Open Source LLM Application Frameworks

LangChain is a framework for building applications that chain large language models with external data sources and third-party tools. It serves as an orchestrator for autonomous agents that use language models to plan and execute multi-step tasks, while providing a toolkit for linking interoperable AI components into sequences to prototype complex model behaviors. The project provides a model agnostic integration layer, allowing users to switch between different language model providers using a standardized interface. It also includes tools for observability and evaluation to track the performance and reliability of deployed applications. The framework covers a broad capability surface including retrieval augmented generation, workflow orchestration, and the creation of specialized agents. It further supports the deployment of stateful workflows and the monitoring of agent performance to debug operational issues.

langchaingo is an LLM application framework for Go designed for building language model-powered applications and autonomous agents. It serves as an orchestration library and tool integration framework that allows developers to link prompt sequences and model calls into complex, multi-step workflows. The project provides a toolkit for implementing retrieval-augmented generation pipelines by processing unstructured documents and retrieving relevant context via vector search. It includes a dedicated integration layer for indexing high-dimensional embeddings and performing similarity searches across various vector database backends. Its broader capabilities cover AI workflow automation, the creation of autonomous agents that use reasoning to execute external tools, and the management of conversation state to maintain context across multi-turn dialogues. The framework also supports integrating external search tools, executing database queries, and triggering third-party workflows.

This project is a Java-based framework integration that provides an AI agent runtime, a graph-based AI workflow engine, and an LLM orchestration framework for Spring applications. It enables the development of stateful autonomous agents and the implementation of retrieval-augmented generation systems using document processing and vector databases. The framework distinguishes itself through a graph-based workflow runtime for designing complex AI pipelines with conditional routing and persistent state. It supports multi-agent orchestration via service-discovery coordination and provides human-in-the-loop mechanisms to mandate manual review or confirmation before automated workflows proceed. The system covers a broad range of capabilities, including structured AI output mapping to ensure type safety, conversational memory management for multi-turn dialogues, and tool-calling loops for executing external functions. It also includes monitoring and observability tools for visualizing agent reasoning and debugging workflow execution through a local interface. Users can bootstrap AI projects and generate source code through a visual configuration interface.

LangChainJS is an AI agent orchestrator and application framework designed for building autonomous systems that use large language models to plan and execute tasks. It serves as an integration library that connects language models with tools, memory, and external data sources to create context-aware logic and complex workflows. The project provides a provider-agnostic interface and model provider abstraction, allowing applications to switch between different language model providers without rewriting core logic. It includes a toolkit for retrieval augmented generation, utilizing retrievers to inject real-time external data and ground model generation in facts. The framework covers the orchestration of stateful agent trajectories, modular chain composition, and pluggable memory backends for persisting conversation history. It also includes observability tools for tracking, debugging, and monitoring model outputs and agent performance in production environments.

JARVIS is a system for large language model task orchestration, deployment management, and automation benchmarking. It utilizes a task orchestrator to decompose complex requests into actionable steps and coordinates various expert models to synthesize final responses. The project includes an AI model deployment manager to handle the local deployment of expert models across different hardware scales. It further provides an AI workflow API consisting of web endpoints used to trigger automated task workflows and retrieve results from model selection stages. The framework incorporates an automation benchmark and evaluation suite to measure the ability of models to automate complex tasks using standardized datasets.

This project is a LangChain-based framework for building retrieval-augmented generation systems, autonomous agents, and multimodal chatbots. It functions as an open-source orchestrator that connects local inference engines and online APIs to manage various large language model deployments. The system distinguishes itself by providing specialized interfaces for local knowledge bases, allowing the loading and vectorization of private documents to create context-aware assistants. It also supports multimodal capabilities, enabling the processing of both text and image inputs through vision-capable models. The platform covers a broad range of capabilities, including autonomous agent orchestration with tool-calling loops, vector-database embedding for semantic search, and the integration of external data querying from search engines and databases. It includes a web-based user interface for managing conversations and configuring system prompts.

LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, it provides a flexible, event-driven architecture for composing modular pipelines, enabling developers to chain data ingestion, transformation, and retrieval steps into sophisticated, multi-agent systems that can coordinate tasks and hand off control between individual agents. The platform covers the entire lifecycle of language model applications, including advanced document processing for parsing and structuring complex file formats, and a diagnostic layer for observability that tracks execution traces and performance metrics. It also includes a suite of evaluation tools for measuring retrieval effectiveness and response quality, alongside mechanisms for query routing and custom post-processing to ensure high-precision information delivery.

