Release v3.4.1

Release v3.4.0
Remove unused test and verification files
2026-11-12 01:41:26 -10:00 · 2026-01-08 14:36:54 +00:00 · 2026-01-08 13:29:57 +00:00 · 2026-01-08 12:50:16 +00:00 · 2026-01-08 12:28:06 +00:00
359 changed files with 6296 additions and 2250 deletions
@@ -538,11 +538,11 @@ python app.py

 Create app.py file and add the code below:
 ```python
-from praisonaiagents import Agent, PraisonAIAgents
+from praisonaiagents import Agent, Agents

 research_agent = Agent(instructions="Research about AI")
 summarise_agent = Agent(instructions="Summarise research agent's findings")
-agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents = Agents(agents=[research_agent, summarise_agent])
 agents.start()
 ```

@@ -3380,11 +3380,11 @@ See [Agent Memory](#6-agent-memory-zero-dependencies) for more options.
 <summary><strong>How do I run multiple agents together?</strong></summary>

 ```python
-from praisonaiagents import Agent, PraisonAIAgents
+from praisonaiagents import Agent, Agents

 agent1 = Agent(instructions="Research topics")
 agent2 = Agent(instructions="Summarize findings")
-agents = PraisonAIAgents(agents=[agent1, agent2])
+agents = Agents(agents=[agent1, agent2])
 agents.start()
 ```

