Alternatives
Products that do what CaptureFlow – LLM codegen/bugfix powered by live application context does
Hi Hacker News, As a dev extensively using GPT-4 for coding, I've realized its effectiveness significantly increases with richer context (e.g., code samples, execution state - props to DevinAI for famously console.logging itself). This inspired me to push the idea further and create CaptureFlow. This tool equips your coding LLM with a debugger-level view into your Python apps, via a simple one-line decorator. Such detailed tracing improves LLM coding capabilities and opens new use cases, such as auto-bug fix and test case generation. CaptureFlow-py offers an extensible end-to-end pipeline…
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- 5WC
Hi all, I'm Ivan, and together with Alex, we're building a diagram visualization tool for codebases. Alex and I are devs, and we've noticed that recently we've been super productive at writing code (prompting :D). But when it comes to understanding big systems, prompting doesn't work that well — for that, diagrams are best imo. Most tools out there don't scale to big projects (e.g. PyTorch), so we're building CodeBoarding — a recursive visualizer for codebases. It starts from the highest level of abstractions and lets you dive deeper. We use static analysis and LLM agents. The control-flow…
2025 · github.com
- 6VP
2021 · github.com
- 7GB
Hey HN, We’re excited to share PySpur, an open-source tool that provides a graph-based interface for building, debugging, and evaluating LLM workflows. Why we built this: Before this, we built several LLM-powered applications that collectively served thousands of users. The biggest challenge we faced was ensuring reliability: making sure the workflows were robust enough to handle edge cases and deliver consistent results. In practice, achieving this reliability meant repeatedly: 1. Breaking down complex goals into simpler steps: Composing prompts, tool calls, parsing steps, and branching…
2024 · github.com
- 8CV
I built Codag because I kept getting lost in my own AI code. You're chaining 3 LLM calls across 5 files. A prompt change breaks something downstream. Which call? Which branch? You grep for "openai.chat", open 8 tabs, trace the flow manually. Codag automates this: - Point it at your codebase and it extracts every LLM call, decision branch, and processing step - Renders an interactive and shareable DAG with clickable nodes that link back to source - Live updates as you edit using tree-sitter — no waiting for re-analysis Supports OpenAI, Anthropic, Gemini, LangChain, LangGraph, CrewAI, and…
Feb 2026 · github.com
- 9CM
I've been vibe-coding tools to automate chunks of my consulting work, fell down a rabbit hole, and started building actual products. Suddenly I'm in a world of unknown-unknowns and known-unknowns. One of the bigger things to solve was understanding code the LLM generated that I didn't fully grasp. What does it touch? What reads and writes where? Is the auth path where I think it is? So I built codeflowmap. Point it at a repo and it maps the dependency and call graph, then surfaces the read / write / auth paths between files and functions. Connect a local model (Ollama) or any…
Jun 2026 · github.com
- 10RA
We built RapidFire AI, an open-source Python tool to speed up LLM fine-tuning and post-training with a powerful level of control not found in most tools: Stop, resume, clone-modify and warm-start configs on the fly—so you can branch experiments while they’re running instead of starting from scratch or running one after another. - Works within your OSS stack: PyTorch, HuggingFace TRL/PEFT), MLflow. - Hyperparallel search: launch as many configs as you want together, even on a single GPU - Dynamic real-time control: stop laggards, resume them later to revisit, branch promising configs in…
Sep 2025 · github.com
- 11LA
We combined Stanford's ACE (agents learning from execution feedback) with the Reflective Language Model pattern. Instead of reading traces in a single pass, an LLM writes and runs Python in a sandbox to programmatically explore them - finding cross-trace patterns that single-pass analysis misses. The framework achieved 2x consistency improvement on τ2-bench.
