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Extracts and structures data from receipts, invoices and transaction documents using configurable LLM prompts for a self-hosted accounting workflow. Offers multi-currency (including crypto) historical conversion, custom fields/prompts, batch processing and Docker-based deployment for local data control.
Autonomously proposes hypotheses, runs experiments, analyzes results, and drafts workshop-level papers via an agentic tree-search pipeline. Unlike template-driven predecessors, it explores open-ended ML research paths but requires GPU/PyTorch and careful sandboxing due to execution of LLM-written code.
Orchestrates a lead agent, isolated parallel sub-agents, long-term memory, and sandboxes for long-horizon tasks — minutes to hours of deep research, coding, and content creation. LangChain/LangGraph-based with extensible skills; v2 is a full rewrite.
Federates MCP, A2A, and REST/gRPC backends behind a single gateway endpoint with centralized discovery, governance, and observability; optimizes agent and tool calling. Includes gRPC→MCP translation, plugin extensibility, OpenTelemetry tracing, and Kubernetes-ready deployment.
Translates full-length books, subtitles, and documents with LLMs while preserving original formatting and structure. Uses intelligent chunking to handle arbitrarily long files, supports local or cloud providers, and resumes interrupted jobs without losing progress.
A code-first collection of runnable tutorials for building production-ready generative-AI agents — step-by-step guides covering stateful workflows, vector memory, RAG, tool integrations, Docker/AWS/RunPod deployment, security guardrails, observability, and multi-agent patterns.
Offline-first knowledge server that bundles local AI chat (Ollama + vector RAG), offline Wikipedia/education/maps, and utility tools behind a Dockerized management UI — designed to keep searchable knowledge available without cloud access.
Browser-based AI development platform that runs tasks inside isolated cloud development environments: natural-language agents read code, run commands, modify files, and integrate results back into Git. Key features include per-task sandboxes, multi-model selection, and an enterprise private-deploy option.
Provides a visual, low-code environment to build, debug, and deploy AI agents—integrates model services (OpenAI, Volcengine), RAG, plugins, workflows, and a Chat SDK for embedding agents into apps.