This project is an LLM autonomous agent framework and orchestration tool designed to build goal-driven agents that automate complex workflows. It functions as a system for converting high-level objectives into a series of autonomous actions and managing the coordination of multiple specialized agents to solve multi-step problems. The framework features a tool integration layer that parses structured model outputs into executable functions and external API calls. It utilizes a non-blocking execution pipeline to manage task orchestration through recursive loops and asynchronous event handling. The system covers the design and orchestration of multi-agent systems, enterprise task automation, and stateful interaction management to maintain context across execution cycles. It includes capabilities for goal-driven task decomposition and the management of internal agent states.

The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monitor tool calls. The project covers a broad range of capabilities, including retrieval augmented generation via vector database integration, human-in-the-loop approval gating for tool use, and a middleware-based request pipeline for security and telemetry. It also supports structured output enforcement, session-based context restoration, and standardized protocols for remote agent connectivity.

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing for explicit node-to-node routing and state management. Furthermore, it includes a human-in-the-loop control layer that enables developers to pause execution at defined breakpoints, allowing for manual inspection, modification, and approval of agent actions during runtime. Beyond its core orchestration capabilities, the framework supports a tiered memory architecture that separates short-term conversation context from long-term persistent data. It also provides comprehensive observability tools for tracing and monitoring execution flows, alongside security features for managing authentication and fine-grained access control. The platform is supported by extensive documentation and standardized interfaces for models, embeddings, and data sources to facilitate the development of production-grade agentic systems.

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, checkpoint-based state persistence for pausing and resuming workflows, and human-in-the-loop interrupt mechanisms for manual approvals. The project covers a wide range of capability areas, including RAG pipeline implementation with semantic tool retrieval and document processing. It provides standardized component abstractions for model integration, a middleware-based interception system for observability and tracing, and tool integration for filesystem and shell command execution. Agent runtimes can be exposed as external services using HTTP and Server-Sent Events for real-time streaming communication.

Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from application code. It serves as a centralized system for developing, versioning, and deploying prompt templates and model configurations across different environments. The platform functions as an AI agent orchestrator with a visual interface for building agent workflows and connecting models to external tools. It further acts as an evaluation framework and observability tool, utilizing OpenTelemetry to capture execution traces, monitor latency, and track token costs. The system covers a broad range of capabilities including judge-based evaluation for scoring model outputs, registry-based prompt management for version control, and environment-based deployment to promote configurations through development and production stages. It also provides tools for converting production traces into test datasets and managing role-based access control for multi-tenant organizations. The platform can be installed using Docker Compose with reverse proxy options for traffic management.

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execution. It features a built-in telemetry pipeline that captures structured execution traces, logs, and performance metrics, allowing for real-time debugging and evaluation of agent behavior. Furthermore, it utilizes sandboxed environments to isolate code execution and filesystem operations, ensuring that agent interactions remain secure and reproducible. Mastra covers a broad capability surface, including multi-agent delegation hierarchies, schema-validated tool execution, and real-time voice interaction. It supports advanced orchestration patterns such as human-in-the-loop approvals, persistent state management for long-running workflows, and retrieval-augmented generation using vector-based semantic memory. These features are designed to work together to support the entire lifecycle of AI-powered applications, from initial development and testing to production deployment. The project is built for TypeScript environments and provides a modular architecture that integrates with existing web stacks and infrastructure. It includes a client SDK for interacting with remote agents and supports various authentication providers to secure API endpoints and agent resources.

Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation history to correct errors and optimize token usage. The framework provides a broad set of capabilities for grounding model responses in factual data using vector databases, graph databases, and tabular datasets. It includes a schema-driven tool execution system that binds models to Python functions and external protocol servers, as well as a comprehensive observability suite for tracing message lineage and monitoring reasoning paths. The library provides installation guidance via import errors when optional dependencies are missing.

DSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-shot demonstrations. By evaluating candidate programs against defined metrics and feedback loops, it systematically improves performance without requiring manual prompt engineering. This process is supported by a programmatic evaluation harness that measures output quality using custom metrics and model-based judges, ensuring consistent behavior across multi-stage pipelines. Beyond core orchestration, the system provides a robust interface for structured data extraction and tool integration. It includes mechanisms for wrapping Python functions as tools, executing iterative reasoning loops, and adapting model outputs into validated data structures. These capabilities are complemented by comprehensive state management and persistence utilities, which allow for the versioning and tracking of program configurations throughout the development lifecycle.

