Extracts structured data from unstructured text with LLMs, mapping every extraction to its exact character span in the source for visual review. Uses few-shot examples, schema enforcement, and multi-pass chunking to handle long documents.
Runs a six-month live experiment where ChatGPT manages a real-money micro-cap portfolio from $100, trading under strict rules with automated stop-losses. Each trade's rationale is logged; returns are benchmarked against the S&P 500 and Russell 2000.
Bundles Langflow, Docling, and OpenSearch into one installable package so you can ingest messy documents, run agentic retrieval with re-ranking, and chat over your own knowledge base. Ships Python/TS SDKs and a built-in MCP server at /mcp.
Exposes Google Analytics Admin and Data APIs as a local Model Context Protocol (MCP) server so LLMs can query accounts, run reports, funnels and realtime queries via standardized MCP tools. Intended for local prototypes and developer integrations; requires Google Cloud credentials.
Closes a learning loop most agents lack: turns experience into reusable skills, refines them mid-task, and full-text searches its own past sessions for recall. Runs from CLI or Telegram/Discord/Slack and schedules unattended cron jobs.
Modular marketplace of focused Claude Code plugins that composes specialized agents and progressive 'agent skills' to orchestrate multi-agent development workflows while minimizing token usage.
An open-source memory layer that turns agent runs and conversations into structured, persistent state recallable across sessions. Captures facts, events, preferences, and relationships automatically; LLM-agnostic with SDK and MCP integration.
Parses PDF resumes into structured JSON using LLMs, enriches profiles with GitHub signals, and outputs explainable category scores, evidence, bonuses and deductions. Runs fully local with Ollama or via Google Gemini; designed for reproducible, fairness-constrained resume scoring in hiring workflows.
Compiles an agent's raw chat logs, documents, and tool traces into three persistent layers — index, learned skills, and user memory — so context survives sessions. Claims 92% Locomo-benchmark accuracy and up to 95% lower token cost than replaying history.
Deep research agent for complex, long-horizon research and prediction tasks. Pairs a 256K context window with up to 300 tool calls per query for web search, extraction, and code execution. Ships as open 30B and 235B models scoring 82.7% on GAIA.
Provides an MCP server exposing 30+ trading tools — real-time prices, technical indicators, Bollinger Band scores, Reddit/news sentiment, and backtesting — designed to integrate with Claude/OpenClaw agents for automated market analysis.
Provides hierarchical, versioned semantic memory for AI agents with Git-like branching, commits, and rollbacks—using semantic paths and cryptographic provenance instead of opaque vector stores. Designed for branch-aware, auditable memory in multi-agent and production workflows.