Brave Search API

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

I built an MCP server so Claude can query repo structure before opening files

I built a tool called Graphenium after repeatedly running into the same issue with Claude on medium-to-large repos. Claude is usually good once it has the right files in context. The weak part is the first few minutes of a session, where it has to reconstruct the shape of the project: search for a symbol read the file follow imports read another file summarize the area notice a missing dependency search again That is not Claude doing anything wrong. It just starts every conversation without a durable model of the repository. Graphenium is my attempt to give it one. It analyzes a repo once, stores the result as a graph, and exposes that graph through MCP. Claude can then use tools like: graph_stats architecture_summary query_graph get_neighbors shortest_path god_nodes summarize_file The intended workflow is not "never read source code." It is: ask the graph where to look open the relevant files then reason from the actual source That matters because the graph output is much smaller than dumping half a repo into the context window just to find the right starting point. Example setup: cargo install graphenium gm run . --no-semantic --no-viz gm setup claude AST-only mode runs locally and does not need an API key. It extracts repository structure using tree-sitter: files, symbols, imports, containment, methods, communities, hubs, and paths. There is also an optional semantic mode: gm run . --provider anthropic That pass can add inferred relationships such as calls, uses, implements, and depends_on. I am careful about trust boundaries here. Every edge has a confidence level: EXTRACTED deterministic static extraction INFERRED useful lead, verify before important edits AMBIGUOUS uncertain relationship, treat as a question So Claude can use the graph as a map, but should still read source before changing code. The repo also includes a Claude Skill at: skills/graphenium/SKILL.md That gives Claude guidance on when to call the graph tools, how to interpret confidence levels, and how to fall back to the CLI if MCP is unavailable. Repo: https://github.com/lambda-alpha-labs/Graphenium I am looking for feedback from Claude Desktop / Claude Code users. The main thing I want to know is whether this actually changes Claude's behavior: does it choose better files earlier, avoid irrelevant reads, and keep more context available for reasoning? submitted by /u/RevolverOcelot86 [link] [comments]

Spawn parallel CC sessions in multiple repos at once

tl;dr Have you ever needed to run the same prompt, but in multiple subdirectories to make a similar change? I built an MCP server that indexes all your repos, lets you query them, makes batch PRs, and gives you a summary of workflow runs. Here's what it does: 1. Indexing, which happens in 2 forms: - Codebase level: runs an agent CLI (with proper context) over all repos to extract what each one does, how they relate, and what the system looks like as a whole. - Repo level: Having the codebase context, it extracts logical info of each repo, and also the libraries, dependencies, etc for lexical search 2. Search, also in 2 forms: - Natural language: where it answers search queries with respect to the codebase and targeted repository context - Structured search: where it returns the result based on actual dependencies (eg "find me repositories that are written with Python, have requirements.txt, and are using FastAPI) 3. Batch change: Simply prompt "find my Python repositories and update library X from vY to vZ"; This will search and find the affected repos, clone them, run a CLI agent like CC on each with the context we already persisted, create and prepare PRs, and give you a report of the results. Tech stack Now it only covers ClaudeCode and Github: mongodb To store the repository tree, dependencies, and workflows redis To store the user's session to track the ongoing batch job claude-cli/Devin Used as the main engine docker-compose to build traefik for routing I would appreciate your feedback and thoughts on this Github: https://github.com/sorena-ai/service-catalog-mcp Demo video: https://infraas.ai/ PS: I reviewed all the code, so if it looks like slop, that's me ^^ submitted by /u/Terrible_Equivalent3 [link] [comments]

