Explore/benchmark/A Baseline Assessment for the Agentic Web Technology Stack
A

Vagner Figueredo de Santana, Diogo Cortiz/A Baseline Assessment for the Agentic Web Technology StackUnknown

The fast pace in which protocols, frameworks, and technologies are being proposed for LLM-based agents results in a complex scenario for anyone aiming to create state-of-the-art AI solutions. Without an assessment on how different protocols, frameworks, and libraries work together to compose an Agentic Web solution, developers and organizations face different challenges (e.g., skilling, governance, infrastructure) even before becoming part of the Agentic Web ecosystem. In this context, we propose a system for performing Agentic Web evaluations called MASTIF (Multi-Agent System TestIng Framework) and present a baseline assessment of Agentic Web technologies, including 3 LLMs, 3 protocols, and 4 agent implementation frameworks, against 300 Mind2Web benchmark tasks. Results suggest that selecting a task-specific technology stack may result in gains of task completion rate, efficiency score, and social value stability scores; and reductions in verbosity and time to complete tasks. With the presented results, we expect to present a baseline in terms of metrics and results for this complex and fast evolving scenario for those aiming at designing, assessing, and developing multi-agent systems.

benchmark
GitHubCompare
Refreshed 7h ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads—30d
Last commit—
Refreshed7h ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
55 / 100
Composite score from 6 signals. How we score →
Active project
55Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 0 days observed0 not enough history
snapshots
not enough history
Repository activity · 0 days observednot enough history from pushed_at
inactivepushed
not enough history
not enough history
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language—
License—
Sourcearxiv
Open source✗ No
Commercial use—
Docs—
Demo—

AgentHub Score

55
Score 55/100
Below average

Alternatives

R
RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems
0 · benchmark
55
C
Commitment To Cooperation With Self-Negotiated Contracts
0 · benchmark
55
E
Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?
0 · benchmark
55
C
Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation
0 · benchmark
55Compare all →

Recent activity

Latest commit ——
Indexed by AgentHub crawler7h ago
Monitor for new releasesongoing