ScrapeGraph AI

ScrapeGraph AI AI

ScrapeGraph AI AI

ScrapeGraph AI AI

ScrapeGraph AI AI

ScrapeGraph AI AI

ScrapeGraph AI AI

ScrapeGraph AI AI

ScrapeGraph AI AI

ScrapeGraph AI AI

ScrapeGraph AI AI

I built a 3D brain that watches AI agents think in real-time (free & gives your agents memory, shared memory audit trail and decision analysis)

Posted yesterday in this sub and just want to thank everyone for the kind words, really awesome to hear. So thought I would drop my new feature here today (spent all last night doing last min changes with your opinions lol) . Basically I spent a few weeks scraping Reddit for the most popular complaints people have about AI agents using GPT Researcher on GitHub. The results were roughly 38% saying their agents forget everything between sessions (hardly shocking), 24% saying debugging multi-agent systems is a nightmare, 17% having no clue how much their agents actually cost to run, 12% wanting session replay, and 9% wanting loop detection. So I went and built something that tries to address all of them at once. The bit you're looking at is a 3D graph where each agent becomes this starburst shape. Every line coming off it is an event, and the length depends on when it happened. Short lines are old events that happened ages ago, long lines are recent ones. My idea was that you can literally watch the thing grow as your agent does more work. A busy agent is a big starburst, a quiet one is small. Colour coding was really important to me. Green means a memory was stored, blue means one was recalled, amber diamonds are decisions your agent made, red cones are loop alerts where the agent got stuck repeating itself, and the cyan lines going between agents are when one agent read another agent's shared memory. So you can glance at it and immediately know what's going on without reading a single log. The visualisation is the flashy bit but the actual dashboard underneath does the boring stuff too. It gives your agents persistent memory through semantic and prefix search, shared memory where agents can read each other's knowledge and actually use it, and my personal favourite which is the audit trail and loop detection. If your agent is looping you can see exactly why, what key it's stuck on, how much it's costing you, and literally press one button to block its writes instantly. Something interesting I found is that loop detection was only the 5th most requested feature in the data, but it's the one that actually saves real money. One user told me it saved them $200 in runaway GPT-4 calls in a single afternoon. The features people ask for and the features that actually matter aren't always the same thing. The demo running here has 5 agents making real GPT-4o and Claude API calls generating actual research, strategy analysis, and compliance checks. Over 500 memories stored. The loops you see are real too, agents genuinely getting stuck trying to verify data behind paywalls or recalculating financial models that won't converge. It's definitely not perfect and I'm slowly adding more stuff based on what people actually want. I would genuinely love to hear from you lot about what you use day to day and the moments that make you think this is really annoying me now, because that's exactly what I want to build next. It runs locally and on the cloud, setup is pretty simple, and adding agents is like 3 lines of code. Any questions just let me know, happy to answer anything. submitted by /u/DetectiveMindless652 [link] [comments]

Burned 5B tokens with Claude Code in March to build a financial research agent.

TL;DR: I built a financial research harness with Claude Code, full stack and open-source under Apache 2.0 (github.com/ginlix-ai/langalpha). Sharing the design decisions around context management, tools and data, and more in case it's useful to others building vertical agents. I have always wanted an AI-native platform for investment research and trading. But almost every existing AI investing platform out there is way behind what Claude Code can do. Generalist agents can technically get work done if you paste enough context and bootstrap the right tools each session, but it's a lot of back and forth. So I built it myself with Claude Code instead: a purpose-built agent harness where portfolio, watchlist, risk tolerance, and financial data sources are first-class context. Open-sourced with full stack (React 19, FastAPI, PostgreSQL, Redis) built on deepagents + LangGraph. Learned a lot along the way and still figuring some things out. Sharing this here to hear how others in the community are thinking about these problems. This post walks through some key features and design decisions. If you've built something similar or taken a different approach to any of these, I'd genuinely love to learn from it. Code execution for finance — PTC (Programmatic Tool Calling) The problem with MCP + financial data: Financial data overflows context fast. Five years of daily OHLCV, multi-quarter financial statements, full options chains — tens of thousands of tokens burned before the model starts reasoning. Direct MCP tool calls dump all of that raw data into the context window. And many data vendors squeeze tens of tools into a single MCP server. Tool schemas alone can eat 50k+ tokens before the agent even starts. You're always fighting for space. PTC solves both sides. At workspace initialization, each MCP server gets translated into a Python module with documentation: proper signatures, docstrings, ready to import. These get uploaded into the sandbox. Only a compact metadata summary per server stays in the system prompt (server name, description, tool count, import path). The agent discovers individual tools progressively by reading their docs from the workspace — similar to how skills work. No upfront context dump. ```python from tools.fundamentals import get_financial_statements from tools.price import get_historical_prices agent writes pandas/numpy code to process data, extract insights, create visualizations raw data stays in the workspace — never enters the LLM context window only the final result comes back ``` Financial data needs post-processing: filtering, aggregation, modeling, charting. That's why it's crucial that data stays in the workspace instead of flowing into the agent's context. Frontier models are already good at coding. Let them write the pandas and numpy code they excel at, rather than trying to reason over raw JSON. This works with any MCP server out of the box. Plug in a new MCP server, PTC generates the Python wrappers automatically. For high-frequency queries, several curated snapshot tools are pre-baked — they serve as a fast path so the agent doesn't take the full sandbox path for a simple question. These snapshots also control what information the agent sees. Time-sensitive context and reminders are injected into the tool results (market hours, data freshness, recent events), so the agent stays oriented on what's current vs stale. Persistent workspaces — compound research across sessions Each workspace maps 1:1 to a Daytona cloud sandbox (or local Docker container). Full Ubuntu environment with common libraries pre-installed. agent.md and a structured directory layout: agent.md — workspace memory (goals, findings, file index) work/ /data/ — per-task datasets work/ /charts/ — per-task visualizations results/ — finalized reports only data/ — shared datasets across threads tools/ — auto-generated MCP Python modules (read-only) .agents/user/ — portfolio, watchlist, preferences (read-only) agent.md is appended to the system prompt on every LLM call. The agent maintains it: goals, key findings, thread index, file index. Start a deep-dive Monday, pick it up Thursday with full context. Multiple threads share the same workspace filesystem. Run separate analyses on shared data without duplication. Portfolio, watchlist, and investment preferences live in .agents/user/. "Check my portfolio," "what's my exposure to energy" — the agent reads from here. It can also manage them for you (add positions, update watchlist, adjust preferences). Not pasted, persistent, and always in sync with what you see in the frontend. Workspace-per-goal: "Q2 rebalance," "data center deep dive," "energy sector rotation." Each accumulates research that compounds across sessions. Past research from any thread is searchable. Nothing gets lost even when context compacts. Two agent modes With PTC and workspaces covered, here's how they come together. PTC Agent is the full research agent — writes and execu