A clean breakdown of RAG vs MCP architectures for AI Agents

Hey everyone, There is still a lot of confusion around how Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP) fit together—specifically when a project actually warrants the engineering complexity of an agent framework versus a clean vector database lookup. I put together a comprehensive video breaking this down, but I wanted to share the core architectural checklist and trade-offs directly here for quick reading: The Core Difference RAG is a Library: It’s a predictable, linear pipeline (Embedding -> Search -> Context Injection -> Response). Perfect for factual grounding, low latency, and deterministic costs. The catch? It’s rigid. If the retrieval step grabs the wrong context, the model can't self-correct. MCP is a Toolkit & Plan: It places the LLM inside an iterative loop (Thought -> Action -> Feedback). It standardizes how models connect to dynamic data, APIs, and file systems. The catch? High latency, unpredictable compute costs, and the risk of strategy loops. The Decision Checklist Data Pattern: Is it a straightforward lookup? Performance Constraints: Need fast, low-cost responses? RAG is simpler to scale and debug MCP unlocks massive agentic capability but requires strict engineering discipline (timeouts, step caps). If you want to see how these two patterns put side-by-side or walk through the full structural breakdown, I went deep into it here: https://youtube.com/watch?v=uBf6pKPjBo0 Curious to hear how others are balancing these two in production right now. Are you keeping them strictly separated, or using MCP servers to expose RAG pipelines as standard tools? submitted by /u/SKD_Sumit [link] [comments]

I built a little shared-memory + persona thing for Claude and finally cleaned it up enough to share

Hi, Two things kept bugging me about working with Claude: It forgets everything between sessions, and Claude Code, Desktop, etc. each keep their own separate notes. Tell one a preference and the others have no clue. The usual fix is to stuff more into CLAUDE.md / global instructions, but that loads on every turn and the model gets noticeably worse as the context fills up. So you end up picking between an assistant that forgets and one that's buried under everything you told it to remember. A while back I hacked together a setup for myself: one memory store kept as plain markdown in a git repo, handed to Claude as an MCP server, plus a small persona block that's the only part actually riding in context. Everything else gets pulled in by search when it's relevant. I've been using it daily and genuinely like it, and enough people asked me to set up something similar for them that I figured I'd just package it properly so anyone can run it. So here it is: npx agent-julia init and a wizard walks you through the setup. (Yeah, the default persona is named Julia. Name yours whatever you want, I'm not precious about it.) What it does: one memory shared across Claude Code and Claude Desktop (Cowork) plain markdown + git, so you own the files and nothing leaves your machine by default keyword search always on, optional local semantic search (no API key, runs offline) a persona/voice you set once, and corrections that stick across surfaces Fair warning: this is v0.1. It works for me, but I've basically been the only real user, so there are definitely rough edges and stuff I haven't thought of. Mobile Dispatch isn't supported either (it can't reach a local server), in case that's a dealbreaker. Mostly I'd love for people to kick the tires and tell me what's confusing, what breaks, or what's missing. Feature ideas very welcome too. Even "this wizard wording is weird" is genuinely useful at this stage. Repo + install instructions: https://github.com/elninopl/agent-julia Thanks for taking a look. submitted by /u/elnino-pl [link] [comments]

Most "AI memory" tools ship zero benchmarks. I come at it from the other side: I wrote a paper on training-free multi-hop retrieval (at ItalySoft) https://zenodo.org/records/20668567, and WikiMoth is that engine packaged small.

on a real 356-note vault: - ~5k tokens to answer a question vs ~482k to paste the whole vault. -99%! - recall@8 = 1.00 on simple lookups: easy. - multi-hop (answer 2-3 links deep): keyword and vector score 0%, link-walking gets 100%! - same query, 5 runs, 1 result. Deterministic. it means that is code not a LLM! `wikimoth install` wires it into Claude Code, and from then on it's hands-off: each session you finish gets saved as one linked markdown note, and your recent notes load back into context at the start of the next session. Claude boots with your memory automatically, no manual step. submitted by /u/ObjectiveEntrance740 [link] [comments]

