ContextIQ
ContextIQ is a suite of free, visual context engineering tools designed for AI engineers. It provides an intuitive graphical interface to inspect and optimize the contextual components of large language model (LLM) applications. The platform helps users understand how data is chunked for retrieval-augmented generation (RAG), visualize agent workflows from GitHub repositories across frameworks like LangGraph, CrewAI, AutoGen, and OpenAI Agents SDK, and analyze complex trace data from LangSmith, Langfuse, or OpenTelemetry with per-node token attribution. The tools also allow engineers to design and compare memory architectures for agents, mapping episodic, semantic, and procedural memory layers with token budgets. A token inspector compares costs and counts across over 20 models, including GPT-4o, Claude, and Gemini, helping teams estimate expenses before deployment. Additionally, the HyDE visualizer demonstrates the retrieval impact of hypothetical document embeddings, and an OIDC inspector scans domains for OpenID Connect configurations. All core tools are free forever without requiring a credit card. For users who need to export diagrams and reports, a paid plan at $5/month unlocks PNG, SVG, and PDF exports, as well as shared links. ContextIQ bridges the gap between conceptual design and operational debugging, enabling faster iteration and more robust AI systems. By centralizing these utilities in a single web interface, ContextIQ reduces the cognitive load of managing LLM context, making it easier to prototype, troubleshoot, and communicate architecture decisions within teams.
- Category
- AI Agents
- Pricing
- Paid
Problem solved
ContextIQ addresses the challenge of managing and optimizing context for large language model applications. AI engineers often struggle to visualize how data flows through agent systems, how chunking strategies affect retrieval, memory architectures, and token costs. ContextIQ provides a suite of free, visual tools that make these aspects tangible, enabling faster debugging and better design decisions.
Use cases
- Visualizing LangGraph agent workflows from GitHub repositories
- Inspecting RAG chunking strategies for document retrieval
- Comparing token counts and costs across LLM models
- Designing agent memory architectures as directed acyclic graphs
- Analyzing OTLP traces from LangSmith for token attribution
- Visualizing HyDE embeddings impact on retrieval
- Scanning domains for OpenID Connect configuration
Audience
- Developers
- ML Engineers
- Data Scientists
- Researchers
Pros
- Free core tools with no credit card required
- Intuitive graphical interface for complex context engineering
- Broad support for frameworks like LangGraph, CrewAI, OpenAI Agents SDK
- Export functionality for documentation and sharing
- Comprehensive token cost comparison across models
Cons
- Export and sharing features behind paywall
- Web-only access, no CLI or API
- Limited to specific frameworks, may not cover all agent builders
- Lacks deep customization or programmatic control
- No self-hosting option for privacy-sensitive environments
Official website: https://contextiq.trango-compute.com/
Canonical LambdaBase page: https://www.lambdabase.com/tools/contextiq