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Research any topic end-to-end — search, analyse, and synthesise — with a single Agent using web search tools. 
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="Researcher",
    instructions="You are a research agent. Search, analyze, and synthesize information.",
    tools=[duckduckgo],
)

agent.start("Research the current state of quantum computing in 2024")
Research agent with web search for comprehensive topic analysis and report generation.
This page uses a plain Agent with a search tool — a flexible custom research recipe. For provider-native Deep Research (OpenAI / Gemini) with the DeepResearchAgent class, see the Deep Research page.

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Quick Start

1

Simple Usage

Attach a search tool and pose a research question.
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="Researcher",
    instructions="You are a research agent. Search, analyze, and synthesize information.",
    tools=[duckduckgo],
)

agent.start("Research the current state of quantum computing in 2024")
2

With Configuration

Return a structured report with a Pydantic schema.
from praisonaiagents import Agent, Task, AgentTeam
from praisonaiagents.tools import duckduckgo
from pydantic import BaseModel

class ResearchReport(BaseModel):
    topic: str
    summary: str
    key_findings: list[str]
    sources: list[str]

agent = Agent(
    name="Researcher",
    instructions="Research topics and return structured reports.",
    tools=[duckduckgo],
)

task = Task(
    description="Research quantum computing in 2024",
    expected_output="Structured research report",
    agent=agent,
    output_pydantic=ResearchReport,
)

AgentTeam(agents=[agent], tasks=[task]).start()

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How It Works

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Simple

Agents: 1 — Single agent handles search, analysis, and synthesis.

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Workflow

  1. Receive research topic
  2. Search web for relevant sources
  3. Analyze and synthesize findings
  4. Generate structured report

​
Setup

pip install praisonaiagents praisonai duckduckgo-search
export OPENAI_API_KEY="your-key"

​
Run — Python

from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="Researcher",
    instructions="You are a research agent. Search, analyze, and synthesize information.",
    tools=[duckduckgo]
)

result = agent.start("Research the current state of quantum computing in 2024")
print(result)

​
Run — CLI

praisonai "Research quantum computing advances" --research --web-search

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Model used by the research-assistant intermediary

Deep research runs a primary researcher agent and a small “research-assistant” intermediary that gathers initial information using tools. The intermediary honours the same cascade as the rest of the CLI: 
--model → MODEL_NAME → OPENAI_MODEL_NAME → gpt-4o-mini

# Both agents use Claude
praisonai --research --model claude-3-5-sonnet-latest "Compare vector databases"
The intermediary consumes tool-call turns. Running it on your main model can raise cost on long research runs — pin it back to gpt-4o-mini by setting OPENAI_MODEL_NAME=gpt-4o-mini and leaving --model unset.

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Run — agents.yaml

framework: praisonai
topic: Research Project
roles:
  researcher:
    role: Research Specialist
    goal: Conduct comprehensive research and analysis
    backstory: You are an expert researcher
    tools:
      - duckduckgo
    tasks:
      research_task:
        description: Research the current state of quantum computing in 2024
        expected_output: A comprehensive research report

praisonai agents.yaml

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Serve API

from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="Researcher",
    instructions="You are a research agent.",
    tools=[duckduckgo]
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Research electric vehicle market trends"}'

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Advanced Workflow (All Features)

Agents: 1 — Single agent with memory, persistence, structured output, and session resumability.

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Workflow

  1. Initialize session for resumable research context
  2. Configure SQLite persistence for research history
  3. Execute multi-source search with structured output
  4. Store findings in memory for follow-up queries
  5. Resume session to continue research

​
Setup

pip install praisonaiagents praisonai duckduckgo-search pydantic
export OPENAI_API_KEY="your-key"

​
Run — Python

from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonaiagents.tools import duckduckgo
from pydantic import BaseModel

# Structured output schema
class ResearchReport(BaseModel):
    topic: str
    summary: str
    key_findings: list[str]
    sources: list[str]
    recommendations: list[str]

# Create session for resumability
session = Session(session_id="research-001", user_id="user-1")

# Agent with memory and tools
agent = Agent(
    name="Researcher",
    instructions="Research topics thoroughly and return structured reports.",
    tools=[duckduckgo],
    memory=True
)

# Task with structured output
task = Task(
    description="Research the current state of quantum computing in 2024",
    expected_output="Structured research report",
    agent=agent,
    output_pydantic=ResearchReport
)

# Run with SQLite persistence
agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

# Resume later
session2 = Session(session_id="research-001", user_id="user-1")
history = session2.search_memory("quantum computing")

​
Run — CLI

praisonai "Research quantum computing" --research --web-search --memory --verbose

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Run — agents.yaml

framework: praisonai
topic: Research Project
memory: true
memory_config:
  provider: sqlite
  db_path: research.db
roles:
  researcher:
    role: Research Specialist
    goal: Conduct comprehensive research
    backstory: You are an expert researcher
    tools:
      - duckduckgo
    memory: true
    tasks:
      research_task:
        description: Research the current state of quantum computing in 2024
        expected_output: Structured research report
        output_json:
          topic: string
          summary: string
          key_findings: array
          sources: array
          recommendations: array

praisonai agents.yaml --verbose

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Serve API

from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="Researcher",
    instructions="Research topics and return structured reports.",
    tools=[duckduckgo],
    memory=True
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Research AI trends", "session_id": "research-001"}'

​
Monitor / Verify

praisonai "test research" --research --web-search --verbose

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Cleanup

rm -f research.db

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Features Demonstrated

FeatureImplementation
WorkflowMulti-step research synthesis
DB PersistenceSQLite via memory_config
Observability--verbose flag
ToolsDuckDuckGo search
ResumabilitySession with session_id
Structured OutputPydantic ResearchReport model

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Best Practices

Instruct the agent to list the URLs it used. Research without provenance is hard to trust and impossible to verify.
Define a Pydantic schema with summary, key_findings, and sources so the report renders consistently and feeds downstream tools.
Deep research runs an assistant that consumes tool-call turns. Set OPENAI_MODEL_NAME=gpt-4o-mini for that helper to avoid inflating cost on long runs.
For OpenAI or Gemini deep-research APIs with built-in browsing, use the DeepResearchAgent class rather than this search-tool recipe.

Provider-native deep research with the DeepResearchAgent class.

Analyze datasets to support data-driven research.