Claude Agent Factory

Hello everyone, We built xSquad - A Claude Agent Factory. We built it mainly to make is easier for not so technical folks to build and contribute to software development. The main benefit is people can now run Claude agents without having to deal with the complexity of setting up the CLI locally, or setup local git and the local copy of the project You can use it to complete tasks on your hobby projects, or power through your huge backlog of low priority tickets that never get done. Product Owners and Scrum Masters can now get 70-80% of their backlog tickets done. Get started in 3 simple steps 1) Login into https://app.xsquads.ai/ using your github credentials. Create a project by selecting your repo 2) Create Tasks on the Kanban board and watch the agents pick and work on them 3) Comeback after a few minutes to PRs waiting for your review. Would love for you to try it and share feedback. You do get a few free credits to try out a couple of tasks. submitted by /u/Normal-Cattle5915 [link] [comments]

browser-search — three tools, zero cost, and your AI agent learns to search and browse the web

I've been using AI agents like OpenCode, Claude Code, and Cursor for months. They're great with code, but when they need to search or browse the web, things get complicated: Cloudflare blocks them, JavaScript-heavy sites don't load, APIs cost money. So I built browser-search. It's three open source tools orchestrated by a skill, fully self-hosted: SearXNG — metasearch engine that queries dozens of search engines at once Camofox — full browser via REST API, always warm, for browsing and interacting CloakBrowser — stealth browser for when the site has Cloudflare, Akamai, or DataDome The agent decides which tool to use. Zero human intervention. Zero API keys. Zero subscriptions. What makes it different: It's a skill, not a plugin — works with any agent that can read instructions Automatic navigation escalation: if Camofox gets blocked, it switches to CloakBrowser Deep Research mode: the agent is instructed to go beyond surface-level answers, cross-verify sources, cover every aspect Integrated Readability.js for clean article extraction (~70% token savings) The SKILL.md is plain text — fork it, tweak it, make it yours MIT licensed on GitHub: https://github.com/Johell1NS/browser-search If you try it, let me know. If you make it better, even more so. If you don't need it, share it with someone who might. Every star, comment, or pull request is welcome — that's what makes open source great. submitted by /u/Ill-Tradition1362 [link] [comments]

browser-search — three tools, zero cost, and your AI agent learns to search and browse the web

I've been using AI agents like OpenCode, Claude Code, and Cursor for months. They're great with code, but when they need to search or browse the web, things get complicated: Cloudflare blocks them, JavaScript-heavy sites don't load, APIs cost money. So I built browser-search. It's three open source tools orchestrated by a skill, fully self-hosted: SearXNG — metasearch engine that queries dozens of search engines at once Camofox — full browser via REST API, always warm, for browsing and interacting CloakBrowser — stealth browser for when the site has Cloudflare, Akamai, or DataDome The agent decides which tool to use. Zero human intervention. Zero API keys. Zero subscriptions. What makes it different: It's a skill, not a plugin — works with any agent that can read instructions Automatic navigation escalation: if Camofox gets blocked, it switches to CloakBrowser Deep Research mode: the agent is instructed to go beyond surface-level answers, cross-verify sources, cover every aspect Integrated Readability.js for clean article extraction (~70% token savings) The SKILL.md is plain text — fork it, tweak it, make it yours MIT licensed on GitHub: https://github.com/Johell1NS/browser-search If you try it, let me know. If you make it better, even more so. If you don't need it, share it with someone who might. Every star, comment, or pull request is welcome — that's what makes open source great. submitted by /u/Ill-Tradition1362 [link] [comments]

Built a small tool that gives coding agents automatic web-search

I kept running into the same problem with Claude Code/Pi/OpenCode. The agent would be halfway through a task, need current docs, hit a rate limit on Tavily (or whatever provider I was using), and suddenly become useless. So I built a tiny CLI called Seek: https://github.com/Rishang/seek The idea is simple: seek search "latest react router changes" Behind the scenes it tries whichever providers you have configured. Tavily -> 429 Exa -> 401 Firecrawl -> success The agent gets a result and keeps working. A few things I added: Currently supports 7 search/fetch providers Automatic failover MCP server (seek mcp) Simple HTTP API (seek serve) Works with Claude Code, Cline, Pi, OpenCode, etc. Single Go binary I built this mostly because I got tired of watching agents fail because a search provider had a bad day. Would love feedback (or reasons this is a terrible idea). GitHub: https://github.com/Rishang/seek That style feels like an actual Reddit user posting, not a company trying to do content marketing. submitted by /u/Horror-Spend-9276 [link] [comments]

I built a solution but not sure if the problem even exists?

