feat: add --save flag to research command for markdown output and bump version to 2.2.93

README.md

@@ -152,6 +152,10 @@ praisonai research "What are the latest AI trends in 2025?"
 # Use Gemini
 praisonai research --model deep-research-pro "Your research query"
 
+# Save output to file (output/research/{query}.md)
+praisonai research --save "Your research query"
+praisonai research -s "Your research query"
+
 # Verbose mode (show debug logs)
 praisonai research -v "Your research query"
 ```

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.2.90" \
+    "praisonai>=2.2.93" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.90" \
+    "praisonai>=2.2.93" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.90" \
+    "praisonai>=2.2.93" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.90" \
+    "praisonai>=2.2.93" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.90`
+- PraisonAI: `>=2.2.93`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`
 
@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.90" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.93" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py

@@ -254,7 +254,17 @@ class DeepResearchAgent:
             max_wait_time: Maximum seconds to wait for research completion
         """
         self.name = name or "DeepResearchAgent"
-        self.instructions = instructions
+        # Default instructions for comprehensive research
+        default_instructions = """
+You are a professional research analyst. When conducting research:
+- Include specific figures, trends, statistics, and measurable outcomes
+- Prioritize reliable, up-to-date sources: peer-reviewed research, official organizations, regulatory agencies
+- Include inline citations and return all source metadata
+- Be analytical, avoid generalities, and ensure data-backed reasoning
+- Structure your response with clear headers and formatting
+- If creating comparisons, use tables for clarity
+"""
+        self.instructions = instructions or default_instructions
         self.verbose = verbose
         self.summary_mode = summary_mode
         self.enable_web_search = enable_web_search
@@ -288,7 +298,7 @@ class DeepResearchAgent:
         self.logger = logging.getLogger(__name__)
         
         if self.verbose:
-            self.logger.info(f"DeepResearchAgent initialized: provider={self.provider.value}, model={self.model}")
+            self.logger.debug(f"DeepResearchAgent initialized: provider={self.provider.value}, model={self.model}")
     
     def _detect_provider(
         self, 
@@ -766,7 +776,7 @@ class DeepResearchAgent:
         interaction = self.gemini_client.interactions.create(**create_params)
         
         if self.verbose:
-            self.logger.info(f"Gemini research started: {interaction.id}")
+            self.logger.debug(f"Gemini research started: {interaction.id}")
         
         start_time = time.time()
         while True:
@@ -774,7 +784,7 @@ class DeepResearchAgent:
             
             if interaction.status == "completed":
                 if self.verbose:
-                    self.logger.info("Gemini research completed")
+                    self.logger.debug("Gemini research completed")
                 break
             elif interaction.status == "failed":
                 error = getattr(interaction, 'error', 'Unknown error')
@@ -789,7 +799,7 @@ class DeepResearchAgent:
                 )
             
             if self.verbose:
-                self.logger.info(f"Research in progress... ({elapsed:.0f}s)")
+                self.logger.debug(f"Research in progress... ({elapsed:.0f}s)")
             
             time.sleep(self.poll_interval)
         
@@ -1008,7 +1018,7 @@ class DeepResearchAgent:
         start_time = time.time()
         
         if self.verbose and not stream:
-            self.logger.info(f"Starting {self.provider.value} deep research: {query[:100]}...")
+            self.logger.debug(f"Starting {self.provider.value} deep research: {query[:100]}...")
         
         try:
             if self.provider == Provider.GEMINI:
@@ -1049,8 +1059,8 @@ class DeepResearchAgent:
         
         elapsed = time.time() - start_time
         if self.verbose:
-            self.logger.info(f"Deep research completed in {elapsed:.1f}s")
-            self.logger.info(f"Report length: {len(result.report)} chars, Citations: {len(result.citations)}")
+            self.logger.debug(f"Deep research completed in {elapsed:.1f}s")
+            self.logger.debug(f"Report length: {len(result.report)} chars, Citations: {len(result.citations)}")
         
         return result
     
@@ -1076,7 +1086,7 @@ class DeepResearchAgent:
         start_time = time.time()
         
         if self.verbose:
-            self.logger.info(f"Starting async {self.provider.value} deep research: {query[:100]}...")
+            self.logger.debug(f"Starting async {self.provider.value} deep research: {query[:100]}...")
