Release Teloa Community edition (0.2.2) · teloa-ai/teloa

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Teloa Community edition (0.2.2) is here: build and run your own AI team

Today, Teloa Community edition (0.2.2) is officially available.

Teloa is an AI-native team workspace. Describe a goal in natural language, create AI employees with ongoing responsibilities, and organize a team around projects, materials, memory and deliverables.

This release brings together AI employees, AI teams, multiple execution harnesses, projects and project memory, local document indexing, voice interaction and versioned delivery. It also turns lessons from product development into two standardization initiatives made publicly available to the industry.

30-second workspace tour

teloa-community-0.2.2-en.mp4

Community 0.2.2 · Actual interface with sample data. Watch with captions

AI employees: persistent roles for ongoing work

Describe the role, working requirements and expected deliverables in natural language or Markdown, then review the proposal to create an AI employee.

For example:

Create a product researcher to organize user feedback, check product assumptions, and deliver source-backed analysis and validation suggestions. List open questions when evidence is insufficient, and ask for my confirmation before publishing externally.

Each employee can have its own responsibilities, working methods, business scope, reference materials, skills and available tools. These settings stay with the role so it can keep handling similar work.

Chat privately with an employee, inspect its tasks, deliverables and accumulated experience, or add it to a team to collaborate on a shared goal.

An employee's role identity is managed separately from the model. Changing models or starting a new task can preserve the same employee's responsibilities and work history.

AI teams: organize responsibilities around shared results

Combine multiple AI employees into a team with clear responsibilities for research, analysis, production and review.

The marketplace provides team templates for product, marketing, sales and other work. They organize roles, skills, reference materials, optional connections and work templates together, reducing repetitive setup. After adding a template, review the members, target business and work arrangements before enabling the capabilities you need.

Use @ mentions in group chats to involve specific employees while retaining the source of assignments, collaboration and result replies. Main messages and topics are organized separately, with distinct entry points for ordinary chats, employee conversations and groups.

Persistent teams carry ongoing responsibilities; temporary execution teams serve the current task. Independent parts can run in parallel before the responsible worker consolidates the results. Collaboration across business areas continues to follow each member's actual access to materials and tools.

Multiple harnesses: DSH, Codex and Claude Code

Teloa connects different execution engines to the same workflow:

DSH handles the main conversation, native execution and tool calls.
Codex handles independent code, file and tool tasks through its native execution provider.
Claude Code / Claude Agent connects to the native Claude execution path, handles independent work and returns the results.

You can ask directly:

Use Codex to review the code this time, and have Claude Agent analyze these files.

The results return to the original task, retaining the work's source and attribution.

Model connections and harnesses are configured separately. Each execution provider uses its own authentication, and external engines require confirmation of their native working directory and permissions before launch. Choose the execution approach that fits the work, then follow its actual status and deliverables in the workspace.

Projects and project memory: carry context into subsequent work

Projects bring together goals, briefs, tasks, employees, groups, materials, automations and deliverables, with dedicated project conversations.

Project goals, active rules, relevant materials and memories can participate in subsequent model requests. Inspect what was used and its sources to understand the basis of an employee's work.

Review memory candidates, trace their sources, withdraw future use, and share a fixed revision. New conversations can choose whether to read and write long-term memory, and the owner decides whether personal preferences participate in a project.

From the first discussion through later deliveries, a project can accumulate reusable background, rules and reasoning.

Local document indexing and local models: put your own materials to work

Add explicitly selected local Markdown files to an index, track material versions, and inspect citations and the locations of source excerpts during retrieval.

Prepare retrieval models after confirmation on the materials page, with options including Qwen3 Embedding and EmbeddingGemma. Chat models handle understanding and generation; embedding models handle retrieval. The two are managed separately.

Through Ollama, check the local chat environment, connect existing models, and confirm a download or cancel model preparation.

The document index runs on the device or container where you deploy Teloa. Answers can use local models or remote models you configure. When you choose a remote chat service, relevant retrieved excerpts may be sent to that service as part of the current context.

Natural-language tasks and versioned deliverables: from a brief to a result

The main task-creation flow centers on a natural-language or Markdown brief. Describe the goal, requirements and delivery format; the source conversation, project and business are carried in from context.

Follow execution, handle required approvals, stop work, preview files and collect deliverables in the same workflow. Even before creating employees, you can work independently with Teloa, the squirrel assistant, or the general-purpose Agent.