I personally find myself working accross different AI tools for different aspects of my project -- Claude to build the core functionality Codex to fix the bugs (to save claude's credits) Chat GPT to get ideas for new features etc etc etc And all of them with multiple accounts as well- TO save some credits Now this works but it creates a mess. This essentially leaves me with problems like Problem 1- I have no memory of Which agent created what feature and to find it I have to spend a lot of time scrolling through my chats with all these AI agents that too on multiple accounts Problem 2- If my credits on one account gets over when I log in to another account now I have to explain it everything I have built so far and that almost always takes more than 1-2 prompts and that again costs me credits on the second account as well. Solution - this project - A place where you can simply copy and paste AI responses, codes, files etc from various AI agents and it will store all of it in one place in a git push style User Interface you can exactly see when and what was changed and in a single click you get a summarized context of the project explaining to AI everything we have done so far in just one prompt. Saves Credits and time all at an affordable price as well. Would you use this ? and feel free to suggest any changes in this project submitted by /u/Sea_Bed520 [link] [comments]

I built a real-time YouTube fact-checker with Claude Code

https://reddit.com/link/1ub7w8v/video/3yyn91ou8i8h1/player First shout-out to u/Debate_Witty and InTruth — I've been independently working on this same problem since last September, with my own launch planned for the end of this week. Seeing the response to their post was genuinely encouraging: it confirms there's real demand here. Glad to be coming at it from a different angle. What I built: a Chrome extension that puts real-time fact-check bubbles over YouTube videos as people speak. It pulls real sources from the web, evaluates the claim against them, and shows the verdict — with the sources — right on screen. How Claude Code helped: Claude Code has been my development environment from day one back in September — first in the terminal, and later through the Claude Code extension in VS Code as I moved over to it. I pair-programmed the entire backend with it: the RAG orchestration, the source-waterfall, the caching layer, the verdict-classification taxonomy, and months of iterative QA. Day-to-day debugging, refactors, and deploys all ran through Claude Code. A solo build of this scope simply wasn't realistic without it. Try it free: there's a real free tier — just install and hit play. Plus is $9.99/mo when you want more. Chrome Web Store: search "PopUp Fact Check for YouTube" Landing page: PopUpFactCheck.com 70-min demo: https://www.youtube.com/watch?v=zkprFltMbXM What makes it different: 🔑 No API keys to bring — no OpenAI/Claude/search keys to sign up for, no config. Just install and play. 🧠 Not just True/False — it reads context, flags misleading framing, and calls out rhetoric and opinion dressed up as fact. 👍👎 Vote any bubble up or down — every fact-check (anonymized) feeds back into the QA pipeline, so it improves the more it's used. Under the hood — for the builders: Developed with Claude Code; verdicts generated by GPT-5.4. RAG, grounded in real retrieved sources — never the model's memory. A source waterfall reaches for the most authoritative first: government data (BLS, FRED, IMF), fact-checkers, and news APIs — then web search (DDGS + Serper). A cache sits in front of everything, so repeated claims across videos and viewers are answered instantly, keeping overhead low. Cost-engineered from the input up. The text the model reads is the video's own transcript/captions — not voice-to-text — so there's no speech-recognition bill on every minute watched. Combined with the cache, that's how the backend stays cheap enough for a $9.99 product to be sustainable. The trade-off, by design: a video needs closed captions available, with CC turned on, for fact-checking to run. A continuous-improvement loop — anonymized results + your votes flow into ongoing QA, on top of months tuning the verdict taxonomy, temporal/misleading detection, source quality, and coherence. To use it: turn on the video's closed captions (CC) and play. For regular videos it reads the full transcript; for live streams it uses the live captions if the feed provides them — and after a live broadcast ends, the full transcript usually takes about an hour to become available. Claims then get checked, with context, as they're spoken. I'd genuinely love your feedback — positive, negative, and especially where it falls short. submitted by /u/userpostingcontent [link] [comments]

