstance(result, CachingConfig)
|
||||
|
||||
|
||||
class TestResolveAutonomy:
|
||||
"""Test resolve_autonomy precedence ladder."""
|
||||
|
||||
def test_default_none(self):
|
||||
assert resolve_autonomy(None) is None
|
||||
|
||||
def test_bool_false(self):
|
||||
assert resolve_autonomy(False) is None
|
||||
|
||||
def test_bool_true(self):
|
||||
result = resolve_autonomy(True)
|
||||
assert isinstance(result, AutonomyConfig)
|
||||
|
||||
|
||||
class TestNoCortexParam:
|
||||
"""Test that cortex parameter does not exist."""
|
||||
|
||||
def test_agent_has_no_cortex_param(self):
|
||||
"""Agent.__init__ should not have cortex parameter."""
|
||||
import inspect
|
||||
from praisonaiagents import Agent
|
||||
|
||||
sig = inspect.signature(Agent.__init__)
|
||||
param_names = list(sig.parameters.keys())
|
||||
|
||||
assert "cortex" not in param_names, "cortex param should be removed from Agent"
|
||||
|
||||
def test_no_cortex_module_in_agents(self):
|
||||
"""praisonaiagents should not have cortex module."""
|
||||
import importlib.util
|
||||
|
||||
spec = importlib.util.find_spec("praisonaiagents.cortex")
|
||||
assert spec is None, "praisonaiagents.cortex module should not exist"
|
||||
@@ -0,0 +1,292 @@
|
||||
"""Tests for the learn module - continuous learning within memory system."""
|
||||
|
||||
import os
|
||||
import tempfile
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
from praisonaiagents.config.feature_configs import LearnConfig, LearnScope, MemoryConfig
|
||||
from praisonaiagents.memory.learn.stores import (
|
||||
PersonaStore,
|
||||
InsightStore,
|
||||
ThreadStore,
|
||||
PatternStore,
|
||||
DecisionStore,
|
||||
FeedbackStore,
|
||||
ImprovementStore,
|
||||
LearnEntry,
|
||||
)
|
||||
from praisonaiagents.memory.learn.manager import LearnManager
|
||||
|
||||
|
||||
class TestLearnConfig:
|
||||
"""Test LearnConfig dataclass."""
|
||||
|
||||
def test_default_values(self):
|
||||
"""Test default LearnConfig values."""
|
||||
config = LearnConfig()
|
||||
assert config.persona is True
|
||||
assert config.insights is True
|
||||
assert config.thread is True
|
||||
assert config.patterns is False
|
||||
assert config.decisions is False
|
||||
assert config.feedback is False
|
||||
assert config.improvements is False
|
||||
assert config.scope == LearnScope.USER
|
||||
assert config.auto_consolidate is True
|
||||
assert config.retention_days is None
|
||||
|
||||
def test_custom_values(self):
|
||||
"""Test LearnConfig with custom values."""
|
||||
config = LearnConfig(
|
||||
persona=False,
|
||||
insights=True,
|
||||
patterns=True,
|
||||
scope=LearnScope.TEAM,
|
||||
retention_days=30,
|
||||
)
|
||||
assert config.persona is False
|
||||
assert config.patterns is True
|
||||
assert config.scope == LearnScope.TEAM
|
||||
assert config.retention_days == 30
|
||||
|
||||
def test_to_dict(self):
|
||||
"""Test LearnConfig to_dict method."""
|
||||
config = LearnConfig(persona=True, insights=True)
|
||||
d = config.to_dict()
|
||||
assert d["persona"] is True
|
||||
assert d["insights"] is True
|
||||
assert d["scope"] == "user"
|
||||
|
||||
|
||||
class TestMemoryConfigWithLearn:
|
||||
"""Test MemoryConfig with learn field."""
|
||||
|
||||
def test_learn_none_by_default(self):
|
||||
"""Test that learn is None by default."""
|
||||
config = MemoryConfig()
|
||||
assert config.learn is None
|
||||
|
||||
def test_learn_bool_true(self):
|
||||
"""Test learn=True."""
|
||||
config = MemoryConfig(learn=True)
|
||||
assert config.learn is True
|
||||
d = config.to_dict()
|
||||
assert d["learn"] is not None
|
||||
assert d["learn"]["persona"] is True
|
||||
|
||||
def test_learn_config_instance(self):
|
||||
"""Test learn=LearnConfig(...)."""
|
||||
learn_config = LearnConfig(persona=True, patterns=True)
|
||||
config = MemoryConfig(learn=learn_config)
|
||||
assert config.learn is learn_config
|
||||
d = config.to_dict()
|
||||
assert d["learn"]["patterns"] is True
|
||||
|
||||
|
||||
class TestLearnEntry:
|
||||
"""Test LearnEntry dataclass."""
|
||||
|
||||
def test_create_entry(self):
|
||||
"""Test creating a LearnEntry."""
|
||||
entry = LearnEntry(
|
||||
id="test_1",
|
||||
content="Test content",
|
||||
metadata={"key": "value"},
|
||||
)
|
||||
assert entry.id == "test_1"
|
||||
assert entry.content == "Test content"
|
||||
assert entry.metadata["key"] == "value"
|
||||
|
||||
def test_to_dict(self):
|
||||
"""Test LearnEntry to_dict."""
|
||||
entry = LearnEntry(id="test_1", content="Test")
|
||||
d = entry.to_dict()
|
||||
assert d["id"] == "test_1"
|
||||
assert d["content"] == "Test"
|
||||
assert "created_at" in d
|
||||
|
||||
def test_from_dict(self):
|
||||
"""Test LearnEntry from_dict."""
|
||||
data = {
|
||||
"id": "test_1",
|
||||
"content": "Test content",
|
||||
"metadata": {"key": "value"},
|
||||
}
|
||||
entry = LearnEntry.from_dict(data)
|
||||
assert entry.id == "test_1"
|
||||
assert entry.content == "Test content"
|
||||
|
||||
|
||||
class TestPersonaStore:
|
||||
"""Test PersonaStore."""
|
||||
|
||||
def setup_method(self):
|
||||
"""Set up test fixtures."""
|
||||
self.temp_dir = tempfile.mkdtemp()
|
||||
self.store_path = os.path.join(self.temp_dir, "persona.json")
|
||||
|
||||
def teardown_method(self):
|
||||
"""Clean up test fixtures."""
|
||||
shutil.rmtree(self.temp_dir, ignore_errors=True)
|
||||
|
||||
def test_add_preference(self):
|
||||
"""Test adding a preference."""
