Framework-agnostic library for connecting and optimizing teams of AI agents built in LangChain, LlamaIndex, CrewAI, Semantic Kernel, or Google ADK. Profiles them down to individual tokens, traces execution, and runs built-in evaluation.
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.
Provider-agnostic framework for orchestrating multi-agent LLM workflows in Python: agents that delegate via handoffs, function/MCP/hosted tools, input/output guardrails, automatic session memory, and a visual tracing UI for debugging runs.
Trains multi-step LLM agents with reinforcement learning (GRPO) on your own tasks, wrapping existing agent code behind an OpenAI-compatible client. Its RULER mode scores trajectories with an LLM judge, so there's no reward function to hand-write.
Provides 7×24 automated customer service for the Xianyu marketplace with multi-expert routing, context-aware dialogue, and a laddered bargaining system. Built in Python and designed to run against an LLM provider with browser-cookie integration for web interactions.
Transforms unstructured financial content—papers, news, blogs, and filings—into a queryable semantic knowledge graph for retrieval-augmented research. Combines domain-tuned LLMs, embedding-based search, and modular ingestion pipelines; aimed at quant research teams and institutional workflows.
Bridges LLM-driven AI assistants to the Unity Editor so models can create scenes, edit C# scripts, manage assets, run tests and automate game-dev workflows. Exposes 47 focused MCP tool entrypoints, supports many MCP clients, and is MIT-licensed for local use.
Twelve engineering principles for building production-grade LLM agents, modeled on the 12-Factor App. Argues the best agents are mostly deterministic software with a few well-placed LLM calls, not a prompt-and-tools loop.
Runs open-source LLMs and multimodal models entirely on mobile devices for offline, private inference. Offers Agent Skills, Thinking Mode, Ask Image, audio scribe, model management and benchmarks, with Gemma 4 and Hugging Face integration.
Builds a table-of-contents tree index over long PDFs and uses LLM tree search to fetch relevant sections — no embeddings, chunking, or vector database. Hits 98.7% on FinanceBench, for financial, legal, and technical docs where relevance needs reasoning.
Provides ready-to-use sample agents for Google’s Agent Development Kit across Python, TypeScript, Go, Java, Kotlin, and Android, from simple assistants to multi-agent workflows.
Combines static code analysis with LLM reasoning to produce interactive architecture diagrams, component-level documentation, and navigable outputs for IDEs, CI, and docs. Emits Mermaid diagrams and incremental updates with CLI and editor integrations.