Why? LangChain added 250ms overhead per request. I needed something simpler.

Performance vs LangChain (1,000 requests): - Avg latency: 420ms -> 65ms - Memory/request: 12MB -> 3MB - Cold start: 2.5s -> 0.3s - Test time: 45s -> 3s

Size: 15KB + 2 dependencies (httpx, pytest) vs LangChain's 15MB+ and 47 packages.

In production: 89% LLM cost reduction via 3-tier Redis caching (88% cache hit rate, verified benchmarks). 4.3M tool dispatches/sec in the core engine.

What it does: 1. Multi-agent orchestration -- route tasks to specialized agents with automatic fallbacks 2. Testing built-in -- MockLLMClient lets you assert agent behavior without API keys 3. Production patterns -- circuit breakers, rate limiting, caching included

Install: pip install agentforge

Live demo: https://ai-orchest-7mnwp9untg7gyyvchzevid.streamlit.app/

Packaged version with docs and deployment guide: https://chunkmaster1.gumroad.com

When to use it: production reliability matters, latency is a concern, you want full test coverage. When not to: prototyping, internal tools, team already on LangChain ecosystem.

Questions welcome.