ContextIQ by Trango Compute is an AI context engineering suite built for AI engineers who want clarity, not guesswork. When you’re building LLM and agent systems, “context” isn’t just a prompt—it’s chunking strategy, memory structure, retrieval quality, token allocation, and traceability. ContextIQ turns those moving parts into visual tools you can inspect quickly and share with your team.
Why context engineering needs better visibility
As AI systems move from prototypes to production, failures often come from context architecture decisions that are hard to explain in code or prose. ContextIQ helps close that gap by making context decisions tangible. Instead of debating behavior, you can review the diagram, the chunk splits, the workflow graph, and the token attribution that explain why an agent behaves the way it does.
Tools that translate complex pipelines into diagrams
ContextIQ’s toolset is designed to answer practical questions fast. For retrieval and RAG, the RAG Chunk Inspector shows exactly how documents split into chunks and compares strategies with a live LLM context preview. For agent orchestration, the Agent Workflow Visualizer lets you paste a GitHub URL and instantly see your agent graph, with support for LangGraph, CrewAI, AutoGen, Google ADK, and OpenAI Agents SDK.
When you need to debug what actually happened, the Agent Trace Inspector takes an OTLP JSON trace (from LangSmith, Langfuse, or OpenTelemetry) and renders the full agent graph—down to per-node token attribution for LangGraph, CrewAI, and OpenAI Agents. If memory is the problem, the Memory Architecture Visualizer maps episodic, semantic, and procedural memory layers as a DAG with token budgets and data flows.
Cost, retrieval quality, and identity checks—made inspectable
ContextIQ also targets the “hidden variables” that affect performance and costs. The Token Inspector compares token counts and costs across GPT-4o, Claude, Gemini, and many other models so you can estimate your monthly bill before committing. For retrieval quality, the HyDE Visualizer compares direct query embeddings vs HyDE-based hypothetical document embeddings, making it easier to see why one approach retrieves better across your corpus.
And for security and integrations, the OIDC Inspector scans domains for OpenID Connect configurations—detecting providers like Google, Microsoft, Auth0, and Okta across subdomains—so you can validate identity setup without guesswork.
Built by engineers, tuned for team workflows
ContextIQ is built by engineers at Trango Compute, and it shows in the product philosophy: simple inputs, auto-layout rendering, and one-click export to the formats teams actually use. You can start for free with the tools, then upgrade when you need exports like PNG, SVG, PDF, and shared links. ContextIQ’s approach supports the same core requirement most AI engineering teams share—make complex decisions reviewable.
If you want a straightforward way to reason about context at scale, start with the suite here: https://contextiq.trango-compute.com/.
Conclusion
ContextIQ by Trango Compute turns context architecture into visual, inspectable artifacts—so you can build more reliable AI systems with less ambiguity. By pairing diagram-first tooling with practical debugging and cost awareness, ContextIQ helps your team move from “it works” to “it works predictably.” Thanks for reading and happy building!