         
         try:
             if self.provider == Provider.GEMINI:
@@ -1151,7 +1161,7 @@ class DeepResearchAgent:
         
         elapsed = time.time() - start_time
         if self.verbose:
-            self.logger.info(f"Async deep research completed in {elapsed:.1f}s")
+            self.logger.debug(f"Async deep research completed in {elapsed:.1f}s")
         
         return result
     

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.163"
+version = "0.0.165"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.163"
+version = "0.0.165"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.90.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.90.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.93.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.93.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli.py

@@ -28,6 +28,13 @@ AUTOGEN_AVAILABLE = False
 PRAISONAI_AVAILABLE = False
 TRAIN_AVAILABLE = False
 try:
+    import importlib.util
+    CHAINLIT_AVAILABLE = importlib.util.find_spec("chainlit") is not None
+except ImportError:
+    pass
+
+def _get_chainlit_run():
+    """Lazy import chainlit to avoid loading .env at startup"""
     # Create necessary directories and set CHAINLIT_APP_ROOT
     if "CHAINLIT_APP_ROOT" not in os.environ:
         chainlit_root = os.path.join(os.path.expanduser("~"), ".praison")
@@ -39,9 +46,7 @@ try:
     os.makedirs(os.path.join(chainlit_root, ".files"), exist_ok=True)
     
     from chainlit.cli import chainlit_run
-    CHAINLIT_AVAILABLE = True
-except ImportError:
-    pass
+    return chainlit_run
 
 try:
     import gradio as gr
@@ -573,6 +578,7 @@ class PraisonAI:
         parser.add_argument("--goal", type=str, help="Goal for context engineering")
         parser.add_argument("--auto-analyze", action="store_true", help="Enable automatic analysis in context engineering")
         parser.add_argument("--research", action="store_true", help="Run deep research on a topic")
+        parser.add_argument("--save", "-s", action="store_true", help="Save research output to file (output/research/)")
         parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose output for research")
         
         # If we're in a test environment, parse with empty args to avoid pytest interference
@@ -714,7 +720,8 @@ class PraisonAI:
                 
                 research_model = getattr(args, 'model', None)
                 verbose = getattr(args, 'verbose', False)
-                self.handle_research_command(research_query, research_model, verbose)
+                save = getattr(args, 'save', False)
+                self.handle_research_command(research_query, research_model, verbose, save)
                 sys.exit(0)
 
         # Only check framework availability for agent-related operations
@@ -812,7 +819,7 @@ class PraisonAI:
             if "CHAINLIT_APP_ROOT" not in os.environ:
                 os.environ["CHAINLIT_APP_ROOT"] = root_path
             chat_ui_path = os.path.join(os.path.dirname(praisonai.__file__), 'ui', 'chat.py')
-            chainlit_run([chat_ui_path])
+            _get_chainlit_run()([chat_ui_path])
         else:
             print("ERROR: Chat UI is not installed. Please install it with 'pip install \"praisonai[chat]\"' to use the chat UI.")
 
@@ -836,7 +843,7 @@ class PraisonAI:
             else:
                 logging.info("Public folder already exists.")
             code_ui_path = os.path.join(os.path.dirname(praisonai.__file__), 'ui', 'code.py')
-            chainlit_run([code_ui_path])
+            _get_chainlit_run()([code_ui_path])
         else:
             print("ERROR: Code UI is not installed. Please install it with 'pip install \"praisonai[code]\"' to use the code UI.")
 
@@ -882,7 +889,7 @@ class PraisonAI:
             else:
                 logging.info("Public folder already exists.")
             chainlit_ui_path = os.path.join(os.path.dirname(praisonai.__file__), 'ui', 'agents.py')
-            chainlit_run([chainlit_ui_path])
+            _get_chainlit_run()([chainlit_ui_path])
         else:
             print("ERROR: Chainlit is not installed. Please install it with 'pip install \"praisonai[ui]\"' to use the UI.")
 
@@ -906,7 +913,7 @@ class PraisonAI:
             else:
                 logging.info("Public folder already exists.")
             realtime_ui_path = os.path.join(os.path.dirname(praisonai.__file__), 'ui', 'realtime.py')
-            chainlit_run([realtime_ui_path])
+            _get_chainlit_run()([realtime_ui_path])
         else:
             print("ERROR: Realtime UI is not installed. Please install it with 'pip install \"praisonai[realtime]\"' to use the realtime UI.")
 
@@ -954,7 +961,7 @@ class PraisonAI:
             print(f"[red]ERROR: Context engineering failed: {e}[/red]")
             sys.exit(1)
 
-    def handle_research_command(self, query: str, model: str = None, verbose: bool = False) -> str:
+    def handle_research_command(self, query: str, model: str = None, verbose: bool = False, save: bool = False) -> str:
         """
         Handle the research command by creating a DeepResearchAgent and running it.