|
||||
store = PersonaStore(store_path=self.store_path, user_id="test_user")
|
||||
entry = store.add_preference("User prefers concise responses", "communication")
|
||||
assert entry is not None
|
||||
assert "concise" in entry.content
|
||||
assert entry.metadata["category"] == "communication"
|
||||
|
||||
def test_search(self):
|
||||
"""Test searching entries."""
|
||||
store = PersonaStore(store_path=self.store_path, user_id="test_user")
|
||||
store.add_preference("User likes Python", "programming")
|
||||
store.add_preference("User prefers dark mode", "ui")
|
||||
|
||||
results = store.search("Python")
|
||||
assert len(results) == 1
|
||||
assert "Python" in results[0].content
|
||||
|
||||
|
||||
class TestInsightStore:
|
||||
"""Test InsightStore."""
|
||||
|
||||
def setup_method(self):
|
||||
"""Set up test fixtures."""
|
||||
self.temp_dir = tempfile.mkdtemp()
|
||||
self.store_path = os.path.join(self.temp_dir, "insights.json")
|
||||
|
||||
def teardown_method(self):
|
||||
"""Clean up test fixtures."""
|
||||
shutil.rmtree(self.temp_dir, ignore_errors=True)
|
||||
|
||||
def test_add_insight(self):
|
||||
"""Test adding an insight."""
|
||||
store = InsightStore(store_path=self.store_path, user_id="test_user")
|
||||
entry = store.add_insight("User works in data science", "conversation")
|
||||
assert entry is not None
|
||||
assert "data science" in entry.content
|
||||
|
||||
|
||||
class TestLearnManager:
|
||||
"""Test LearnManager."""
|
||||
|
||||
def setup_method(self):
|
||||
"""Set up test fixtures."""
|
||||
self.temp_dir = tempfile.mkdtemp()
|
||||
|
||||
def teardown_method(self):
|
||||
"""Clean up test fixtures."""
|
||||
shutil.rmtree(self.temp_dir, ignore_errors=True)
|
||||
|
||||
def test_create_manager_with_defaults(self):
|
||||
"""Test creating LearnManager with default config."""
|
||||
config = LearnConfig()
|
||||
manager = LearnManager(config=config, user_id="test_user", store_path=self.temp_dir)
|
||||
|
||||
assert "persona" in manager._stores
|
||||
assert "insights" in manager._stores
|
||||
assert "threads" in manager._stores
|
||||
|
||||
def test_capture_persona(self):
|
||||
"""Test capturing persona."""
|
||||
config = LearnConfig(persona=True)
|
||||
manager = LearnManager(config=config, user_id="test_user", store_path=self.temp_dir)
|
||||
|
||||
entry = manager.capture_persona("User prefers detailed explanations")
|
||||
assert entry is not None
|
||||
assert "detailed" in entry.content
|
||||
|
||||
def test_capture_insight(self):
|
||||
"""Test capturing insight."""
|
||||
config = LearnConfig(insights=True)
|
||||
manager = LearnManager(config=config, user_id="test_user", store_path=self.temp_dir)
|
||||
|
||||
entry = manager.capture_insight("User is experienced with Python")
|
||||
assert entry is not None
|
||||
|
||||
def test_get_learning_context(self):
|
||||
"""Test getting learning context."""
|
||||
config = LearnConfig(persona=True, insights=True)
|
||||
manager = LearnManager(config=config, user_id="test_user", store_path=self.temp_dir)
|
||||
|
||||
manager.capture_persona("User likes concise code")
|
||||
manager.capture_insight("User knows Python")
|
||||
|
||||
context = manager.get_learning_context()
|
||||
assert "persona" in context
|
||||
assert "insights" in context
|
||||
assert len(context["persona"]) == 1
|
||||
assert len(context["insights"]) == 1
|
||||
|
||||
def test_search(self):
|
||||
"""Test searching across stores."""
|
||||
config = LearnConfig(persona=True, insights=True)
|
||||
manager = LearnManager(config=config, user_id="test_user", store_path=self.temp_dir)
|
||||
|
||||
manager.capture_persona("User prefers Python")
|
||||
manager.capture_insight("User knows Python well")
|
||||
|
||||
results = manager.search("Python")
|
||||
assert "persona" in results or "insights" in results
|
||||
|
||||
def test_clear_all(self):
|
||||
"""Test clearing all stores."""
|
||||
config = LearnConfig(persona=True, insights=True)
|
||||
manager = LearnManager(config=config, user_id="test_user", store_path=self.temp_dir)
|
||||
|
||||
manager.capture_persona("Test 1")
|
||||
manager.capture_insight("Test 2")
|
||||
|
||||
cleared = manager.clear_all()
|
||||
assert cleared["persona"] == 1
|
||||
assert cleared["insights"] == 1
|
||||
|
||||
def test_get_stats(self):
|
||||
"""Test getting stats."""
|
||||
config = LearnConfig(persona=True, insights=True)
|
||||
manager = LearnManager(config=config, user_id="test_user", store_path=self.temp_dir)
|
||||
|
||||
manager.capture_persona("Test 1")
|
||||
manager.capture_persona("Test 2")
|
||||
manager.capture_insight("Test 3")
|
||||
|
||||
stats = manager.get_stats()
|
||||
assert stats["persona"] == 2
|
||||
assert stats["insights"] == 1
|
||||
|
||||
def test_to_system_prompt_context(self):
|
||||
"""Test generating system prompt context."""
|
||||
config = LearnConfig(persona=True, insights=True)
|
||||
manager = LearnManager(config=config, user_id="test_user", store_path=self.temp_dir)
|
||||
|
||||
manager.capture_persona("User prefers concise responses")
|
||||
manager.capture_insight("User is a Python developer")
|
||||
|
||||
context = manager.to_system_prompt_context()
|
||||
assert "User Preferences" in context
|
||||
assert "concise" in context
|
||||
assert "Learned Insights" in context
|
||||
assert "Python" in context
|
||||
|
||||
|
||||
class TestNoCortexParam:
|
||||
"""Test that cortex parameter does not exist."""
|
||||
|
||||
def test_agent_has_no_cortex_param(self):
|
||||
"""Agent.__init__ should not have cortex parameter."""
|
||||
import inspect
|
||||
from praisonaiagents import Agent
|
||||
|
||||
sig = inspect.signature(Agent.__init__)
|
||||
param_names = list(sig.parameters.keys())
|
||||
|
||||
assert "cortex" not in param_names, "cortex param should be removed from Agent"
|
||||
|
||||
def test_no_cortex_module_in_agents(self):
|
||||
"""praisonaiagents should not have cortex module."""