Preview and download deliverables, then request revisions to a selection or the whole document. Each revision retains a clear version. Earlier results such as v1 and v2 remain available with their original attribution, and acceptance can refer to the specific version adopted at the time.

The workspace previews Markdown, code, images, PDF, HTML, Office files and spreadsheets so you can inspect the actual delivery.

Business workflows, dashboards and the marketplace

Create or adjust business configurations in natural language, review and adopt the draft, then import records or connect authorized data sources.

Business content and statistical dashboards are organized separately, so you can inspect individual records and observe changes across the business. Tasks, materials and deliverables can continue within the same business context.

Discover AI employees, AI teams, skills, apps and connectors through dedicated marketplace entries. The apps page manages installed capabilities and connections.

For more complex work, use step-by-step Workflow orchestration, PTC tool composition, and Ralph iteration when explicitly requested by the owner. Optional capabilities such as Browser Use, Computer Use and IM channels are configured and authorized separately as needed.

Voice input, TTS and calls: collaborate by speaking

Teloa brings voice input, recognition, speech output and calls into the workspace:

Voice input: SenseVoice supports local transcription. Recognized text enters an editable draft for the owner to review and send.
Call recognition: choose MiniMax cloud recognition or local SenseVoice.
Text-to-speech: MiniMax, OpenAI and ElevenLabs are supported, with separate model, voice and credential settings.
Voices and previews: MiniMax offers Mandarin and English voice selection and previews. Other providers use their corresponding model and voice settings.
Continuous speech and call controls: MiniMax supports continuous audio output, reducing repeated starts between short phrases, while retaining mute, interruption and hang-up controls.

Call an employee or the squirrel assistant, or leave a voice message to add work requirements. Calls remain associated with the original conversation, and you can continue reviewing work and deliverables after a call ends.

Auto Dream, employee experience and personal digital twins

Auto Dream organizes private daily summaries from work evidence and records employee experience with its source and scope. You can inspect it and withdraw its use.

Employee experience, project memory and personal digital twin materials are managed separately. A twin can accumulate judgment examples supplied by its owner and observations of working habits to help organize personal working methods. Each material retains its own scope of use.

Methods you have confirmed as useful can also become skills or recurring-plan drafts. Work arrangements are explicitly enabled by the owner, allowing recurring work to develop into processes that can be inspected and adjusted.

Two open standards: how AI works and acts safely

To describe how AI workers are organized and where their security boundaries lie, Teloa has made two standardization initiatives publicly available to the industry, for developers, enterprise users and other platforms to implement, review and improve together.

AI Worker Standard 1.0

Initiated and maintained by Teloa, AI Worker Standard 1.0 provides working definitions and collaboration contracts for AI Workers, AI Teams and AI Organizations.

It describes:

Worker identity, responsibilities and definition versions that persist across tasks.
Inputs, outputs, dependencies, authorization conditions and acceptance requirements.
Responsibilities, handoffs and shared deliverables between team members.
Organizational accountability, resource constraints and change governance.

The standard distinguishes role definitions, actual employee instances and individual executions. It includes a core specification, a work and collaboration profile, and JSON and YAML examples to help different implementations express what a worker is responsible for, how it collaborates and how its deliverables are checked.

Read AI Worker Standard 1.0

Enterprise Agent Identity and Authorization Security Baseline

Initiated and maintained by Teloa AI Security, the Enterprise Agent Identity and Authorization Security Baseline v1.0 contains 240 verifiable requirements across 10 security domains.

The baseline covers identity authentication, delegation and permissions, authorization decisions, credential management, execution isolation, context security, output visibility, data and memory, lifecycle governance, and audit and response.

It provides L1 baseline, L2 standard and L3 enhanced assessment levels, together with online and offline self-assessment tools, maturity reports and Excel assessment sheets for platform design, supplier evaluation and security review.

Both specifications are open to industry use and collaboration. Product conformance must be checked against the specific version, deployment scope and actual evidence.

Read the security baseline and self-assessment tools

Get started with Teloa 0.2.2

Teloa Community edition (0.2.2) is available through GitHub, npm and Docker Hub for local deployment.

Start with a real project: describe the goal, select materials, create the employees you need, and let the team work toward deliverables you can review and accept.

