> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Deep research and analysis

> Combine Team modes, Workflow steps, grounding, and typed research outputs.

Research, strategy, and investment teams use deep research systems when a question requires several independent investigations and a decision-ready deliverable. Agno combines specialist teams, explicit workflows, parallel execution, knowledge, and typed outputs so each stage and final result can be inspected.

```python theme={null}
from agno.workflow import Parallel, Step, Workflow

# The analyst agents are defined separately. See the investment-team repo
# under Developer Resources for the full set.
investment_workflow = Workflow(
    id="investment-workflow",
    name="Investment Review Pipeline",
    steps=[
        Step(name="Market Assessment", agent=market_analyst),
        Parallel(
            Step(name="Fundamental Analysis", agent=financial_analyst),
            Step(name="Technical Analysis", agent=technical_analyst),
            name="Deep Dive",
        ),
        Step(name="Risk Assessment", agent=risk_officer),
        Step(name="Investment Memo", agent=memo_writer),
        Step(name="Committee Decision", agent=committee_chair),
    ],
)

result = investment_workflow.run("Run a full investment review on NVDA")
# result.content holds the Committee Decision.
# result.step_results holds the output from each outer step.
```

This review has five stages, with fundamental and technical analysis running in parallel. Replace the investment agents and prompt to apply the pattern to another research mandate. Attach a database to the Workflow to keep each run's input, step outputs, and final result.

## Deep research patterns

> **[Orchestration patterns](https://agno-v2.mintlify.site/use-cases/deep-research/orchestration-patterns.md)**
>
> Route, coordinate, broadcast, task, and explicit Workflow pipelines.

> **[Parallel investigation](https://agno-v2.mintlify.site/use-cases/deep-research/parallel-investigation.md)**
>
> Run independent specialists concurrently, then synthesize their results.

> **[Grounding research](https://agno-v2.mintlify.site/use-cases/deep-research/grounding-research.md)**
>
> Give each specialist a mandate, a research library, and access to prior work.

> **[Structured deliverable](https://agno-v2.mintlify.site/use-cases/deep-research/structured-deliverable.md)**
>
> Return a typed decision and preserve its supporting memo.

> **[Institutional learning](https://agno-v2.mintlify.site/use-cases/deep-research/institutional-learning.md)**
>
> Share reviewed learnings through a common store and namespace.

> **[Serve and embed](https://agno-v2.mintlify.site/use-cases/deep-research/serve-and-embed.md)**
>
> Expose the pipeline through AgentOS for applications, schedules, and dashboards.

## Developer Resources

* [Workflows](https://agno-v2.mintlify.site/workflows/overview.md)
* [Teams](https://agno-v2.mintlify.site/teams/overview.md)
* [Workflows cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/04_workflows)
* [Investment committee on GitHub](https://github.com/agno-agi/investment-team)


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.