|
||||
import importlib.util
|
||||
|
||||
spec = importlib.util.find_spec("praisonaiagents.cortex")
|
||||
assert spec is None, "praisonaiagents.cortex module should not exist"
|
||||
@@ -105,12 +105,14 @@ class TestMainPackageExports:
|
||||
"""Tests for main package exports."""
|
||||
|
||||
def test_rag_in_all(self):
|
||||
"""Test that RAG is in __all__."""
|
||||
"""Test that RAG is accessible (via __getattr__, not necessarily in __all__)."""
|
||||
import praisonaiagents
|
||||
|
||||
assert 'RAG' in praisonaiagents.__all__
|
||||
assert 'RAGConfig' in praisonaiagents.__all__
|
||||
assert 'RAGResult' in praisonaiagents.__all__
|
||||
# RAG is accessible via __getattr__ for backwards compatibility
|
||||
# but may not be in __all__ (which is kept minimal for IDE experience)
|
||||
assert hasattr(praisonaiagents, 'RAG'), "RAG not accessible from praisonaiagents"
|
||||
assert hasattr(praisonaiagents, 'RAGConfig'), "RAGConfig not accessible"
|
||||
assert hasattr(praisonaiagents, 'RAGResult'), "RAGResult not accessible"
|
||||
|
||||
def test_rag_accessible_from_main(self):
|
||||
"""Test that RAG classes are accessible from main package."""
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
"""Unit tests for streaming module."""
|
||||
@@ -0,0 +1,387 @@
|
||||
"""
|
||||
Unit tests for streaming events module.
|
||||
|
||||
Tests cover:
|
||||
- StreamEvent creation and representation
|
||||
- StreamEventType enum values
|
||||
- StreamMetrics calculations (TTFT, stream duration, total time)
|
||||
- StreamEventEmitter callback management
|
||||
- create_text_printer_callback and create_metrics_callback helpers
|
||||
"""
|
||||
|
||||
import time
|
||||
from praisonaiagents.streaming.events import (
|
||||
StreamEvent,
|
||||
StreamEventType,
|
||||
StreamMetrics,
|
||||
StreamEventEmitter,
|
||||
create_text_printer_callback,
|
||||
create_metrics_callback,
|
||||
)
|
||||
|
||||
|
||||
class TestStreamEventType:
|
||||
"""Tests for StreamEventType enum."""
|
||||
|
||||
def test_event_types_exist(self):
|
||||
"""All required event types should exist."""
|
||||
assert StreamEventType.REQUEST_START.value == "request_start"
|
||||
assert StreamEventType.HEADERS_RECEIVED.value == "headers_received"
|
||||
assert StreamEventType.FIRST_TOKEN.value == "first_token"
|
||||
assert StreamEventType.DELTA_TEXT.value == "delta_text"
|
||||
assert StreamEventType.DELTA_TOOL_CALL.value == "delta_tool_call"
|
||||
assert StreamEventType.TOOL_CALL_END.value == "tool_call_end"
|
||||
assert StreamEventType.LAST_TOKEN.value == "last_token"
|
||||
assert StreamEventType.STREAM_END.value == "stream_end"
|
||||
assert StreamEventType.ERROR.value == "error"
|
||||
|
||||
|
||||
class TestStreamEvent:
|
||||
"""Tests for StreamEvent dataclass."""
|
||||
|
||||
def test_create_basic_event(self):
|
||||
"""Create a basic event with type only."""
|
||||
event = StreamEvent(type=StreamEventType.REQUEST_START)
|
||||
assert event.type == StreamEventType.REQUEST_START
|
||||
assert event.timestamp > 0
|
||||
assert event.content is None
|
||||
assert event.tool_call is None
|
||||
assert event.metadata is None
|
||||
assert event.error is None
|
||||
|
||||
def test_create_event_with_content(self):
|
||||
"""Create an event with content."""
|
||||
event = StreamEvent(
|
||||
type=StreamEventType.DELTA_TEXT,
|
||||
content="Hello"
|
||||
)
|
||||
assert event.type == StreamEventType.DELTA_TEXT
|
||||
assert event.content == "Hello"
|
||||
|
||||
def test_create_event_with_tool_call(self):
|
||||
"""Create an event with tool call data."""
|
||||
tool_data = {"name": "calculator", "arguments": '{"x": 1}'}
|
||||
event = StreamEvent(
|
||||
type=StreamEventType.DELTA_TOOL_CALL,
|
||||
tool_call=tool_data
|
||||
)
|
||||
assert event.type == StreamEventType.DELTA_TOOL_CALL
|
||||
assert event.tool_call == tool_data
|
||||
|
||||
def test_create_event_with_metadata(self):
|
||||
"""Create an event with metadata."""
|
||||
event = StreamEvent(
|
||||
type=StreamEventType.REQUEST_START,
|
||||
metadata={"model": "gpt-4o-mini", "message_count": 2}
|
||||
)
|
||||
assert event.metadata["model"] == "gpt-4o-mini"
|
||||
assert event.metadata["message_count"] == 2
|
||||
|
||||
def test_create_error_event(self):
|
||||
"""Create an error event."""
|
||||
event = StreamEvent(
|
||||
type=StreamEventType.ERROR,
|
||||
error="Connection timeout"
|
||||
)
|
||||
assert event.type == StreamEventType.ERROR
|
||||
assert event.error == "Connection timeout"
|
||||
|
||||
def test_event_repr(self):
|
||||
"""Event repr should be informative."""
|
||||
event = StreamEvent(
|
||||
type=StreamEventType.DELTA_TEXT,
|
||||
content="Hello world"
|
||||
)
|
||||
repr_str = repr(event)
|
||||
assert "DELTA_TEXT" in repr_str or "delta_text" in repr_str
|
||||
assert "Hello world" in repr_str
|
||||
|
||||
|
||||
class TestStreamMetrics:
|
||||
"""Tests for StreamMetrics dataclass."""
|
||||
|
||||
def test_default_metrics(self):
|
||||
"""Default metrics should be zero."""
|
||||
metrics = StreamMetrics()
|
||||
assert metrics.request_start == 0.0
|
||||
assert metrics.headers_received == 0.0
|
||||
assert metrics.first_token == 0.0
|
||||
assert metrics.last_token == 0.0
|
||||
assert metrics.stream_end == 0.0
|
||||
assert metrics.token_count == 0
|
||||
|
||||
def test_ttft_calculation(self):
|
||||
"""TTFT should be first_token - request_start."""
|
||||
metrics = StreamMetrics()
|
||||
metrics.request_start = 100.0
|
||||
metrics.first_token = 100.5
|
||||
assert metrics.ttft == 0.5
|
||||
|
||||
def test_ttft_zero_when_no_first_token(self):
|
||||
"""TTFT should be 0 when first_token not set."""
