We 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 https://openai.com/index/introducing-gpt-5-4/).Our solution uses small language models (SLMs): we look at model internals and train classifiers to detect which parts of the context carry the most signal. When a tool returns output, we compress it conditioned on the intent of the tool call—so if the agent called grep looking for error handling patterns, the SLM keeps the relevant matches and strips the rest.If the model later needs something we removed, it calls expand() to fetch the original output. We also do background compaction at 85% window capacity and lazy-load tool descriptions so the model only sees tools relevant to the current step.The proxy also gives you spending caps, a dashboard for tracking running and past sessions, and Slack pings when an agent is sitting there waiting on you.Repo is here: https://github.com/Compresr-ai/Context-Gateway. You can try it with: curl -fsSL https://compresr.ai/api/install | sh Happy to go deep on any of it: the compression model, how the lazy tool loading works, or anything else about the gateway. Try it out and let us know how you like it!
FL score
out of 100
Verdict
high confidence
Competition
7
competitors found, emerging market, funded players
Trend
5 community mentions
An open-source proxy that intelligently compresses AI agent context to reduce LLM costs and improve performance, targeting developers of coding agents.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Context Gateway – Compress agent context before it hits the LLM”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Strong problem and a clear technical gap, but the build complexity for a solo founder is high due to the machine learning components.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Solid value proposition in a growing market, but execution complexity might challenge a solo founder's resources.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A highly technical solution to a clear problem, but the creator fit for a solo builder to maintain complex ML and build a business is challenging.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong value proposition and reachable audience, but ongoing validation of the core compression efficacy and business model is key.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a desperate need for a specific user, with clear paths for validation and future relevance.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
An AI code completion assistant with a large context window to understand entire codebases and provide fast, relevant code suggestions.
Pricing: Free Tier: $0/month. Pro: $10/month (1 million token context window, $5/month chat credits). Team: $10/month per user (all Pro features plus centralized user management).
An AI assistant that provides developers with AI-powered code completions and suggestions, focusing on integrating generative AI into the end-to-end software development lifecycle.
Pricing: Dev Preview: Free. Dev Plan: $9/user/month. Enterprise Plan: $39/user/month.
Develops a platform for building specialized Retrieval-Augmented Generation (RAG) agents for enterprise use, enhancing the accuracy and reliability of generative AI models with facts from external sources.
Pricing: Parse (text only): $3/1,000 pages; Parse (multimodal): $40/1,000 pages. Rerank-v2: $0.05 per million tokens; Rerank-v2-mini: $0.02 per million tokens. Input: $3/1M tokens. Output: $15/1M tokens. Free account plus $25 in credit towards compute.
An AI-powered answer engine that provides instant, reliable answers to complex queries with sources and citations, and offers API access for developers.
Pricing: Standard: Free. Pro: $20/month or $200/year. Max: $200/month (individual plans). Enterprise Pro: $40/user/month or $400 annually. Enterprise Max: $325/user/month or $3,250 annually. API access is credit-based, pay-as-you-go.
An AI-powered platform revolutionizing scientific research through its agentic system, designed to accelerate drug discovery and streamline research workflows.
Pricing: Not publicly disclosed. Focus on enterprise solutions for biotech and research institutions.
An open-source, batteries-included agent harness that provides an easy path to build agents with planning, subagent spawning, and filesystem integration, implementing various features for context compression.
Pricing: Open-source, so generally free to use, but costs would be associated with the underlying LLMs and infrastructure.
A feature within Agno that manages agent context by compressing tool call results to reduce token usage and extend the context window, while preserving critical information.
Pricing: Not explicitly detailed, but offered as a feature within the Agno platform. Likely usage-based or included in platform subscription.
What they charge
What people say, 5 mentions
How I built a niche AI-driven Marketplace for a high-trust community (Next.js + Supabase)
r/SaaS
Anyone struggling to orchestrate multiple AI agents without blowing budget?
r/SaaS
Claude Code MCP changes the game in agentic GTM
r/SaaS
Some lessons I learnt building my agentic social networking app
r/SaaS
SaaS builders: Have you seen prompt injection / agent tool-abuse in production? How are you handling it?
r/SaaS
Recent news
Automatic Context Compression in LLM Agents: Why Agents Need to Forget—and How to Help Them Do It Well
The AI Forum - Medium, March 15, 2026
ACON: Optimizing Context Compression for Long-horizon LLM Agents
OpenReview / arXiv.org, February 11, 2026 (OpenReview) / October 1, 2025 (arXiv)
Context Management for Deep Agents
LangChain Blog, January 28, 2026
Market signals
The market for LLM context compression is a growing niche within the broader AI industry. Recent articles highlight the critical need for effective context management as AI agents tackle longer and more complex tasks, leading to 'context rot' and increased costs. Funding rounds in related areas, like AI coding assistants (Supermaven, Tabnine) and enterprise AI platforms (Contextual AI, Perplexity AI, Mithril), indicate strong investor confidence in technologies that enhance LLM efficiency and performance. Several companies have recently secured significant funding, suggesting a robust and expanding market for solutions that address LLM limitations.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI