Open source locally hosted cross-domain "calculator" for Your preferred AI agent working with vast amount personal data feeding only relevant data to the LLM

Hey everyone I posted this on the hermes agent page and it seemed like people found it useful so hopefully it can be of use for you as well. I just open sourced OpenHealthAtlas to help your AI agent adhere more to reality and stop hallucinating on health data of course all local and model agnostic. I created it to help myself with muscle imbalances causing back pain and generally to see what I do in a day that makes me have a good or bad day over a period of a year or two. The tool basically works as a calculator for the AI agent in order to optimize the context window of the agent the raw compute is done by Open Health Atlas and the agent is able to pull levers in order to compare, analyse and synthesize hypotheses for possible reasons for some outcome. The System is intended to be used with a harness like Openclaw or Hermes (I first built it to Hermes. But it should be agnostic) and telegram but can also be used directly via the MCP https://github.com/kajeesan/Open-Health-Atlas An example of how you could use it: Part of why I built this was frustration with going to a doctor or physiotherapist and coming away without a clear way forward. But I also realised it’s unfair to expect someone to piece everything together from a short appointment and whatever I happen to remember that day. They don’t have months of context about my training, sleep, food, mood and symptoms. When I started training for a race, for example, I was running much more than usual and developed IT band problems. I wanted a better way to explore what had changed around that time and bring something more useful to the conversation than “my knee hurts.” Atlas and the AI handle different parts of that. Atlas organises the records, runs the calculations and keeps the results traceable. Hermes uses the model’s broader medical and physiological knowledge to interpret the evidence, ask follow-up questions and explore possible explanations. The agent can request the relevant information as it needs it, rather than having my entire history dumped into every conversation. That also creates a feedback loop. We can discuss a possible change to training, an exercise or a food choice, then I can report what happened after trying it. Did symptoms improve? Did energy or sleep change? The next conversation has more evidence to work with. Improvement doesn’t automatically prove what caused it, but it helps us revisit the original idea. I tried to design it around me hating to log stuff every day: I get tired of logging everything. If this required a detailed journal every day, I probably wouldn’t keep using it. In my configured setup, a scheduled morning check reviews the previous day and recent trends, then sends me a Telegram message with things to keep in mind. That can include meal ideas based on my recorded food and what we’ve discussed, alongside context from sleep, heart rate and activity. I can talk through those suggestions with the agent. At the end of the day, I can simply tap red, yellow or green to say how the day felt, with more detail if I want. That gives the system something to compare the measurements against: my actual experience. Training is only one example. Mood, meals, recovery, symptoms, good days and bad days all belong to the same person. I wanted that history connected and available to me and my agent, with the routine work happening in the background so we can keep asking better questions, and give a doctor or physiotherapist better context too. What is next? If you try it, let me know where you get stuck, what feels confusing, or which data source you’d want to connect. I’m planning to add screen time from my computer and phone too, so I can explore how it relates to sleep, mood and good or bad days. Contributions are very welcome bug fixes, importers, tests and documentation would all help. One area I’d especially appreciate help with is performance. On my setup, the heavier analyses can take three to four minutes because they run many comparisons and repeated statistical tests. I’d love to make those faster while keeping the results and evidence checks intact. If you spot an improvement, feel free to open an issue or submit a PR. If you would like the thoughts on how the system is structured with the references to the medical papers that I used to base the calculations on let me know