VoidCat RDC

For Developers & AI Engineers

Production AI frameworks, MCP toolchains, reasoning engines, and multi-agent patterns. Build and deploy at scale.

Explore Reasoning Core

The Challenge

Building production AI applications requires more than API calls:

  • Fragmentation: Reasoning, retrieval, planning, memory are separate libraries. Integration burden is enormous.
  • Production gaps: Most frameworks are prototypes. Missing observability, cost tracking, error handling at scale.
  • Model dependency: Lock-in to single provider. Switching models or vendors requires rewriting.
  • Tool integration: Connecting external services (APIs, databases, code execution) is ad-hoc and fragile.
  • Evaluation burden: No standard metrics for reasoning quality, cost efficiency, task success rate.

The VoidCat Solution

VoidCat Reasoning Core provides complete, production-ready infrastructure for building agentic AI systems. Focus on application logic; framework handles the rest.

Framework

  • RAG pipeline (semantic chunking, retrieval, evaluation)
  • Sequential reasoning (step-by-step chains, token optimization)
  • Planning engine (task decomposition, multi-step execution)
  • Memory management (short/long term, semantic compression)

Integration

  • Multi-model support (OpenAI, Claude, Llama, custom)
  • MCP protocol for tool integration
  • Pre-built connectors (Redis, Pinecone, PostgreSQL)
  • FastAPI-native async/await patterns

Operations

  • Full observability (logging, metrics, traces)
  • Cost tracking (per-token accounting)
  • Error handling & fallback strategies
  • Evaluation framework with built-in metrics

Architecture Patterns

Tool-RAG

Combine retrieval with tool calls. Agent retrieves documents, calls APIs, synthesizes responses. Best for knowledge-work + external data.

Router

Classify user intent, route to appropriate agent. Enables specialization: different agents for search, analysis, coding.

Hierarchical

Multi-level agents. Parent coordinates, children execute specialized tasks. Scale complex problems.

Hybrid Cloud/Local

Local reasoning engine for fast inference. Cloud fallback for complex tasks. Optimize latency + cost.

Agentic Loops

Agent plans, executes, reflects, adapts. Build self-improving systems with evaluation feedback.

Multi-Model Ensemble

Chain different models. Fast model for routing, powerful model for synthesis. Cost + quality optimization.

Developer Experience

SDK & CLI

Python SDK for framework. CLI for local development, testing, deployment.

Documentation & Examples

Comprehensive docs, 10+ example applications, best practices guide, production checklists.

Local Dev Environment

Docker compose setup. Redis, Postgres, Ollama pre-configured. Zero-config onboarding.

Testing & Evaluation

Built-in test harness. Compare reasoning quality across models, prompts, configurations with metrics.

Deployment Templates

Kubernetes, Lambda, EC2, Docker manifests. One-command deployments with observability.

Community

GitHub discussions, Discord community, monthly webinars, blog tutorials.

Use Cases

Research Teams

Benchmark reasoning chains. Evaluate prompt variations with built-in metrics. Publish reproducible results.

Startup Teams

Fast MVP development. Pre-built components reduce time to market. Focus on product differentiation.

Enterprise Platform Teams

White-label reasoning engines. Multi-tenant support. Custom model endpoints. Usage-based billing.

Autonomous Agent Builders

Full stack in one framework. RAG + reasoning + planning + memory. Production-hardened concurrency.

LLM Application Builders

Complex workflows: search + analysis + writing. Multi-step chains with fallbacks. Cost optimized.

ML/AI Engineers

Deploy custom models alongside cloud APIs. Compare performance/cost. Route traffic dynamically.

Technical Specifications

Capability Details
Language Python 3.10+; async/await; type hints for IDE support
Frameworks FastAPI, Starlette, asyncio; compatible with Django Async
Scale 1,000s concurrent agents; Kubernetes-native; auto-scaling ready
Models Supported OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), Google (Gemini), Meta (Llama), Mistral, custom endpoints
Storage Backends Redis (sessions), Pinecone/Weaviate (vectors), PostgreSQL (structured), S3 (blobs)
Observability Prometheus, OpenTelemetry, Datadog, ELK; structured logging; trace propagation
Licensing Open source (MIT) + Enterprise with support SLA

Getting Started

  • Step 1: Install SDK: pip install voidcat-reasoning-core
  • Step 2: Run example: python -m voidcat.examples.simple_rag
  • Step 3: Read docs at docs.voidcat.org
  • Step 4: Join community Discord for support

Get Support

Questions? Schedule a technical consultation with our team.

Contact Engineering