@@ -1,71 +0,0 @@
-"""
-Auto-generated API server for PraisonAI agents.
-"""
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-from praisonai import PraisonAI
-import os
-
-app = Flask(__name__)
-
-# CORS configuration
-CORS(app)
-
-# Authentication
-AUTH_ENABLED = False
-AUTH_TOKEN = None
-
-def check_auth():
-    """Check authentication if enabled."""
-    if not AUTH_ENABLED:
-        return True
-    
-    token = request.headers.get('Authorization', '').replace('Bearer ', '')
-    return token == AUTH_TOKEN
-
-@app.route('/health', methods=['GET'])
-def health():
-    """Health check endpoint."""
-    return jsonify({"status": "ok", "service": "praisonai-api"})
-
-@app.route('/chat', methods=['POST'])
-def chat():
-    """Chat endpoint for agent interaction."""
-    if not check_auth():
-        return jsonify({"error": "Unauthorized"}), 401
-    
-    data = request.get_json()
-    if not data or 'message' not in data:
-        return jsonify({"error": "Message required"}), 400
-    
-    try:
-        praisonai = PraisonAI(agent_file="agents.yaml")
-        result = praisonai.run()
-        
-        return jsonify({
-            "response": result,
-            "status": "success"
-        })
-    except Exception as e:
-        return jsonify({
-            "error": str(e),
-            "status": "error"
-        }), 500
-
-@app.route('/agents', methods=['GET'])
-def list_agents():
-    """List available agents."""
-    if not check_auth():
-        return jsonify({"error": "Unauthorized"}), 401
-    
-    return jsonify({
-        "agents": ["default"],
-        "agent_file": "agents.yaml"
-    })
-
-if __name__ == '__main__':
-    app.run(
-        host='0.0.0.0',
-        port=8080,
-        debug=False
-    )
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=3.3.0" \
+    "praisonai>=3.4.1" \
    "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>=3.3.0" \
+    "praisonai>=3.4.1" \
    "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>=3.3.0" \
+    "praisonai>=3.4.1" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -1,203 +0,0 @@
-# Profiling CLI
-
-PraisonAI provides unified profiling capabilities for performance analysis and debugging, with **full visibility from ENTER to first response**.
-
-## Quick Start
-
-```bash
-# Basic profiling
-praisonai "What is 2+2?" --profile
-
-# Deep profiling with call graph, decision trace, and module breakdown
-praisonai "What is 2+2?" --profile --profile-deep
-
-# JSON output format
-praisonai "What is 2+2?" --profile --profile-format json
-```
-
-## Time to First Response (TTFR)
-
-**TTFR** is the key user-perceived latency metric. It measures the time from when you press ENTER until the first visible output appears.
-
-### What contributes to TTFR?
-
-1. **CLI Entry** - Time to reach the CLI entry point
-2. **CLI Parse** - Argparse/Typer routing time
-3. **Routing** - Command routing to handler
-4. **Imports** - Loading required modules
-5. **Agent Init** - Initializing the agent
-6. **Network Request** - Sending request to LLM API
-7. **First Token** - Receiving first token (streaming)
-8. **First Output** - Rendering first visible output
-
-### How to reduce TTFR
-
-1. **Warm runs** - Imports are cached after first run
-2. **Streaming mode** - See output as tokens arrive
-3. **Lighter models** - Faster response times
-4. **Local models** - Eliminate network latency
-
-## CLI Flags
-
-| Flag | Description | Default |
-|------|-------------|---------|
-| `--profile` | Enable profiling with timing breakdown | Disabled |
-| `--profile-deep` | Enable deep profiling with call graph (higher overhead) | Disabled |
-| `--profile-format` | Output format: `text` or `json` | `text` |
-
-## Profile Command
-
-For more detailed profiling options, use the `profile` subcommand:
-
-```bash
-# Profile a query
-praisonai profile query "What is 2+2?"
-
-# With deep call tracing
-praisonai profile query "Hello" --deep
-
-# Limit functions shown
-praisonai profile query "Test" --limit 20
-
-# Save artifacts to directory
-praisonai profile query "Test" --save ./profile_results
-
-# JSON output
-praisonai profile query "Test" --format json
-```
-
-### Profile Query Options
-
-| Option | Description | Default |
-|--------|-------------|---------|
-| `--model, -m` | Model to use | Default model |
-| `--stream/--no-stream` | Use streaming mode | No stream |
-| `--deep` | Enable deep call tracing | Disabled |
-| `--limit, -n` | Top N functions to show | 30 |
-| `--sort, -s` | Sort by: `cumulative` or `tottime` | `cumulative` |
-| `--show-callers` | Show caller functions | Disabled |
-| `--show-callees` | Show callee functions | Disabled |
-| `--save` | Save artifacts to path | None |
-| `--format, -f` | Output format: `text` or `json` | `text` |
-
-## Other Profile Commands
-
-```bash
-# Profile module import times
-praisonai profile imports
-
-# Profile CLI startup time
-praisonai profile startup
-
-# Run comprehensive profiling suite
-praisonai profile suite
-
-# Create performance snapshots for comparison
-praisonai profile snapshot --baseline
-praisonai profile snapshot current --compare
-
-# Configure performance optimizations
-praisonai profile optimize --show
-```
-
-## Output Format
-
-### Text Report
-
-```
-======================================================================
-PraisonAI Profile Report
-======================================================================
-
-Run ID:     abc12345
-Timestamp:  2024-01-02T12:00:00.000000Z
-Method:     cli_direct
-Version:    3.0.1
-
-## Execution Timeline
---------------------------------------------
-  Imports                   :   500.00 ms
-  Agent Init                :     0.50 ms
-  Execution                 :  2000.00 ms
-  ───────────────────────────────────────────
-  ⏱ Time to First Response  :  2500.50 ms
-  TOTAL                     :  2500.50 ms
-
-## Timing Breakdown
----------------------------------------
-  Imports:            500.00 ms
-  Agent Init:           0.50 ms
-  Execution:         2000.00 ms
-  ─────────────────────────────────────
-  TOTAL:             2500.50 ms
-
-## Decision Trace (deep profile only)
----------------------------------------
-  Agent Config:    default
-  Model:           gpt-4
-  Streaming:       disabled
-  Profile Layer:   2
-
-## Top Functions by Cumulative Time
----------------------------------------------------------------------
-Function                                    Calls   Cumul (ms)
----------------------------------------------------------------------
-request                                         1      1800.00
-send                                            1      1750.00
-...
-```
-
-### JSON Report
-
-```json
-{
-  "schema_version": "1.0",
-  "run_id": "abc12345",
-  "timestamp": "2024-01-02T12:00:00.000000Z",
-  "invocation": {
-    "method": "cli_direct",
-    "flags": {"layer": 1, "stream": false, "model": null},
-    "praisonai_version": "3.0.1",
-    "python_version": "3.12.0"
-  },
-  "timing": {
-    "total_ms": 2500.5,
-    "imports_ms": 500.0,
-    "agent_init_ms": 0.5,
-    "execution_ms": 2000.0
-  },
-  "functions": [...],
-  "response_preview": "four"
-}
-```
-
-## Profiling Layers
-
-| Layer | Overhead | Features |
-|-------|----------|----------|
-| 0 | < 1ms | Wall-clock phases only |
-| 1 | ~5% | + cProfile function stats |
-| 2 | ~15% | + Call graph (callers/callees) |
-
- `--profile` uses Layer 1
- `--profile --profile-deep` uses Layer 2
-
-## Performance Tips
-
-1. **Use Layer 0 for production monitoring** - minimal overhead
-2. **Use Layer 1 for development profiling** - good balance
-3. **Use Layer 2 only for deep debugging** - higher overhead
-
-## Observational Equivalence
-
-The following commands produce equivalent profile reports:
-
-```bash
-# CLI direct
-praisonai "prompt" --profile
-
-# Profile command
-praisonai profile query "prompt"
-```
-
-Both use the same unified profiling architecture and produce identical schema output.
@@ -1,257 +0,0 @@
-# Profiling Module
-
-PraisonAI provides a unified profiling module for programmatic performance analysis.
-
-## Quick Start
-
-```python
-from praisonai.cli.execution import (
-    ExecutionRequest,
-    Profiler,
-    ProfilerConfig,
-)
-
-# Create execution request
-request = ExecutionRequest(prompt="What is 2+2?")
-
-# Configure profiler
-config = ProfilerConfig(layer=1)
-
-# Run with profiling
-profiler = Profiler(config)
-result, report = profiler.profile_sync(request)
-
-# Access results
-print(f"Output: {result.output}")
-print(f"Total time: {report.timing.total_ms}ms")
-```
-
-## ExecutionRequest
-
-Immutable request descriptor for execution.
-
-```python
-from praisonai.cli.execution import ExecutionRequest
-
-# Basic request
-request = ExecutionRequest(prompt="Hello")
-
-# With model
-request = ExecutionRequest(
-    prompt="Hello",
-    model="gpt-4",
-    agent_name="MyAgent",
-)
-
-# With streaming
-request = ExecutionRequest(
-    prompt="Hello",
-    stream=True,
-)
-```
-
-### Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `prompt` | str | Required | The prompt to execute |
-| `agent_name` | str | "Assistant" | Name of the agent |
-| `agent_instructions` | str | None | Custom instructions |
-| `model` | str | None | Model to use |
-| `stream` | bool | False | Enable streaming |
-| `tools` | tuple | () | Tools to provide |
-
-## ProfilerConfig
-
-Configuration for profiling behavior.
-
-```python
-from praisonai.cli.execution import ProfilerConfig
-
-# Layer 0: Wall-clock only (< 1ms overhead)
-config = ProfilerConfig(layer=0)
-
-# Layer 1: + cProfile stats (~5% overhead)
-config = ProfilerConfig(layer=1)
-
-# Layer 2: + Call graph (~15% overhead)
-config = ProfilerConfig(
-    layer=2,
-    show_callers=True,
-    show_callees=True,
-)
-
-# From CLI flags
-config = ProfilerConfig.from_flags(
-    profile=True,
-    deep=True,
-    network=False,
-)
-```
-
-### Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `layer` | int | 1 | Profiling layer (0, 1, or 2) |
-| `limit` | int | 30 | Max functions to show |
-| `sort_by` | str | "cumulative" | Sort by: cumulative, time, calls |
-| `show_callers` | bool | False | Show caller functions |
-| `show_callees` | bool | False | Show callee functions |
-| `track_network` | bool | False | Track network requests |
-| `output_format` | str | "text" | Output format: text, json |
-| `save_path` | str | None | Path to save artifacts |
-
-## Profiler
-
-The main profiler class that wraps execution.
-
-```python
-from praisonai.cli.execution import Profiler, ProfilerConfig, ExecutionRequest
-
-config = ProfilerConfig(layer=1)
-profiler = Profiler(config)
-
-request = ExecutionRequest(prompt="Hello")
-result, report = profiler.profile_sync(request, invocation_method="api")
-```
-
-### Methods
-
-#### `profile_sync(request, invocation_method="profile_command")`
-
-Profile synchronous execution.
-
-**Parameters:**
- `request`: ExecutionRequest - The execution request
- `invocation_method`: str - How this was invoked (for report)
-
-**Returns:** Tuple of (ExecutionResult, ProfileReport)
-
-## ProfileReport
-
-The canonical profile report with timing and statistics.
-
-```python
-# Access timing breakdown
-print(f"Total: {report.timing.total_ms}ms")
-print(f"Imports: {report.timing.imports_ms}ms")
-print(f"Agent Init: {report.timing.agent_init_ms}ms")
-print(f"Execution: {report.timing.execution_ms}ms")
-
-# Access function stats (layer 1+)
-if report.functions:
-    for func in report.functions[:5]:
-        print(f"{func.name}: {func.cumulative_time_ms}ms")
-
-# Access call graph (layer 2)
-if report.call_graph:
-    print(f"Edges: {len(report.call_graph.edges)}")
-
-# Export formats
-json_str = report.to_json()
-text_str = report.to_text()
-dict_data = report.to_dict()
-```
-
-### Schema Version
-
-The report uses schema version `1.0`. Schema is frozen after release - new fields are always optional with defaults.
-
-### Mandatory Fields
-
- `schema_version`: str
- `run_id`: str
- `timestamp`: str (ISO 8601)
- `invocation`: InvocationInfo
- `timing`: TimingBreakdown
- `response_preview`: str
-
-### Optional Fields
-
- `functions`: List[FunctionStat] (layer 1+)
- `call_graph`: CallGraph (layer 2)
- `network`: List[RequestTiming] (if track_network)
-
-## ExecutionResult
-
-Result of execution with output and metadata.
-
-```python
-# Access output
-print(result.output)
-print(result.success)
-print(result.run_id)
-
-# Check for errors
-if result.error:
-    print(f"Error: {result.error}")
-
-# Duration
-print(f"Duration: {result.duration_ms}ms")
-```
-
-## Data Bounds
-
-Profile data is strictly bounded to prevent memory issues:
-
-| Data Type | Maximum |
-|-----------|---------|
-| Function stats | 1000 |
-| Call graph edges | 5000 |
-| Network requests | 100 |
-
-## Example: Full Profiling Workflow
-
-```python
-from praisonai.cli.execution import (
-    ExecutionRequest,
-    Profiler,
-    ProfilerConfig,
-)
-
-# Configure deep profiling
-config = ProfilerConfig(
-    layer=2,
-    limit=50,
-    show_callers=True,
-    show_callees=True,
-)
-
-# Create request
-request = ExecutionRequest(
-    prompt="Explain quantum computing in simple terms",
-    model="gpt-4",
-)
-
-# Profile execution
-profiler = Profiler(config)
-result, report = profiler.profile_sync(request)
-
-# Print text report
-print(report.to_text())
-
-# Save JSON report
-with open("profile.json", "w") as f:
-    f.write(report.to_json())
-
-# Analyze timing
-print(f"\nTiming Analysis:")
-print(f"  Import overhead: {report.timing.imports_ms / report.timing.total_ms * 100:.1f}%")
-print(f"  Execution time: {report.timing.execution_ms / report.timing.total_ms * 100:.1f}%")
-
-# Top 5 hotspots
-if report.functions:
-    print(f"\nTop 5 Hotspots:")
-    for func in report.functions[:5]:
-        print(f"  {func.name}: {func.cumulative_time_ms:.2f}ms ({func.calls} calls)")
-```
-
-## Invariants
-
-The profiling module guarantees:
-
-1. **Output Equivalence**: Profiled execution produces identical output to non-profiled execution
-2. **Schema Stability**: Schema version 1.0 is frozen; new fields are always optional
-3. **Bounded Data**: All profile data respects maximum bounds
-4. **Zero Overhead When Disabled**: No profiling code runs unless explicitly enabled
@@ -1,53 +0,0 @@
-# Templates CLI
-
-```bash
-praisonai templates list
-```
-
-```bash
-praisonai templates search video
-```
-
-```bash
-praisonai templates info ai-video-editor
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4 --offline
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4 --strict-tools
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --tools ./my_tools.py
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --tools-dir ./tools
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --tools-source praisonai_tools.video
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --no-template-tools-py
-```
-
-```bash
-praisonai templates init my-project --template ai-video-editor
-```
-
-```bash
-praisonai templates cache clear
-```
-
-```bash
-praisonai templates cache list
-```
@@ -1,104 +0,0 @@
-# Templates Module
-
-## Loading Templates
-
-```python
-from praisonai.templates.loader import TemplateLoader
-
-loader = TemplateLoader()
-
-# Load from local path
-template = loader.load("./my-template")
-
-# Load from URI
-template = loader.load("ai-video-editor")
-
-# Load offline
-template = loader.load("ai-video-editor", offline=True)
-
-# Check requirements
-missing = loader.check_requirements(template)
-```
-
-## Template Discovery
-
-```python
-from praisonai.templates.discovery import TemplateDiscovery
-
-discovery = TemplateDiscovery(
-    include_package=True,
-    include_defaults=True,
-)
-
-# Find template
-result = discovery.find_template("ai-video-editor")
-
-# List all templates
-templates = discovery.list_templates()
-```
-
-## Template Config
-
-```python
-from praisonai.templates.loader import TemplateConfig
-
-# Access template properties
-template.name
-template.description
-template.version
-template.author
-template.requires
-template.workflow_file
-template.agents_file
-template.path
-```
-
-## Tool Override Integration
-
-```python
-from praisonai.templates.tool_override import (
-    create_tool_registry_with_overrides,
-    resolve_tools,
-)
-
-# Build registry with template sources
-tools_sources = template.requires.get("tools_sources", [])
-
-registry = create_tool_registry_with_overrides(
-    tools_sources=tools_sources,
-    template_dir=str(template.path),
-    include_defaults=True,
-)
-
-# Resolve tool names
-tools = resolve_tools(["shell_tool"], registry=registry)
-```
-
-## TEMPLATE.yaml Schema
-
-```yaml
-name: my-template
-version: "1.0.0"
-description: Template description
-
-requires:
-  tools: [shell_tool]
-  packages: [praisonai-tools]
-  env: [OPENAI_API_KEY]
-  tools_sources:
-    - praisonai_tools.video
-    - ./local_tools.py
-
-workflow: workflow.yaml
-agents: agents.yaml
-
-config:
-  input:
-    type: string
-    required: true
-  output:
-    type: string
-
-defaults:
-  preset: podcast
-```
@@ -1,65 +0,0 @@
-# Tools CLI
-
-```bash
-praisonai tools list
-```
-
-```bash
-praisonai tools info shell_tool
-```
-
-```bash
-praisonai tools info internet_search
-```
-
-```bash
-praisonai tools search "web"
-```
-
-```bash
-praisonai tools search "file"
-```
-
-```bash
-praisonai tools doctor
-```
-
-```bash
-praisonai tools doctor --json
-```
-
-```bash
-praisonai tools resolve shell_tool
-```
-
-```bash
-praisonai tools resolve internet_search --template ai-video-editor
-```
-
-```bash
-praisonai tools resolve my_tool --tools ./my_tools.py
-```
-
-```bash
-praisonai tools resolve my_tool --tools-dir ./tools
-```
-
-```bash
-praisonai tools discover
-```
-
-```bash
-praisonai tools discover --include praisonai_tools.video
-```
-
-```bash
-praisonai tools discover --entrypoints
-```
-
-```bash
-praisonai tools show-sources
-```
-
-```bash
-praisonai tools show-sources --template ai-video-editor
-```
@@ -1,90 +0,0 @@
-# Tools Module
-
-## Tool Registry
-
-```python
-from praisonai.templates.tool_override import (
-    create_tool_registry_with_overrides,
-    resolve_tools,
-)
-
-# Create registry with defaults
-registry = create_tool_registry_with_overrides(include_defaults=True)
-
-# Create registry with overrides
-registry = create_tool_registry_with_overrides(
-    override_files=["my_tools.py"],
-    override_dirs=["./tools"],
-    include_defaults=True,
-)
-
-# Create registry with tools_sources
-registry = create_tool_registry_with_overrides(
-    tools_sources=["praisonai_tools.video"],
-    template_dir="./my-template",
-    include_defaults=True,
-)
-
-# Resolve tool names to callables
-tools = resolve_tools(
-    ["shell_tool", "internet_search"],
-    registry=registry,
-)
-```
-
-## Tool Override Loader
-
-```python
-from praisonai.templates.tool_override import ToolOverrideLoader
-
-loader = ToolOverrideLoader()
-
-# Load from file
-tools = loader.load_from_file("my_tools.py")
-
-# Load from directory
-tools = loader.load_from_directory("./tools")
-
-# Load from module
-tools = loader.load_from_module("praisonai_tools.video")
-
-# Get default tool directories
-dirs = loader.get_default_tool_dirs()
-```
-
-## Resolution Order
-
-1. CLI `--tools` files (highest priority)
-2. CLI `--tools-dir` directories
-3. Template `tools_sources` (from TEMPLATE.yaml)
-4. Template-local `tools.py`
-5. Default dirs (`~/.praison/tools`, `~/.config/praison/tools`)
-6. Package discovery (`praisonai_tools`)
-7. Built-in tools (lowest priority)
-
-## Creating Custom Tools
-
-```python
-from praisonai_tools import tool
-
-@tool
-def my_custom_tool(query: str) -> str:
-    """My custom tool description.
-    
-    Args:
-        query: The search query
-    """
-    return f"Result for: {query}"
-```
-
-## Template tools_sources
-
-```yaml
-# TEMPLATE.yaml
-requires:
-  tools: [shell_tool]
-  tools_sources:
-    - praisonai_tools.video
-    - ./local_tools.py
-    - ./tools_dir/
-```
@@ -0,0 +1,158 @@
+"""
+Agent-Centric API Example
+
+Demonstrates the new consolidated feature parameters for PraisonAI Agents.
+These params provide a cleaner, more intuitive API with progressive disclosure.
+
+New Consolidated Params:
+- memory: Union[bool, MemoryConfig, MemoryManager] - Memory and session management
+- knowledge: Union[bool, List[str], KnowledgeConfig, KnowledgeBase] - RAG and retrieval
+- planning: Union[bool, PlanningConfig] - Planning mode
+- reflection: Union[bool, ReflectionConfig] - Self-reflection
+- guardrails: Union[bool, Callable, GuardrailConfig] - Safety validation
+- web: Union[bool, WebConfig] - Web search and fetch
+- context: Union[bool, ManagerConfig, ContextManager] - Context management
+"""
+
+from praisonaiagents import (
+    Agent,
+    # Config classes for progressive disclosure
+    MemoryConfig,
+    KnowledgeConfig,
+    PlanningConfig,
+    ReflectionConfig,
+    GuardrailConfig,
+    WebConfig,
+)
+
+
+def example_simple_enable():
+    """Example 1: Simple boolean enable (easiest)"""
+    print("\n=== Example 1: Simple Boolean Enable ===")
+    
+    agent = Agent(
+        instructions="You are a helpful assistant",
+        memory=True,      # Enable memory with defaults
+        reflection=True,  # Enable self-reflection
+        web=True,         # Enable web search + fetch
+    )
+    print(f"Agent created with memory={agent._memory_instance is not None}")
+    print(f"  self_reflect={agent.self_reflect}")
+    print(f"  web_search={agent.web_search}")
+
+
+def example_with_config():
+    """Example 2: Using config objects (more control)"""
+    print("\n=== Example 2: Using Config Objects ===")
+    
+    agent = Agent(
+        instructions="You are a research assistant",
+        memory=MemoryConfig(
+            backend="file",
+            user_id="researcher_001",
+            auto_memory=True,
+        ),
+        reflection=ReflectionConfig(
+            min_iterations=1,
+            max_iterations=5,
+            llm="gpt-4o-mini",
+        ),
+        web=WebConfig(
+            search=True,
+            fetch=False,  # Only search, no full page fetch
+            max_results=3,
+        ),
+    )
+    print(f"Agent created with user_id={agent.user_id}")
+    print(f"  {agent.max_reflect}")
+    print(f"  web_search={agent.web_search}, web_fetch={agent.web_fetch}")
+
+
+def example_guardrails():
+    """Example 3: Guardrails with callable or config"""
+    print("\n=== Example 3: Guardrails ===")
+    
+    # Option A: Direct callable
+    def my_validator(output):
+        """Simple validator that checks response length."""
+        is_valid = len(output.raw) < 1000
+        return (is_valid, output if is_valid else "Response too long")
+    
+    agent_a = Agent(
+        instructions="Be concise",
+        guardrails=my_validator,  # Direct callable
+    )
+    print(f"Agent A: guardrail is callable = {callable(agent_a.guardrail)}")
+    
+    # Option B: Using GuardrailConfig
+    agent_b = Agent(
+        instructions="Be helpful and safe",
+        guardrails=GuardrailConfig(
+            llm_validator="Ensure the response is helpful, accurate, and safe",
+            max_retries=3,
+        ),
+    )
+    print(f"Agent B: guardrail = {agent_b.guardrail}")
+    print(f"  max_retries = {agent_b.max_guardrail_retries}")
+
+
+def example_planning():
+    """Example 4: Planning mode"""
+    print("\n=== Example 4: Planning Mode ===")
+    
+    agent = Agent(
+        instructions="You are a project planner",
+        planning=PlanningConfig(
+            reasoning=True,
+            read_only=True,  # Plan mode - only read operations
+        ),
+    )
+    print(f"Agent created with planning={agent.planning}")
+    print(f"  plan_mode={agent.plan_mode}")
+    print(f"  planning_reasoning={agent.planning_reasoning}")
+
+
+def example_backward_compatible():
+    """Example 5: Using new consolidated params"""
+    print("\n=== Example 5: Consolidated Params ===")
+    
+    # New consolidated params
+    agent = Agent(
+        instructions="Test agent",
+        reflection=True,
+        web=WebConfig(search=True, fetch=False),
+    )
+    print(f"Consolidated params work: self_reflect={agent.self_reflect}")
+    print(f"  web_search={agent.web_search}, web_fetch={agent.web_fetch}")
+
+
+def example_real_chat():
+    """Example 6: Real chat with new API"""
+    print("\n=== Example 6: Real Chat ===")
+    
+    agent = Agent(
+        instructions="You are a helpful math tutor. Be concise.",
+        reflection=False,
+        web=False,
+        verbose=False,
+    )
+    
+    response = agent.start("What is the square root of 144?")
+    print(f"Response: {response}")
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("PraisonAI Agent-Centric API Examples")
+    print("=" * 60)
+    
+    example_simple_enable()
+    example_with_config()
+    example_guardrails()
+    example_planning()
+    example_backward_compatible()
+    example_real_chat()
+    
+    print("\n" + "=" * 60)
+    print("All examples completed!")
+    print("=" * 60)
@@ -128,6 +128,78 @@ def main():
    budget_dict = budgeter.to_dict()
    print(f"Budget as dict: {budget_dict}")
    
