Quick summary Automate predictable CRUD: FastCRUD reduces repeated endpoint and data-access wiring. Expose relationships deliberately: control fields and response sizes. Keep business rules explicit: permissions, tenant boundaries and transactions remain design choices. Use custom endpoints when needed: complex domain workflows may need a service layer. One FastAPI tool I’ve found genuinely useful lately is FastCRUD. […]
Quick summary Evaluate the whole graph: assess answers, routing, tool use, reliability and cost. Use deliberate test cases: cover ordinary requests, edge cases and safe failures. Compare against a baseline: combine offline tests with production feedback. Apply focused human review: use a consistent rubric where judgement matters. LangGraph evaluation turns a convincing multi-agent demo into […]
Quick summary Checkpoint state: persistence lets a graph pause, recover and continue. Resume the same thread: reuse its identifier and supply the review decision. Design for replay: repeated execution must not duplicate consequential actions. Use durable storage: production needs an appropriate backend and retention policy. LangGraph persistence is what makes a multi-agent workflow safe to […]
LangGraph routing is the point where a multi-agent graph stops broadcasting work and starts making deliberate choices. It decides which specialist should handle a request, which tasks can run at the same time, and when their findings are ready to combine. In the previous tutorial on LangGraph shared state, we defined safe information flow. Here, […]
Quick summary Define a state contract: make field ownership, inputs and updates explicit. Separate context types: shared facts, private material and long-term memory serve different purposes. Return partial updates: use deliberate reducers instead of mutating a shared snapshot. Test data flow: verify merge behaviour and the context each worker receives. LangGraph shared state is where […]
Quick summary The article reviews seven Python 3.15 changes: its focus includes imports, immutable data, API design and developer tooling. Adopt features for a concrete need: measure startup or workload behaviour before changing working code. Check compatibility first: dependencies and native extensions can determine when a runtime upgrade is practical. Upgrade with evidence: test in […]
Quick summary Give coordination a clear owner: a supervisor selects specialists and remains responsible for the final response. Keep workers focused: supply bounded tasks and request concise, structured results. Control the workflow in code: enforce budgets, permissions and stopping conditions outside model instructions. Start simple: introduce specialists only when separate responsibilities make the system easier […]
Quick summary Follow a progressive learning path: move from a stateful conversational agent to coordinated specialists. Add one capability at a time: the series covers supervisors, shared context, routing, persistence and evaluation. Make responsibilities explicit: separate planning, execution, data flow and recovery. Build for observability: favour understandable state and testable behaviour over adding more agents. […]
If by “turbo vec” you mean the project source, it is a project aimed at a familiar AI-infrastructure problem: storing and searching embedding vectors without treating memory, storage, and retrieval quality as afterthoughts. Its repository describes TurboVec as a vector index built on TurboQuant, written in Rust, with Python bindings. That description is useful, but […]