Semantic Kernel is an artificial intelligence orchestration framework designed to integrate large language models with existing codebases. It functions as an agentic workflow engine, providing a standardized interface that connects generative models to traditional application logic, data sources, and external tools to automate complex, multi-step business tasks. The platform distinguishes itself through a modular plugin architecture and a planner-based reasoning engine that decomposes high-level goals into executable sequences of functions. By utilizing a connector-based abstraction layer, it decouples core orchestration logic from specific model providers and vector databases, allowing for consistent retrieval and execution across diverse infrastructure. The framework includes a middleware-based request pipeline for managing cross-cutting concerns such as telemetry and safety filtering, alongside a prompt template engine for dynamic context injection. These components support the development of scalable, enterprise-ready systems that maintain security and compliance while coordinating multiple language models and specialized tools.

Rowboat is an LLM orchestration platform and multimodal AI agent framework. It coordinates large language models with external tools, automated web monitoring, and local data vaults to execute actions and retrieve real-time information. The system operates as a local-first knowledge base, converting meeting notes and emails into a linked markdown knowledge graph. It functions as an automated market intelligence tool that tracks competitors and trends across the web to maintain updated information summaries. The platform covers a broad range of productivity and automation capabilities, including the generation of professional documents and meeting briefs, voice audio processing for speech-to-text and text-to-speech, and a provider-agnostic model layer for switching between hosted APIs and local language models.

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex queries through iterative processing and tool-calling, while its hybrid retrieval orchestration combines vector similarity and full-text search with re-ranking to improve the accuracy of retrieved context. The framework also features event-driven streaming, which delivers incremental results from long-running pipelines to the user interface in real-time. Beyond its core reasoning capabilities, the platform includes a suite of functional modules for the entire lifecycle of document-based applications. This includes multi-modal parsing for extracting text, tables, and visual elements from diverse file formats, as well as administrative tools for managing document collections, vector stores, and multi-user access. The system is designed to be interface-agnostic, allowing developers to wrap third-party libraries and external services into standardized, reusable processing units. The project provides a web-based user interface for interactive querying and configuration, and it supports deployment of private, isolated instances through predefined templates.

Fabric is a command-line orchestrator designed to automate complex data processing and content generation tasks by chaining artificial intelligence models with modular prompt templates. It functions as a terminal-based tool that utilizes standard input and output streams, allowing users to pipe data directly into predefined reasoning strategies. By providing a model-agnostic abstraction layer, the system decouples execution logic from specific artificial intelligence vendors, normalizing requests and responses across different service providers. The platform distinguishes itself through its pattern-based orchestration, which enables the organization, storage, and reuse of custom prompt collections for consistent task execution. It includes a built-in server component that exposes these local prompt workflows as standard web endpoints, allowing external software and graphical interfaces to interact with custom logic as if it were a native model. Users can manage these interactions through a dedicated directory for private templates or via a graphical web dashboard, providing flexibility in how automated workflows are configured and monitored. Beyond its core orchestration capabilities, the tool offers a suite of utilities for development tasks, including document analysis, code context generation, and system interaction. It supports advanced reasoning techniques, such as chain-of-thought processing, and allows for specific model-to-pattern mapping to balance performance and operational costs. The system maintains state and configuration through local filesystem storage, ensuring portability across different operating environments.

BAML is a prompt engineering framework and LLM client generator that defines AI prompts as type-safe functions. It serves as a structured data extraction tool and workflow orchestrator, transforming unstructured model responses into strongly typed objects using a custom schema language and alignment algorithms. The project distinguishes itself by using a compiler to generate language-specific boilerplate code for API communication and output parsing. It features a dedicated environment for designing complex prompt templates with conditional logic and reusable snippets, and employs genetic algorithms for automated prompt optimization based on performance benchmarks. The platform covers a broad range of capability areas, including provider-agnostic request routing with multi-stage fallback orchestration and an observability suite for token tracking and distributed tracing. It supports multimodal AI processing for images, audio, and PDFs, while providing tools for AI workflow validation and schema-driven output parsing. The system includes a command-line interface for project initialization and automated client generation, as well as IDE integration for real-time prompt testing and syntax validation.