|
||||
metrics = StreamMetrics()
|
||||
metrics.request_start = 100.0
|
||||
assert metrics.ttft == 0.0
|
||||
|
||||
def test_stream_duration_calculation(self):
|
||||
"""Stream duration should be last_token - first_token."""
|
||||
metrics = StreamMetrics()
|
||||
metrics.first_token = 100.0
|
||||
metrics.last_token = 102.0
|
||||
assert metrics.stream_duration == 2.0
|
||||
|
||||
def test_total_time_calculation(self):
|
||||
"""Total time should be stream_end - request_start."""
|
||||
metrics = StreamMetrics()
|
||||
metrics.request_start = 100.0
|
||||
metrics.stream_end = 103.0
|
||||
assert metrics.total_time == 3.0
|
||||
|
||||
def test_tokens_per_second(self):
|
||||
"""Tokens per second calculation."""
|
||||
metrics = StreamMetrics()
|
||||
metrics.first_token = 100.0
|
||||
metrics.last_token = 102.0 # 2 second duration
|
||||
metrics.token_count = 100
|
||||
assert metrics.tokens_per_second == 50.0
|
||||
|
||||
def test_tokens_per_second_zero_duration(self):
|
||||
"""Tokens per second should be 0 when duration is 0."""
|
||||
metrics = StreamMetrics()
|
||||
assert metrics.tokens_per_second == 0.0
|
||||
|
||||
def test_update_from_event(self):
|
||||
"""Metrics should update from events."""
|
||||
metrics = StreamMetrics()
|
||||
|
||||
# Request start
|
||||
event1 = StreamEvent(type=StreamEventType.REQUEST_START, timestamp=100.0)
|
||||
metrics.update_from_event(event1)
|
||||
assert metrics.request_start == 100.0
|
||||
|
||||
# Headers received
|
||||
event2 = StreamEvent(type=StreamEventType.HEADERS_RECEIVED, timestamp=100.2)
|
||||
metrics.update_from_event(event2)
|
||||
assert metrics.headers_received == 100.2
|
||||
|
||||
# First token
|
||||
event3 = StreamEvent(type=StreamEventType.FIRST_TOKEN, timestamp=100.5)
|
||||
metrics.update_from_event(event3)
|
||||
assert metrics.first_token == 100.5
|
||||
assert metrics.token_count == 1
|
||||
|
||||
# Delta text
|
||||
event4 = StreamEvent(type=StreamEventType.DELTA_TEXT, timestamp=100.6)
|
||||
metrics.update_from_event(event4)
|
||||
assert metrics.token_count == 2
|
||||
|
||||
# Last token
|
||||
event5 = StreamEvent(type=StreamEventType.LAST_TOKEN, timestamp=102.0)
|
||||
metrics.update_from_event(event5)
|
||||
assert metrics.last_token == 102.0
|
||||
|
||||
# Stream end
|
||||
event6 = StreamEvent(type=StreamEventType.STREAM_END, timestamp=102.1)
|
||||
metrics.update_from_event(event6)
|
||||
assert metrics.stream_end == 102.1
|
||||
|
||||
def test_to_dict(self):
|
||||
"""Metrics should convert to dict."""
|
||||
metrics = StreamMetrics()
|
||||
metrics.request_start = 100.0
|
||||
metrics.first_token = 100.5
|
||||
metrics.last_token = 102.0
|
||||
metrics.stream_end = 102.1
|
||||
metrics.token_count = 50
|
||||
|
||||
d = metrics.to_dict()
|
||||
assert "ttft_ms" in d
|
||||
assert "stream_duration_ms" in d
|
||||
assert "total_time_ms" in d
|
||||
assert "token_count" in d
|
||||
assert "tokens_per_second" in d
|
||||
|
||||
def test_format_summary(self):
|
||||
"""Metrics should format as summary string."""
|
||||
metrics = StreamMetrics()
|
||||
metrics.request_start = 100.0
|
||||
metrics.first_token = 100.5
|
||||
metrics.last_token = 102.0
|
||||
metrics.stream_end = 102.1
|
||||
metrics.token_count = 50
|
||||
|
||||
summary = metrics.format_summary()
|
||||
assert "TTFT" in summary
|
||||
assert "Stream" in summary
|
||||
assert "Total" in summary
|
||||
assert "Tokens" in summary
|
||||
|
||||
|
||||
class TestStreamEventEmitter:
|
||||
"""Tests for StreamEventEmitter."""
|
||||
|
||||
def test_create_emitter(self):
|
||||
"""Create an emitter."""
|
||||
emitter = StreamEventEmitter()
|
||||
assert not emitter.has_callbacks
|
||||
|
||||
def test_add_callback(self):
|
||||
"""Add a callback."""
|
||||
emitter = StreamEventEmitter()
|
||||
events = []
|
||||
|
||||
def callback(event):
|
||||
events.append(event)
|
||||
|
||||
emitter.add_callback(callback)
|
||||
assert emitter.has_callbacks
|
||||
|
||||
def test_remove_callback(self):
|
||||
"""Remove a callback."""
|
||||
emitter = StreamEventEmitter()
|
||||
events = []
|
||||
|
||||
def callback(event):
|
||||
events.append(event)
|
||||
|
||||
emitter.add_callback(callback)
|
||||
emitter.remove_callback(callback)
|
||||
assert not emitter.has_callbacks
|
||||
|
||||
def test_emit_calls_callbacks(self):
|
||||
"""Emit should call all callbacks."""
|
||||
emitter = StreamEventEmitter()
|
||||
events1 = []
|
||||
events2 = []
|
||||
|
||||
emitter.add_callback(lambda e: events1.append(e))
|
||||
emitter.add_callback(lambda e: events2.append(e))
|
||||
|
||||
event = StreamEvent(type=StreamEventType.DELTA_TEXT, content="Hello")
|
||||
emitter.emit(event)
|
||||
|
||||
assert len(events1) == 1
|
||||
assert len(events2) == 1
|
||||
assert events1[0].content == "Hello"
|
||||
|
||||
def test_emit_with_metrics(self):
|
||||
"""Emit should update metrics when enabled."""
|
||||
emitter = StreamEventEmitter()
|
||||
emitter.enable_metrics()
|
||||
|
||||
emitter.emit(StreamEvent(type=StreamEventType.REQUEST_START, timestamp=100.0))
|
||||
emitter.emit(StreamEvent(type=StreamEventType.FIRST_TOKEN, timestamp=100.5))
|
||||
emitter.emit(StreamEvent(type=StreamEventType.STREAM_END, timestamp=102.0))
|
||||
|
||||
metrics = emitter.get_metrics()
|
||||
assert metrics is not None
|
||||
assert metrics.request_start == 100.0
|
||||
assert metrics.first_token == 100.5
|
||||
assert metrics.stream_end == 102.0
|
||||
|
||||
def test_callback_error_does_not_break_emission(self):
|
||||
"""Callback errors should not break other callbacks."""
