cloudwego/eino

Features

• Agentic Workflow Orchestration - Integrates agents as functional units within larger graphs to combine reasoning with orchestration.
• Agent Workflow Orchestrations - Runs multiple agents in sequential chains, iterative loops, or concurrent execution to create processing pipelines.
• Autonomous Agents - Provides a framework for creating autonomous agents that integrate LLMs with memory and tool usage via reasoning loops.
• AI Agent Orchestrators - Coordinates multiple specialized agents using supervisor, sequential, and parallel patterns to solve complex tasks.
• LLM Orchestrators - Provides a graph-based engine to orchestrate complex language model workflows and route data between models and tools.
• Agent Handoffs - Enables the transfer of execution control and conversation context from a parent agent to a specialized child agent.
• Event-Driven Agent Loops - Implements a push-based event loop that manages transitions between agent idle and active processing states.
• AI Agent - Provides a high-level abstraction that encapsulates model invocation and tools via a unified interface.
• Multi-Agent Coordination Systems - Coordinates specialized agents using sequential, parallel, and supervisor patterns to collaborate on complex tasks.
• Hierarchical Task Delegation - Provides hierarchical task delegation by wrapping specialized agents as tools for a parent agent to invoke.
• Sequential Agent Execution - Executes a series of agents in a strict linear order by passing outputs between subsequent agents.
• Reasoning-Action Loops - Provides a think-act-observe loop that coordinates iterative cycles of model reasoning and tool execution.
• Agent Delegation - Allows parent agents to delegate independent tasks by wrapping autonomous sub-agents as tools.
• Agent Orchestration Loops - Implements iterative reasoning and execution loops that allow agents to repeat sequences until goals are met.
• Agent Session Management - Stores session events and provides history rewriting capabilities to maintain consistent conversation state.
• Agent State Management - Manages conversation history and execution checkpoints to support resumable workflows and human-in-the-loop interrupts.
• Agent State Persistence - Saves and restores the execution state of autonomous agents via checkpoints to ensure continuity after interrupts.
• Agentic Model Integrations - Provides specialized model interfaces that support server tools and prefix caching for agentic tasks.
• Agentic Planning - Provides an architecture that separates high-level reasoning and planning from low-level task execution.
• Knowledge Base Retrieval - Fetches documents using vector similarity or hybrid methods to support knowledge base answering.
• Agentic Reasoning Loops - Implements an iterative reasoning loop where models call external tools and refine answers.
• Agentic Execution Loops - Orchestrates a processing loop that buffers inputs and handles asynchronous event streams.
• Agent Capability Extensions - Expands agent action spaces by injecting general-purpose tools and filesystem access.
• Agent Construction Frameworks - Provides a framework for integrating custom data, memory, and toolsets into autonomous agent workflows.
• Execution Interrupts - Pauses and resumes agent workflows based on runtime events, persisting state to a checkpoint store.
• Graph-Based State Orchestrations - Implements graph-based state orchestrations to route data and control flow between models, tools, and retrievers.
• Human-in-the-loop Workflows - Interrupts agent execution using checkpoints to manage interactive tasks and manual approval flows.
• Dynamic Interrupt Mechanisms - Allows individual nodes in a workflow to trigger dynamic interrupts to pause execution for manual inspection or modification.
• Multi-Agent Orchestration Patterns - Implements hierarchical invocation and dynamic handoff architectural strategies to coordinate multiple specialized agents.
• Agent Execution Runtimes - Ships a core engine managing serialized execution loops, state checkpoints, and execution interrupts.
• Safe-Point Cancellations - Enables the interruption of active agent execution at precise safe points, such as after model responses.
• Agent Prompt Templates - Implements systems for rendering message templates by substituting variables to control agent behavior and reasoning.
• Model Provider Integrations - Provides unified interfaces for connecting and configuring multiple language model providers and retrieval systems.
• Schema-Based Tool Definitions - Uses JSON Schema to define precise input requirements for tools to ensure AI models provide valid arguments.
• Agentic Task Orchestrators - Directs execution from a parent agent to specialized sub-agents to handle bounded responsibilities via task routing.
• Agentic Workflow Graphs - Connects agents to external components through a graph-based orchestration system to extend functional capabilities.
• Nested Workflow Hierarchies - Enables the creation of complex, nested AI workflows by integrating functional flows as specialized nodes within larger orchestration graphs.
• AI Component Abstractions - Creates consistent interfaces for chat models, embeddings, and retrievers to serve as a reliable foundation for AI applications.
