Langflow

RAGFlow is an open-source retrieval-augmented generation (RAG) engine from infiniflow that provides a context layer between documents and large language models, combining document understanding with agent capabilities. It ingests heterogeneous sources including Word, Slides, Excel, text files, images, scanned documents, structured data, and web pages; extracts knowledge from complex formats; and uses configurable template-based chunking, multiple retrieval methods, and fused re-ranking to assemble context for language-model applications. Its workflows provide traceable citations and visualized text chunks for human review, and it supports configurable language and embedding models, agent templates, orchestration, APIs, and self-hosting. A hosted cloud service is also available.

Langfuse is an open-source LLM engineering platform for developing, monitoring, evaluating, and debugging AI applications. It ingests traces for LLM calls and related operations such as retrieval, embeddings, and agent actions, with integrations including OpenTelemetry, LangChain, the OpenAI SDK, LiteLLM, and other LLM frameworks. Its features include prompt management with version control and collaborative iteration, LLM-as-a-judge and code-based evaluations, user feedback and manual labeling, datasets for test sets and benchmarks, trace replay and debugging, metrics, and a playground for testing prompts and model configurations. Langfuse exposes APIs and typed Python and JavaScript/TypeScript SDKs for custom LLMOps workflows. It is available as a managed cloud service or for self-hosting with Docker Compose, virtual machines, Kubernetes via Helm, or Terraform templates.

MLflow is an open-source AI engineering platform for agents, large language models, and machine-learning models, developed in the MLflow repository. It provides experiment tracking for models, parameters, metrics, and evaluation results, along with production observability, evaluation, prompt versioning and optimization, and model lifecycle tools. Its agent and LLM observability captures application traces using OpenTelemetry and supports different LLM providers and agent frameworks; its AI Gateway provides an OpenAI-compatible interface for routing requests, managing rate limits and credentials, handling fallbacks, controlling costs, and applying guardrails. MLflow can be run as a server and accessed through Python, TypeScript/JavaScript, Java, and other programming languages, with integrations including OpenTelemetry and MCP.

LangChain is an open-source framework and agent engineering platform for building agents and applications powered by large language models. It chains interoperable components and third-party integrations, providing standard interfaces for models, embeddings, vector stores, retrievers, tools, and other data sources. It supports sequential agentic workflows, context and tool-call orchestration, model substitution, and application development primarily through Python; the project also provides a separate JavaScript/TypeScript library. LangChain can be used standalone or with related tools for agent orchestration, evaluation, observability, debugging, and deployment.

DeerFlow is an open-source long-horizon super-agent harness developed in the ByteDance repository. Built on LangGraph and LangChain, it orchestrates a lead agent with sub-agents, progressively loaded skills, memory, tools, and sandboxed execution environments for research, coding, file work, report generation, slide creation, web-page creation, and other multi-step tasks. The runtime provides a filesystem for uploads, workspaces, and outputs; configurable web search, web fetching, browser control, Bash and file tools; MCP servers; reusable Markdown-based skills; long-term memory; scheduled tasks; and messaging-app channels. Sub-agents receive scoped context, tools, and termination conditions, and can run bounded parallel work before returning structured results to the lead agent. Sandbox modes include local execution, isolated Docker containers, Kubernetes-backed execution, and E2B-based environments, depending on configuration. DeerFlow can run locally or with Docker Compose and exposes a Web UI and Gateway at the documented local address. Its setup wizard generates configuration for model providers, search, sandbox, Bash, and file-writing options; the project also documents integrations for LangSmith, Langfuse, and Monocle tracing, personal access tokens, custom agents, MCP background tasks, and extensible Python packages. Version 2.0 is a ground-up rewrite of the earlier 1.x Deep Research framework; active development is on the 2.0 line.