+    # Example 6: Overflow handling strategies overview
+    print("\n6. Overflow Handling Strategies")
+    print("-" * 40)
+    strategies = [
+        ("truncate", "Remove oldest messages first", "Fast, simple", "Loses early context"),
+        ("sliding_window", "Keep N most recent messages", "Preserves recent", "Loses early context"),
+        ("prune_tools", "Truncate old tool outputs", "Keeps messages", "May lose tool details"),
+        ("summarize", "Replace old messages with summary", "Preserves meaning", "Slower, uses API"),
+        ("smart", "Combine strategies intelligently", "Best balance", "More complex"),
+    ]
+    print(f"{'Strategy':<15} | {'Description':<30} | {'Pros':<18} | {'Cons'}")
+    print("-" * 90)
+    for name, desc, pros, cons in strategies:
+        print(f"{name:<15} | {desc:<30} | {pros:<18} | {cons}")
+    
+    # Example 7: Simulated overflow playbook
+    print("\n7. Overflow Playbook (Thresholds → Actions)")
+    print("-" * 40)
+    playbook = [
+        (0.70, "INFO", "Monitor usage, no action needed"),
+        (0.80, "NOTICE", "Consider optimization soon"),
+        (0.90, "WARNING", "Trigger auto-compact if enabled"),
+        (0.95, "CRITICAL", "Aggressive optimization required"),
+        (1.00, "OVERFLOW", "Immediate truncation to prevent API error"),
+    ]
+    print(f"{'Usage':<8} | {'Level':<10} | {'Action'}")
+    print("-" * 60)
+    for threshold, level, action in playbook:
+        print(f"{threshold:>6.0%}   | {level:<10} | {action}")
+    
+    # Example 8: Strategy comparison table
+    print("\n8. Strategy Comparison")
+    print("-" * 40)
+    print("Strategy         | Preserves      | Loses          | Best For")
+    print("-" * 70)
+    print("truncate         | Recent msgs    | Old msgs       | Simple chats")
+    print("sliding_window   | Last N msgs    | Earlier msgs   | Long conversations")
+    print("prune_tools      | All messages   | Tool details   | Tool-heavy agents")
+    print("summarize        | Context meaning| Exact wording  | Important history")
+    print("smart            | Balanced       | Minimal        | Production use")
+    
+    # Example 9: Default strategy for interactive mode
+    print("\n9. Default Strategy: Interactive Mode")
+    print("-" * 40)
+    print("Interactive mode default: context=False (zero overhead)")
+    print("To enable context management in interactive mode:")
+    print("  praisonai chat --context  # Enable with defaults")
+    print("  Or in code:")
+    print("    agent = Agent(instructions='...', context=True)")
+    print("")
+    print("When enabled, defaults are:")
+    print("  - auto_compact: True")
+    print("  - compact_threshold: 0.8 (80% usage)")
+    print("  - strategy: smart")
+    print("  - output_reserve: model-specific (8K-16K)")
+    
+    # Example 10: Default strategy for auto-agents mode
+    print("\n10. Default Strategy: Auto-Agents Mode")
+    print("-" * 40)
+    print("Auto-agents mode default: context=False (zero overhead)")
+    print("To enable for multi-agent workflows:")
+    print("    from praisonaiagents import Agents")
+    print("    agents = Agents(agents=[...], context=True)")
+    print("")
+    print("Recommended config for long tasks:")
+    print("    context=ManagerConfig(")
+    print("        auto_compact=True,")
+    print("        compact_threshold=0.7,  # Earlier trigger for safety")
+    print("        strategy='smart',")
+    print("        output_reserve=16384,   # Larger reserve for complex outputs")
+    print("    )")
+    
    print("\n" + "=" * 60)
    print("✓ Context budgeting examples complete!")
    print("=" * 60)
@@ -2,14 +2,14 @@
 """
 Multi-Agent Context Isolation Example

-Demonstrates how to use multi-agent context with PraisonAIAgents via context= param,
+Demonstrates how to use multi-agent context with Agents via context= param,
 and also shows low-level MultiAgentLedger usage for advanced scenarios.

 Agent-Centric Quick Start:
-    from praisonaiagents import Agent, PraisonAIAgents
+    from praisonaiagents import Agent, Agents
    from praisonaiagents.context import ManagerConfig
    
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[agent1, agent2],
        context=ManagerConfig(policy="isolated"),  # or "shared"
    )
@@ -17,7 +17,7 @@ Agent-Centric Quick Start:

 import os
 import tempfile
-from praisonaiagents import Agent, PraisonAIAgents
+from praisonaiagents import Agent, Agents
 from praisonaiagents.context import (
    ManagerConfig,
    MultiAgentLedger,
@@ -49,7 +49,7 @@ def agent_centric_example():
    # Multi-agent setup with isolated context policy
    # (each agent maintains its own context)
    print("Created agents with context=True")
-    print("Use PraisonAIAgents(agents=[...], context=ManagerConfig(policy='isolated'))")
+    print("Use Agents(agents=[...], context=ManagerConfig(policy='isolated'))")
    print("for multi-agent context isolation")


@@ -13,10 +13,10 @@ from praisonaiagents import Agent
 from praisonaiagents.context import (
    ContextBudgeter,
    ContextLedgerManager,
-    ContextMonitor,
    get_optimizer,
    OptimizerStrategy,
    estimate_messages_tokens,
+    format_percent,
 )


@@ -37,7 +37,7 @@ def main():
    
    # Get budget info
    budget = budgeter.allocate()
-    print(f"\nModel: gpt-4o-mini")
+    print("\nModel: gpt-4o-mini")
    print(f"Context budget: {budget.usable:,} tokens")
    print(f"Output reserve: {budget.output_reserve:,} tokens")
    
@@ -76,7 +76,7 @@ def main():
        current_tokens = ledger.get_total()
        utilization = budgeter.get_utilization(current_tokens)
        
-        print(f"[Context: {current_tokens} tokens, {utilization:.1%} used]")
+        print(f"[Context: {current_tokens} tokens, {format_percent(utilization)} used]")
        
        # Check if optimization needed
        if utilization > 0.8:
@@ -101,7 +101,7 @@ def main():
    final_utilization = budgeter.get_utilization(final_tokens)
    
    print(f"Total tokens used: {final_tokens:,}")
-    print(f"Context utilization: {final_utilization:.1%}")
+    print(f"Context utilization: {format_percent(final_utilization)}")
    print(f"Messages in conversation: {len(conversation)}")
    print(f"Remaining capacity: {budgeter.get_remaining(final_tokens):,} tokens")
    
@@ -156,7 +156,7 @@ def example_5_multi_agent():
    print("Example 5: Multi-Agent Context Management")
    print("=" * 60)
    
-    from praisonaiagents import PraisonAIAgents, Task
+    from praisonaiagents import Agents, Task
    
    config = ManagerConfig(
        auto_compact=True,
@@ -181,7 +181,7 @@ def example_5_multi_agent():
    task2 = Task(description="Write a summary", agent=writer)
    
    # Create multi-agent system with context management
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[researcher, writer],
        tasks=[task1, task2],
        context=config,  # Shared context config
@@ -4,7 +4,7 @@ Fixed example demonstrating proper guardrail usage with PraisonAI Agents.
 This addresses the issues reported in issue #875.
 """

-from praisonaiagents import Agent, Task, GuardrailResult, PraisonAIAgents, TaskOutput
+from praisonaiagents import Agent, Task, GuardrailResult, Agents, TaskOutput
 from typing import Tuple, Any
 import trafilatura

@@ -67,7 +67,7 @@ agent = Agent(
    goal="Summarize web content concisely",
    instructions="You are a helpful assistant that summarizes web content",
    llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    self_reflect=False,
+    reflection=False,
    verbose=True,
    tools=[get_url_context]  # FIX: tools must be a list, not a single function
 )
@@ -77,7 +77,7 @@ task_with_guardrailresult = Task(
    name="summarise article with GuardrailResult",
    description="get the context of this url: https://blog.google/technology/ai/dolphingemma/ and produce a summary below 500 characters",
    agent=agent,
-    guardrail=validate_length_guardrailresult,  # Using GuardrailResult
+    guardrails=validate_length_guardrailresult,  # Using GuardrailResult
    expected_output="summary of the article below 500 characters",
    max_retries=3  # Will retry up to 3 times if guardrail fails
 )
@@ -87,7 +87,7 @@ task_with_tuple = Task(
    name="summarise article with tuple",
    description="get the context of this url: https://blog.google/technology/ai/dolphingemma/ and produce a summary below 500 characters",
    agent=agent,
-    guardrail=validate_length_tuple,  # Using Tuple[bool, Any]
+    guardrails=validate_length_tuple,  # Using Tuple[bool, Any]
    expected_output="summary of the article below 500 characters",
    max_retries=3
 )
@@ -97,13 +97,13 @@ task_with_llm_guardrail = Task(
    name="summarise with LLM guardrail",
    description="get the context of this url: https://blog.google/technology/ai/dolphingemma/ and produce a summary",
    agent=agent,
-    guardrail="Ensure the summary is professional, factual, and between 100-500 characters",
+    guardrails="Ensure the summary is professional, factual, and between 100-500 characters",
    expected_output="professional summary of the article"
 )

 # Run with GuardrailResult example
 print("=== Running with GuardrailResult guardrail ===")
-agents_gr = PraisonAIAgents(
+agents_gr = Agents(
    agents=[agent],
    tasks=[task_with_guardrailresult]
 )
@@ -113,7 +113,7 @@ agents_gr = PraisonAIAgents(

