Aidena

— AI STACK RECOMMENDATION

AI Multi-Agent Research Assistant with Web Browsing

Multi-agent system that autonomously browses the web, gathers research data, and synthesizes comprehensive reports. Scales from startup MVP to production with built-in observability and cost tracking.

Stays alive for 365 days after the last visit.

Data & Analytics

AI Multi-Agent Research Assistant with Web Browsing

Multi-agent system that autonomously browses the web, gathers research data, and synthesizes comprehensive reports. Scales from startup MVP to production with built-in observability and cost tracking.

high confidence

Core Stack ℹ︎

CrewAI

Primary

Multi-agent orchestration framework perfect for coordinating specialized research agents (researcher, analyst, writer) with built-in memory, tool integration, and visual Studio editor for workflow design.

$0/month

Browserbase

Primary

Cloud-hosted headless browser infrastructure enabling agents to autonomously browse, scrape, and interact with websites at scale. Handles anti-bot protections and stealth mode essential for research workflows.

$0-$500/month

Claude Sonnet 4

Primary

Balanced LLM with strong reasoning and analysis capabilities for synthesizing research findings into coherent reports. Cost-effective for high-volume research tasks.

$50-$200/month

Complete the Stack ℹ︎

Firecrawl

Alternative

Web scraping and crawling API that extracts clean, LLM-ready Markdown from any webpage. Complements Browserbase for structured data extraction from research sources.

$0-$100/month

AgentOps

Alternative

Observability platform for monitoring agent sessions, tracking costs, and debugging multi-agent workflows. Critical for understanding research agent behavior and optimizing performance.

$0-$200/month

Chroma

Alternative

Embedding database for storing and retrieving research findings, enabling agents to reference previous research and avoid duplicate work across reports.

$0/month

Getting started

  1. 1Set up CrewAI with three specialized agents: Researcher (gathers web data), Analyst (evaluates sources), Writer (synthesizes reports).
  2. 2Integrate Browserbase as the primary tool for web browsing tasks in each agent.
  3. 3Add Firecrawl as a secondary extraction tool for structured data from research pages.
  4. 4Connect Claude Sonnet 4 as the LLM backbone for all agents.
  5. 5Deploy Chroma locally or in-memory to store research findings and embeddings.
  6. 6Integrate AgentOps SDK to track agent sessions, token usage, and costs.
  7. 7Create a simple API endpoint (FastAPI/Flask) to trigger research workflows and return synthesized reports.
  8. 8Test with sample research queries and iterate on agent prompts and tool selection.
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