- Agents have cross-session memory: Your agents are able to automatically write memory when you tell them anything worth remembering or when it discovers something that might be useful in the future.
- Browser automation: Agents can operate a headless browser, navigate, fill forms, click, read pages, and take screenshots. Browsers are also managed by the host machine, so agents interact with them using proxy.
- Human in the loop: Whenever there is a critical decision to make, the agent pauses and asks the human for a set of options.
- Team knowledge base and skill system.
Overall, Valmis is designed to run on the cloud and collaborate with humans to get work done (Valmis means "done" and "completed" in Estonian). If you have any questions about the project, please leave a comment, and I'll reply to all questions.
Also here is something fun: Valmis is probably the first AI agent that is able to play real chess with legit moves. We all know LLMs are notoriously terrible at playing chess and always hallucinate moves. So we added a tool to the agent called chess-engine, which basically requires the agent not to rely on text generation to produce moves, but instead to produce each move strictly based on the calculation of a lightweight chess engine built in. And AI can be a great (and sportsmanlike!) chess player.
This is an example I created to show how LLMs can actually do rigorous work. We cannot always trust the output generated (or hallucinated most of the time) by LLMs, but if we add a deterministic tool layer and instruct the model to rely only on the tool's output, we can get more accurate results. In this case, the tool used is a lightweight chess engine.
Looks good, I’ll spin up an instance and give it a try. Btw, does it support multiple users? And the chess engine thing is brilliant, I guess you can extend your logic to other fields, can tools be extended by the end user?
It doesn’t current support multiuser, but the system is designed to support multiple users with different roles and permissions. I’m also adding a feature to share credentials between teams, which will be rolled out soon. For the tools call question, yes, this will be a generic mechanism for producing rigorous results from agents. I’m planning to extend it to other fields such as calculation, data analysis and deep research. The basic idea is to give zero trust to any outcome generated through LLMs’ text generation to avoid hallucinations, and only trust results from tool calls.
Other cool features:
- Agents have cross-session memory: Your agents are able to automatically write memory when you tell them anything worth remembering or when it discovers something that might be useful in the future.
- Browser automation: Agents can operate a headless browser, navigate, fill forms, click, read pages, and take screenshots. Browsers are also managed by the host machine, so agents interact with them using proxy.
- Human in the loop: Whenever there is a critical decision to make, the agent pauses and asks the human for a set of options.
- Team knowledge base and skill system.
Overall, Valmis is designed to run on the cloud and collaborate with humans to get work done (Valmis means "done" and "completed" in Estonian). If you have any questions about the project, please leave a comment, and I'll reply to all questions.
Also here is something fun: Valmis is probably the first AI agent that is able to play real chess with legit moves. We all know LLMs are notoriously terrible at playing chess and always hallucinate moves. So we added a tool to the agent called chess-engine, which basically requires the agent not to rely on text generation to produce moves, but instead to produce each move strictly based on the calculation of a lightweight chess engine built in. And AI can be a great (and sportsmanlike!) chess player.
This is an example I created to show how LLMs can actually do rigorous work. We cannot always trust the output generated (or hallucinated most of the time) by LLMs, but if we add a deterministic tool layer and instruct the model to rely only on the tool's output, we can get more accurate results. In this case, the tool used is a lightweight chess engine.
Looks good, I’ll spin up an instance and give it a try. Btw, does it support multiple users? And the chess engine thing is brilliant, I guess you can extend your logic to other fields, can tools be extended by the end user?
It doesn’t current support multiuser, but the system is designed to support multiple users with different roles and permissions. I’m also adding a feature to share credentials between teams, which will be rolled out soon. For the tools call question, yes, this will be a generic mechanism for producing rigorous results from agents. I’m planning to extend it to other fields such as calculation, data analysis and deep research. The basic idea is to give zero trust to any outcome generated through LLMs’ text generation to avoid hallucinations, and only trust results from tool calls.