Built a news tool with Claude I've wanted for a long time

I've always hated when news stories just die and I never hear about them again. I built a site that searches for updates weekly starting from a particular article. It uses a combination of the Serper Google API, Haiku for small judgements, and Sonnet for larger synthesis. It costs about $0.05 for a weekly scan, so that's not bad. The basic way it works is to take the original article and use Haiku to extract search terms and phrases to keep up to date with the story. Not quite, but something like "whatever happened to that story about X". Then, it tries every week to make an update or just say nothing if it can't find anything. Check it out at www.signaltracker.news I've already added tags and some other small things. I'd be interested in feedback, especially if you can think of a better way of doing it. submitted by /u/Nearby-Nebula4104 [link] [comments]

I built a local MCP gateway (Conduit) almost entirely with Claude Code, what it does and what I learned

Since this is r/ClaudeAI, the how might be as useful as the what. I built this with Claude Code in the last 48 hours. What it is: a local desktop app that puts all your MCP servers behind one gateway, so they work across Claude Desktop, Claude Code, Cursor, and other tools. You set up and authenticate each server once instead of reconfiguring it in every client. It's free and open source (MIT), Windows now, macOS/Linux coming. Why I built it: I use Claude Desktop + Claude Code plus a couple other tools, and managing MCP servers across all of them was painful, the same servers configured in each app, API keys in plaintext, and every agent buried under hundreds of tool definitions. What it does: Lazy discovery: your agent sees 3 meta-tools instead of 400 and searches/calls on demand, so context stays small Keys live in your OS keychain, not config files Per-agent profiles, an audit log, and a built-in tool playground How Claude helped (the interesting part): It's a Tauri app, Rust gateway + React frontend, and I'm not a strong Rust dev. Claude Code wrote most of the gateway, the MCP protocol handling, the OAuth 2.1 flow, and the keychain integration. The hardest part was Windows-specific bugs, and it diagnosed each from the symptoms: MSIX path virtualization (packaged apps like Claude Desktop silently redirect %APPDATA% into a sandbox, so the app was reading the wrong config) a stdio server with no read timeout deadlocking the whole health check OAuth callback-port collisions when re-authing quickly What I learned: if you're routing many MCP servers through one place, "lazy discovery" (a few meta-tools the agent searches, instead of exposing all of them) is the thing that keeps context usable, most clients silently drop tools past ~40-128 anyway. Free to try: conduit.southforgeai.com · code: github.com/tsouth89/conduit Happy to answer anything about building it with Claude Code. And genuinely curious, what's the most annoying part of your MCP setup right now? edit: added a 30 second demo https://reddit.com/link/1uaj131/video/9b6vsvkvnf8h1/player submitted by /u/kydude [link] [comments]

A map of the Agentic Future

Hey guys, I have been thinking a lot about where the current tech paradigm may ultimately lead. Everyday I see a ton of new products : better assistants, better automation, better this, faster that… But what is going on here is much deeper than a betterment of existing use cases. My current hypothesis is that we are shifting from a world of direct interaction to a world of representation where everyone and everything will have an agent. And I mean it : corporations, brands, places, institutions, your dentist, that guy on eBay selling vintage armchairs, you… All will have an agent. This shift, that I call the Agentic Shift, will have deep implications on a broad spectrum of domains And at some point my agent may even meet yours without us ever meeting. This diagram is my attempt at mapping that transition: the Agentic Shift, a move from direct interaction to delegation, and ultimately from delegation to representation. I'd love to get the conversation going on this subject. What is your take on it? What am I missing? Where do you think this reasoning breaks down? submitted by /u/ReversedK [link] [comments]

Megathread Summary: I Asked Multiple Reddit Communities How to Build a Living Memory /Context Engine for Business. Here's what everyone had to say.

I am trying to build a living memory/context engine for my business, something that can remember projects, decisions, timelines, risks, and conversations across emails, documents, notes, chats, and meetings. Since this is new territory for me, I asked several Reddit communities for advice. The responses were incredibly thoughtful, and many people shared architectures, engineering trade-offs, tools, and lessons learned from building similar systems. I consolidated the best ideas into a single summary. If you're exploring the same problem, especially if you're just getting started like me, I hope this will help. Core Philosophies & Perspectives Query-First Design: Do not build the storage layer first. Write out 20 real-world queries you will ask tomorrow and architect backward, because the retrieval interface shapes the system more than the storage layer. Chief of Staff vs. Search Engine: The goal is not just retrieving raw data, but synthesis. Like Microsoft Clarity’s bulk insights, the system should process updates and proactively tell you what projects need attention, what changed, and what the blockers are. The "Daily Mirror" Briefing: Focus on what the user needs to know at the start of the next session to continue without context loss, rather than striving for perfect archival completeness. Four Separate Problems: Treating user queries as a single search issue will fail; "latest status" is a retrieval problem, "unresolved issues" is state tracking, "decisions made" is entity extraction, and "important updates" requires significance scoring. Architecture & Strategies Append-Only Event Logs First: Avoid starting with a massive knowledge graph or vector database. Ingest everything as a timestamped, append-only event log, and build the knowledge graph later as a derived query layer on top. Artifact-Mediated Continuity: To prevent identity collapse over long timelines, separate retrieval (facts) from reconstruction (identity and working context). Use a "Principal-owned Artifact System" with files like MEMORY.md for project state, "Texture Packs" for behavior descriptions, and "Lane Files" structured around the Five W's. Parallel Retrieval Paths: Pure vector search fails at scale. Run vector search (for semantic similarity) alongside a graph/relational lookup (for exact entities) in parallel, because neither covers the query surface alone. Hybrid search (semantic + BM25 keyword) is heavily recommended. Split Memory by Lifespan & Namespace: Sector your memory from day one. Split durable facts (stable preferences, user info) from working context (recent events), applying different decay rates and routing queries to the appropriate layer. Continuous Summarization: Instead of treating everything as unstructured documents, use an LLM pipeline to continuously extract structured facts from new inputs to update project briefs, decision logs, and risk trackers automatically. The Hardest Engineering Challenges Entity Resolution (The Silent Killer): Different sources will refer to the same thing differently (e.g., "Project X" vs "the X pilot"). Without an entity registry mapping aliases to canonical IDs before writing, your graph will become a mess of duplicates. Ontology & Classification: The hardest part is often getting the system to universally understand the difference between a "decision", a "discussion", or a "risk" across varying data structures like emails versus meeting transcripts. Temporal Relevance & Stale Context: A "decision" stays load-bearing for months, whereas a "status update" decays in days. If you don't encode decay rates and version records, stale facts will outrank fresh ones and confidently contradict recent updates. Significance Scoring: Standard retrieval returns everything recent, not everything important. Write-time scoring fails because significance is retrospective; a better approach is "adaptive salience," where chunks gain weight when retrieved and decay when ignored. Context Moodiness: Especially in greenfield projects, meaningful status updates can be muddied by confounding, irrelevant, or noisy data. Tools & Tech Stack Recommendations Storage / Databases: Vector stores like pgvector for semantic search, paired with key-value or relational databases for exact lookup. Airtable, Databricks, Notion, and Obsidian were also noted as strong foundational or single-source-of-truth layers. AI Models & Agents: Claude Code, OpenAI Codex, Hermes-agent (by Nous Research), AsanaAI, and ClickUp Brain. Injecting local LLMs where appropriate can help cut down on continuous API costs. Middleware & Pipelines: Kapex: Memory middleware built specifically to score node significance, governing lifecycle so resolved stuff fades and unresolved stuff persists. Sauna.ai: An engine built out of Wordware that fits this use case. Automation: Make.com or n8n for routing deterministic logic and LLM reasoning. The "Party Model": A CRM data integration framework