Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
AgentBench serves as a comprehensive evaluation framework tailored to measure the effectiveness and performance of autonomous AI agents. It features a uniform set of benchmarks designed to assess various dimensions of an agent's behavior, including their proficiency in task-solving, decision-making, adaptability, and interactions with simulated environments. By conducting evaluations on tasks spanning multiple domains, AgentBench aids developers in pinpointing both the strengths and limitations in the agents' performance, particularly regarding their planning, reasoning, and capacity to learn from feedback. This framework provides valuable insights into an agent's capability to navigate intricate scenarios that mirror real-world challenges, making it beneficial for both academic research and practical applications. Ultimately, AgentBench plays a crucial role in facilitating the ongoing enhancement of autonomous agents, ensuring they achieve the required standards of reliability and efficiency prior to their deployment in broader contexts. This iterative assessment process not only fosters innovation but also builds trust in the performance of these autonomous systems.
Description
Bench47 empowers teams and their AI-driven agents with a unified, clear memory and skill set. Key decisions, incidents, strategies, and discoveries are documented in an easy-to-read Markdown format, allowing for inspection, editing, and exportation by users. Various MCP-compatible agents, including Claude Code, Codex, and Cursor, can share the same knowledge base and contribute new insights. This system enables teams to maintain up-to-date skills across different machines, connect knowledge with code modifications, and seamlessly transfer tasks between agents while retaining context. Additionally, agents can initiate workflows for both development and production through alerts, webhooks, and scheduled tasks. The flexibility of Bench47 allows it to operate on chosen machines, adhering to the access levels established by the team. Furthermore, it can be utilized as a hosted service, self-hosted within the client's infrastructure, or deployed exclusively in the customer's cloud environment, ensuring adaptability to various organizational needs. This versatility makes Bench47 an invaluable tool for enhancing collaboration and efficiency in AI-driven projects.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Claude Code
No
Codex CLI
No
Gemini CLI
No
GitHub
No
Linear
No
Model Context Protocol (MCP)
No
Sentry
No
Integrations
Claude Code
Yes
Codex CLI
Yes
Gemini CLI
Yes
GitHub
Yes
Linear
Yes
Model Context Protocol (MCP)
Yes
Sentry
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$20/month
The Free plan is $0 for one person. Pro is $20/month and includes five seats. Team is $40/month for 10 seats, with each additional seat priced at $5/month, for teams of up to 100 people. Annual billing saves 20%; prices are in USD before tax. Enterprise, self-hosted, and dedicated deployments are quoted. Agent-provider usage is billed separately.
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
AgentBench
Country
China
Website
llmbench.ai/agent
Vendor Details
Company Name
AltaCoda
Founded
2025
Country
United States
Website
bench47.com