Tale — Shared Workspace for Teams and AI Agents
Turn company problems into tasks your team and AI agents solve together, then review the reports and files they deliver.
- Source repo
- tale-project/tale
- Stars
- ★ 32
- Last updated
- today
- License
- MIT
- Primary language
- TypeScript
- FA score
- 50/100 · Major gaps
At a glance
- Works with
- Universal · cross-platformCodex · Claude Code · OpenAI API · Claude API
- You'll need
- Typical use
- A team reviewing a launch brief: create one project task with the brief and meeting notes, and have an agent produce a Markdown report of conflicting dates, missing owners and open decisions with citations.
- Main limitation
- Self-hosting is not turnkey: production needs DNS, TLS, backups and access controls on top of Docker and Compose, plus several GB for images and data.
- Source review
- 50/100 · Major gaps
What does this agent do, and when should you use it?
Tale is an open-source project workspace where teammates and AI agents share a task board, project knowledge, agent configuration and review flow. You create a project task with the problem statement, source files and completion criteria, then assign it to a person or a project agent whose instructions, harness, model and tools you configure individually. Agents run in persistent sandbox workspaces, with concurrency limited by your configured capacity, and a manager agent can delegate ready work when granted the required tools. Around the board, the platform provides Chat with an Arena mode that compares two model responses side by side, Knowledge for retrieval-ready documents and web content, versioned Automations workflows, and Connectors backed by credentials your workspace controls. Administrators manage members, roles, providers, policies, usage and audit records. You can self-host it with Docker Compose on your own infrastructure or use the managed Cloud service; the code is MIT licensed.
Tale reads the task brief, source files and completion criteria you attach to a project task and hands the work to a configured project agent. Before running, you choose that agent's harness (Claude Code, Codex, Cursor, Gemini CLI, Hermes, OpenClaw, OpenCode, Pi, Qwen Code and others), its model and its tools; the agent then executes in a persistent sandbox workspace and returns reports and delivered files to the board for review. You follow progress on the board, steer the agent with @mentions in task comments, inspect the reports and files, and either ask for corrections or accept the result; a manager agent with the right tools can delegate ready tasks and coordinate follow-up work. The wider platform adds Chat and Arena for comparing two model responses, Knowledge for preparing documents, knowledge entries and website content for retrieval (upload and indexing are separate steps), versioned Automations that connect steps and run manually or through configured triggers, Connectors for external services using workspace-controlled credentials, and Administration for members, roles, providers, policies, usage and audit records.
- A team reviewing a launch brief: create one project task with the brief and meeting notes, and have an agent produce a Markdown report of conflicting dates, missing owners and open decisions with citations.
- Engineering teams that want agents to actually work in a repository or build an internal tool: assign the task to a project agent running Claude Code or Codex in a sandbox, then review the delivered files.
- A project manager who needs one shared board where human-assigned and agent-assigned cards sit in the same status columns with their deliverables.
- Knowledge-heavy teams turning documents, knowledge entries and website content into retrieval sources and checking the cited sources behind answers.
- An operations team repeating a defined process as a versioned Automation that starts on a schedule or through connector approvals, then reviewing each run's steps and inputs.
- An administrator consolidating governance: members, roles, providers, policies, usage and audit records, plus Guardrails pages for content-safety and data policy status.
How do you install or deploy this agent?
The documented local path uses the published CLI on macOS or Linux; no repository clone and no Bun installation are needed:
curl -fsSL https://raw.githubusercontent.com/tale-project/tale/main/scripts/install-cli.sh | bash
tale init my-project
cd my-project
tale devYou need Docker with Compose, several GB of space for images and data, and a model provider credential for your first reply. On Apple Silicon, Docker Desktop's amd64 emulation is needed by the bundled object store; on ARM64 Linux, configure emulation before starting. The CLI can help install or start Docker.
To develop from source you need the Bun version pinned in package.json, a compatible Node.js runtime, and Docker for backing services; full repository checks also need Python and uv:
bun install --frozen-lockfile
bun run setup:check
bun run devDocumentation-only work needs no platform database or provider:
bun run --filter @tale/docs dev
bun run --filter @tale/ui-docs devHow do you use this agent?
After tale dev starts, open the printed URL, create the first account and organization, add a credential under Settings > AI providers, and send your first message. Press Ctrl-C to stop; running tale dev again in the same directory resumes with your data.