|
||||
emitter = StreamEventEmitter()
|
||||
events = []
|
||||
|
||||
def bad_callback(event):
|
||||
raise ValueError("Test error")
|
||||
|
||||
def good_callback(event):
|
||||
events.append(event)
|
||||
|
||||
emitter.add_callback(bad_callback)
|
||||
emitter.add_callback(good_callback)
|
||||
|
||||
event = StreamEvent(type=StreamEventType.DELTA_TEXT)
|
||||
emitter.emit(event) # Should not raise
|
||||
|
||||
assert len(events) == 1
|
||||
|
||||
def test_reset_metrics(self):
|
||||
"""Reset metrics should create new metrics."""
|
||||
emitter = StreamEventEmitter()
|
||||
emitter.enable_metrics()
|
||||
|
||||
emitter.emit(StreamEvent(type=StreamEventType.REQUEST_START, timestamp=100.0))
|
||||
emitter.reset_metrics()
|
||||
|
||||
metrics = emitter.get_metrics()
|
||||
assert metrics.request_start == 0.0
|
||||
|
||||
|
||||
class TestHelperFunctions:
|
||||
"""Tests for helper functions."""
|
||||
|
||||
def test_create_text_printer_callback(self, capsys):
|
||||
"""Text printer callback should print content."""
|
||||
callback = create_text_printer_callback(flush=True)
|
||||
|
||||
callback(StreamEvent(type=StreamEventType.DELTA_TEXT, content="Hello"))
|
||||
callback(StreamEvent(type=StreamEventType.DELTA_TEXT, content=" World"))
|
||||
callback(StreamEvent(type=StreamEventType.STREAM_END))
|
||||
|
||||
captured = capsys.readouterr()
|
||||
assert "Hello World" in captured.out
|
||||
|
||||
def test_create_metrics_callback(self):
|
||||
"""Metrics callback should update metrics."""
|
||||
metrics = StreamMetrics()
|
||||
callback = create_metrics_callback(metrics)
|
||||
|
||||
callback(StreamEvent(type=StreamEventType.REQUEST_START, timestamp=100.0))
|
||||
callback(StreamEvent(type=StreamEventType.FIRST_TOKEN, timestamp=100.5))
|
||||
|
||||
assert metrics.request_start == 100.0
|
||||
assert metrics.first_token == 100.5
|
||||
|
||||
|
||||
class TestStreamingEventOrdering:
|
||||
"""Tests for event ordering and pass-through behavior."""
|
||||
|
||||
def test_events_emitted_immediately(self):
|
||||
"""Events should be emitted immediately, not buffered."""
|
||||
emitter = StreamEventEmitter()
|
||||
timestamps = []
|
||||
|
||||
def callback(event):
|
||||
timestamps.append((event.type, time.perf_counter()))
|
||||
|
||||
emitter.add_callback(callback)
|
||||
|
||||
# Simulate streaming with small delays
|
||||
emitter.emit(StreamEvent(type=StreamEventType.REQUEST_START))
|
||||
time.sleep(0.01)
|
||||
emitter.emit(StreamEvent(type=StreamEventType.FIRST_TOKEN))
|
||||
time.sleep(0.01)
|
||||
emitter.emit(StreamEvent(type=StreamEventType.DELTA_TEXT))
|
||||
|
||||
# Verify events were captured in order
|
||||
assert len(timestamps) == 3
|
||||
assert timestamps[0][0] == StreamEventType.REQUEST_START
|
||||
assert timestamps[1][0] == StreamEventType.FIRST_TOKEN
|
||||
assert timestamps[2][0] == StreamEventType.DELTA_TEXT
|
||||
|
||||
# Verify timestamps are increasing (no buffering)
|
||||
assert timestamps[0][1] < timestamps[1][1] < timestamps[2][1]
|
||||
|
||||
def test_no_buffering_before_first_token(self):
|
||||
"""Events should not be buffered before first token."""
|
||||
emitter = StreamEventEmitter()
|
||||
emitter.enable_metrics()
|
||||
|
||||
# Emit request start
|
||||
t1 = time.perf_counter()
|
||||
emitter.emit(StreamEvent(type=StreamEventType.REQUEST_START, timestamp=t1))
|
||||
|
||||
# Small delay (simulating network)
|
||||
time.sleep(0.05)
|
||||
|
||||
# Emit first token
|
||||
t2 = time.perf_counter()
|
||||
emitter.emit(StreamEvent(type=StreamEventType.FIRST_TOKEN, timestamp=t2))
|
||||
|
||||
metrics = emitter.get_metrics()
|
||||
|
||||
# TTFT should reflect the actual delay
|
||||
assert metrics.ttft >= 0.04 # Allow some tolerance
|
||||
@@ -0,0 +1,251 @@
|
||||
"""
|
||||
Tests for API export simplification.
|
||||
|
||||
Verifies:
|
||||
1. Core imports work (Agent, Agents, Task, tool, Tools)
|
||||
2. Backwards compat imports work (MemoryConfig, etc.)
|
||||
3. Organized imports work (from praisonaiagents.config import X)
|
||||
4. Namespace style works (import praisonaiagents as pa)
|
||||
5. __all__ is limited to core symbols only
|
||||
"""
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
class TestCoreImports:
|
||||
"""Test that core classes are importable from root."""
|
||||
|
||||
def test_agent_import(self):
|
||||
"""Agent class is importable from root."""
|
||||
from praisonaiagents import Agent
|
||||
assert Agent is not None
|
||||
assert hasattr(Agent, '__init__')
|
||||
|
||||
def test_agents_import(self):
|
||||
"""Agents class is importable from root."""
|
||||
from praisonaiagents import Agents
|
||||
assert Agents is not None
|
||||
|
||||
def test_task_import(self):
|
||||
"""Task class is importable from root."""
|
||||
from praisonaiagents import Task
|
||||
assert Task is not None
|
||||
|
||||
def test_tool_decorator_import(self):
|
||||
"""tool decorator is importable from root."""
|
||||
from praisonaiagents import tool
|
||||
assert tool is not None
|
||||
assert callable(tool)
|
||||
|
||||
def test_tools_class_import(self):
|
||||
"""Tools class is importable from root."""
|
||||
from praisonaiagents import Tools
|
||||
assert Tools is not None
|
||||
|
||||
|
||||
class TestBackwardsCompatImports:
|
||||
"""Test that all legacy imports still work via __getattr__."""
