Agent-Native

useAgent is an open-source AI coworker that gives Claude Code, Codex, OpenCode, and Pi a shared workspace with isolated Linux computers, repositories, terminals, browsers, and team tools. It can handle research, websites, spreadsheets, reports, presentations, and code changes, returning usable files, artifacts, or pull requests through a web app, Slack, REST API, or scheduled tasks. Each run is stored as an event log in Postgres and uses a common event contract across supported agent engines. Daytona or CubeSandbox provides the isolated workstation, while integrations cross a trusted gateway as typed tools so credentials remain on the control plane rather than entering the sandbox. The workspace also supports versioned skills and playbooks, team knowledge and memory, approval-gated tools, revisioned workpieces, and durable sessions. The repository describes useAgent as alpha software with changing APIs and schemas. It can be self-hosted on Linux using Bun and Postgres with pgvector, and is licensed under AGPL-3.0-only; a commercial license is available for proprietary embedding, distribution, OEM, or white-label use.

Embabel is an open-source framework for authoring agentic flows on the JVM. Written in Kotlin with a natural Java usage model and built on Spring, it combines LLM-prompted interactions with manually written code and strongly typed domain models. Flows are expressed through actions, goals, conditions, and domain objects; they can be authored with Spring-style annotations such as @Agent, @Goal, @Condition, and @Action, or with an idiomatic Kotlin DSL. The framework dynamically formulates plans toward goals using a pluggable, non-LLM planning step. Its default approach is Goal Oriented Action Planning (GOAP), while Utility AI is also supported for selecting actions by utility scores rather than strict preconditions and postconditions. Conditions are reassessed after each action, and the system replans based on new information and observed effects, allowing known actions to be combined in novel orders and enabling runtime decisions such as parallelization. The planning model is independent of the actions, goals, and plans themselves, and can extend application capabilities by adding domain objects, actions, goals, and conditions without changing finite-state-machine definitions. Execution is provided through an AgentPlatform with focused, closed, and open modes. Focused mode runs a requested agent from application code; closed mode classifies user intent or an incoming event to select an agent; and open mode searches across available goals and actions to build a custom agent, while allowing developers to restrict goal selection. Open mode can find paths not explicitly envisioned by developers and combine functionality from multiple providers, but individual steps must still be defined by the application. Embabel supports mixing language models, including local models for selected tasks, separates the programming model from platform internals for local or production execution, and integrates with Spring dependency injection, AOP, persistence, transactions, and testing. The framework is designed for unit and end-to-end agent testing.

AgentConnect is an open-source, self-hostable platform that integrates autonomous AI agents (ACP agents such as Claude Code, Codex, and Gemini CLI) into communication platforms including Slack, Telegram, Discord, and GitHub. It provides tagging of agents and facilities to assign roles, tools, and memory so agents can act within those channels.

Hyperagent is an AI agent platform for assigning work to a fleet of agents that research, build, deliver and maintain outputs using an organization's data, tools and working style. Its agents operate through real browsers and shells, can search, code, make decisions and generate artifacts such as live websites, videos, presentations, documents and dashboards. Agents run in individual computing environments, expose their execution as it happens, and can learn new memories and skills over time. Work is delivered through Slack, email, Telegram, webhooks or scheduled runs, and agents are deployed and managed from a central command center.

AgentOps is an open-source observability and developer-tools platform for building, evaluating, and monitoring AI agents. Its Python SDK records agent sessions and LLM calls for dashboard-based session replays, step-by-step execution graphs, debugging, analytics, cost tracking, and benchmarking. Developers instrument code with decorators for sessions, agents, operations, tasks, and workflows; these support nested span hierarchies, input and output recording, exception handling, asynchronous functions, generators, and custom attributes. The SDK provides integrations with agent frameworks and LLM systems including CrewAI, AG2/AutoGen, Agno, LangGraph, and the OpenAI Agents SDK, and the platform can be self-hosted with its dashboard and API backend. The app is released under the MIT license.