• AI Workflow Orchestration - Builds AI agents and logic using chains and state machines to coordinate model components.
• Chat Completion Services - Provides APIs for generating conversational responses using structured message sequences and real-time streams.
• Chat Model Integrations - Offers standardized interfaces for connecting various local and cloud-based chat models into agentic workflows.
• Document Grounding - Fetches relevant documents from vector stores to provide factual grounding context for AI responses.
• Context Window Management - Prevents token overflow by truncating long outputs or summarizing conversation history based on defined thresholds.
• Document Indexing - Transforms raw text from various sources into chunks or embeddings for vector storage in RAG pipelines.
• Event-Driven Model Streaming - Streams data from a model using an event-driven runner to deliver real-time outputs as they are generated.
• Executable Tool Definitions - Creates tool interfaces with metadata and execution logic that enable models to perform specific tasks.
• External Tool Execution - Executes local functions, server-side tools, and external capabilities based on model-generated tool calls.
• External Tool Integration - Connects agents to external capabilities such as web search and shell commands to extend functional range.
• Tool Calling - Enables models to identify and execute external functions by binding tool definitions to model instances.
• Human Approval - Provides a mechanism to pause agent workflows for human review and approval of tool executions.
• Human-in-the-Loop Runtimes - Provides the runtime control plane to pause execution at specific points and resume based on user intervention.
• Language Model Interaction Patterns - Implements standardized patterns for communicating with large language models via single calls or incremental streams.
• Multi-Agent Systems - Manages groups of specialized autonomous agents using supervisor and layered patterns to solve complex tasks.
• Language Model Response Generators - Provides a generation layer that wraps large language models to produce complete conversational responses.
• LLM Application Frameworks - Provides a framework for building AI agents and orchestrating LLM workflows using a graph-based execution model.
• LLM Response Streaming - Supports incremental delivery of language model outputs to reduce perceived latency in real-time applications.
• Incremental Inference Streaming - Streams model outputs to clients incrementally as they are generated to provide real-time partial results.
• AI Provider Interfaces - Provides unified interfaces for models, tools, and retrievers, enabling provider swapping without altering orchestration logic.
• Multi-Agent Orchestrators - Coordinates multiple specialized AI agents using supervisor patterns and reasoning loops to solve complex tasks.
• Multi-Model Workflow Coordinators - Provides a framework to sequence multiple language model components as nodes within a coordinated execution graph.
• Conversational Message Schemas - Organizes dialogue into typed messages with specific roles to support complex reasoning and tool execution.
• Standardized AI Component Abstractions - Provides standardized component abstractions for integrating models, tools, and retrievers into a unified framework.
• State Persistence - Captures and maintains the runtime state of agent graphs in a key-value store to support restoration after interrupts.
• Supervisor Agent Configurations - Implements the supervisor-worker delegation pattern to allocate tasks to sub-agents and aggregate results.
• Text Embedding Generators - Transforms natural language text into vector embeddings using third-party providers for semantic search.
• Tool Calling - Implements mechanisms for language models to request and execute external functions to perform actions or retrieve data.
• Workflow-as-a-Tool Exposure - Encapsulates chains, graphs, or workflows as callable tools that can be invoked by an agent.
• Streaming Workflow Execution - Processes data in partial frames across a graph to allow downstream nodes to start execution early.
• Workflow State Persistences - Implements storage mechanisms for saving execution checkpoints to resume paused workflows after system restarts.
• Checkpoint-Based State Restoration - Restores agent execution states from checkpoints to allow workflows to resume exactly where they were interrupted.
• Interrupted State Rehydration - Retrieves persisted state and new user inputs to restore a component's context and resume processing.
• AI Agent Development Tools - Offers a comprehensive kit for creating autonomous agents with tool integration, ReAct reasoning, and human-in-the-loop controls.
• Tool Orchestration - Configures local and server-side tools while managing tool loading and selection logic.
• AI Architectural Patterns - Provides templates for frequent AI architectures, including ReAct agents and multi-agent systems.
• Component Abstractions - Provides standardized component abstractions for models and tools, allowing implementation swapping without changing orchestration logic.
• Conditional Branching - Directs data flow to specific downstream nodes based on conditional checks.
• Event-Driven AI Runtimes - Processes model and tool outputs as an asynchronous stream of frames to provide real-time responses.
• Graph-Based Workflow Orchestrators - Builds workflows with branching and loops using a graph-based structure to manage state flow.
• LLM Reasoning Workflows - Structures language model tasks into deterministic graphs and chains to automate multi-step business logic.
