Alternatives
Products that do what Context Grip does
Web tool to slice codebase clutter & save 50%+ on LLM tokens
- 1ST
2024 · supermaven.com
- 2
- 3BA
I am working on a modular open source framework called Griptape that allows Python developers to create LLM pipelines and DAGs for complex workflows that use rules and memory. Griptape can be thought of as "Airflow for LLMs," providing an alternative to the agent-based LangChain approach. Developers can also build reusable LLM tools with explicit JSON schemas that can be executed in any environment (local, containerized, cloud, etc.) and integrated into Griptape workflows. They can also be easily converted into ChatGPT Plugin APIs and LangChain tools via adapters. Tools can be thought of as…
2023 · github.com
- 4
- 5MJ
2025 · github.com
- 6GF
Hi HN, we’re Jamie and Matti, co-founders of Twigg. During our master’s we continually found the same pain points cropping up when using LLMs. The linear nature of typical LLMs interfaces - like ChatGPT and Claude - made it really easy to get lost without any easy way to visualise or navigate your project. Worst of all, none of them are well suited for long term projects. We found ourselves spending days using the same chat, only for it to eventually break. Transferring context from one chat to another is also cumbersome. We decided to build something more intuitive to the ways humans think.…
Oct 2025 · twigg.ai
- 7BT
2024 · github.com
- 8
- 9
RAG-ready web scraping that cuts your LLM token costs
Apr 2026 · geekflare.com
- 10BT
Small codebases were always a good thing. With coding agents, there's now a huge advantage to having a codebase small enough that an agent can hold the full thing in context. Repo Tokens is a GitHub Action that counts your codebase's size in tokens (using tiktoken) and updates a badge in your README. The badge color reflects what percentage of an LLM's context window the codebase fills: green for under 30%, yellow for 50-70%, red for 70%+. Context window size is configurable and defaults to 200k (size of Claude models). It's a composite action. Installs tiktoken, runs ~60 lines of inline…
Feb 2026 · github.com
- 11
- 12CT
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…
Feb 2026 · github.com
- 13AW
I've been presenting at local meetups about Context Engineering, RAG, Skills, etc.. I even have a vbrownbag coming up on LinkedIn about this topic so I figured I would make a basic example that uses bedrock so I can use it in my talks or vbrownbags. Hopefully it's useful.
Apr 2026 · github.com
- 14LT
Current AI-assisted CLI tools are often part of larger systems and work better on Linux. I built llm-term to address these. It's a Rust-based tool that compiles into a single binary file. You only need to download the binary, add it to your PATH, and configure your OpenAI key to get started. While llm-term offers an option for gpt-4o, it works great with gpt-4o-mini. So it's not costly. I appreciate any feedback or suggestions.
2024 · github.com
- 15TC
Hi HN, I built a CLI for uploading documents and querying them with an LLM agent that uses search tools rather than stuffing everything into the context window. I recorded a demo using the CrossFit 2025 rulebook that shows how this approach compares to traditional RAG and direct context injection[1]. The core insight is that LLMs running in loops with tool access are unreasonably effective at this kind of knowledge retrieval task[2]. Instead of hoping the right chunks make it into your context, the agent can iteratively search, refine queries, and reason about what it finds. The CLI handles…
2025 · github.com
- 16FA
LLM agents rely on tool calls — but tool responses are huge. Gmail, CRMs, and APIs return bloated JSON LLMs choke on large responses You only need 2–3 fields, but frameworks give you zero control Toolflow is an AI-native framework to fix this: * Filter tool responses before they hit the LLM * Context modes: `minimal`, `full`, `custom`, or `ai` * Composable TypeScript tool registry GitHub: [https://github.com/dksingh1997/toolflow](https://github.com/dksingh1997/toolflow) Would love feedback — especially from those building with LLMs in production.
2025 · github.com
- 17CC
Show HN: Context Copy - VS Code extension for quickly copying context I've developed a VS Code extension called "Context Copy" that allows developers to swiftly copy multiple files with their full context, formatted as markdown. It's designed to streamline the process of sharing code snippets with AI assistants (like ChatGPT or Claude) or in documentation. Key features: - Select multiple files in VS Code Explorer - Right-click to copy full context as markdown - Automatic language detection for syntax highlighting - Includes file paths for better context preservation - Copies directly to…
2024 · marketplace.visualstudio.com
- 18AT
Oct 2025 · blog.nilenso.com
- 19OS
Hello HN, I’ve been building AI agents lately and ran into a common "Context Bloat" problem. When an agent has 20+ skills, stuffing every system prompt, reference doc, and tool definition into a single request quickly hits token limits and degrades model performance (the "lost in the middle" problem). To solve this, I built OpenSkills, an open-source SDK that implements a Progressive Disclosure Architecture for agent skills. The Core Concept: Instead of loading everything upfront, OpenSkills splits a skill into three layers: Layer 1 (Metadata): Light-weight tags and triggers (always loaded…
Jan 2026
- 20
- 21CC
If you had to build a context window manager in 24h, would you stick to the existing model or come up with something better? Here's what I did: 1. Built a proxy that intercepts Codex's calls to OpenAI and rewrites them on the fly. 2. Replayed 3,807 rounds of SWE-bench Verified traces through it: avg prompt 44k → 6k tokens (-87%). 3. Posted it to HN to get the next reduction applied to my confidence interval — starting with the inevitable "How about accuracy?" npx -y pando-proxy · github.com/human-software-us/pando-proxy
Apr 2026 · npmjs.com
- 22
Private AI that injects your knowledge into every LLM query
May 2026 · api.contextcut-pro.com
- 23MZ
Jul 2026 · github.com
- 24UE
I've created uithub, a tool that allows developers to easily get LLM context for their coding questions and perform AI repo analysis at scale. Here's what it does: - Get Context: Simply change the 'g' in github.com to 'u' to access AI-powered insights on any GitHub repo. - Flexible Querying: Fetch entire repos, specific branches/subfolders, or filter by file type and size. - API for Developers: Power the next generation of development tools with our API. Key features: - Customizable token limits - File type filtering - Multiple response formats - Size-based file exclusion I built this…
2024 · uithub.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →