What is AgentCPM-Report
AgentCPM-Report is a localized deep research agent jointly launched by the Natural Language Processing Laboratory of Tsinghua University, Renmin University of China, ModelBest, and the OpenBMB open-source community, based on an 8B-parameterMiniCPM4.1model. The model can generate logically rigorous, deeply insightful long-form reports of tens of thousands of words in a local offline environment, supports fully offline deployment, and eliminates the risk of cloud data leakage. Through deep retrieval and chain-of-thought reasoning, it achieves report generation capabilities comparable to top closed-source systems, making it suitable for high-privacy scenarios. It is the first deep research system that can be locally deployed and reaches a practical level.
Main features of AgentCPM-Report
-
Deep research and report generation: The agent can automatically retrieve and integrate information based on user instructions or questions, generating logically rigorous and content-rich long-form reports.
-
Localized and private deployment: Supports fully offline local deployment, ensuring data is processed locally, eliminating the risk of cloud data leakage, and making it suitable for handling highly private data.
-
Efficient knowledge base mounting: Through the UltraRAG framework, it can quickly mount and understand local private knowledge bases, transforming core confidential data into professional decision-making reports.
-
Multi-round deep retrieval and reasoning: Through an average of 40 rounds of deep retrieval and nearly 100 rounds of chain-of-thought reasoning, it achieves comprehensive mining and reorganization of information.
-
Flexible deployment methods: The agent provides one-click Docker deployment and supports drag-and-drop knowledge base construction, allowing users to get started quickly without writing code.
Technical principles of AgentCPM-Report
-
"Writing as reasoning" mode: The agentadopts an iterative refinement framework, simulating the writing process of human experts. Through the alternating stages of "drafting" and "deepening," it gradually optimizes the report content.It breaks down complex long-form writing tasks into a series of small goals, solving local problems in each cycle, and generating high-quality reports with a relatively small parameter scale.
- Decomposition of four core capabilities: It breaks down the report generation task into four major modules: intelligent retrieval, fluent writing, scientific planning, and precise decision-making, with targeted reinforcement training.
- Three-stage training method: Includes supervised fine-tuning, atomic capability reinforcement, and full-process optimization, ensuring the model can continuously improve performance at each stage.
-
UltraRAG framework：A low-code RAG (Retrieval-Augmented Generation) framework used to efficiently mount and understand local knowledge bases, support rapid construction of vectorized indexes, and improve retrieval efficiency.
Project address of AgentCPM-Report
- GitHub repository：https://github.com/OpenBMB/AgentCPM/tree/main/AgentCPM-Report
- HuggingFace model library：https://huggingface.co/openbmb/AgentCPM-Report
Application scenarios of AgentCPM-Report
-
Corporate strategic planning: AgentCPM-Report can process sensitive financial and market data locally, generating structured strategic planning and analysis reports, ensuring the security and confidentiality of core corporate information.
-
Financial industry: Financial institutions use its offline deployment capability to process investment analysis and risk assessment data, generate professional reports, and ensure the absolute security of financial information.
-
Scientific research and academia: Researchers integrate experimental data and internal literature into a local knowledge base to generate high-quality research reports while protecting the confidentiality of research results.
-
Government and Public Sector: Government departments use the tool to write policy analysis and project evaluation reports, meeting high privacy requirements and supporting scientific decision-making.
-
Healthcare Industry: Healthcare institutions process sensitive medical data locally to generate clinical research and treatment plan reports, ensuring patient privacy and data security.