• Inter-Component Data Streaming - Transmits fragmented values incrementally while automatically converting between streaming and non-streaming data types.
• Data-Driven Routing - Selects downstream execution paths based on complete inputs or the first available stream frames.
• Model Tool Calls - Maps AI model outputs to executable third-party tool calls and feeds the results back into the model.
• Concurrent Agent Execution - Executes tasks asynchronously and returns a stream of events containing messages and behavioral signals.
• Execution Control Flows - Provides mechanisms to pause, resume, or interrupt agent execution threads via behavioral signals during task processing.
• Modular Agent Skill Executions - Enables language models to dynamically invoke modular skills through custom system prompts and isolated contexts.
• Agent Execution Tracing - Provides tools for inspecting the history of thoughts, tool calls, and observations during agentic workflows.
• Behavioral Interceptors - Provides middleware and global callbacks to intercept and modify agent logic for custom concerns.
• Agent Interfaces - Implements standard interfaces to ensure predictable composition of specialized AI agent behaviors.
• Agent Monitoring - Collects internal messages and state changes to track the status and lifecycle of AI agents.
• Agent Planning Frameworks - Enables autonomous agents to generate and execute step-by-step task plans for multi-stage scenarios.
• Middleware State Interceptors - Uses a middleware layer to handle project instructions and dynamically adjust visible tools via search.
• Input-Driven Turn Preemption - Interrupts a running agent turn to start a new one based on new input at safe points.
• Loop Termination Controls - Implements the ability to stop the entire execution loop immediately or after the current turn completes.
• Hierarchical State Access - Provides a mechanism for sub-graphs to read and interact with the state of their parent graphs during execution.
• Node-Level State Transformations - Provides pre- and post-handlers to modify data and state as it flows through the nodes of an orchestration graph.
• Execution Breakpoints - Implements configurable pause points in agent workflows for debugging, inspection, and manual approval.
• Frontend Interrupt Signaling - Coordinates with the frontend to pause execution and signal the need for human approval through the UI.
• Cross-Agent Context Managers - Maintains shared state and conversation history between specialized agents during transfers and tool calls.
• Conversation History Trackers - Maintains message history arrays across multiple execution cycles to enable context-aware multi-turn interactions.
• Multi-turn Interaction Managers - Manages stateful conversations and tool-calling workflows using a push-based event loop for continuous interaction.
• Agent Result Aggregators - Includes systems for merging and summarizing outputs from multiple concurrent specialist agent runs.
• Execution Middleware - Provides extension points to modify configurations and wrap model or tool invocations during execution.
• Turn Preemption - Supports interrupting current agent execution at safe points to immediately process high-priority input.
• Agent Streaming Interfaces - Outputs agent progress as an asynchronous iterator of events providing real-time metadata and responses.
• Stateful Terminations - Ends active agent execution using safe-point cancellation and checkpoint persistence.
• Agent Response Streamers - Implements incremental streaming of agent outputs and tool interactions to client-side interfaces.
• Filesystem Tooling - Provides tools for agents to perform reading, writing, and searching across local or abstract storage.
• AI Agent Skills - Provides a way to load documentation from the filesystem as reusable skill guides for agents.
• AI Agent Workflow Definition - Defines deterministic execution paths and looping processes for agent workflows via structured definitions.
• Reasoning Process Monitors - Outputs the internal thought process and reasoning steps produced by a model during execution for auditing.
• Context Compression - Implements summarization of old messages to keep dialogue history within the model's token limits.
• Instruction Injections - Enables the embedding of custom guidelines and reference documentation into AI agent contexts.
• Conversation History Management - Provides mechanisms to compress and truncate message history and tool outputs for token window efficiency.
• Context Optimization Tools - Optimizes conversation context by truncating large tool responses and offloading historical data.
• Model Output Transformers - Transforms chat model messages into custom data structures using dedicated parsing nodes within a workflow.
• Dynamic Skill Injection - Provides a middleware layer for injecting and purging agent capabilities at runtime.
• Dynamic Tool Discovery - Dynamically discovers and loads tool schemas on demand to minimize prompt noise and token usage.
• External Tool Integrations - Integrates with third-party platforms using standardized context protocols and schema conversion.
• Conversation History Condensation - Uses language models to condense dialogue history when token counts exceed predefined thresholds.
• RAG Pipelines - Implements RAG pipelines that ingest documents into vector stores to retrieve grounding context for LLM responses.
• LLM Stream Processing - Handles real-time model outputs through stream processing and concatenation to minimize response latency.
• Native Tool Call Parsers - Implements native parsing and streaming of tool call arguments to route model output correctly in real-time.
