Technical articles, benchmarks, and insights about building reliable AI systems with OpenSymbolic.
PlanExecute forbids loops and conditionals on purpose. DesignExecute adds them back, with guardrails, for the problems that actually need control flow. Here's when to reach for it, and what stays the same.
Go joins Python and C# on the MultiHopRAG benchmark. Different runtime, different vector store, single static binary. Accuracy: 81.6%. The framework holds.
We swapped the language, the vector store, the code executor, and the type system. Accuracy moved by 0.9pp. The framework is the invariant, not the infrastructure.
Multi-agent systems don't need new infrastructure. They use the same patterns that connect microservices today: typed interfaces, explicit wiring, and the auth and observability stack you already have.
OpenSymbolicAI achieves near-perfect scores on all 1,225 TravelPlanner tasks, outperforms LangChain and CrewAI head-to-head, and maps the model landscape across 11 LLMs and 4 providers.
A deep dive into the design decisions behind OpenSymbolicAI's core blueprint: why plans are code, why execution is sandboxed, and why the planner only gets called once.
Plain English gets you to 50% on day one. Specs get you to 70%. Code gets you to 99.999%. The question is which ceiling you're willing to live under.
Why security in OpenSymbolicAI is architecturally guaranteed, not hoped for. Introducing the Symbolic Firewall.
Tool-calling costs 2.3x more and fails 20% of the time. Here's the math.
Tool-calling re-reads everything on every call. Here's exactly where the tokens go.
We gave a tool-calling agent detailed instructions. It ignored them. Here's why.
A visual comparison of token usage between ReAct (Reasoning+Acting) and PlanExecute approaches.
Why teaching agents through executable examples beats massive prompts with chain-of-thought reasoning, illustrated through a RAG agent implementation.
A hands-on walkthrough showing how primitives and decompositions create the compounding improvement loop that prompt engineering lacks.
Why language models keep getting better while AI agents remain stubbornly brittle, and how to fix it with structural separation of concerns.
How do we build non-deterministic intelligence on top of a deterministic foundation, without compromising either?