ContextIQ

ContextIQ — Context Engineering for AI Engineers

Building production AI systems means wrestling with invisible problems. You ship a RAG pipeline and retrieval quality is poor — but you can't see why. Your agent graph works in testing but you can't explain the token costs to your team. Your auth integration breaks in staging but the OpenID Connect config looks fine. These aren't model problems. They're context problems.

ContextIQ is a suite of free tools built specifically for AI engineers who need to see inside their systems — not guess.

RAG Chunk Inspector

The single most common reason RAG pipelines underperform is bad chunking. When your chunks are too large, you flood the context window with noise. Too small, and the retrieved fragments lack enough meaning to be useful. The problem is that chunking strategies are invisible — you configure a splitter, run it, and hope.

RAG Chunk Inspector lets you paste any document and instantly see exactly how it splits across three strategies: tiktoken-based, sentence-boundary, and paragraph-boundary. You see chunk sizes, token counts, and a live LLM context preview side by side. You stop guessing which strategy fits your corpus and start knowing.

Agent Workflow Visualizer

As agent systems grow — more nodes, more edges, more conditional branches — the code becomes the only authoritative map of the system, and reading it is slow and error-prone. Onboarding a new engineer or reviewing a pull request on a LangGraph workflow means mentally simulating a graph from source files.

Agent Workflow Visualizer takes a GitHub URL and renders the full agent graph in seconds. It supports LangGraph, CrewAI, AutoGen, Google ADK, and OpenAI Agents SDK. The graph is the documentation. You can share it, review it, and reason about it without running the code.

Agent Trace Inspector

You ran your agent. Something went wrong — or something was slower and more expensive than expected — and now you need to understand what actually happened at runtime. Log output gives you lines. What you need is structure.

Agent Trace Inspector takes an OTLP JSON trace exported from LangSmith, Langfuse, or any OpenTelemetry-compatible backend and renders the full execution graph with per-node token attribution. You can see which node consumed the most tokens, where latency concentrated, and how the graph actually executed versus how you designed it. It supports LangGraph, CrewAI, and OpenAI Agents.