Deepcon – Get the most accurate context for coding agents
I got frustrated watching Claude Code fail at using modern APIs (ask it about GPT-5 and it says it doesn't exist). Existing solutions like Context7 dump thousands of tokens of irrelevant docs into context. So I built DeepCon. How it works: - Crawled 10,000+ official docs using agentic browser automation and structured them hierarchically - Query decomposer breaks down requests, searches in parallel, then merges only relevant context - Returns just what's needed: 2.4x fewer tokens than Context7 Results on our benchmark: DeepCon achieved 90% accuracy vs Context7's 65% on real-world tasks with…
Deepcon – Get the most accurate context for coding agents launched on November 6, 2025 with 7 votes, #1,843 of 9,796 launches that month and more than 75% of that year's launches.
What it does
Token-optimized context—the only tool your coding agent needs
Token-optimized context— the only tool your coding agent needs. Docs, code, web, deep research integrated. Tested across 20 real-world scenarios implementing Autogen, LangGraph, OpenAI Agents, Agno, and OpenRouter SDK. Each scenario evaluated by 3 LLMs (GPT-5, Grok-4, Deepseek-v3.2) for completeness and relevance. Deepcon provided sufficient context in 18/20 scenarios, outperforming all competitors by 25+ percentage points. Token efficiency matters. Deepcon achieves 90% success with only 2,365 avg tokens—less than half of Context7's 5,626 tokens at 65% success. GPT-5 API via client.responses.create() with model: "gpt-5". Supports input prompts, returns output_text. Import from "openai"…from deepcon.ai
In the maker’s words, at launch
I got frustrated watching Claude Code fail at using modern APIs (ask it about GPT-5 and it says it doesn't exist). Existing solutions like Context7 dump thousands of tokens of irrelevant docs into context. So I built DeepCon. How it works: - Crawled 10,000+ official docs using agentic browser automation and…
Does a similar job
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Context GatewayMar 2026 · github.com · ▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
Deep Work PlanJun 2026 · deepworkplan.com · ▲114Models matter. Context matters more. Give your agent a plan.
Ktx – Open-source executable context layer for data agentsMay 2026 · github.com · ▲93Hi HN, we’re open-sourcing ktx. It’s an executable context layer that makes agents reliable on your data stack. We built it after going through the experience of building production-grade data agents for dozens of companies. If you’ve also tried building them, or simply tried using Claude Code or Codex on your data warehouse, you’ll know that accuracy is the #1 issue. Agents are great at generating valid SQL, but it’s not always correct SQL. To cite a few examples of “agents gone wrong”: - Stale column + hidden business rule: when preparing a board report, a finance analyst asks Claude Code…
UltraContext – A simple context API for AI agents with auto-versioningJan 2026 · ultracontext.ai · ▲21Hey HN! I'm Fabio and I built UltraContext, a simple context API for AI agents with automatic versioning. After two years building AI agents in production, I experienced firsthand how frustrating it is to manage context at scale. Storing messages, iterating system prompts, debugging behavior and multi-agent patterns—all while keeping track of everything without breaking anything. It was driving me insane. So I built UltraContext. The mental model is git for context: - Updates and deletes automatically create versions (history is never lost) - Replay state at any point The API is 5 methods:…
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