GitHub Release · npm · Docker Hub

Website · Documentation · Marketplace · Full release notes

简体中文

Teloa 社区版（0.2.2）正式发布：构建并运行你自己的 AI 团队

今天，Teloa 社区版（0.2.2）正式发布。

Teloa 是一个 AI 原生团队工作台。你可以用自然语言提出目标，创建持续负责工作的 AI 员工，组织团队分工，让工作围绕项目、资料、记忆和成果持续推进。

这次发布完整带来了 AI 员工、AI 团队、多 Harness 执行、项目与项目记忆、本地文档索引、语音交互和版本化成果交付，也将产品实践沉淀为两项面向行业公开的标准成果。

30 秒，看看真实工作台

teloa-community-0.2.2-zh-CN.mp4

社区版 0.2.2 实际界面，使用示例数据。观看字幕版

AI 员工：为持续工作建立长期岗位

用一段自然语言或 Markdown，描述你需要的岗位、工作要求和交付方式，核对后即可创建 AI 员工。

例如：

创建一位产品研究员，负责整理用户反馈、核实产品假设，输出带来源的分析与验证建议。资料不足时明确列出问题，对外发布前交由我确认。

每位员工可以拥有自己的职责、工作方式、业务范围、参考资料、技能和可用工具。这些配置随岗位保留，让同类工作能够持续开展。

你可以与员工私聊，查看它承担的任务、交付的成果和积累的经验，也可以把它加入团队，围绕共同目标协作。

员工的岗位身份与具体模型分开管理。更换模型或开始新任务，仍然可以延续同一个员工的职责与工作历史。

AI 团队：围绕共同成果组织分工

Teloa 支持将多个 AI 员工组合成团队，让研究、分析、制作、复核等工作形成明确分工。

市场提供产品、营销、销售等团队模板，将岗位、技能、参考资料、可选连接和工作模板一起组织，减少从零配置的重复操作。添加后，你可以核对成员、目标业务与工作安排，再启用需要的能力。

在群聊中，可以通过 @ 指定员工参与，让交办、协作和结果回帖保留来源。主消息和话题分别组织，普通会话、员工私聊与群也有各自的入口。

长期团队承接持续职责，临时执行小组服务于当前任务。 独立部分可以并行处理，再由负责者汇总交付。跨业务协作继续遵循每位成员实际获得的资料和工具授权。

多 Harness：接入 DSH、Codex 与 Claude Code

Teloa 将不同执行引擎接入同一工作流程：

DSH 承担主会话、原生运行与工具调用。
Codex 通过原生执行提供方处理独立的代码、文件与工具任务。
Claude Code / Claude Agent 接入原生 Claude 执行链路，承接独立工作并回传结果。

你可以直接提出：

这次用 Codex 检查代码，让 Claude Agent 分析这些文件。

执行结果会接回原任务，继续沿用工作的来源与归属。

模型连接和 Harness 分别配置；各执行提供方使用对应认证，外部引擎启动前确认其原生工作目录与权限。你可以选择适合当前工作的执行方式，并在工作台跟进真实状态与交付结果。

项目与项目记忆：让后续工作带着上下文

项目将目标、简报、任务、员工、群、资料、自动化和成果归集起来，并提供专属项目会话。

项目目标、有效规则、相关资料与记忆可以参与后续模型请求。你可以查看“本次使用”的内容与来源，了解员工依据什么完成工作。

项目记忆支持审阅候选、追溯来源、撤回后续使用，以及按固定修订分享。新会话可以选择是否读写长期记忆，个人偏好是否参与项目也由本人决定。

从第一次讨论到后续交付，项目能够逐步积累可复用的背景、规则和判断依据。

本地文档索引与本地模型：把自己的资料用起来

Teloa 支持将你明确选择的本机 Markdown 文件加入索引，跟踪资料版本，并在检索时查看引用与原文摘录位置。

检索模型可在资料页确认后准备，包括 Qwen3 Embedding、EmbeddingGemma 等选项。聊天模型负责理解与生成，嵌入模型负责资料检索，两者分别管理。

通过 Ollama，可以检查本地聊天环境、接入已有模型，并确认下载或取消模型准备。

资料索引运行在你部署 Teloa 的设备或容器中。回答可以使用本地模型，也可以使用自行配置的远程模型；选择远程聊天服务时，相关检索摘录可能随本次上下文发送至该服务。