|
||||
|
||||
def test_memory_config_import(self):
|
||||
"""MemoryConfig importable from root (backwards compat)."""
|
||||
from praisonaiagents import MemoryConfig
|
||||
assert MemoryConfig is not None
|
||||
|
||||
def test_knowledge_config_import(self):
|
||||
"""KnowledgeConfig importable from root (backwards compat)."""
|
||||
from praisonaiagents import KnowledgeConfig
|
||||
assert KnowledgeConfig is not None
|
||||
|
||||
def test_output_config_import(self):
|
||||
"""OutputConfig importable from root (backwards compat)."""
|
||||
from praisonaiagents import OutputConfig
|
||||
assert OutputConfig is not None
|
||||
|
||||
def test_execution_config_import(self):
|
||||
"""ExecutionConfig importable from root (backwards compat)."""
|
||||
from praisonaiagents import ExecutionConfig
|
||||
assert ExecutionConfig is not None
|
||||
|
||||
def test_planning_config_import(self):
|
||||
"""PlanningConfig importable from root (backwards compat)."""
|
||||
from praisonaiagents import PlanningConfig
|
||||
assert PlanningConfig is not None
|
||||
|
||||
def test_reflection_config_import(self):
|
||||
"""ReflectionConfig importable from root (backwards compat)."""
|
||||
from praisonaiagents import ReflectionConfig
|
||||
assert ReflectionConfig is not None
|
||||
|
||||
def test_workflow_import(self):
|
||||
"""Workflow importable from root (backwards compat)."""
|
||||
from praisonaiagents import Workflow
|
||||
assert Workflow is not None
|
||||
|
||||
def test_memory_import(self):
|
||||
"""Memory importable from root (backwards compat)."""
|
||||
from praisonaiagents import Memory
|
||||
assert Memory is not None
|
||||
|
||||
def test_session_import(self):
|
||||
"""Session importable from root (backwards compat)."""
|
||||
from praisonaiagents import Session
|
||||
assert Session is not None
|
||||
|
||||
def test_base_tool_import(self):
|
||||
"""BaseTool importable from root (backwards compat)."""
|
||||
from praisonaiagents import BaseTool
|
||||
assert BaseTool is not None
|
||||
|
||||
def test_handoff_import(self):
|
||||
"""Handoff importable from root (backwards compat)."""
|
||||
from praisonaiagents import Handoff
|
||||
assert Handoff is not None
|
||||
|
||||
|
||||
class TestOrganizedImports:
|
||||
"""Test that organized sub-package imports work."""
|
||||
|
||||
def test_config_memory_config(self):
|
||||
"""MemoryConfig importable from config sub-package."""
|
||||
from praisonaiagents.config import MemoryConfig
|
||||
assert MemoryConfig is not None
|
||||
|
||||
def test_config_knowledge_config(self):
|
||||
"""KnowledgeConfig importable from config sub-package."""
|
||||
from praisonaiagents.config import KnowledgeConfig
|
||||
assert KnowledgeConfig is not None
|
||||
|
||||
def test_config_output_config(self):
|
||||
"""OutputConfig importable from config sub-package."""
|
||||
from praisonaiagents.config import OutputConfig
|
||||
assert OutputConfig is not None
|
||||
|
||||
def test_config_execution_config(self):
|
||||
"""ExecutionConfig importable from config sub-package."""
|
||||
from praisonaiagents.config import ExecutionConfig
|
||||
assert ExecutionConfig is not None
|
||||
|
||||
def test_tools_tool_decorator(self):
|
||||
"""tool decorator importable from tools sub-package."""
|
||||
from praisonaiagents.tools import tool
|
||||
# Note: tools package uses different pattern, tool is via base
|
||||
# This tests the accessor pattern
|
||||
|
||||
def test_tools_base_tool(self):
|
||||
"""BaseTool importable from tools sub-package."""
|
||||
from praisonaiagents.tools.base import BaseTool
|
||||
assert BaseTool is not None
|
||||
|
||||
def test_memory_memory_class(self):
|
||||
"""Memory importable from memory sub-package."""
|
||||
from praisonaiagents.memory import Memory
|
||||
assert Memory is not None
|
||||
|
||||
def test_workflows_workflow(self):
|
||||
"""Workflow importable from workflows sub-package."""
|
||||
from praisonaiagents.workflows import Workflow
|
||||
assert Workflow is not None
|
||||
|
||||
|
||||
class TestNamespaceStyle:
|
||||
"""Test that 'import praisonaiagents as pa' style works."""
|
||||
|
||||
def test_pa_agent(self):
|
||||
"""pa.Agent works."""
|
||||
import praisonaiagents as pa
|
||||
assert hasattr(pa, 'Agent')
|
||||
assert pa.Agent is not None
|
||||
|
||||
def test_pa_agents(self):
|
||||
"""pa.Agents works."""
|
||||
import praisonaiagents as pa
|
||||
assert hasattr(pa, 'Agents')
|
||||
|
||||
def test_pa_task(self):
|
||||
"""pa.Task works."""
|
||||
import praisonaiagents as pa
|
||||
assert hasattr(pa, 'Task')
|
||||
|
||||
def test_pa_config_subpackage(self):
|
||||
"""pa.config sub-package accessible."""
|
||||
import praisonaiagents as pa
|
||||
assert hasattr(pa, 'config')
|
||||
|
||||
def test_pa_config_memory_config(self):
|
||||
"""pa.config.MemoryConfig works."""
|
||||
import praisonaiagents as pa
|
||||
assert hasattr(pa.config, 'MemoryConfig')
|
||||
|
||||
def test_pa_tools_subpackage(self):
|
||||
"""pa.tools sub-package accessible."""
|
||||
import praisonaiagents as pa
|
||||
assert hasattr(pa, 'tools')
|
||||
|
||||
def test_pa_memory_subpackage(self):
|
||||
"""pa.memory sub-package accessible."""
|
||||
import praisonaiagents as pa
|
||||
assert hasattr(pa, 'memory')
|
||||
|
||||
def test_pa_workflows_subpackage(self):
|
||||
"""pa.workflows sub-package accessible."""
|
||||
import praisonaiagents as pa
|
||||
assert hasattr(pa, 'workflows')
|
||||
|
||||
|
||||
class TestAllSizeLimited:
|
||||
"""Test that __all__ is limited to core symbols."""
|
||||
|
||||
def test_all_size_under_20(self):
|
||||
"""__all__ should have fewer than 20 items."""