• Multimodal Processing Tools - Handles varied input and output formats beyond text, enabling tools to process diverse media types.
• Failover Mechanisms - Implements automatic retries and failover logic to switch to backup models upon primary failure.
• Model Task Retries - Implements automatic retry strategies and input modification for failed model inference tasks.
• Model Tool Bindings - Binds functional tools to a model for execution within graph nodes to perform real-world actions.
• Modular AI Components - Provides reusable and swappable building blocks for models and retrievers to ensure architectural flexibility.
• Multimodal Document Reading - Extracts content from images and PDFs using specialized readers to return binary data.
• Custom Tool Logic Interfaces - Defines specialized tool interfaces that execute custom logic and return structured multimodal or text results.
• Multimodal Tool Implementations - Supports the creation of tools that can output streaming data and multimodal content for model recognition.
• Provider Abstraction Layers - Uses standardized interfaces to replace model providers without altering the underlying orchestration logic.
• External Workflow Interrupts - Enables external triggers to pause active agent execution and save the current node's input for later handling.
• Prefix Caching - Reduces cost and latency by storing prefix and session data to avoid redundant processing.
• Prompt Templates - Implements a system for filling variables into predefined message templates to generate structured model inputs.
• RAG Toolkits - Ships a toolkit for document ingestion, embedding generation, and semantic retrieval to ground LLM responses.
• Specialist Task Routing - Implements a host-specialist pattern to recognize intent and route requests to the appropriate domain-expert agent.
• Global Run-Local State - Maintains a global key-value store to pass and retrieve data between agents during a single execution run.
• Sequential Step Orchestrators - Links processing steps into a linear pipeline with optional parallel execution and conditional branching.
• Structured Message Block Management - Preserves reasoning segments, multimodal data, and tool events as ordered blocks within a single message.
• Agentic Goal Decomposition - Uses LLMs to recursively break high-level objectives into a structured, actionable todo list.
• Tool Output Processors - Corrects malformed JSON or converts technical errors into friendly prompts to improve agent robustness.
• Incremental Result Streaming - Sends tool execution results back to the caller incrementally as they are produced.
• Tool Selection Optimizations - Optimizes tool loadouts by filtering available tools via keyword search or retrieval to prevent context overflow.
• Visual Workflow Orchestration - Renders application logic as visual graphs to simplify the design and organization of component connections.
• Workflow Definitions - Creates predefined flowcharts with clear topologies to ensure predictable and deterministic execution paths.
• Workflow Visualizations - Generates diagrams of chains and workflows to audit and debug the execution paths of AI agents.
• Document Parsing and Extraction - Extracts content from text and HTML using parsers to prepare unstructured data for LLM ingestion.
• Observability and Tracing - Monitors execution paths and debugs orchestrations using built-in callbacks and external observability platforms.
• Document Processing - Manages the loading and transformation of raw documents into structured formats suitable for retrieval.
• Document Transformation Pipelines - Provides document transformation pipelines for splitting and filtering content to meet token and format requirements.
• Tool Output Summarization - Reduces token usage by truncating overlong tool results or clearing historical outputs.
• Stream Conversions - Transforms streaming data between nodes to simplify manual stream handling during orchestration.
• Vector-Database-Backed Retrievals - Performs semantic similarity searches against vector indices to fetch relevant grounding context for model prompts.
• Vector Document Indexing - Implements workflows for indexing documents into vector databases to enable semantic association searches.
• Document Parsing Engines - Uses document parsing engines to convert raw data streams into structured formats based on file extensions.
• Multi-Format Document Ingestion - Ingests content from local files, URLs, and cloud storage in multiple formats for AI workflows.
• Orchestration Graph Visualizers - Renders orchestration graph structures via IDE plugins to facilitate visual debugging and construction.
• Resumption Data Injection - Allows passing specific data back to a target interrupt point during resumption to influence workflow logic.
• Search & Information Retrieval - Utilizes multi-query and routing strategies to discover and retrieve relevant grounding information from large datasets.
• Semantic Retrieval Engines - Indexes textual documents and retrieves relevant content using vector similarity matching.
• Tool Semantic Retrieval - Selects the most relevant tools via semantic search to avoid context overload in large tool libraries.
• Tool-Specific Semantic Retrieval - Filters available tools via semantic search to prevent context overflow in large tool libraries.
• Vector Indexing - Integrates with vector databases to manage and store high-dimensional embeddings for semantic retrieval.
• Approval Workflows - Provides mechanisms for human intervention points to approve critical operations and resume paused automated processes.
