Builds event-driven multi-agent AI systems that use a Solace event mesh for agent-to-agent messaging, task delegation, and artifact exchange. Emphasizes asynchronous orchestration, plugin-based extensibility, and integrations with LLMs and external systems.
Connects to Gmail, Calendar, and meeting notes to build a local, Obsidian-compatible Markdown graph it acts on — drafting emails, briefs, and decks. Memory accumulates instead of resetting each session; runs on local or hosted models, extensible via MCP.
Wires retrievers, rerankers, and generators as standalone MCP servers orchestrated in YAML, so iterative RAG logic fits in dozens of lines instead of glue code. Adds loops, conditional branches, one-command web UIs, and shared evaluation benchmarks.
Runs stateful AI agents as Cloudflare Durable Objects — each keeps its own storage and lifecycle, hibernating when idle and waking on demand. Adds WebSocket state sync, type-safe RPC, resumable LLM streaming, MCP roles, and durable workflows.
Lets you build, generate, and run multi-agent LLM workflows from natural-language prompts with no coding. Automatically profiles agents, creates tools/workflows, and supports multiple LLM providers plus CLI/Docker deployment.
Structures AI-assisted development as deterministic YAML workflows—planning, implementation, validation, review, and PR creation—so agent runs are repeatable and isolated. Mixes deterministic nodes with AI nodes and runs from CLI, Web UI, or chat integrations.
Curates 80+ hands-on LLM-powered examples, tutorials and recipes for building agents, RAG systems, voice assistants, and agentic workflows. Includes starter templates, course playlists, and reference apps for rapid prototyping and learning.
Provides curated ComfyUI workflow templates and subgraph blueprints that package reusable node graphs, preview assets, and publishing pipelines for image/video generation. Includes a browsable Astro site with i18n, CI-driven sync/publish scripts, and PyPI packaging for easy distribution.
Keeps codebases, PDFs, Slack, and docs continuously indexed for RAG and knowledge graphs by recomputing only what changed, not the whole dataset. You declare target state in Python; a Rust engine maintains it with per-row lineage back to the source.
Coordinates role-playing agents to automate real-world tasks — web search and browsing, code execution, document parsing, and multimodal handling. Built on the CAMEL-AI framework; scored 69.09% on the GAIA benchmark, topping open-source frameworks.
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.