AgentCPM-Report - A writing intelligence agent developed in collaboration with Tsinghua University and other open-source developers such as Mianbi AI.

What is AgentCPM-Report?

AgentCPM-Report is a collaborative project between Tsinghua University's Natural Language Processing Laboratory, Renmin University of China, and Wallfacer Intelligence. OpenBMB This localized deep research agent, jointly developed by the open-source community, is based on the MiniCPM4.1 model with 8B parameters. The model can generate logically rigorous and insightful reports of tens of thousands of words in a local offline environment, supporting fully offline deployment and eliminating the risk of data leakage from the cloud. Through deep retrieval and thought chain deduction, it achieves report generation capabilities comparable to top-tier closed-source systems, making it suitable for high-privacy scenarios. It is the first locally deployable and practically applicable deep research system.

Main functions of AgentCPM-Report

• In-depth research and report generationThe intelligent system can automatically retrieve and integrate information based on user-input instructions or questions, generating a logically rigorous and content-rich long report.
• Localized and private deploymentIt supports fully offline local deployment, ensuring that data is processed locally, eliminating the risk of data leakage in the cloud, and is suitable for processing highly private data.
• High-efficiency knowledge base mountingThe UltraRAG framework enables the rapid mounting and understanding of local private knowledge bases, transforming core confidential data into professional decision-making reports.
• Multi-round deep search and reasoningThrough an average of 40 rounds of in-depth retrieval and nearly 100 rounds of thought chain deduction, the information is comprehensively mined and reorganized.
• Flexible deployment methodsThe intelligent agent offers 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" ModelIntelligent agentAn iterative refinement framework is adopted to simulate the writing process of human experts, and the report content is gradually optimized by alternating between the two stages of "drafting" and "deepening".The complex task of writing a long article is broken down into a series of smaller goals. Each cycle solves a local problem, generating a high-quality report with a smaller parameter scale.
• Breakdown of the four core capabilitiesThe report generation task is broken down into four modules: intelligent retrieval, fluent writing, scientific planning, and accurate decision-making, with targeted training provided.
• Three-stage training methodThis includes supervised fine-tuning, atomic capability enhancement, and end-to-end optimization to ensure that the model's performance continues to improve at every stage.
• UltraRAG framework:A low-code RAG (Retrieval-Augmented Generation) framework for efficiently mounting and understanding local knowledge bases, supporting the rapid construction of vectorized indexes, and improving retrieval efficiency.

AgentCPM-Report project address

• GitHub repository: https://github.com/OpenBMB/AgentCPM/tree/main/AgentCPM-Report
• HuggingFace model libraryhttps://huggingface.co/openbmb/AgentCPM-Report

Application scenarios of AgentCPM-Report

• Corporate strategic planningAgentCPM-Report can process sensitive financial and market data locally, generating structured strategic planning and analysis reports to ensure the security and confidentiality of core corporate information.
• Financial industryFinancial institutions use its offline deployment capabilities to process investment analysis and risk assessment data, generate professional reports, and ensure the absolute security of financial information.
• Scientific research and academicResearchers 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 sectorGovernment departments use tools to write policy analyses and project evaluation reports, meeting high privacy requirements and assisting in scientific decision-making.
• healthcare industryHealthcare institutions can process sensitive medical data locally, generating clinical research and treatment plan reports to ensure patient privacy and data security.