自然语言任务与版本化成果：从交代工作到取得交付

任务的主要创建入口围绕一段自然语言或 Markdown 说明展开。交代目标、要求与交付格式，来源会话、项目和所属业务由上下文带入。

在同一工作路径中，你可以跟进执行、处理必要审批、停止工作、预览文件并取得成果。即使尚未创建员工，也可以先与小松鼠或通用 Agent 独立完成工作。

成果支持预览、下载，以及针对选段或整份内容反馈续改。每轮修改保留明确版本，v1、v2 等历史成果和原归属继续可查，验收也可以指向当时采用的具体版本。

工作台支持预览 Markdown、代码、图片、PDF、HTML、Office 文件和表格，便于直接检查交付内容。

业务、看板与应用市场：把工作接入实际业务

用自然语言创建或调整业务配置，审阅草稿后采用，再导入记录或连接已授权的数据来源。

业务内容与统计看板分别组织，既能查看具体记录，也能观察业务变化。任务、资料与成果可以围绕同一业务持续推进。

市场按 AI 员工、AI 团队、技能、应用与连接器等入口组织能力发现；应用页管理已安装能力和连接。

复杂工作还可以使用 Workflow 分步编排、PTC 工具组合，以及本人明确要求的 Ralph 迭代。Browser Use、Computer Use 和 IM 通道等可选能力，按实际需要分别配置与授权。

语音输入、TTS 与通话：用说话的方式协作

Teloa 将语音输入、识别、播报与通话接入工作台：

语音输入：支持 SenseVoice 本地转写，识别结果进入可编辑草稿，由本人核对发送。
通话识别：可选择 MiniMax 云端识别或本机 SenseVoice。
语音播报：支持 MiniMax、OpenAI、ElevenLabs，分别配置模型、音色与凭据。
音色与试听：MiniMax 提供普通话、英文音色选择与试听，其他提供方按对应模型和音色配置。
连续播报与通话控制：MiniMax 支持连续音频播报，减少小句之间反复启动的停顿，并保留静音、打断和挂断控制。

你可以与员工或小松鼠通话，也可以通过语音留言补充工作要求。通话与原会话保持关联，结束通话后仍可继续查看工作和成果。

Auto Dream、员工经验与个人分身

Auto Dream 根据工作证据整理私有每日小结，沉淀带来源和范围的员工经验，支持查看与撤回。

员工经验、项目记忆和个人分身资料分别管理。分身可以积累本人提供的判断样本与习惯观察，用于整理个人工作方式；这些材料保留各自的使用范围。

确认有效的方法，还可以复用为技能或例行计划草稿。工作安排由本人明确启用，让重复工作逐步形成可检查、可调整的流程。

两项开放标准：定义 AI 怎样工作，也定义它如何安全行事

围绕 AI 工作者的组织方式与安全边界，Teloa 已面向行业公开两项标准化成果，供开发者、企业用户与其他平台实现、评审和共同完善。

AI Worker 开放定义标准 1.0

由 Teloa 发起维护的 AI Worker Standard 1.0，为 AI Worker、AI Team 和 AI Organization 提供工作定义与协作合同。

它明确描述：

AI 工作者跨任务延续的身份、职责与定义版本。
工作的输入、输出、依赖、授权条件与验收要求。
团队成员之间的分工、交接和共同成果。
组织层面的责任、资源约束与变更治理。

标准区分岗位定义、实际员工实例和单次执行，并提供核心规范、工作与协作应用规范，以及 JSON、YAML 示例，帮助不同实现准确表达一个 AI 工作者承担什么、怎样协作、交付如何核验。

阅读 AI Worker Standard 1.0

企业智能体身份与授权安全基线

由 Teloa AI Security 发起维护的《企业智能体身份与授权安全基线》v1.0，包含 240 项可验证要求，覆盖 10 个安全领域。

基线围绕智能体行为明确身份认证、委托与权限、授权决策、凭证管理、执行隔离、上下文安全、结果可见性、数据与记忆、生命周期治理及审计响应。

它提供 L1 底线、L2 标准、L3 增强三级评估框架，并配套在线、离线自评工具、成熟度报告和 Excel 评估表，可用于平台设计、供应商评估与安全评审。

两项规范均向行业开放使用与共建，产品符合性按具体版本、部署范围和实际证据核验。

阅读安全基线与自评工具

开始使用 Teloa 0.2.2

Teloa 社区版（0.2.2）现已通过 GitHub、npm 和 Docker Hub 正式发布，支持本地部署。

从一个真实项目开始，描述目标、选择资料、创建需要的员工，再让团队围绕可验收的成果开展工作。