 # Run with Tuple example
 print("\n=== Running with Tuple[bool, Any] guardrail ===")
-agents_tuple = PraisonAIAgents(
+agents_tuple = Agents(
    agents=[agent],
    tasks=[task_with_tuple]
 )
@@ -123,7 +123,7 @@ agents_tuple = PraisonAIAgents(

 # Run with LLM guardrail example
 print("\n=== Running with LLM-based guardrail ===")
-agents_llm = PraisonAIAgents(
+agents_llm = Agents(
    agents=[agent],
    tasks=[task_with_llm_guardrail]
 )
@@ -14,7 +14,7 @@ Usage:

 from praisonai_tools.observability import obs
 from praisonai_tools.observability.base import SpanKind
-from praisonaiagents import Agent, PraisonAIAgents
+from praisonaiagents import Agent, Agents

 # Initialize observability
 obs.init()
@@ -13,7 +13,7 @@ Features demonstrated:

 import sys
 sys.path.insert(0, '/Users/praison/praisonai-package/src/praisonai-agents')
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents


 def get_weather(city: str) -> str:
@@ -106,7 +106,7 @@ def example_multi_agent():
        agent=writer
    )
    
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[researcher, writer],
        tasks=[task1, task2],
        process="sequential",
@@ -5,7 +5,7 @@ This example demonstrates hierarchical agent patterns using PraisonAI
 for structured task delegation and coordinated workflows.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import internet_search

 print("=== AutoAgents Hierarchical Generation Example ===\n")
@@ -58,7 +58,7 @@ execution_task = Task(
 )

 # Run hierarchical workflow
-agents_system = PraisonAIAgents(
+agents_system = Agents(
    agents=[manager_agent, research_agent],
    tasks=[planning_task, execution_task],
    process="sequential",
@@ -5,7 +5,7 @@ This example demonstrates a comprehensive code agent that can write, analyze, de
 It includes self-reflection for improving code quality and multiple specialized tools.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import CodeInterpreter

 # Initialize code execution tool
@@ -32,9 +32,9 @@ code_writer = Agent(
    goal="Write clean, efficient, and well-documented Python code",
    backstory="You are a senior Python developer with 10+ years of experience in writing production-quality code.",
    instructions="Write Python code that follows PEP 8 standards, includes proper error handling, and has clear documentation.",
-    self_reflect=True,
-    min_reflect=2,
-    max_reflect=5
+    reflection=True,
+    
+    
 )

 code_reviewer = Agent(
@@ -84,7 +84,7 @@ def demonstrate_code_agent():
    )
    
    # Create workflow
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[code_writer, code_reviewer, code_executor],
        tasks=[write_task, review_task, execute_task],
        process="sequential",
@@ -108,9 +108,9 @@ general_code_agent = Agent(
 4. Refactor code for better performance
 5. Add tests and documentation""",
    tools=[code_interpreter, analyze_code_complexity],
-    self_reflect=True,
-    min_reflect=1,
-    max_reflect=3
+    reflection=True,
+    
+    
 )

 if __name__ == "__main__":
@@ -4,7 +4,7 @@ from praisonaiagents.tools import duckduckgo
 agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
 agent.start("I want to go London next week, find me a good hotel and flight")

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Create Image Analysis Agent
 image_agent = Agent(
@@ -14,7 +14,7 @@ image_agent = Agent(
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
    llm="gpt-5-nano",
-    self_reflect=False
+    reflection=False
 )

 # 1. Task with Image URL
@@ -35,8 +35,8 @@ task2 = Task(
    images=["image.jpg"] 
 )

-# Create PraisonAIAgents instance
-agents = PraisonAIAgents(
+# Create Agents instance
+agents = Agents(
    agents=[image_agent],
    tasks=[task1, task2],
    process="sequential",
@@ -4,7 +4,7 @@ from praisonaiagents.tools import duckduckgo
 agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
 agent.start("I want to go London next week, find me a good hotel and flight")

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Create Image Analysis Agent
 image_agent = Agent(
@@ -14,7 +14,7 @@ image_agent = Agent(
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
    llm="gpt-5-nano",
-    self_reflect=False
+    reflection=False
 )

 # 1. Task with Image URL
@@ -35,8 +35,8 @@ task2 = Task(
    images=["image.jpg"] 
 )

-# Create PraisonAIAgents instance
-agents = PraisonAIAgents(
+# Create Agents instance
+agents = Agents(
    agents=[image_agent],
    tasks=[task1, task2],
    process="sequential",
@@ -5,7 +5,7 @@ This example demonstrates a mathematical problem-solving agent that can handle v
 including calculations, equation solving, and step-by-step explanations.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from typing import Dict, Any
 import math
 import re
@@ -65,9 +65,9 @@ calculation_agent = Agent(
    backstory="You are an expert mathematician who can handle complex calculations with precision.",
    instructions="Perform the requested calculations step by step, showing your work clearly.",
    tools=[basic_calculator, solve_quadratic, calculate_statistics],
-    self_reflect=True,
-    min_reflect=1,
-    max_reflect=2
+    reflection=True,
+    
+    
 )

 problem_solver_agent = Agent(
@@ -81,9 +81,9 @@ problem_solver_agent = Agent(
 3. Determine what needs to be found
 4. Show each step of the solution
 5. Verify the answer makes sense""",
-    self_reflect=True,
-    min_reflect=2,
-    max_reflect=4
+    reflection=True,
+    
+    
 )

 math_tutor_agent = Agent(
@@ -123,7 +123,7 @@ def solve_math_problem_workflow():
        context=[solve_task]
    )
    
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[problem_solver_agent, calculation_agent, math_tutor_agent],
        tasks=[understand_task, solve_task, verify_task],
        process="sequential",
@@ -148,9 +148,9 @@ math_assistant = Agent(

 Always show your work step by step.""",
    tools=[basic_calculator, solve_quadratic, calculate_statistics],
-    self_reflect=True,
-    min_reflect=1,
-    max_reflect=3
+    reflection=True,
+    
+    
 )

 if __name__ == "__main__":
@@ -6,7 +6,7 @@ agent-based selection for cost optimization and performance.
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.agent import RouterAgent
 from praisonaiagents.llm.model_router import ModelRouter, ModelProfile, TaskComplexity

@@ -49,7 +49,7 @@ def example_auto_routing():
    )
    
    # Run the tasks
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[research_agent],
        tasks=[simple_task, moderate_task, complex_task],
        process="sequential",
@@ -118,7 +118,7 @@ def example_cost_optimized_workflow():
    )
    
    # Run workflow
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[analyzer, writer],
        tasks=[analysis_task, writing_task],
        process="sequential",
@@ -249,7 +249,7 @@ def example_custom_routing():
    ]
    
    # Run tasks
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[coder],
        tasks=tasks,
        process="sequential",
@@ -1,10 +1,11 @@
-from praisonaiagents import Agent, Tools
+from praisonaiagents import Agent, Tools, ReflectionConfig
 from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
 from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
 from praisonaiagents.tools import duckduckgo # Web Search Tool

 agent = Agent(
-    instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10, 
+    instructions="You are a Programming Agent",
+    reflection=ReflectionConfig(min_iterations=5, max_iterations=10),
    tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
 )
 agent.start(
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Create Video Analysis Agent
 video_agent = Agent(
@@ -8,7 +8,7 @@ video_agent = Agent(
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
    llm="gpt-5-nano",
-    self_reflect=False
+    reflection=False
 )

 # Task with Video File
@@ -24,8 +24,8 @@ task1 = Task(
    images=["video.mp4"]  
 )

-# Create PraisonAIAgents instance
-agents = PraisonAIAgents(
+# Create Agents instance
+agents = Agents(
    agents=[video_agent],
    tasks=[task1],
    process="sequential",
@@ -1,12 +1,12 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language

 agent = Agent(
    instructions="You are a Wikipedia Agent", 
    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
-    self_reflect=True,
-    min_reflect=3,
-    max_reflect=5,
+    reflection=True,
+    
+    
 )
 agent.start(
    "What is the history of AI?"
@@ -32,7 +32,7 @@ import os
 from pathlib import Path

 # Import our market research system
-from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+from praisonaiagents import Agent, Task, Agents, Tools

 # Create FastAPI app
 app = FastAPI(
@@ -266,7 +266,7 @@ async def generate_market_research_report(job_id: str, config: MarketResearchCon
        job_status[job_id]["message"] = "Setting up research workflow..."
        
        # Create workflow
-        workflow = PraisonAIAgents(
+        workflow = Agents(
            agents=list(agents.values()),
            tasks=tasks,
            process="sequential",
@@ -16,7 +16,7 @@ Environment Variables:

 import cv2
 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents


 def capture_and_analyze():
@@ -58,7 +58,7 @@ def capture_and_analyze():
            )
            
            # Run analysis
-            agents = PraisonAIAgents(
+            agents = Agents(
                agents=[vision_agent],
                tasks=[task],
                process="sequential",
@@ -18,7 +18,7 @@ import cv2
 import time
 import os
 from datetime import datetime
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents


 class ContinuousCameraMonitor:
@@ -71,7 +71,7 @@ class ContinuousCameraMonitor:
            images=[image_path]
        )
        
-        agents = PraisonAIAgents(
+        agents = Agents(
            agents=[self.vision_agent],
            tasks=[task],
            process="sequential",
@@ -17,7 +17,7 @@ Environment Variables:
 import cv2
 import os
 from datetime import datetime
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents


 def capture_camera_frame(camera_id=0):
@@ -114,7 +114,7 @@ def analyze_with_multiple_agents():
    )
    
    # Run multi-agent analysis
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[security_agent, object_detector, scene_analyst],
        tasks=[security_task, object_task, scene_task],
        process="parallel",  # Run all analyses in parallel
@@ -18,7 +18,7 @@ import cv2
 import os
 import time
 from datetime import datetime
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents


 class CameraVideoAnalyzer:
@@ -131,7 +131,7 @@ class CameraVideoAnalyzer:
            images=[video_path]  # PraisonAI supports video files in images parameter
        )
        
-        agents = PraisonAIAgents(
+        agents = Agents(
            agents=[self.video_agent],
            tasks=[task],
            process="sequential",
@@ -27,9 +27,9 @@ pdf_agent = Agent(
    instructions="Read the provided PDF document and answer questions about its content. Be specific and cite relevant sections when possible.",
    knowledge=["document.pdf"],  # Replace with your PDF file path
    knowledge_config=config,
-    self_reflect=True,
-    min_reflect=1,
-    max_reflect=3
+    reflection=True,
+    
+    
 )

 # Example usage
@@ -24,7 +24,7 @@ from pathlib import Path
 project_root = Path(__file__).parent.parent.parent.parent / "src" / "praisonai-agents"
 sys.path.insert(0, str(project_root))

-from praisonaiagents import Agent, Task, PraisonAIAgents, create_context_agent
+from praisonaiagents import Agent, Task, Agents, create_context_agent

 class ContextEngineeringWorkflow:
    """
@@ -312,7 +312,7 @@ class ContextEngineeringWorkflow:
        print("\n⚙️ Executing Context Engineering Workflow")
        print("-" * 50)
        
-        agents_workflow = PraisonAIAgents(
+        agents_workflow = Agents(
            agents=[
                self.product_manager,
                self.architect, 
@@ -11,7 +11,7 @@ Author: Generated for GitHub Issue #23
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import read_csv, write_csv

 def create_sample_urls_csv():
@@ -81,7 +81,7 @@ def method_1_simple_loop():
    )
    
    # Initialize the agents system
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[loop_agent],
        tasks=[loop_task],
        process="workflow",
@@ -145,7 +145,7 @@ def method_2_manual_csv():
    )
    
    # Initialize and run
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[csv_agent],
        tasks=[csv_task],
        process="sequential"
@@ -211,7 +211,7 @@ def method_3_url_processing():
    )
    
    # Initialize and run
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[url_agent],
        tasks=[url_task],
        process="sequential"
@@ -319,7 +319,7 @@ def method_4_advanced_processing():
    )
    
    # Initialize and run with sequential processing
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[validator_agent, processor_agent],
        tasks=[validation_task, processing_task],
        process="sequential"  # Validation must complete before processing
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import logging
 import os

