Build production-grade AI agents at speed
Config-driven workflows and visual iteration let you design, test, and deploy state-of-the-art agents faster than pure code. LLM-agnostic, prompt-tunable, and built for teams who demand quality.
Platform highlights
Build powerful agents without sacrificing quality — visual authoring, rapid prompt iteration, secure execution, and full observability to take agents from prototype to production.
Dynamic agent generation
Generate agents from LLM-friendly config files and visualize them in the UI for human-in-the-loop refinement and rapid iteration.
Iterate with control
Design high-quality agents quickly — visual authoring plus prompt engineering tools let teams iterate safely and maintain engineering-grade quality.
Security & sandboxing
Typed tool contracts, sandboxed templating, and runtime isolation protect your data and systems while enabling powerful integrations.
Traceability & cost controls
Per-run telemetry, token accounting, and timing metrics help you optimize prompts, routing, and provider selection for cost and performance.
Integrations & adapters
Plug any tool or MCP server via our adapter registry. In-house deep integrations and certified adapters are on the roadmap.
Stateful memory
Hierarchical memory with provenance, adaptive token budgeting, and lifecycle controls provides reliable context across sessions.
Architectural principles
Principles: modular components with typed contracts, declarative authoring and deterministic execution, security-first sandboxing, end-to-end observability and provenance, and adapter-driven integrations.
Read the extended overview in the Architecture doc.
Dependency injection
LLM providers, tool registries, and persistence backends are injected at runtime to enable modularity, testing, and provider swaps without changes to business logic.
Protocol-based interfaces
Minimal Python Protocols define explicit contracts for core components. Adapters map external implementations to these protocols for compatibility and flexibility.
State isolation
Executors are stateless and safe for multi-tenant concurrent operation. All persistent state is externalized to pluggable backends for scalability.
Reference implementations
AspireAI
Goal-setting and task management chatbot built on sutra + smrti. Multi-agent triage with declarative specialist agents and a FastAPI backend.
No Offence (game)
A social simulation using sutra-orchestrated agents with smrti-driven personalities. Game prototype; early development.
*Prototype
Documentation
Product documentation, API references, and implementation guides are available to customers and partners.
Collaborate
We're available for technical discussions, consulting, and partnerships. Whether you're evaluating agent infrastructure or planning integrations, let's connect.
contact@konf.devHow it works
konf.dev is a browser-first platform that makes agent development visual, composable, and production-ready. Behind the UI sits a modular orchestration stack that handles execution, context, and integrations so teams can focus on building product features.
sutra — Workflow execution
Reliable, declarative workflow engine that compiles visual flows into executable graphs. Ensures deterministic routing, retries, and observability for production agents.
smrti — Stateful memory
Hierarchical memory manager providing short-term working memory, semantic vector storage, and long-term episodic persistence. Enables agents to maintain context across interactions.
astra — Tool integration
Adapter-driven tool registry that exposes APIs and third-party services as pluggable components. Makes integrations secure, testable, and hot-swappable.
These internal engines are intentionally modular: they let konf.dev offer a no-code UX while giving engineering teams extensibility and operational guarantees. Mentioned here for technical readers and partners; full architecture details are available on request.