待翻譯：Frags framework for precision in AI agents

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

Notifications You must be signed in to change notification settings Fork 1 Star 8 BranchesTags Open more actions menu Folders and files NameName Last commit message Last commit date Latest commit History 218 Commits 218 Commits .github/workflows .github/workflows anthropic anthropic chatgpt chatgpt cli cli evaluators evaluators examples examples gemini gemini httpfactory httpfactory log log mcpauth mcpauth ollama ollama resources resources schema schema scoper scoper scriptengines scriptengines spec spec test_data test_data util util .gitignore .gitignore LICENSE LICENSE README.md README.md ai.go ai.go config.go config.go config_test.go config_test.go context.go context.go context_config.go context_config.go context_test.go context_test.go dependencies.go dependencies.go dependencies_test.go dependencies_test.go external_functions.go external_functions.go external_functions_test.go external_functions_test.go function_callers.go function_callers.go go.mod go.mod go.sum go.sum mcp.go mcp.go runner.go runner.go runner_test.go runner_test.go scripting.go scripting.go session_manager.go session_manager.go session_manager_test.go session_manager_test.go sessions.go sessions.go sessions_test.go sessions_test.go thread_safe.go thread_safe.go thread_safe_test.go thread_safe_test.go tools.go tools.go transformers.go transformers.go transformers_test.go transformers_test.go Repository files navigation Note: The project is still in development, but you can already try it out. What is Frags? Frags is an advanced AI/LLM Agent dedicated to executing complex workflows of data retrieval, transformation, extraction and aggregation. It is designed to be highly customizable and extensible, allowing you to integrate it with your own tools and processes. Its main goal is precision and focus, and it's a system dedicated to engineers and specialists rather than a code-free quick fix. Frags comes as a CLI tool and as a library to be integrated into Golang projects. Main features Multi LLM: Frags supports multiple LLMs, allowing you to choose the one that best suits your needs. Dedicated almost exclusively to producing structured content: the purpose of Frags is to be integrated in advanced workflows, therefore its output needs to be perfectly predictable and consumable by a machine. Orchestration system: Frags is not an agent to which you ask a question a simple answer back. The whole purpose of Frags is to allow the user to describe complex data retrieval, transformation, extraction and aggregation to produce complex data structures. Advanced support for tools: Frags has a whole standardized system to integrate with internal (as in: provided by the integrator) and external tools (as in: MCP servers). Anti-context-bloating: Frags multi-session system allows you to define and organize what is present in the LLM context, based on the session task, improving focus and reducing the risk of hallucinations Output segmentation: Frags allows you to segment your output into multiple parts, allowing you to overcome output token limitations, and improving answer quality. Advanced pre/post-processing: Frags allows you to define custom pre/post-processing steps, scripts, tools, and transformers, reducing the amount of LLM work where not necessary, reducing cost, improving performance and answer quality. Modularity: Frags is designed to be easily extensible, allowing you to add new features and integrate them with your own tools. Use cases Research/Paper: when your needs go beyond getting a straight answer, but need a whole structured research on sophisticated topics. The context reduction, focus enhancement, document ingestion combined with internet search allows you to design how your paper should contain in each section, allowing the LLM to focus on each objective and produce data you can process however you want. Data extraction: Frags allows you to define complex data extraction pipelines, allowing you to extract data from documents, making sure the output is structured and predictable. This makes it easy to plug into other systems that expect predefined fields and values. Data transformation/analysis: From data retrieval (via the Internet, databases or any MCP tool available) to analysis or transformation, Frags can guide the process and provide solid data structures, skimming the context and increasing the credibility of the results. Reporting: Connect Frags to data sources and define complex reporting templates that describe the entire status of a system, division or company. Connect the output to a reporting tool to produce quality reports. Notes augmentation: give Frags your notes and design how you want the LLM to expand them into a fully featured document. Chatbot augmentation: improve your chatbot from an "answers machine" to an "explanation engine." Creative writing: Frags can be used to generate creative content, allowing you to design how your writing should look like, and how it should be structured. Find the full documentation in the Frags Wiki Resources Readme AGPL-3.0 license Activity Stars 8 stars Watchers 1 watching Forks 1 fork Report repository