@@ -37,7 +37,7 @@ knowledge_task = Task(
 )

 # Create and start the agents
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[knowledge_agent],
    tasks=[knowledge_task],
    process="sequential",
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Define the configuration for the Knowledge instance
 config = {
@@ -29,7 +29,7 @@ rag_task = Task(
 )

 # Build Agents
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[rag_agent],
    tasks=[rag_task],
    user_id="user1"
@@ -8,7 +8,7 @@ This example demonstrates how to extract and utilize reasoning patterns in Prais
 - Decision rationale extraction
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from typing import Dict, List, Tuple
 from pydantic import BaseModel
 import json
@@ -49,9 +49,9 @@ cot_agent = Agent(
 6. Assess your confidence level

 Always make your thinking process explicit and transparent.""",
-    self_reflect=True,
-    min_reflect=2,
-    max_reflect=3
+    reflection=True,
+    
+    
 )

 # Example 2: Reasoning Extractor Agent
@@ -100,7 +100,7 @@ def demonstrate_reasoning_extraction():
        context=[problem_task]
    )
    
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[cot_agent, reasoning_extractor],
        tasks=[problem_task, extract_task],
        process="sequential",
@@ -184,7 +184,7 @@ def advanced_reasoning_patterns():
        context=[deductive_task, inductive_task, abductive_task]
    )
    
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[cot_agent, reasoning_validator],
        tasks=[deductive_task, inductive_task, abductive_task, validation_task],
        process="sequential",
@@ -224,7 +224,7 @@ pattern_reasoning_agent = Agent(
 3. Counterfactual: Explore what-if scenarios
 4. Systems thinking: Consider interconnections
 5. Probabilistic: Assess likelihoods""",
-    self_reflect=True
+    reflection=True
 )

 # Example 8: Interactive Reasoning Extraction
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 agent = Agent(
    instructions="You are a loop agent that creating a loop of tasks.",
@@ -12,7 +12,7 @@ task = Task(
    input_file="tasks.csv"
 )

-agents = PraisonAIAgents(
+agents = Agents(
    agents=[agent],
    tasks=[task],
    process="workflow",
@@ -8,7 +8,7 @@ This example demonstrates various routing patterns in PraisonAI including:
 - Dynamic task selection
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from typing import Dict, Any
 import random

@@ -77,7 +77,7 @@ def conditional_routing_example():
        agent=processor_agent
    )
    
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[analyzer_agent, processor_agent],
        tasks=[analysis_task, premium_task, standard_task, basic_task],
        process="workflow",
@@ -110,7 +110,7 @@ def loop_pattern_example():
        agent=validator_agent
    )
    
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[processor_agent, validator_agent],
        tasks=[process_task, finalize_task],
        process="workflow",
@@ -185,7 +185,7 @@ def decision_tree_example():
    
    all_tasks = [categorize_task, technical_task, business_task] + terminal_tasks
    
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[analyzer_agent, processor_agent],
        tasks=all_tasks,
        process="workflow",
@@ -208,7 +208,7 @@ dynamic_router = Agent(
 5. Current system load

 Provide clear routing decisions with reasoning.""",
-    self_reflect=True
+    reflection=True
 )

 def dynamic_routing_example():
@@ -272,7 +272,7 @@ def dynamic_routing_example():
        agent=processor_agent
    )
    
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[dynamic_router, processor_agent],
        tasks=[route_task, escalate_task, automate_task, review_task, standard_task],
        process="workflow",
@@ -8,7 +8,7 @@ This example demonstrates the self-reflection capabilities in PraisonAI agents:
 - Impact on output quality
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from typing import Dict, Any
 import time

@@ -24,9 +24,9 @@ basic_reflection_agent = Agent(
 3. Identify areas for improvement
 4. Revise and enhance
 5. Repeat until satisfied""",
-    self_reflect=True,  # Enable self-reflection
-    min_reflect=1,      # Minimum 1 reflection iteration
-    max_reflect=3       # Maximum 3 reflection iterations
+    reflection=True,  # Enable self-reflection
+          # Minimum 1 reflection iteration
+           # Maximum 3 reflection iterations
 )

 # Example 2: Advanced Reflection with Different Strategies
@@ -41,9 +41,9 @@ quality_focused_agent = Agent(
 - Clarity of presentation
 - Logical flow
 - Supporting evidence""",
-    self_reflect=True,
-    min_reflect=3,  # Higher minimum for quality
-    max_reflect=5   # Allow more iterations
+    reflection=True,
+      # Higher minimum for quality
+       # Allow more iterations
 )

 efficiency_balanced_agent = Agent(
@@ -52,9 +52,9 @@ efficiency_balanced_agent = Agent(
    goal="Achieve good quality with reasonable reflection time",
    backstory="You balance quality with efficiency, knowing when good enough is sufficient.",
    instructions="Create good content with 1-2 rounds of improvement.",
-    self_reflect=True,
-    min_reflect=1,
-    max_reflect=2  # Limit iterations for efficiency
+    reflection=True,
+    
+      # Limit iterations for efficiency
 )

 # Example 3: No Reflection for Comparison
@@ -64,7 +64,7 @@ no_reflection_agent = Agent(
    goal="Provide immediate responses without reflection",
    backstory="You provide quick, first-draft responses.",
    instructions="Respond immediately with your first thought.",
-    self_reflect=False  # Disable reflection
+    reflection=False  # Disable reflection
 )

 # Demonstration function to show reflection in action
@@ -113,9 +113,9 @@ def reflection_workflow_example():
        role="Initial content creator",
        goal="Create first draft with self-improvement",
        backstory="You create initial drafts and refine them through reflection.",
-        self_reflect=True,
-        min_reflect=2,
-        max_reflect=3
+        reflection=True,
+        
+        
    )
    
    # Reviewer without reflection (provides feedback)
@@ -124,7 +124,7 @@ def reflection_workflow_example():
        role="Content reviewer",
        goal="Provide constructive feedback",
        backstory="You review content and provide specific improvement suggestions.",
-        self_reflect=False  # No reflection needed for reviewing
+        reflection=False  # No reflection needed for reviewing
    )
    
    # Final polisher with reflection
@@ -133,9 +133,9 @@ def reflection_workflow_example():
        role="Final content polisher",
        goal="Perfect the content based on feedback",
        backstory="You take feedback and create the final, polished version.",
-        self_reflect=True,
-        min_reflect=1,
-        max_reflect=2
+        reflection=True,
+        
+        
    )
    
    # Workflow tasks
@@ -162,7 +162,7 @@ def reflection_workflow_example():
        context=[create_task, review_task]
    )
    
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[creator_agent, reviewer_agent, polisher_agent],
        tasks=[create_task, review_task, polish_task],
        process="sequential",
@@ -202,9 +202,9 @@ custom_reflection_agent = Agent(
    backstory="You adapt your reflection process based on content quality.",
    instructions="""Create content and reflect on it.
 Continue improving until quality standards are met or maximum iterations reached.""",
-    self_reflect=True,
-    min_reflect=1,
-    max_reflect=5
+    reflection=True,
+    
+    
 )

 # Example 6: Reflection Impact Analysis
@@ -5,7 +5,7 @@ This example demonstrates self-reflection capabilities using PraisonAI's
 built-in reflection features for iterative improvement and quality optimization.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import internet_search

 print("=== Self-Reflection Optimization Patterns Example ===\n")
@@ -17,10 +17,10 @@ reflection_agent = Agent(
    goal="Demonstrate self-reflection and iterative improvement patterns",
    backstory="Expert researcher with strong self-reflection and continuous improvement capabilities",
    tools=[internet_search],
-    self_reflect=True,
-    min_reflect=2,
-    max_reflect=4,
-    reflect_llm="gpt-5-nano",
+    reflection=True,
+    
+    
+    
    verbose=True
 )

@@ -9,7 +9,7 @@ This is the simplest possible implementation for processing URLs from a CSV file
 Author: Generated for GitHub Issue #23
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import write_csv

 def create_url_csv():
@@ -59,7 +59,7 @@ def main():
    )
    
    # Step 4: Run the agents
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[url_agent],
        tasks=[url_task],
        process="workflow",
@@ -4,7 +4,7 @@ Example: Using Latency Tracking Tool with PraisonAI
 This example shows how to track latency without modifying core files.
 """

-from praisonaiagents import Agent, PraisonAIAgents
+from praisonaiagents import Agent, Agents
 from latency_tracker_tool import (
    latency_tracking_tool, 
    create_tracked_agent,
@@ -5,7 +5,7 @@ This shows how to add latency tracking to an MCP server
 without modifying PraisonAI core files.
 """

-from praisonaiagents import Agent, PraisonAIAgents
+from praisonaiagents import Agent, Agents
 from hosted_server import HostedMCPServer  # Import from local file
 from latency_tracker_tool import tracker, get_latency_metrics
 import json
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import cot_save, cot_upload_to_huggingface
 from pydantic import BaseModel
 import os
@@ -131,7 +131,7 @@ upload_to_huggingface_task = Task(
 )

