AgentScope 1.0 Open Source: A controllable multi-agent framework for developers, covering the entire lifecycle of building, deploying, and monitoring
This is a set of AI and large model infrastructure built for developers. AgentScope 1.0 uses a three-layer architecture to cover agent construction and orchestration, production-level deployment and security execution, visual development and monitoring and evaluation, emphasizing modular, asynchronous and intelligent context management, adapting to automated workflows with multiple agents and tool calls, and facilitating collaboration with AI tools such as ChatGPT and Claude.
1. Three-layer architecture and capability matrix
1. AgentScope Core: orchestrateable, interruptable, and scalable
The core framework adopts a highly modular and asynchronous design, providing flexible tool use, real-time interrupt and recovery, context governance and memory policies, and supports multi-agent collaboration and routing. It is convenient to incorporate ChatGPT and Claude into the unified orchestration.
2. AgentScope Runtime: Secure sandbox and one-click deployment
Theruntime provides an isolated tool sandbox to ensure the security of code execution and file operation. It has one-click deployment, logging, and observability capabilities, and is compatible with mainstream agent frameworks and large models, making it convenient for AI tools to be stably launched in the production environment.
3. AgentScope Studio: Visual development and evaluation monitoring
Studio provides developers with visual debugging, execution flow tracking, state change and resource consumption monitoring, and a built-in evaluation system to help quickly locate bottlenecks, reproduce experiments, and optimize prompts and strategies.
2. Engineering landing route
1. Automated process from configuration to go-live
Roles, tools, routes and SLAs are defined in YAML or configuration-driven. Enter the security sandbox and log system of Runtime after local joint debugging. Complete playback, evaluation and Kanban precipitation in Studio to achieve continuous delivery of AI tools.
2. Collaborate with mainstream large models and tools
Mix ChatGPT and Claude to complete planning and review under the same arrangement, and then execute tool calls by specific models; Stabilize latency and throughput in peak scenarios through rate limiting, retries, caching, and concurrency caps.
(1) Best practice list
(a) Set up system prompts and terminology for key agents
(b) Set up whitelists, timeouts and audit logs for tools
(c) Enable interrupt recovery and checkpoints to reduce the failure rate
3. Business-oriented value
1. Stable and controllable
Asynchronous scheduling and interrupt recovery make long tasks more controllable; Security sandbox reduces the risk of overreach and data leakage to meet compliance requirements.
2. Observable and Optimizeable
End-to-end observation from the call chain to resource consumption, with evaluation and regression sets, continuously improve the quality and automation efficiency of machine learning.
3. Migrable and Integrable
Compatible with mainstream frameworks and tool ecosystems, it is convenient to integrate existing ChatGPT, Claude, internal APIs, and databases into a unified AI tool chain.
4. Applicable scenarios and boundaries
1. Applicable scenarios
Multi-round customer service, code assistant, data governance, retrieval and reporting, O&M and terminal automation, multi-agent research and simulation, etc.
2. Boundaries and attention
Long trajectory tasks need to control the context and cost; External tools must be minimally privileged and strictly audited; Human review and dual-model cross-verification are introduced for key decisions.
Frequently Asked Questions (Q&A)
Q: How does AgentScope work with ChatGPT and Claude?
A: ChatGPT is used for task decomposition and retrieval planning, Claude is used for security and style review, and the core execution is completed by AgentScope multi-agent orchestration and tool sandbox, forming a closed loop of intelligence and automation.
Q: What specific problems does Runtime's security sandbox solve?
A: Put file operations, code execution, and network access into an isolated environment, and cooperate with permissions and audits to reduce data and security risks caused by tool calls, which is suitable for enterprise compliance scenarios.
Q: What benefits can Studio review and visualization bring?
A: Real-time observation of execution flow and state changes, locating bottlenecks and anomalies; Combine playback and indicator dashboards to quickly optimize prompts, routing, and concurrency strategies to improve the stability of AI tools.
Q: What are the advantages compared to the "only one large model" solution?
A: Multi-agent and multi-model orchestration are more flexible: ChatGPT and Claude are good at planning and reviewing, while other models are good at execution and tool calling; AgentScope unifies governance context, permissions, and fault tolerance, reducing overall costs and failure rates.
