AI Agent Frameworks - An open-source TypeScript framework for building production-ready AI agents with memory, tools, and workflows.
Tool-Based Architectures - Implements a tool-based agent architecture where tasks are executed via typed, schema-validated tools.
Agent Delegation - Automatically generates handoff tools that let a parent agent delegate tasks to specialized child agents.
Agent Evaluation Tools - Runs offline and live evaluation suites with scorers and datasets to measure agent accuracy.
HTTP Agent Servers - Ships a Hono server integration that exposes each agent as an HTTP endpoint for external invocation.
Agent Memory Storage - Connects persistent memory adapters so agents retain important context across separate runs.
Agent Memory Stores - Persists conversation context and intermediate results in a LibSQL-backed store shared across agents, enabling retries and cross-agent data reuse.
Knowledge Base Retrieval - Fetches relevant information from vector databases or document stores to ground LLM responses.
Hybrid Search Retrievers - Provides hybrid search with metadata filtering and reranking for accurate knowledge base retrieval.
Agent Tooling - Enables agents to invoke external functions and interact with systems through a type-safe tool-calling API.
Agent Streaming Interfaces - Emits granular events covering tool calls, reasoning steps, and completion status for fine-grained monitoring.
Agent Workspace Environments - Gives each agent a persistent filesystem, sandbox execution, search, and skills available across conversations.
Memory and Context Systems - Provides persistent memory and context management with pluggable storage adapters for agent state.
OpenAI-Compatible APIs - Connects to any OpenAI-compatible API, including local servers and custom endpoints.
MCP Server Connections - Integrates with MCP servers to make their tools available to agents for execution.
Agentic Workflow Automations - Defines declarative multi-step pipelines with branching, parallel execution, and human-in-the-loop pauses.
Agentic Workflow Engines - Provides a declarative multi-step workflow system with branching, parallel execution, and human-in-the-loop.
Agentic Workflow Orchestration - Composes, branches, and orchestrates multi-step agent pipelines with a chain API supporting pause and resume.
Streaming Chat Responses - Sends text chunks to the client incrementally as the agent produces them for real-time UI updates.
AI Safety Guardrails - Defines content filters and behavioral boundaries to keep AI agent actions within safe limits.
Synchronous Text Completion - Produces a complete text reply from an agent in a single synchronous call for immediate display.
Automatic Knowledge Injections - Automatically retrieves relevant knowledge base content and injects it into each agent response.
Conversation Management Systems - Creates, reads, updates, deletes, clones, and searches conversation records and their messages through a RESTful API.
Conversation Memory Stores - Retains past messages in memory with optional persistent storage adapters for cross-turn reference.
Conversation State Persistence - Saves messages and step records with configurable checkpointing for safe multi-step tool chains.
External Knowledge Integrators - Connects agents to external documents and databases for retrieval-augmented generation.
External System Integrations - Binds agent or workflow outputs to external services using a catalog of pre-built integrations.
Function Calling Interfaces - Invokes user-defined functions to fetch data, perform actions, or interact with external systems.
LLM Provider Integrations - Switches between different LLM providers by updating configuration without altering agent logic.
Runtime Provider Switching - Changes the underlying language model by altering the model string or importing a different provider.
Multi-Provider Abstractions - Provides a unified API that routes requests to dozens of LLM providers with model resolution by string.
Multi-Agent Orchestrators - Coordinates a supervisor agent that delegates tasks to specialized subagents in a defined handoff sequence.
RAG Knowledge Management - Connects agents to external documents via retrieval-augmented generation with chunking and embedding.
Agent Deployments - Provides managed infrastructure deployment for AI agents with one-click GitHub integration.
Agent Text Streamers - Delivers agent text output incrementally over SSE so clients see results as they are produced.
Database Memory Persistence - Stores conversation state and order history in a local SQLite database using an adapter, isolating data per conversation for reliable recall.
AI Guardrails - Provides pre- and post-model guardrails that validate inputs and outputs with abort-on-violation enforcement.
Declarative Workflow Engines - Ships a declarative workflow engine for composing multi-step agent pipelines with branching and retry.
RESTful Workflow APIs - Provides RESTful endpoints to trigger and manage workflow executions programmatically.
Agent Execution Tracing - Traces execution flow, messages, and tool usage to debug complex agent interactions in real time.
Agent Observability - Captures execution traces, replays sessions, and monitors agent decisions for debugging.
Replayable Session Logs - Traces every LLM call, tool execution, and interaction to replay sessions and inspect payloads for root cause analysis.
LLM Execution Tracing - Records spans, logs, and tool events from agent runs for step-by-step inspection.
Agent Observability Platforms - Captures execution traces, logs, and metrics for debugging and performance monitoring of agent runs.
Agent Interaction Monitors - Registers agents and workflows with a console for live execution traces and debugging.
Execution Context Management - Passes run-specific data like user IDs into every workflow step for dynamic, user-aware execution.
Tool Execution Contexts - Provides operation metadata, cancellation signals, and tool-specific context to tool execute functions.
Tool - Registers onStart and onEnd hooks on individual tools to observe or override execution results.
Streaming Output Modifiers - Processes each streaming chunk from the model in real-time, allowing modification, removal, or abortion of the stream before content reaches the user.