 # Initialize workflow
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
    tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
    process="workflow",
@@ -0,0 +1,192 @@
+"""
+Escalation Pipeline Example.
+
+Demonstrates the progressive escalation from direct response to autonomous mode.
+"""
+
+import asyncio
+from praisonaiagents.escalation import (
+    EscalationPipeline,
+    EscalationStage,
+    EscalationConfig,
+    EscalationTrigger,
+    DoomLoopDetector,
+    ObservabilityHooks,
+    EventType,
+)
+
+
+def demo_signal_detection():
+    """Demonstrate signal detection from prompts."""
+    print("\n" + "="*60)
+    print("SIGNAL DETECTION DEMO")
+    print("="*60)
+    
+    trigger = EscalationTrigger()
+    
+    # Test various prompts
+    test_prompts = [
+        ("What is Python?", "Simple question"),
+        ("Read the file src/main.py", "File reference"),
+        ("Refactor the authentication module and add tests", "Complex task"),
+        ("First, analyze the code. Then, fix the bug. Finally, test it.", "Multi-step"),
+    ]
+    
+    for prompt, description in test_prompts:
+        signals = trigger.analyze(prompt)
+        stage = trigger.recommend_stage(signals)
+        
+        print(f"\n📝 Prompt: {prompt[:50]}...")
+        print(f"   Description: {description}")
+        print(f"   Signals: {[s.value for s in signals]}")
+        print(f"   Recommended Stage: {stage.name}")
+
+
+def demo_doom_loop_detection():
+    """Demonstrate doom loop detection."""
+    print("\n" + "="*60)
+    print("DOOM LOOP DETECTION DEMO")
+    print("="*60)
+    
+    detector = DoomLoopDetector()
+    detector.start_session()
+    
+    # Simulate repeated identical actions
+    print("\n🔄 Simulating repeated identical actions...")
+    for i in range(4):
+        detector.record_action(
+            action_type="read_file",
+            args={"path": "config.py"},
+            result="same content",
+            success=True,
+        )
+        
+        if detector.is_doom_loop():
+            loop_type = detector.get_loop_type()
+            recovery = detector.get_recovery_action()
+            print(f"   ⚠️  Doom loop detected after {i+1} actions!")
+            print(f"   Type: {loop_type.value}")
+            print(f"   Recovery: {recovery.value}")
+            break
+        else:
+            print(f"   Action {i+1}: No loop detected")
+    
+    # Show stats
+    stats = detector.get_stats()
+    print(f"\n📊 Stats: {stats}")
+
+
+def demo_observability():
+    """Demonstrate observability hooks."""
+    print("\n" + "="*60)
+    print("OBSERVABILITY DEMO")
+    print("="*60)
+    
+    hooks = ObservabilityHooks()
+    
+    # Register event handlers
+    def on_stage_change(event):
+        print(f"   📍 Stage changed: {event.data}")
+    
+    def on_tool_call(event):
+        print(f"   🔧 Tool called: {event.data}")
+    
+    hooks.on(EventType.STAGE_ESCALATE, on_stage_change)
+    hooks.on(EventType.TOOL_CALL_END, on_tool_call)
+    
+    # Simulate events
+    print("\n🎯 Simulating execution events...")
+    hooks.start_execution("session_123")
+    
+    hooks.set_stage(EscalationStage.DIRECT)
+    hooks.emit(EventType.STEP_START, {"step": 1})
+    hooks.emit(EventType.TOOL_CALL_END, {"tool": "read_file", "path": "main.py"})
+    hooks.emit(EventType.STEP_END, {"step": 1})
+    
+    hooks.emit(EventType.STAGE_ESCALATE, {"from": "DIRECT", "to": "HEURISTIC"})
+    hooks.set_stage(EscalationStage.HEURISTIC)
+    
+    hooks.end_execution(1500.0)
+    
+    # Show summary
+    summary = hooks.get_summary()
+    print(f"\n📊 Summary: {summary}")
+
+
+def demo_stage_descriptions():
+    """Show descriptions of each escalation stage."""
+    print("\n" + "="*60)
+    print("ESCALATION STAGES")
+    print("="*60)
+    
+    trigger = EscalationTrigger()
+    
+    stages = [
+        (EscalationStage.DIRECT, "Simple questions, no tools needed"),
+        (EscalationStage.HEURISTIC, "File references, code blocks"),
+        (EscalationStage.PLANNED, "Edit intent, test/build tasks"),
+        (EscalationStage.AUTONOMOUS, "Multi-step, refactoring tasks"),
+    ]
+    
+    for stage, example in stages:
+        desc = trigger.get_stage_description(stage)
+        print(f"\n🎯 Stage {stage.value}: {stage.name}")
+        print(f"   Description: {desc}")
+        print(f"   Example use: {example}")
+
+
+async def demo_pipeline_analysis():
+    """Demonstrate pipeline analysis."""
+    print("\n" + "="*60)
+    print("PIPELINE ANALYSIS DEMO")
+    print("="*60)
+    
+    config = EscalationConfig(
+        max_steps=20,
+        max_time_seconds=60,
+        auto_escalate=True,
+    )
+    
+    pipeline = EscalationPipeline(config=config)
+    
+    prompts = [
+        "What is a Python decorator?",
+        "Read the file src/auth.py and explain it",
+        "Refactor the user authentication to use JWT tokens",
+    ]
+    
+    for prompt in prompts:
+        stage = pipeline.analyze(prompt)
+        print(f"\n📝 Prompt: {prompt[:50]}...")
+        print(f"   Recommended Stage: {stage.name}")
+
+
+def main():
+    """Run all demos."""
+    print("\n" + "="*60)
+    print("PRAISONAI ESCALATION PIPELINE DEMO")
+    print("="*60)
+    print("\nThis demo shows the progressive escalation system that")
+    print("automatically adjusts execution complexity based on task signals.")
+    
+    # Run demos
+    demo_stage_descriptions()
+    demo_signal_detection()
+    demo_doom_loop_detection()
+    demo_observability()
+    asyncio.run(demo_pipeline_analysis())
+    
+    print("\n" + "="*60)
+    print("DEMO COMPLETE")
+    print("="*60)
+    print("\nKey takeaways:")
+    print("1. Simple tasks stay at DIRECT stage (no tools)")
+    print("2. File references trigger HEURISTIC stage")
+    print("3. Edit/test tasks trigger PLANNED stage")
+    print("4. Multi-step/refactor tasks trigger AUTONOMOUS stage")
+    print("5. Doom loop detection prevents infinite loops")
+    print("6. Observability hooks enable monitoring")
+
+
+if __name__ == "__main__":
+    main()
@@ -5,7 +5,7 @@ This example demonstrates callback systems using PraisonAI's built-in
 callback functionality for monitoring agent interactions and tool usage.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.display_callback import register_display_callback
 from praisonaiagents.tools import internet_search

@@ -1,7 +1,7 @@
 import asyncio
 import time
 from typing import List, Dict
-from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from praisonaiagents import Agent, Task, Agents, TaskOutput
 from praisonaiagents.main import (
    display_error,
    display_interaction,
@@ -98,7 +98,7 @@ sync_agent = Agent(
    goal="Perform synchronous searches and return structured results",
    backstory="Expert in sync operations and data organization",
    tools=[sync_search_tool],
-    self_reflect=False,
+    reflection=False,
    verbose=True,
    markdown=True
 )
@@ -109,7 +109,7 @@ async_agent = Agent(
    goal="Perform asynchronous searches and return structured results",
    backstory="Expert in async operations and data organization",
    tools=[async_search_tool],
-    self_reflect=False,
+    reflection=False,
    verbose=True,
    markdown=True
 )
@@ -160,7 +160,7 @@ workflow_async_task = Task(
 def run_sync_example():
    """Run synchronous example"""
    display_instruction("\nRunning Synchronous Example...", console)
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[sync_agent],
        tasks=[sync_task],
        verbose=1,
@@ -172,7 +172,7 @@ def run_sync_example():
 async def run_async_example():
    """Run asynchronous example"""
    display_instruction("\nRunning Asynchronous Example...", console)
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[async_agent],
        tasks=[async_task],
        verbose=1,
@@ -184,7 +184,7 @@ async def run_async_example():
 async def run_mixed_example():
    """Run mixed sync/async example"""
    display_instruction("\nRunning Mixed Sync/Async Example...", console)
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[sync_agent, async_agent],
        tasks=[sync_task, async_task],
        verbose=1,
@@ -196,7 +196,7 @@ async def run_mixed_example():
 async def run_workflow_example():
    """Run workflow example with both sync and async tasks"""
    display_instruction("\nRunning Workflow Example...", console)
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[sync_agent, async_agent],
        tasks=[workflow_sync_task, workflow_async_task],
        verbose=1,
@@ -1,7 +1,7 @@
 import asyncio
 import time
 from typing import List, Dict
-from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from praisonaiagents import Agent, Task, Agents, TaskOutput
 from duckduckgo_search import DDGS
 from pydantic import BaseModel

@@ -53,7 +53,7 @@ async_agent = Agent(
    goal="Perform fast parallel searches with structured results",
    backstory="Expert in efficient data retrieval and parallel search operations",
    tools=[async_search_tool],
-    self_reflect=False,
+    reflection=False,
    verbose=True,
    markdown=True
 )
@@ -63,7 +63,7 @@ summary_agent = Agent(
    role="Research Synthesizer",
    goal="Create concise summaries from multiple search results",
    backstory="Expert in analyzing and synthesizing information from multiple sources",
-    self_reflect=True,
+    reflection=True,
    verbose=True,
    markdown=True
 )
@@ -114,8 +114,8 @@ async def run_parallel_tasks():
        context=parallel_tasks
    )
    
-    # Create a single PraisonAIAgents instance with both agents
-    agents = PraisonAIAgents(
+    # Create a single Agents instance with both agents
+    agents = Agents(
        agents=[async_agent, summary_agent],
        tasks=parallel_tasks + [summary_task],
        verbose=1,
@@ -1,7 +1,7 @@
 import asyncio
 import time
 from typing import List, Dict
-from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from praisonaiagents import Agent, Task, Agents, TaskOutput
 from duckduckgo_search import DDGS
 from pydantic import BaseModel

@@ -60,7 +60,7 @@ async_agent = Agent(
    goal="Perform fast and efficient asynchronous searches with structured results",
    backstory="Expert in parallel search operations and data retrieval",
    tools=[async_search_tool],
-    self_reflect=False,
+    reflection=False,
    verbose=True,
    markdown=True
 )
@@ -72,7 +72,7 @@ summary_agent = Agent(
    backstory="""Expert in analyzing and synthesizing information from multiple sources.
 Skilled at identifying patterns, trends, and connections between different topics.
 Specializes in creating clear, structured summaries that highlight key insights.""",
-    self_reflect=True,  # Enable self-reflection for better summary quality
+    reflection=True,  # Enable self-reflection for better summary quality
    verbose=True,
    markdown=True
 )
@@ -103,7 +103,7 @@ async_task = Task(
 async def run_single_task():
    """Run single async task"""
    print("\nRunning Single Async Task...")
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[async_agent],
        tasks=[async_task],
        verbose=1,
@@ -187,7 +187,7 @@ Present the summary in a clear, structured format with sections for findings, pa
    )
    
    # First run parallel search tasks
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[async_agent],
        tasks=parallel_tasks,  # Only run search tasks first
        verbose=1,
@@ -212,7 +212,7 @@ Present the summary in a clear, structured format with sections for findings, pa
    summary_task.context = completed_tasks
    
    # Run summarization task with summary agent
-    summary_agents = PraisonAIAgents(
+    summary_agents = Agents(
        agents=[summary_agent],  # Use summary agent for synthesis
        tasks=[summary_task],
        verbose=1,
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import time

 def get_environment_state():
@@ -86,7 +86,7 @@ feedback_task = Task(
 )

 # Create workflow manager
-workflow = PraisonAIAgents(
+workflow = Agents(
    agents=[llm_caller, action_agent, feedback_agent],
    tasks=[monitor_task, action_task, feedback_task],
    process="workflow",
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import json
 from e2b_code_interpreter import Sandbox

@@ -25,13 +25,13 @@ code_agent = Agent(
    name="code_agent",
    llm="gpt-5-nano",
    backstory="Expert in writing Python scripts",
-    self_reflect=False
+    reflection=False
 )
 execution_agent = Agent(
    name="execution_agent",
    llm="gpt-5-nano",
    backstory="Expert in executing Python scripts",
-    self_reflect=False,
+    reflection=False,
    tools=[code_interpreter]
 )

@@ -46,5 +46,5 @@ execution_agent_task = Task(
    agent=execution_agent
 )

-agents = PraisonAIAgents(agents=[code_agent, execution_agent], tasks=[code_agent_task, execution_agent_task])
+agents = Agents(agents=[code_agent, execution_agent], tasks=[code_agent_task, execution_agent_task])
 agents.start()
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Create generator and evaluator agents
 generator = Agent(
@@ -53,7 +53,7 @@ evaluate_task = Task(
 )

 # Create workflow manager
-workflow = PraisonAIAgents(
+workflow = Agents(
    agents=[generator, evaluator],
    tasks=[generate_task, evaluate_task],
    process="workflow",
@@ -1,7 +1,7 @@
 from praisonaiagents import (
    Agent, 
    Task, 
-    PraisonAIAgents, 
+    Agents, 
    error_logs, 
    register_display_callback,
    sync_display_callbacks,
@@ -129,9 +129,9 @@ def main():
        tools=[internet_search_tool],
        llm="gpt-5-nano",
        markdown=True,
-        reflect_llm="gpt-5-nano",
-        min_reflect=2,
-        max_reflect=4
+        
+        
+        
    )
    
    writer = Agent(
@@ -190,7 +190,7 @@ def main():
    )

    # Create and run agents manager
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[researcher, writer],
        tasks=[task1, task2, task3, task4],
        verbose=True,
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from typing import List, Dict, Union
 from duckduckgo_search import DDGS
 from langchain_community.tools import YouTubeSearchTool
@@ -112,7 +112,7 @@ data_agent = Agent(
    goal="Perform internet searches to collect relevant information.",
    backstory="Expert in finding and organizing internet data from multiple sources.",
    tools=[internet_search_tool, youtube_search_tool, wikipedia_search_tool],
-    self_reflect=False
+    reflection=False
 )

 # 3. Tasks
@@ -141,7 +141,7 @@ validate_task = Task(
 )

 # 4. Workflow
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[data_agent],
    tasks=[collect_task, validate_task],
    verbose=1,
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
+from praisonaiagents import Agent, Task, Agents, error_logs
 from duckduckgo_search import DDGS

 def my_callback(output):
@@ -41,9 +41,9 @@ researcher = Agent(
    tools=[internet_search_tool],
    llm="gpt-5-nano",
    markdown=True,
-    reflect_llm="gpt-5-nano",
-    min_reflect=2,
-    max_reflect=4
+    
+    
+    
 )
 writer = Agent(
    name="Writer",
@@ -99,7 +99,7 @@ task4 = Task(
 )

 # Create and run agents manager
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[researcher, writer],
    tasks=[task1, task2, task3, task4],
    verbose=False,
@@ -24,7 +24,7 @@ import sys
 # Add the local development path to use the current implementation
 sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', '..', 'src', 'praisonai-agents'))

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import python_tools, file_tools, shell_tools
 from praisonaiagents.main import register_approval_callback
 from praisonaiagents.approval import (
@@ -111,7 +111,7 @@ def main():
    ]
    