• Execution Lifecycle Controls - Supports timeouts and cancellation signals during graph invocation to prevent resource leaks.
• LLM Response Parsers - Converts JSON messages and tool call results from language models into structured types for further processing.
• Custom Component Wrapping - Wraps user-defined code into standardized components to integrate specific business logic into a workflow.
• Agent Workflow Interception - Injects custom logic at lifecycle stages to modify instructions, state, or tool invocations.
• Filesystem Directory Operations - Performs low-level filesystem operations including directory listing and content editing on the local disk.
• Orchestration Node Debugging - Allows executing workflows from any specific node using mock inputs to inspect and debug results.
• Parallel Execution - Runs multiple nodes concurrently with shared inputs and aggregates results into a map.
• Visual Code Builders - Allows designing component topologies in a visual editor and exporting them as executable source code.
• Shell Command Execution - Executes system commands with standard or streaming output to interact with the host operating system.
• Agent Turn Preemption - Cancels an active agent execution immediately when a new user message is received to prioritize the new request.
• Tool Execution Hooks - Uses middleware to hook into the tool execution lifecycle for logging and argument processing.
• Visual Flow Orchestrators - Provides visual representations of graph and chain topologies to help users understand data flow between nodes.
• Workflow State Recoveries - Provides utilities to retrieve status and metadata from suspended runs to facilitate the restoration of workflow execution.
• Type Safety - Ensures data compatibility between upstream and downstream nodes using type safety.
• Graph Engine Selection - Chooses between cyclic and acyclic engines to manage node trigger logic for different graph types.
• Concurrent Task Runners - Executes lists of tasks concurrently with a defined concurrency limit and aggregates the final results.
• Cross-Cutting Concern Automations - Automates cross-cutting concerns like retries, approvals, and error handling using middleware and handlers.
• Custom Function Embedding - Integrates custom functions into workflows using invocation, streaming, or transformation modes.
• Tool Execution Error Reporters - Converts tool execution errors into descriptive strings to allow language models to understand and respond to failures.
• Graph Execution Tracing - Tracks data flow through visual graphs during runtime to identify and resolve logic errors in AI workflows.
• Execution Pausing - Pauses graph execution at specified nodes and resumes later with the option to modify state.
• Hierarchical Checkpoint Management - Provides independent state modification and breakpoint control for nested sub-graphs through extended checkpointing capabilities.
• AI Model Middleware - Includes AI model middleware to intercept and transform request-response cycles for logging and retries.
• Execution Graph Compositions - Composes business logic into reusable execution graphs or chains to organize AI sequences.
• Output Data Extraction - Uses custom extractor functions to select specific data from a predecessor's output.
• Model Call Interceptors - Provides architectural hooks to intercept and modify language model requests and responses to customize agent behavior.
• Retry Policies - Automatically re-executes failed chat model requests based on configurable retry policies.
• Retry Strategies - Implements automatic retry of failed model invocations using an exponential backoff strategy for resilience.
• Agent Coordination State - Provides persistent knowledge bases for sharing state and coordination data across multiple agents during execution.
• Hierarchical Nesting - Embeds compiled graphs within other graphs as nodes to create hierarchical workflows.
• Workflow Orchestrators - Connects components in a forward-only sequence to create predictable step-by-step data pipelines.
• Agent Observability - Tracks autonomous decision paths and agent-specific accuracy metrics through lifecycle callbacks.
• Monitoring and Observability - Provides a comprehensive platform for collecting telemetry to observe the behavior of components and graphs.
• Model Interaction Monitors - Captures and verifies the interaction between applications and AI model providers during generation.
• Observability Tracing - Records execution spans and traces across distributed application components to monitor latency and token usage.
• Token Usage Analytics - Tracks and reports LLM token consumption and compression efficiency to monitor operational costs.
• Visual Workflow Builders - Features a drag-and-drop interface for composing application graphs and generating corresponding source code.
• PDF and HTML Content Extraction - Converts raw data from PDFs or HTML into standardized document objects with metadata.
• Request-Scoped State - Maintains request-level state accessible across different nodes through handlers that modify inputs and outputs.
• Real-Time Data Streaming - Implements patterns for handling real-time message fragments and converting streams between workflow components.
• Tool Call Wrappers - Intercepts and modifies the behavior of standard and streaming tool calls by wrapping endpoints.
• AI Agent Frameworks - Component-based framework for production-ready AI application orchestration.
• AI and Machine Learning - Framework for building LLM and AI applications.
• Application Frameworks - Golang AI development framework with component integrations.

Star-Verlauf