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Research Assistant

Break complex research questions into sub-tasks, dispatch specialist agents in parallel, and synthesize web and internal data into structured, scored reports.

researchadvancedHTTP WebhookWebSocketCronMCP ServerView on GitHub
connic init my-project --templates=research-assistant
Quickstart

Overview

Decomposes complex research questions into sub-tasks and dispatches them to specialist agents in parallel using trigger_agent. The web researcher searches the internet while the knowledge analyst queries internal data. The orchestrator synthesizes all results, identifies conflicts between sources, scores confidence, and produces a structured report. Uses extended reasoning (32k budget) for deep synthesis and stores reports for future reference.

Use cases

Market research

Research competitors, market trends, and industry developments by combining web search with internal data.

Due diligence

Investigate companies, technologies, or partnerships with multi-source research and confidence scoring.

Knowledge gap analysis

Identify what your organization knows vs. what's publicly available and highlight areas needing more research.

Architecture

Webhook / WebSocket
research-orchestrator
web-researcher
knowledge-analyst
Web Search
Knowledge Base

Scaffolded project structure

Running connic init my-project --templates=research-assistant creates this file tree.

research-assistant/
  agents/
    research-orchestrator.yaml
    web-researcher.yaml
    knowledge-analyst.yaml
  tools/
    research_tools.py
  schemas/
    research-report.json
  requirements.txt
  README.md

Get started

Install the template, create a Connic project, and deploy. Choose Git (automatic on push) or CLI (works with any provider).

Prerequisites

  • Python 3.10+
  • A Connic account (create a project first)
  • API key for your LLM provider (e.g. Gemini, OpenAI) to add in project variables
Create project
1

Install and scaffold

Install the SDK and create a project from this template.

terminal
pip install connic-composer-sdk
connic init my-project --templates=research-assistant

Then cd my-project

2

Deploy

Pick your deployment method. Git auto-deploys on push; CLI works with GitLab, Bitbucket, or no Git.

Git integration

  1. In Connic: Project Settings → Git Repository, connect your GitHub repo
  2. Settings → Environments: map branch (e.g. main) to Production
  3. Push your scaffolded project to that repo
terminal
git add .
git commit -m "Add Research Assistant template"
git push origin main

CLI deploy

  1. In Connic: Project Settings → CLI, create an API key and copy project ID
  2. Run connic login in your project folder
  3. connic test to try with hot-reload, or connic deploy for production
terminal
connic login
connic test    # Ephemeral dev env with hot-reload
connic deploy # Deploy to production
3

Connect and configure

Add an HTTP Webhook (sync) connector for on-demand research requests. The response contains the structured research report. Add your LLM provider API key in Project Settings → LLM Provider API Keys.

Full connector setup guideQuickstart guideConnector docs

Template source

Browse the full template, contribute improvements, or fork for your own use.

connic-org/connic-awesome-agents/tree/main/research-assistant

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