Mar 2026 · github.com
- 12CV
I worked as a software engineer at Amazon, SAP, and on open source. In all 3 places I have struggled with the friction of understanding codebases before I can make a contribution. I think this brain-fatiguing process can be improved. I am trying to solve it with a tool I built over the last 4 years called CodeCanvas: https://docs.code-canvas.com CodeCanvas visualizes codebases through interactive diagrams linked directly to source code. Users can record 'simulations' to demonstrate data flow and business logic. I’ve also recently added an LLM chat where it takes only the relevant…
2025 · pie-crepe-38f.notion.site
- 13FA
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
- 14LF
Hey HN, I built SWE-Kit, LLM toolkit (Function callable tools) which makes building agents specialised in coding like Devin very easy. I noticed a typical pattern while building local agents: creating & perfecting LLM tools to interact with system or codebase was the repeated and time-consuming. We created a layer that simplifies building agents that can interact with code, file system, git, shell and allows you to quickly solve for a wide variety of coding agent use cases. Aren’t there open coding agents already? Well, yes, but most folks would want to solve their specific use case like a…
2024 · swekit.dev
- 15GY
Hi all, I've been working on this devtool for 1 month now for myself at first and I'll be curious to see if it's something that could work for you as well. So basically, it detects bugs in your website in production from real user sessions, an llm clusters them by severity and it provides the complete context of the issue that you can copy-paste into your coding agent to fix it in one go. Why did I create it? I've been shipping fast with tools like Cursor and Claude Code. The problem? When bugs happen in production, these tools have zero context about what actually went wrong. Sentry is…
Nov 2025 · sonarly.dev
- 16EL
Hey HN! I built Experiment to solve a common frustration in LLM development: the lack of proper tools for prompt engineering experimentation. Here's what makes it different: Key Features: - Load and edit chat completion logs from CSV files - Fork and modify specific conversation entries - Run inference via Anthropic, Mistral, and OpenAI - Define custom tools using JSONSchema format - Visual tool usage analysis with collapsible, sorted key-value pairs - Full mobile support and available as installable PWA Technical Highlights: - Built with React using custom isomorphic architecture -…
2025 · github.com
- 17NT
I built a CLI tool that turns codebases and PRs into diagrams so you can quickly understand how things fit together. Originally made it because I couldn't follow my own AI-generated repos. Just shipped a big update: - Switched from D2 to Mermaid for rendering - Tree-sitter AST parsing + agentic flow instead of raw LLM calls. ~50x faster. - Works on any GitHub repo or PR, not just local - Dropped the web frontend, it's just a CLI now - Published as a pip package Still a ton to improve and I'm building fast. Feedback, issues, PRs all welcome.
Feb 2026 · github.com
- 18AS
Hi HN, I built ast-visualizer.com because I wanted a way to visualize the architecture/structure of a Python repo before dived into the code. Most tools tell you what the code does; I wanted to see how it's built. The Problem: Onboarding onto a large codebase is a nightmare. LLMs help with single functions, but they struggle to show you the "God Objects," circular dependencies, or high-complexity hotspots across 50+ files. What it does: Dependency Graph: Visualizes imports and file complexity to find architectural bottlenecks. Radial AST Heatmaps: Maps individual files and color-codes…
Feb 2026 · ast-visualizer.com
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Hey everyone, My friend and I built a simple bug fixing app that listens for alerts/issues from Sentry, contextualizes it against your codebase, and any other data sources you wish to connect (right now we support Notion, Google Docs, and Slack), and deploys an ai agent to write a PR for review in Github or Gitlab to solve the bug. Our current demo shows the end-to-end process for a trivial bug fix, but we have been testing it with open source python repos like http-pie, comparing how our agent solves a bug compared to a human engineer and it gets fairly close. We are working on adding…
2023 · resolvd.ai
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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
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A few weeks ago I got rejected from a role and the feedback was that I needed a deeper understanding of FAISS and LlamaIndex. So I built triage.flow — an AI assistant that lets you explore and understand GitHub repositories through a chat interface. It clones a repo, indexes it using FAISS + BM25 + tree-sitter parsing, and powers a full UI where you can: - Ask natural-language questions like “how does auth work?” or “explain @src/components/Modal.tsx” - Mention specific files/folders with @filename.ts (autocomplete supported) - See how the agent thinks in real time (Thought →…
2025 · github.com
- 22IM
Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com
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2020 · github.com
- 24IB
I’ve spent the last 2.5 months building a product that runs LLM-powered code reviews on my pull requests — and I just launched it. The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood. Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all with minimal need for another human in the loop. Things I want to try out in the next months : -…
2025 · codii.dev
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