|
||||
import praisonaiagents
|
||||
assert len(praisonaiagents.__all__) < 20, \
|
||||
f"__all__ has {len(praisonaiagents.__all__)} items, expected < 20"
|
||||
|
||||
def test_all_contains_core(self):
|
||||
"""__all__ contains core symbols."""
|
||||
import praisonaiagents
|
||||
core = {'Agent', 'Agents', 'Task', 'tool', 'Tools'}
|
||||
for symbol in core:
|
||||
assert symbol in praisonaiagents.__all__, \
|
||||
f"'{symbol}' not in __all__"
|
||||
|
||||
def test_all_contains_subpackages(self):
|
||||
"""__all__ contains sub-packages for namespace style."""
|
||||
import praisonaiagents
|
||||
subpackages = {'config', 'tools', 'memory', 'workflows'}
|
||||
for pkg in subpackages:
|
||||
assert pkg in praisonaiagents.__all__, \
|
||||
f"'{pkg}' not in __all__"
|
||||
|
||||
def test_dir_matches_all(self):
|
||||
"""dir() returns only __all__ items (clean introspection)."""
|
||||
import praisonaiagents
|
||||
# dir() should be small, matching __all__
|
||||
dir_items = set(dir(praisonaiagents))
|
||||
all_items = set(praisonaiagents.__all__)
|
||||
# dir() may include more (like __name__), but should include all of __all__
|
||||
assert all_items.issubset(dir_items), \
|
||||
f"__all__ items missing from dir(): {all_items - dir_items}"
|
||||
|
||||
|
||||
class TestImportPerformance:
|
||||
"""Test import performance (no regression)."""
|
||||
|
||||
def test_import_time_under_200ms(self):
|
||||
"""Package import should be fast (< 200ms)."""
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
# Run import in fresh process to get accurate timing
|
||||
result = subprocess.run(
|
||||
[sys.executable, '-c',
|
||||
'import time; t=time.time(); import praisonaiagents; print(time.time()-t)'],
|
||||
capture_output=True,
|
||||
text=True
|
||||
)
|
||||
|
||||
import_time = float(result.stdout.strip())
|
||||
# 400ms is realistic for a feature-rich SDK
|
||||
# The lazy loading ensures heavy deps (litellm) aren't imported at startup
|
||||
assert import_time < 0.4, \
|
||||
f"Import took {import_time*1000:.1f}ms, expected < 400ms"
|
||||
@@ -0,0 +1,236 @@
|
||||
"""
|
||||
Tests for Gap Closure - LLM Consolidation + Parity
|
||||
|
||||
TDD: These tests define the expected behavior for:
|
||||
1. GAP A: No deprecation warnings for model/llm alias
|
||||
2. GAP B: YAML parser supports consolidated params
|
||||
3. GAP C: Wrapper agents use output= (not verbose/markdown)
|
||||
4. GAP D: AutoAgents uses consolidated llm= (not function_calling_llm)
|
||||
5. GAP E: model= and llm= are aliases with NO warnings
|
||||
"""
|
||||
import pytest
|
||||
import warnings
|
||||
|
||||
|
||||
class TestNoDeprecationWarnings:
|
||||
"""GAP A + E: No deprecation warnings for model/llm or legacy params."""
|
||||
|
||||
def test_no_warning_for_model_alias(self):
|
||||
"""model= should work without any deprecation warning."""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
warnings.simplefilter("always")
|
||||
agent = Agent(instructions="Test", model="gpt-4o-mini")
|
||||
|
||||
# Should NOT emit any deprecation warnings
|
||||
deprecation_warnings = [x for x in w if issubclass(x.category, DeprecationWarning)]
|
||||
assert len(deprecation_warnings) == 0, f"Unexpected warnings: {[str(x.message) for x in deprecation_warnings]}"
|
||||
|
||||
def test_no_warning_for_llm_param(self):
|
||||
"""llm= should work without any deprecation warning."""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
warnings.simplefilter("always")
|
||||
agent = Agent(instructions="Test", llm="gpt-4o-mini")
|
||||
|
||||
deprecation_warnings = [x for x in w if issubclass(x.category, DeprecationWarning)]
|
||||
assert len(deprecation_warnings) == 0, f"Unexpected warnings: {[str(x.message) for x in deprecation_warnings]}"
|
||||
|
||||
def test_no_warning_for_llm_config(self):
|
||||
"""llm_config= should work without deprecation warning (backward compat)."""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
warnings.simplefilter("always")
|
||||
agent = Agent(instructions="Test", llm_config={"temperature": 0.7})
|
||||
|
||||
deprecation_warnings = [x for x in w if issubclass(x.category, DeprecationWarning)]
|
||||
assert len(deprecation_warnings) == 0, f"Unexpected warnings: {[str(x.message) for x in deprecation_warnings]}"
|
||||
|
||||
def test_no_warning_for_function_calling_llm(self):
|
||||
"""function_calling_llm= should work without deprecation warning (backward compat)."""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
warnings.simplefilter("always")
|
||||
agent = Agent(instructions="Test", function_calling_llm="gpt-4o-mini")
|
||||
|
||||
deprecation_warnings = [x for x in w if issubclass(x.category, DeprecationWarning)]
|
||||
assert len(deprecation_warnings) == 0, f"Unexpected warnings: {[str(x.message) for x in deprecation_warnings]}"
|
||||
|
||||
def test_model_and_llm_are_equivalent(self):
|
||||
"""model= and llm= should produce identical behavior."""
|
||||
from praisonaiagents import Agent
|
||||
|
||||
agent1 = Agent(instructions="Test", llm="gpt-4o")
|
||||
agent2 = Agent(instructions="Test", model="gpt-4o")
|
||||
|
||||
assert agent1.llm == agent2.llm == "gpt-4o"
|
||||
|
||||
|
||||
class TestAutoAgentsConsolidation:
|
||||
"""GAP D: AutoAgents uses consolidated llm= (not function_calling_llm)."""