    # Create and run process
-    process = PraisonAIAgents(
+    process = Agents(
        agents=[agent],
        tasks=tasks,
        verbose=True
@@ -171,7 +171,7 @@ async def async_demo():
        expected_output="Async execution result"
    )
    
-    process = PraisonAIAgents(
+    process = Agents(
        agents=[agent],
        tasks=[task],
        verbose=True
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from langchain_community.tools import YouTubeSearchTool
 from langchain_community.utilities import WikipediaAPIWrapper

@@ -9,7 +9,7 @@ agent = Agent(
    goal="Search for information from multiple sources",
    backstory="I am an AI assistant that can search YouTube and Wikipedia.",
    tools=[YouTubeSearchTool, WikipediaAPIWrapper],
-    self_reflect=False
+    reflection=False
 )

 # Create tasks to demonstrate both tools
@@ -21,7 +21,7 @@ task = Task(
 )

 # Create and start the workflow
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[agent],
    tasks=[task],
    verbose=True
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import logging
 import os

@@ -25,7 +25,7 @@ def main():
    # else:
    #     logger.info("Creating new memory database")
    
-    # Create task config (without memory config since it's moved to PraisonAIAgents)
+    # Create task config (without memory config since it's moved to Agents)
    task_config = {}

    # Create agents with different roles
@@ -100,8 +100,8 @@ def main():
        agent=retriever
    )

-    # Initialize PraisonAIAgents with memory configuration
-    agents = PraisonAIAgents(
+    # Initialize Agents with memory configuration
+    agents = Agents(
        agents=[researcher, retriever],
        tasks=[store_task, verify_task, query_task, query_both_task],
        verbose=True,  # Use same verbose level as memory
@@ -114,7 +114,7 @@ def main():
        }
    )

-    # agents = PraisonAIAgents(
+    # agents = Agents(
    #     agents=[researcher, retriever],
    #     tasks=[store_task, verify_task, query_task, query_both_task],
    #     verbose=True,  # Use same verbose level as memory
@@ -1,4 +1,4 @@
-from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+from praisonaiagents.agents.agents import Agent, Task, Agents
 from praisonaiagents.tools import duckduckgo

 # Test facts
@@ -54,7 +54,7 @@ blog_task = Task(
    agent=blog_agent
 )

-agents = PraisonAIAgents(
+agents = Agents(
    agents=[fact_agent, research_agent, blog_agent],
    tasks=[fact_task, research_task, research_task2, research_task3, research_task4, blog_task],
    memory=True
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Create Vision Analysis Agent
 vision_agent = Agent(
@@ -8,7 +8,7 @@ vision_agent = Agent(
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
    llm="gpt-5-nano",
-    self_reflect=False
+    reflection=False
 )

 # 1. Task with Image URL
@@ -42,8 +42,8 @@ task3 = Task(
    images=["video.mp4"]  
 )

-# Create PraisonAIAgents instance
-agents = PraisonAIAgents(
+# Create Agents instance
+agents = Agents(
    agents=[vision_agent],
    tasks=[task1, task2, task3],
    process="sequential",
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import time

 def get_time_check():
@@ -93,7 +93,7 @@ synthesize_task = Task(
 )

 # Create workflow manager
-workflow = PraisonAIAgents(
+workflow = Agents(
    agents=[router, worker1, worker2, worker3, synthesizer],
    tasks=[router_task, worker1_task, worker2_task, worker3_task, synthesize_task],
    process="workflow",
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from datetime import datetime
 import asyncio

@@ -75,7 +75,7 @@ aggregate_task = Task(
 async def main():

    # Create workflow manager
-    workflow = PraisonAIAgents(
+    workflow = Agents(
        agents=[agent1, agent2, agent3, aggregator],
        tasks=[task1, task2, task3, aggregate_task],
        process="workflow"
@@ -1,6 +1,6 @@
 from praisonaiagents.agent import Agent
 from praisonaiagents.task import Task
-from praisonaiagents.agents import PraisonAIAgents
+from praisonaiagents.agents import Agents
 from typing import List, Dict
 import time

@@ -64,7 +64,7 @@ final_task = Task(
 )

 # Create the workflow manager
-workflow = PraisonAIAgents(
+workflow = Agents(
    agents=[agent1, agent2, agent3],
    tasks=[initial_task, analysis_task, final_task],
    process="workflow",  # Use workflow process type
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+from praisonaiagents import Agent, Task, Agents, Tools
 from pydantic import BaseModel

 class AnalysisReport(BaseModel):
@@ -15,7 +15,7 @@ researcher = Agent(
    verbose=True,
    llm="gpt-5-nano",
    tools=[Tools.internet_search],
-    self_reflect=False
+    reflection=False
 )

 # Create an analyst agent
@@ -26,7 +26,7 @@ analyst = Agent(
    backstory="Senior data analyst with expertise in pattern recognition",
    verbose=True,
    llm="gpt-5-nano",
-    self_reflect=False
+    reflection=False
 )

 # Define structured tasks
@@ -46,7 +46,7 @@ analysis_task = Task(
 )

 # Initialize and run agents
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[researcher, analyst],
    tasks=[research_task, analysis_task],
    process="sequential",
@@ -33,7 +33,7 @@ def get_structured_analysis(query: str, verbose: bool = True) -> AnalysisReport:
        goal="Analyze topics and provide structured reports",
        backstory="Expert at gathering information and providing structured analysis",
        verbose=verbose,
-        self_reflect=True,  # Enable self-reflection for better quality
+        reflection=True,  # Enable self-reflection for better quality
        markdown=True,
        llm="gpt-5-nano"
    )
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import os
 import requests
 from typing import Any, Dict, List, Optional
@@ -94,7 +94,7 @@ agent = Agent(
    goal="Search for information about 'AI Agents Framework'",
    backstory="I am an AI assistant that can search GitHub.",
    tools=[EXASearchTool],
-    self_reflect=False
+    reflection=False
 )

 # Create tasks to demonstrate both tools
@@ -106,7 +106,7 @@ task = Task(
 )

 # Create and start the workflow
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[agent],
    tasks=[task],
    verbose=True
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics

 # Create Wikipedia agent
@@ -8,7 +8,7 @@ news_agent = Agent(
    goal="Collect and analyze news articles from various sources.",
    backstory="Expert in news gathering and content analysis.",
    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
-    self_reflect=False
+    reflection=False
 )


@@ -22,7 +22,7 @@ news_task = Task(


 # Run agent
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[news_agent],
    tasks=[news_task],
    process="sequential"
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from typing import List, Dict
 from duckduckgo_search import DDGS

@@ -35,7 +35,7 @@ data_agent = Agent(
    goal="Perform internet searches to collect relevant information.",
    backstory="Expert in finding and organising internet data.",
    tools=[internet_search_tool],
-    self_reflect=False
+    reflection=False
 )

 # 3. Tasks
@@ -64,7 +64,7 @@ validate_task = Task(
 )

 # 4. AI Agents Workflow
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[data_agent],
    tasks=[collect_task, validate_task],
    verbose=1,
@@ -1,4 +1,4 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import random
 from typing import List, Dict, Union
 import json
@@ -158,7 +158,7 @@ data_agent = Agent(
    goal="Collect and validate data about individuals",
    backstory="Expert in gathering and validating demographic data",
    tools=[random_data_of_individuals],
-    self_reflect=False
+    reflection=False
 )

 process_agent = Agent(
@@ -167,7 +167,7 @@ process_agent = Agent(
    goal="Process and categorize individual data",
    backstory="Expert in data analysis and categorization",
    tools=[process_data_of_individuals],
-    self_reflect=False
+    reflection=False
 )

 # Modify tasks to use Pydantic models
@@ -224,8 +224,8 @@ process_task = Task(
    output_json=ProcessedPerson
 )

-# Create PraisonAIAgents instance with workflow process
-workflow = PraisonAIAgents(
+# Create Agents instance with workflow process
+workflow = Agents(
    agents=[data_agent, process_agent],
    tasks=[collect_task, validate_task, process_task],
    verbose=1,
@@ -12,7 +12,7 @@ Features demonstrated:
 - Quality assurance workflows
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.task import TaskOutput
 from typing import Tuple, Any
 import re
@@ -125,7 +125,7 @@ email_task = Task(
    description="Write a business proposal for a new software project, including contact information",
    expected_output="Professional business proposal with contact email addresses",
    agent=email_agent,
-    guardrail=email_format_guardrail,
+    guardrails=email_format_guardrail,
    max_retries=3
 )

@@ -135,7 +135,7 @@ content_task = Task(
    description="Write an analysis of cloud computing benefits for small businesses",
    expected_output="Comprehensive analysis of cloud computing benefits (minimum 50 words)",
    agent=content_agent,
-    guardrail=word_count_guardrail,
+    guardrails=word_count_guardrail,
    max_retries=3
 )

@@ -145,7 +145,7 @@ business_task = Task(
    description="Provide advice on handling difficult client relationships",
    expected_output="Professional advice maintaining appropriate business tone",
    agent=business_agent,
-    guardrail=professional_tone_guardrail,
+    guardrails=professional_tone_guardrail,
    max_retries=3
 )

@@ -155,7 +155,7 @@ research_task = Task(
    description="Analyze the impact of artificial intelligence on job markets",
    expected_output="Research-backed analysis with citations and evidence",
    agent=research_agent,
-    guardrail=factual_accuracy_guardrail,
+    guardrails=factual_accuracy_guardrail,
    max_retries=3
 )

@@ -176,12 +176,12 @@ llm_guardrail_task = Task(
    agent=llm_guardrail_agent,
    
    # LLM-based guardrail using natural language validation
-    guardrail="Validate that this marketing content is: 1) Factually accurate with no false claims, 2) Compliant with advertising standards (no misleading statements), 3) Engaging and professional in tone, 4) Includes a clear call-to-action. If any criteria are not met, explain what needs to be improved.",
+    guardrails="Validate that this marketing content is: 1) Factually accurate with no false claims, 2) Compliant with advertising standards (no misleading statements), 3) Engaging and professional in tone, 4) Includes a clear call-to-action. If any criteria are not met, explain what needs to be improved.",
    max_retries=2
 )

 # Execute tasks with guardrails
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[email_agent, content_agent, business_agent, research_agent, llm_guardrail_agent],
    tasks=[email_task, content_task, business_task, research_task, llm_guardrail_task],
    verbose=True
@@ -5,7 +5,7 @@ This example demonstrates production guardrail patterns using PraisonAI's
 built-in validation and safety mechanisms for secure agent operations.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import internet_search

 print("=== Production Guardrails Patterns Example ===\n")
@@ -38,7 +38,7 @@ production_agent = Agent(
    goal="Generate safe, compliant content for production use",
    backstory="Production-ready agent with built-in safety and compliance checks",
    tools=[internet_search],
-    guardrail=content_guardrail,
+    guardrails=content_guardrail,
    max_retries=3,
    verbose=True
 )
@@ -5,7 +5,7 @@ This example demonstrates an advanced system that dynamically creates agents and
 based on user inputs, including conditional logic, custom configurations, and file output.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import duckduckgo
 import os
 from typing import Dict, List
@@ -40,7 +40,7 @@ class DynamicAgentSystem:
            goal=f"Conduct {inputs['depth']} research on {inputs['topic']} in {inputs['language']}",
            backstory=f"Multilingual expert specializing in {inputs['depth']} analysis",
            tools=[duckduckgo],
-            self_reflect=inputs['depth'] == 'detailed',
+            reflection=inputs['depth'] == 'detailed',
            llm=self.llm_config
        )
        agents.append(research_agent)
@@ -137,7 +137,7 @@ class DynamicAgentSystem:
        
        # Run the system
        print(f"\n🚀 Starting {process} analysis for '{inputs['topic']}'...")
-        praison_agents = PraisonAIAgents(
+        praison_agents = Agents(
            agents=agents,
            tasks=tasks,
            process=process,
@@ -6,7 +6,7 @@ to create flexible agent configurations that can be pre-configured via environme
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Set via environment or use default
 default_topic = os.getenv("RESEARCH_TOPIC", "AI trends")
@@ -46,7 +46,7 @@ print(f"  - Max Retries: {max_retries}")
 print("\n" + "="*50 + "\n")

 # Run agents
-agents = PraisonAIAgents(agents=[agent], tasks=[task])
+agents = Agents(agents=[agent], tasks=[task])
 result = agents.start()