I created my first MPC using Claude!

I used Claude Code to build America's Law Graph, a knowledge graph of 529,000+ US statute sections across all 50 states, USC, and CFR. Claude Code wrote most of the Spring Boot API, the Python data pipeline, the Neo4j graph derivation, and the React frontend. The whole thing from scraping state legislature websites to deploying on GCP was pair-programmed with Claude. The problem I was solving: every time I had a business idea, I couldn't answer "what are the legal implications?" without getting hallucinated citations from ChatGPT. So I built a knowledge graph that Claude can actually query through MCP. The MCP server has 11 tools: search legislation, traverse the citation graph, compare jurisdictions, get risk surfaces for business descriptions, semantic search, and more. You ask Claude "what California employment laws apply to remote workers" and instead of hallucinating, it queries the graph and returns actual statute sections with real citations and cross-references. It's free to try. No API key needed for the free tier (100 calls/day). Install it right now: npx america-law-graph. Or add it to your claude_desktop_config.json. It's also on Smithery as u/vestara and you can search manually at americalawgraph.ai. I'd love feedback from anyone using Claude for compliance, startup legal questions, or regulatory research. What tools would make this more useful for your workflow? submitted by /u/Significant-Ruin1348 [link] [comments]

I gave Claude Code a knowledge graph so it remembers everything across sessions

I got tired of re-explaining decisions to every new Claude Code session. So, I built a system that lets Claude search its own conversation history before answering. If you didn't know, Claude Code stores every conversation as a JSONL file (one JSON object per line) in your project directory under ~/.claude/projects/. Each line is a message with the role (user, assistant, tool), the full text content, timestamps, a unique ID, and a parentUuid that points to the earlier message it's responding to. Those parent references form a DAG (Directed Acyclic Graph), because conversations aren't linear. Every tool call branches, every interruption forks. A single session can have dozens of branches. It's all there on disk after every session, just not searchable. Total Recall makes all of that searchable by Claude. Every JSONL transcript gets ingested into a SQLite database with full-text search, vector embeddings (local Ollama, no cloud), and semantic cross-linking. So if you mentioned a restaurant with great chile rellenos two weeks ago in some random session, you don't have to track it down across dozens of conversations. You just ask Claude, "What was that restaurant with the great chile rellenos?" and it runs the search (keyword and vector) and has the answer. When you ask a question about something from a prior session, Claude queries the database and gets back the actual conversation excerpts where you discussed that topic. Not a summary. The real messages, in order, with the surrounding context. The retrieval is DAG-aware. Claude Code conversations aren't flat lists; they branch every time there's a tool call or an interruption. The system walks the parent chain backward from each search hit, so you get the reasoning thread that led to that point, not a random orphaned answer. Sessions get tagged by project, so queries are scoped. My AI runtime project doesn't pollute results when I'm working on a pitch deck. I also wrote a "where were we" script that shows the last 20 messages from the most recent session. You literally ask, where were we, and it remembers. That alone changed how I work. There's a ChatGPT importer too (I used it extensively before switching to Claude and hated having to remember which discussions happened where). It authenticates via Playwright, then calls the backend API to pull full conversation trees with timestamps and model metadata. It downloads DALL-E images and code interpreter outputs. Four attempts to get this working (DOM scraping, screenshots, text dumps) before landing on the API approach. Running on my machine: 28K chunks, 63K semantic links, 255 MB, 49 sessions across 6 projects. Auto-ingests every 15 minutes. I don't think about it. Everything is local. SQLite + Ollama + nomic-embed-text. One file you can copy to another machine. I open-sourced it today: https://github.com/aguywithcode/total-recall The repo has the full pipeline (ingest, embed, link, retrieve, browse), the ChatGPT scraper, setup instructions, and a CLAUDE.md integration guide. There's also a background doc with the full build story if you want the details on the collaboration process. Happy to answer questions. submitted by /u/browniepoints77 [link] [comments]