         
@@ -962,6 +969,7 @@ class PraisonAI:
             query: Research query/topic
             model: Model for deep research (optional, defaults to o4-mini-deep-research)
             verbose: Enable verbose output (default: False)
+            save: Save output to file (default: False)
             
         Returns:
             str: Research report
@@ -985,13 +993,10 @@ class PraisonAI:
             
             print(f"Model: {model}")
             
-            # Create DeepResearchAgent
-            agent = DeepResearchAgent(
-                model=model,
-                verbose=verbose
-            )
+            # Create DeepResearchAgent (verbose=True is default for streaming output)
+            agent = DeepResearchAgent(model=model)
             
-            # Execute the research (streaming is enabled by default)
+            # Execute the research
             result = agent.research(query)
             
             print("\n[bold green]Research Complete![/bold green]")
@@ -1009,6 +1014,42 @@ class PraisonAI:
                     if url:
                         print(f"     {url}")
             
+            # Save output to file if --save flag is set
+            if save:
+                import re
+                # Get first 10 words from query for filename
+                words = query.split()[:10]
+                filename_base = ' '.join(words)
+                # Sanitize filename: remove invalid characters
+                filename_base = re.sub(r'[<>:"/\\|?*\x00-\x1f]', '', filename_base)
+                filename_base = filename_base.strip()[:100]  # Limit length
+                if not filename_base:
+                    filename_base = "research_output"
+                
+                # Create output directory
+                output_dir = os.path.join(os.getcwd(), "output", "research")
+                os.makedirs(output_dir, exist_ok=True)
+                
+                # Build markdown content
+                md_content = f"# {query}\n\n"
+                md_content += result.report
+                if result.citations:
+                    md_content += "\n\n## Citations\n\n"
+                    for i, citation in enumerate(result.citations, 1):
+                        title = getattr(citation, 'title', 'Untitled')
+                        url = getattr(citation, 'url', '')
+                        if url:
+                            md_content += f"{i}. [{title}]({url})\n"
+                        else:
+                            md_content += f"{i}. {title}\n"
+                
+                # Save to file
+                output_path = os.path.join(output_dir, f"{filename_base}.md")
+                with open(output_path, 'w', encoding='utf-8') as f:
+                    f.write(md_content)
+                
+                print(f"\n[bold green]Saved to:[/bold green] {output_path}")
+            
             return result.report
             
         except ImportError as e:

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.2.90 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.93 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.90"
+version = "2.2.93"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.163",
+    "praisonaiagents>=0.0.165",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -102,7 +102,7 @@ autogen-v4 = [
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.90"
+version = "2.2.93"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -120,7 +120,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.163"
+praisonaiagents = ">=0.0.165"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/uv.lock

@@ -4044,7 +4044,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.2.90"
+version = "2.2.93"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -4198,7 +4198,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.22" },
-    { name = "praisonaiagents", specifier = ">=0.0.163" },
+    { name = "praisonaiagents", specifier = ">=0.0.165" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4253,7 +4253,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.163"
+version = "0.0.165"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4262,9 +4262,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/33/6d/68553b1c16f82ab9d0f4466227df65990103d89428aa49963253b85ed877/praisonaiagents-0.0.163.tar.gz", hash = "sha256:f1dded526613e353d10ea0ee7158d2e544efa06aae289d3b84efe7f03275883b", size = 298039, upload-time = "2025-12-13T01:13:18.845Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c3/49/37f8518b2205760cec66ac44482c1cb9e33c0cb568c4c6cb07f9908dceb3/praisonaiagents-0.0.165.tar.gz", hash = "sha256:b2a43a44d51454f9917a2345407bf86fdc46046bafac96601c98d1d21fb2601a", size = 298269, upload-time = "2025-12-13T02:18:34.883Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d8/21/2e2f9719dcf31c99474c1b61a62be78c79cdfde0abd40b5d100419515279/praisonaiagents-0.0.163-py3-none-any.whl", hash = "sha256:518c46a618b609af4718c2c77779c75494bc4751f97ebb52864c490483cc2240", size = 315110, upload-time = "2025-12-13T01:13:17.363Z" },
+    { url = "https://files.pythonhosted.org/packages/26/e7/c3b21f4c68629612ab4e4d128fca5fdabe141df16b0861679e2736adebe7/praisonaiagents-0.0.165-py3-none-any.whl", hash = "sha256:65b880f6d4c5d05fadf86153338306e12004316da9dc9f83942b9e16f2133851", size = 315370, upload-time = "2025-12-13T02:18:33.197Z" },
 ]
 
 [[package]]