 # Save to environment-specified location if provided
@@ -5,7 +5,7 @@ This example shows how to use multiple user inputs to create a more sophisticate
 agent system with tools, multiple agents, and context passing between tasks.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import duckduckgo

 # Get multiple inputs
@@ -19,7 +19,7 @@ search_agent = Agent(
    goal=f"Find {num_results} relevant results about {topic}",
    backstory="Expert in internet research and data collection",
    tools=[duckduckgo],
-    self_reflect=False
+    reflection=False
 )

 # Create analysis agent
@@ -47,7 +47,7 @@ analyze_task = Task(
 )

 # Run sequential process
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[search_agent, analysis_agent],
    tasks=[search_task, analyze_task],
    process="sequential"
@@ -5,7 +5,7 @@ This example demonstrates how to use basic user input to create dynamic agents a
 The user is prompted for a search query, and the agent searches for information about it.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Get user input
 user_query = input("What would you like to search for? ")
@@ -26,5 +26,5 @@ task = Task(
 )

 # Run
-agents = PraisonAIAgents(agents=[agent], tasks=[task])
+agents = Agents(agents=[agent], tasks=[task])
 agents.start()
@@ -10,7 +10,7 @@ To run this example:
 """

 import streamlit as st
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import duckduckgo

 # Set page configuration
@@ -125,7 +125,7 @@ if st.button("🚀 Submit", type="primary", use_container_width=True):
                    )
                    
                    # Run agents
-                    agents = PraisonAIAgents(
+                    agents = Agents(
                        agents=[agent],
                        tasks=[task],
                        process=process_type
@@ -5,7 +5,7 @@ This example demonstrates graph memory capabilities with PraisonAI agents
 for knowledge graph construction and relationship-aware memory retrieval.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import internet_search

 print("=== Advanced Graph Memory Integration Example ===\n")
@@ -59,7 +59,7 @@ query_task = Task(
 )

 # Run with graph memory integration
-agents_system = PraisonAIAgents(
+agents_system = Agents(
    agents=[knowledge_agent],
    tasks=[build_task, query_task],
    memory=True,
@@ -5,7 +5,7 @@ This example demonstrates memory search optimization using PraisonAI's
 built-in memory capabilities with semantic search and quality filtering.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import internet_search

 print("=== Advanced Memory Search and Optimization Example ===\n")
@@ -60,7 +60,7 @@ search_task = Task(
 )

 # Run with memory optimization
-agents_system = PraisonAIAgents(
+agents_system = Agents(
    agents=[research_agent],
    tasks=[build_memory_task, search_task],
    memory=True,
@@ -20,7 +20,7 @@ Prerequisites:
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 def main():
    """Demonstrate Gemini embedding usage"""
@@ -82,7 +82,7 @@ def main():
    
    # Configure with Gemini embeddings
    # Option 1: Using stable text-embedding-004
-    agents = PraisonAIAgents(
+    agents = Agents(
        agents=[researcher, retriever],
        tasks=[store_task, retrieve_task],
        verbose=True,
@@ -98,7 +98,7 @@ def main():
    )
    
    # Alternative: Using experimental model with advanced features
-    # agents = PraisonAIAgents(
+    # agents = Agents(
    #     agents=[researcher, retriever],
    #     tasks=[store_task, retrieve_task],
    #     verbose=True,
@@ -16,7 +16,7 @@ Setup:
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 def main():
    # Example with Neo4j (uncomment and configure as needed)
@@ -150,7 +150,7 @@ def main():
    )
    
    # Run the multi-agent system with graph memory
-    agents_system = PraisonAIAgents(
+    agents_system = Agents(
        agents=[researcher],
        tasks=[build_knowledge_task, query_knowledge_task, expand_knowledge_task],
        verbose=1,
@@ -34,9 +34,7 @@ agent = Agent(
    llm=llm_config,                  # Pass the detailed configuration
    verbose=True,                    # Enable detailed output
    markdown=True,                   # Format responses in markdown
-    self_reflect=True,              # Enable self-reflection
-    max_reflect=3,                  # Maximum reflection iterations
-    min_reflect=1                   # Minimum reflection iterations
+    reflection=True               # Enable self-reflection
 )

 # Test the agent
@@ -4,7 +4,7 @@ from praisonaiagents import Agent
 agent = Agent(
    instructions="You are a helpful assistant",
    llm="gemini/gemini-1.5-flash-8b",
-    self_reflect=True,
+    reflection=True,
    verbose=True
 )

@@ -3,7 +3,7 @@ from praisonaiagents import Agent
 agent = Agent(
    instructions="You are a helpful assistant",
    llm="gemini/gemini-1.5-flash-8b",
-    self_reflect=True,
+    reflection=True,
    verbose=True
 )

@@ -20,7 +20,7 @@ Features demonstrated:
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import mongodb_tools

 # Ensure OpenAI API key is set
@@ -190,7 +190,7 @@ def main():
    print("🚀 Starting Comprehensive MongoDB Business System...")
    print("=" * 60)
    
-    business_system = PraisonAIAgents(
+    business_system = Agents(
        agents=[data_manager, knowledge_curator, business_analyst, customer_service],
        tasks=business_tasks,
        memory=True,
@@ -19,7 +19,7 @@ Features demonstrated:
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Ensure OpenAI API key is set
 if not os.environ.get("OPENAI_API_KEY"):
@@ -124,7 +124,7 @@ def main():
    print("🚀 Starting MongoDB Knowledge Management System...")
    print("=" * 60)
    
-    knowledge_system = PraisonAIAgents(
+    knowledge_system = Agents(
        agents=[research_agent, knowledge_agent],
        tasks=knowledge_tasks,
        memory=True,
@@ -19,7 +19,7 @@ Features demonstrated:
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents

 # Ensure OpenAI API key is set
 if not os.environ.get("OPENAI_API_KEY"):
@@ -91,7 +91,7 @@ def main():
    print("🚀 Starting MongoDB Memory Research System...")
    print("=" * 60)
    
-    research_system = PraisonAIAgents(
+    research_system = Agents(
        agents=[research_agent],
        tasks=research_tasks,
        memory=True,
@@ -19,7 +19,7 @@ Features demonstrated:
 """

 import os
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import mongodb_tools

 # Ensure OpenAI API key is set
@@ -113,7 +113,7 @@ def main():
    print("🚀 Starting MongoDB Tools Demo System...")
    print("=" * 60)
    
-    mongodb_system = PraisonAIAgents(
+    mongodb_system = Agents(
        agents=[db_agent, analysis_agent],
        tasks=mongodb_tasks,
        verbose=True
@@ -10,7 +10,7 @@ Demonstrates performance monitoring in a multi-agent workflow:
 Shows how to monitor complex multi-agent systems.
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.telemetry import (
    monitor_function, track_api_call, get_function_stats, 
    analyze_function_flow, generate_comprehensive_report
@@ -101,7 +101,7 @@ def main():
    with track_api_call("multi_agent_workflow"):
        print("\n🚀 Starting multi-agent workflow...")
        
-        workflow = PraisonAIAgents(
+        workflow = Agents(
            agents=[researcher, analyst, writer],
            tasks=[research_task, analysis_task, report_task], 
            process="sequential",
@@ -11,7 +11,7 @@ Shows how to monitor complex organizational agent structures.
 """

 import time
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.telemetry import (
    monitor_function, track_api_call, get_function_stats,
    visualize_execution_flow, generate_comprehensive_report
@@ -13,7 +13,7 @@ Shows how to create a comprehensive performance monitoring solution.
 import time
 import json
 from datetime import datetime, timedelta
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.telemetry import (
    monitor_function, track_api_call, performance_monitor,
    get_performance_report, get_function_stats, get_api_stats,
@@ -139,7 +139,7 @@ def simulate_complex_workflow():
    
    # Execute workflow
    with track_api_call("complex_workflow_execution"):
-        workflow = PraisonAIAgents(
+        workflow = Agents(
            agents=[planner, executor, reviewer],
            tasks=[planning_task, execution_task, review_task],
            process="sequential",
@@ -21,7 +21,7 @@ Features Demonstrated:

 import time
 import random
-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import duckduckgo
 from praisonaiagents.telemetry import (
    monitor_function, track_api_call, performance_monitor,
@@ -213,7 +213,7 @@ def execute_monitored_task(agent, task_description):
        agent=agent
    )
    
-    agents_workflow = PraisonAIAgents(
+    agents_workflow = Agents(
        agents=[agent],
        tasks=[task],
        verbose=False
@@ -12,7 +12,7 @@ Features demonstrated:
 - State-based workflow management
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import duckduckgo
 import random

@@ -203,7 +203,7 @@ for request in sample_requests:
    )
    
    # Execute the workflow with conditional routing
-    workflow_agents = PraisonAIAgents(
+    workflow_agents = Agents(
        agents=[
            intake_agent, specialist_agent, review_agent, 
            coordinator_agent
@@ -12,7 +12,7 @@ Features demonstrated:
 - Process-level orchestration
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.tools import duckduckgo

 # Create Manager Agent
@@ -83,7 +83,7 @@ coordination_task = Task(
 )

 # Create Hierarchical Process
-agents = PraisonAIAgents(
+agents = Agents(
    agents=[manager, researcher, data_analyst, writer],
    tasks=[research_task, analysis_task, writing_task, coordination_task],
    process="hierarchical",  # Hierarchical process type
@@ -87,7 +87,7 @@ def main():
        and technology. You provide concise, accurate summaries of important news.""",
        tools=[search_tool],
        verbose=True,
-        self_reflect=False  # Disable reflection for faster execution
+        reflection=False  # Disable reflection for faster execution
    )
    
    # Define the task
@@ -12,7 +12,7 @@ Features demonstrated:
 - Session-specific memory and knowledge
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 from praisonaiagents.session import Session
 from praisonaiagents.tools import duckduckgo
 import tempfile
@@ -62,7 +62,7 @@ research_task1 = Task(
 )

 # Create agents with session
-agents_session1 = PraisonAIAgents(
+agents_session1 = Agents(
    agents=[research_agent],
    tasks=[research_task1],
    session=session1,
@@ -102,7 +102,7 @@ analysis_task = Task(
    context_variables={"previous_research": str(result1)}
 )

-agents_session2 = PraisonAIAgents(
+agents_session2 = Agents(
    agents=[analysis_agent],
    tasks=[analysis_task], 
    session=session2,
@@ -153,7 +153,7 @@ synthesis_task = Task(
    }
 )

-agents_recovery = PraisonAIAgents(
+agents_recovery = Agents(
    agents=[synthesis_agent],
    tasks=[synthesis_task],
    session=recovery_session,
@@ -204,7 +204,7 @@ review_task = Task(
    }
 )

-agents_reviewer = PraisonAIAgents(
+agents_reviewer = Agents(
    agents=[review_agent],
    tasks=[review_task],
    session=reviewer_session,
@@ -10,7 +10,7 @@ Run this example:
    python 01_basic_state_management.py
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import json
 from typing import Dict, Any

@@ -150,7 +150,7 @@ task4 = Task(
 )

 # Create workflow (global variable for state access in tools)
-workflow = PraisonAIAgents(
+workflow = Agents(
    agents=[project_manager, developer, finance_manager],
    tasks=[task1, task2, task3, task4],
    verbose=True,
@@ -10,7 +10,7 @@ Run this example:
    python 02_state_in_tool_functions.py
 """

-from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents import Agent, Task, Agents
 import time
 import json
 from typing import Dict, Any
@@ -273,7 +273,7 @@ task6 = Task(
 )

 # Create workflow (global variable for state access in tools)
-workflow = PraisonAIAgents(
+workflow = Agents(
    agents=[progress_tracker, data_processor, error_handler, config_manager],
    tasks=[task1, task2, task3, task4, task5, task6],
    verbose=True,
Author	SHA1	Message	Date
Mervin Praison	1e46093edf	Release v3.4.1	2026-01-08 14:36:54 +00:00
Mervin Praison	1a71aac6b8	Release v3.4.0	2026-01-08 13:29:57 +00:00
Mervin Praison	287f42dfee	Remove unused test and verification files	2026-01-08 12:50:16 +00:00
Mervin Praison	57b637948f	Release v3.3.1	2026-01-08 12:28:06 +00:00