
Show HN: OpenSymbolicAI – Agents with typed variables, not just context stuffingHi HN,We've spent the last year building AI agents and kept hitting the same wall: prompt engineering doesn't feel like software engineering. It feels like guessing.We built OpenSymbolicAI to turn agent development into actual programming. It is an open-source framework (MIT) that lets you build agents using typed primitives, explicit decompositions, and unit tests.THE MAIN PROBLEM: CONTEXT WINDOW ABUSEMost agent frameworks (like ReAct) force you to dump tool outputs back into the LLM's context window to decide the next step.Agent searches DB.Agent gets back 50kb of JSON.You paste that 50kb back into the prompt just to ask "What do I do next?"This is slow, expensive, and confuses the model.THE SOLUTION: DATA AS VARIABLESIn OpenSymbolicAI, the LLM generates a plan (code) that manipulates variables. The actual heavy data (search results, PDF contents, API payloads) is stored in the Python/runtime variables and is never passed through the LLM context until a specific primitive actually ne
