
Jo Bergli worked on the CogitoNTNU/jarvis repository, delivering a series of architectural improvements and feature expansions over four months. He overhauled the graph agent and tool integration, introducing modular node design and robust routing logic to support scalable tool usage and more reliable LLM-driven responses. Using Python, Docker, and LangChain, he modernized agent frameworks, standardized model naming, and improved code maintainability through targeted refactoring and cleanup. Jo also enhanced deployment reliability by resolving Docker Compose issues and ensuring persistent storage for ChromaDB. His work addressed both backend stability and frontend user experience, enabling faster, safer feature delivery and automation.

March 2025: Stabilized deployment and storage for CogitoNTNU/jarvis by addressing Docker Compose issues. Removed duplicate phoenix service to eliminate startup conflicts and corrected persistent storage mappings after a rebase to ensure ChromaDB and related services retain data reliably. These changes improved startup reliability, data integrity, and developer onboarding for jarvis deployments.
March 2025: Stabilized deployment and storage for CogitoNTNU/jarvis by addressing Docker Compose issues. Removed duplicate phoenix service to eliminate startup conflicts and corrected persistent storage mappings after a rebase to ensure ChromaDB and related services retain data reliably. These changes improved startup reliability, data integrity, and developer onboarding for jarvis deployments.
February 2025 performance summary for CogitoNTNU/jarvis. Focused on modernization, reliability, and code hygiene to enable faster, safer delivery of features and more predictable AI behavior in graph-based components.
February 2025 performance summary for CogitoNTNU/jarvis. Focused on modernization, reliability, and code hygiene to enable faster, safer delivery of features and more predictable AI behavior in graph-based components.
November 2024, CogitoNTNU/jarvis: Delivered key product improvements and tooling enhancements focused on reliability, UX, and automation. Highlights include LLM Output Formatting improvements to ensure tool usage prompts return only the selected option name; expansion of perplexity-based tools and agent orchestration (perplexity_agent, agent_decision state, routing, and tool deciders including calendar and other tools), with corresponding prompt updates; frontend debugging utility to retrieve all displayed chat messages; and toolset expansion exposing core utilities (add_tool, find_files, read_file, read_pdf). These changes improve user experience, make tool invocation more reliable, and broaden automated capabilities.
November 2024, CogitoNTNU/jarvis: Delivered key product improvements and tooling enhancements focused on reliability, UX, and automation. Highlights include LLM Output Formatting improvements to ensure tool usage prompts return only the selected option name; expansion of perplexity-based tools and agent orchestration (perplexity_agent, agent_decision state, routing, and tool deciders including calendar and other tools), with corresponding prompt updates; frontend debugging utility to retrieve all displayed chat messages; and toolset expansion exposing core utilities (add_tool, find_files, read_file, read_pdf). These changes improve user experience, make tool invocation more reliable, and broaden automated capabilities.
Concise monthly summary for CogitoNTNU/jarvis (Oct 2024): Delivered a major overhaul of the Jarvis Graph Agent and Tools integration, improving the tool decision flow, prompt handling, event routing, and overall response quality. Reinstated a stable graph agent after refactors and added modular nodes and a router to support diverse tool usage, enabling more scalable tool integration. Implemented several stability and correctness fixes, including ensuring LLM calls are properly invoked, frontend streaming delivery, and cleanup of initialization logic. These changes reduced tool-usage latency, enhanced reliability, and established a foundation for future tool expansions and performance improvements. Technologies/skills demonstrated include Graph Agent architecture, modular node design, prompt engineering, tool routing, LLM invocation management, and streaming frontend delivery for faster user feedback.
Concise monthly summary for CogitoNTNU/jarvis (Oct 2024): Delivered a major overhaul of the Jarvis Graph Agent and Tools integration, improving the tool decision flow, prompt handling, event routing, and overall response quality. Reinstated a stable graph agent after refactors and added modular nodes and a router to support diverse tool usage, enabling more scalable tool integration. Implemented several stability and correctness fixes, including ensuring LLM calls are properly invoked, frontend streaming delivery, and cleanup of initialization logic. These changes reduced tool-usage latency, enhanced reliability, and established a foundation for future tool expansions and performance improvements. Technologies/skills demonstrated include Graph Agent architecture, modular node design, prompt engineering, tool routing, LLM invocation management, and streaming frontend delivery for faster user feedback.
Overview of all repositories you've contributed to across your timeline