Description
ContextIQ, developed by Trango Compute Inc., offers a comprehensive suite of tools designed for AI context engineering and agentic commerce. This platform empowers AI engineers to meticulously reason about how AI agents remember, the effectiveness of retrieval strategies, and the precise allocation of tokens across conversations. The tools are built to bridge the gap between AI systems working in a notebook and reliably performing in production, addressing complex context architecture decisions that are often difficult to convey through code or prose alone.
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The suite includes a variety of visual tools such as the RAG Chunk Inspector for debugging chunking strategies, the Agent Workflow Visualizer to map workflows from popular frameworks like LangGraph and CrewAI, and the Memory Architecture Visualizer for designing agent memory layers. For cost optimization, the Token Inspector allows comparison of token counts and API costs across over 20 LLMs, including GPT-6 Astra, Claude Sonnet 5, and Gemini 3.1 Pro. The HyDE Visualizer helps compare direct query embedding against Hypothetical Document Embeddings for LangChain and LlamaIndex pipelines.
ContextIQ also provides critical tooling for agentic commerce. The x402 Inspector validates payment challenges, while the ERC-8004 Inspector looks up on-chain agent identity and reputation across multiple EVM mainnets. These two tools combine into the Agent Commerce Gate, a Pro feature that offers an allow/warn/block decision before an agent makes a payment, ensuring transparency and trust in autonomous transactions. The platform emphasizes transparent pricing, with many core tools available for free, and a Pro subscription offering advanced features like export capabilities and early access to new tools.
Targeted at AI engineers, agent framework operators, B2B agentic-commerce platforms, and security/compliance reviewers, ContextIQ aims to simplify the development and deployment of robust AI systems. It supports open standards like x402 and ERC-8004, providing an API-first approach for automation. The tools help users understand agent codebases, spot structural issues in workflows, and audit agent payment activities with independently verifiable settlement receipts.
- CategoryPrompt Engineering Tools
- Pricing ModelFreemium
- DeploymentCloud
- PlatformsWeb
ContextIQ's Core Features
RAG Chunk Inspector: Debug tiktoken, sentence, and paragraph chunking strategies
Agent Workflow Visualizer: Map LangGraph, CrewAI, AutoGen, Google ADK, and OpenAI Agents SDK workflows from GitHub repos
Memory Architecture Visualizer: Design LangGraph, CrewAI, AutoGen, and OpenAI Agents SDK memory layers as a DAG
Token Inspector: Compare token counts and API costs across 20+ LLMs (GPT-6 Astra, Claude Sonnet 5, Gemini 3.1 Pro, DeepSeek-V4)
HyDE Visualizer: Compare direct query embedding vs. Hypothetical Document Embeddings using all-MiniLM-L6-v2 and BGE-Small-en-v1.5
OIDC Inspector: Discover and inspect OpenID Connect provider configurations (Google, Microsoft Azure AD, Auth0, Okta, AWS Cognito, Keycloak)
AI Agent Protocol Inspector: Detect MCP, A2A, and ARD support for any URL with live handshake and agent card parsing
Agent Trace Inspector: Visualize full agent graphs from OTLP JSON traces (LangSmith, Langfuse, OpenTelemetry) with per-node token attribution
DNS Inspector: View all DNS records for any domain, including SPF/DMARC verdicts
x402 Inspector: Validate x402 payment challenges, including HTTP 402 response shape, USDC amount encoding, and address formats
ERC-8004 Inspector: Look up on-chain agent identity and reputation on Ethereum, Base, Arbitrum, Polygon, Avalanche, and BNB Chain
Agent Commerce Gate: Combine ERC-8004 identity/reputation with x402 payment validation for allow/warn/block decisions
Export to JSON, Markdown, PDF, PNG, SVG
Share diagrams via public links
Early access to new tools
How to use ContextIQ?
Select a Tool: Choose from 11 live tools for context engineering or agentic commerce.
Input Data: Provide a GitHub URL for workflow visualization, a domain for OIDC/DNS inspection, or an endpoint for payment validation.
Inspect and Analyze: Use visualizers and inspectors to debug, compare, and verify AI agent components and transactions.
Refine AI Systems: Apply insights to optimize LLM context, agent memory, and payment protocols.
Export and Share: Upgrade to Pro to export diagrams and data in various formats or share via public links.
ContextIQ's Use Cases
- Debugging RAG Pipelines
- Visualizing Agent Workflows
- Designing Agent Memory
- Optimizing LLM Costs
- Validating Agent Payments
- Auditing Agent Activity
- Inspecting OpenID Connect
- Understanding Agent Traces
- Enhancing Agent Trust