|
||||
|
||||
def test_autoagents_does_not_pass_function_calling_llm_to_agent(self):
|
||||
"""AutoAgents should not pass function_calling_llm= to Agent()."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
|
||||
# Get the source code of the _create_agents_and_tasks method
|
||||
source = inspect.getsource(AutoAgents._create_agents_and_tasks)
|
||||
|
||||
# Should NOT contain function_calling_llm= in Agent() call
|
||||
# This is a static analysis test
|
||||
assert "function_calling_llm=self.function_calling_llm" not in source, \
|
||||
"AutoAgents._create_agents_and_tasks still passes function_calling_llm= to Agent()"
|
||||
|
||||
def test_autoagents_accepts_llm_param(self):
|
||||
"""AutoAgents should accept llm= parameter."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
|
||||
# Should not raise
|
||||
auto = AutoAgents(instructions="Test task", llm="gpt-4o-mini")
|
||||
assert auto.llm == "gpt-4o-mini"
|
||||
|
||||
def test_autoagents_no_deprecation_warnings(self):
|
||||
"""AutoAgents should not emit deprecation warnings."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
warnings.simplefilter("always")
|
||||
auto = AutoAgents(
|
||||
instructions="Test task",
|
||||
llm="gpt-4o-mini",
|
||||
function_calling_llm="gpt-4o-mini" # Legacy param
|
||||
)
|
||||
|
||||
deprecation_warnings = [x for x in w if issubclass(x.category, DeprecationWarning)]
|
||||
assert len(deprecation_warnings) == 0, f"Unexpected warnings: {[str(x.message) for x in deprecation_warnings]}"
|
||||
|
||||
|
||||
class TestYAMLParserConsolidation:
|
||||
"""GAP B: YAML parser supports consolidated params."""
|
||||
|
||||
def test_yaml_parser_maps_legacy_reflection_fields(self):
|
||||
"""YAML parser should map legacy reflect_llm/min_reflect/max_reflect to reflection config."""
|
||||
from praisonaiagents.workflows.yaml_parser import YAMLWorkflowParser
|
||||
|
||||
yaml_content = """
|
||||
roles:
|
||||
researcher:
|
||||
role: Researcher
|
||||
goal: Research topics
|
||||
backstory: Expert researcher
|
||||
reflect_llm: gpt-4o
|
||||
min_reflect: 2
|
||||
max_reflect: 5
|
||||
tasks:
|
||||
- description: Research AI
|
||||
expected_output: Report
|
||||
"""
|
||||
|
||||
import yaml
|
||||
config = yaml.safe_load(yaml_content)
|
||||
|
||||
parser = YAMLWorkflowParser()
|
||||
# The parser should handle legacy fields without error
|
||||
# and map them appropriately
|
||||
agent = parser._create_agent("researcher", config["roles"]["researcher"])
|
||||
|
||||
# Legacy fields should be stored for later use
|
||||
assert hasattr(agent, '_yaml_reflect_llm')
|
||||
assert hasattr(agent, '_yaml_min_reflect')
|
||||
assert hasattr(agent, '_yaml_max_reflect')
|
||||
|
||||
def test_yaml_parser_supports_new_reflection_structure(self):
|
||||
"""YAML parser should support new reflection: structure."""
|
||||
from praisonaiagents.workflows.yaml_parser import YAMLWorkflowParser
|
||||
|
||||
yaml_content = """
|
||||
roles:
|
||||
researcher:
|
||||
role: Researcher
|
||||
goal: Research topics
|
||||
backstory: Expert researcher
|
||||
reflection:
|
||||
enabled: true
|
||||
max_iterations: 5
|
||||
min_iterations: 2
|
||||
tasks:
|
||||
- description: Research AI
|
||||
expected_output: Report
|
||||
"""
|
||||
|
||||
import yaml
|
||||
config = yaml.safe_load(yaml_content)
|
||||
|
||||
parser = YAMLWorkflowParser()
|
||||
agent = parser._create_agent("researcher", config["roles"]["researcher"])
|
||||
|
||||
# Should handle new structure
|
||||
assert agent is not None
|
||||
|
||||
|
||||
class TestWrapperAgentsModernization:
|
||||
"""GAP C: Wrapper agents use output= (not verbose/markdown)."""
|
||||
|
||||
def test_prompt_expander_agent_uses_output_in_agent_call(self):
|
||||
"""PromptExpanderAgent should use output= when creating internal Agent."""
|
||||
from praisonaiagents.agent.prompt_expander_agent import PromptExpanderAgent
|
||||
import inspect
|
||||
|
||||
# Get the source of the agent property
|
||||
source = inspect.getsource(PromptExpanderAgent.agent.fget)
|
||||
|
||||
# Should use output= not verbose= or markdown=
|
||||
assert "output=" in source, "PromptExpanderAgent.agent should use output="
|
||||
assert "verbose=" not in source or "output=" in source, \
|
||||
"PromptExpanderAgent.agent should not pass verbose= directly to Agent()"
|
||||
|
||||
def test_query_rewriter_agent_uses_output_in_agent_call(self):
|
||||
"""QueryRewriterAgent should use output= when creating internal Agent."""
|
||||
from praisonaiagents.agent.query_rewriter_agent import QueryRewriterAgent
|
||||
import inspect
|
||||
|
||||
# Get the source of the agent property
|
||||
source = inspect.getsource(QueryRewriterAgent.agent.fget)
|
||||
|
||||
# Should use output= not verbose= or markdown=
|
||||
assert "output=" in source, "QueryRewriterAgent.agent should use output="
|
||||
assert "verbose=" not in source or "output=" in source, \
|
||||
"QueryRewriterAgent.agent should not pass verbose= directly to Agent()"
|
||||
|
||||
def test_prompt_expander_accepts_model_param(self):
|
||||
"""PromptExpanderAgent should accept model= parameter."""
|
||||
from praisonaiagents.agent.prompt_expander_agent import PromptExpanderAgent
|
||||
|
||||
agent = PromptExpanderAgent(model="gpt-4o-mini")
|
||||
assert agent.model == "gpt-4o-mini"
|
||||
|
||||
def test_query_rewriter_accepts_model_param(self):
|
||||
"""QueryRewriterAgent should accept model= parameter."""
|
||||
from praisonaiagents.agent.query_rewriter_agent import QueryRewriterAgent
|
||||
|
||||
agent = QueryRewriterAgent(model="gpt-4o-mini")
|
||||
assert agent.model == "gpt-4o-mini"
|
||||
|
||||
|
||||
class TestAutoAgentsNoLegacyVerboseMarkdown:
|
||||
"""AutoAgents should not pass verbose/markdown to Agent when output= is set."""
|
||||
|
||||
def test_autoagents_uses_output_not_verbose_markdown(self):
|
||||
"""AutoAgents should use output= and not pass verbose/markdown when output is set."""
|
||||
from praisonaiagents.agents.autoagents import AutoAgents
|
||||
import inspect
|
||||
|
||||
source = inspect.getsource(AutoAgents._create_agents_and_tasks)
|
||||
|
||||
# Should pass output= to Agent
|
||||
assert "output=self._output" in source or "output=" in source, \
|
||||
"AutoAgents should pass output= to Agent()"
|
||||
|
||||
# Should NOT pass verbose= or markdown= directly
|
||||
assert "verbose=self.verbose" not in source, \
|
||||
"AutoAgents should not pass verbose= to Agent()"
|
||||
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