A typical agent flow: create a project task with the problem, source files and completion criteria; configure a project agent (instructions, harness, model, tools); start the task and read the agent's report. An illustrative brief for a launch-review task looks like this:
Compare the attached launch brief and meeting notes. Produce a Markdown report listing conflicting dates, missing owners, and open decisions. Cite the source file and passage for each finding. Separate confirmed facts from questions, and leave the source files unchanged.Before accepting a result, open the delivered report, verify its citations against both files, and check that every requested category is covered; when evidence is missing, ask for corrections in the task or follow the task review guide. Before serving a team, complete production preparation — tale deploy uses separate data volumes from the local development instance.
What are this agent's strengths and limitations?
- People and agents share one workspace: the board, @mention steering in task comments, and report/file review all happen in place instead of across separate tools.
- Per-agent runtime choice is explicit — Claude Code, Codex, Cursor, Gemini CLI, Hermes, OpenClaw, OpenCode, Pi, Qwen Code — alongside model and tool selection.
- Agents execute in persistent sandbox workspaces with concurrency bounded by configured capacity, giving isolation and an explicit capacity limit.
- Bring your own provider API keys (routed through Tale's model gateway) or use supported vendor subscriptions with compatible runtimes.
- Two deployment paths with the same feature set: MIT-licensed Docker Compose self-hosting, or the managed Cloud service; Enterprise adds operation and support rather than features.
- Self-hosting is not turnkey: production needs DNS, TLS, backups and access controls on top of Docker and Compose, plus several GB for images and data.
- Vendor subscriptions only work with compatible runtimes, cannot power ordinary Chat, and bypass Tale's gateway metering and spending caps, so cost visibility suffers.
- Self-hosting does not automatically keep data local: model providers, connectors and external tools can process data outside Tale's stores, so residency has to be configured deliberately.
- Developer setup pulls in a mix of toolchains (Bun, Node.js, Docker, Python, uv), which raises the bar for source work.
- Runtime availability depends on your deployment, credentials and sandbox capacity rather than being guaranteed out of the box.
- Only four screenshots and the linked docs describe the UI; there is no public hosted demo in the repository itself.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Tale — Shared Workspace for Teams and AI Agents This agent | 50 · Major gaps | — | ★ 32 | today | TypeScript | Codex · Claude Code · OpenAI API · Claude API |
| Atom Platform | 59 · Major gaps | CLIFree + model costs | ★ 901 | 3d ago | Python | OpenAI API · Claude API |
| OpenCompany | 44 · Major gaps | Desktop appFree + model costs | ★ 970 | 7d ago | Python | OpenAI API · Claude API |
| GAIA — Personal AI Assistant | 73 · Some gaps | Hosted serviceFreemium | ★ 305 | 7d ago | Python | — |
How does FollowAgents rate this agent?
Why each dimension lost points
The README is unusually explicit about data residency, the model gateway, connectors and external tools, and warns that self-hosting does not keep every request local, which supports transparency. However, the repository contains no visible least-privilege design, sandbox capability constraints, user-confirmation flow or rollback mechanism; credential and sensitive-data handling is documented only at a high level, and external effects (connector actions, automation triggers) lack verifiable approval or recovery evidence, so most items score 1. Dependency security scores 2 because package.json pins exact versions, uses overrides and patchedDependencies, and ships a vulnerability-scan script, though no scan results are provided.
Workflow and script naming is consistent and the CI structure is coherent, supporting self-consistency at 2. Dependency availability can only be inferred from package.json and lockfile references; no lockfile content or install verification is shown, so 1. Failure messages are concrete in CI (::error:: lines, failure summaries), so 2.
The README addresses teams, developers, self-hosted and cloud users with rich scenarios. Capability boundaries (runtimes, models, tools, sandbox capacity) are described, but trigger precision (automation triggers, @-mention delegation semantics) lacks verifiable detail, so 1. Environment fit (Docker, ARM64 emulation, Bun/Node versions) is documented, so 2.
Information architecture is clear, install notes are concrete (CLI, Docker, source development), examples and FAQ are present, and the MIT license is complete, earning 3. But the version is only 0.1.0 with no CHANGELOG, naming stability and maintenance responsibility (ownership, update path) are not clearly stated, and known limitations are only scattered, so those items score 1.
Output usability is visible in tasks, reports, delivered files and review flow, and marginal value for team collaboration is plausible; cost-benefit lacks quantified evidence (resource consumption, concurrency limits, pricing only via external link), so 1.
Many README claims point to external documentation sites that cannot be cross-checked from the repository. The only test files cover marketing-ui dependency declarations and test-environment stubs, which cannot corroborate product-level claims. Fact/inference separation is partially present (e.g., self-hosting is not fully local), but the overall evidence chain is thin, so each item scores 1.
- Security, data-residency and governance claims in the README point mainly to external documentation sites and cannot be independently verified from the repository; do not treat them as validated facts.
- No concrete implementation of least privilege, user confirmation, rollback or external-effect approval is visible in the repository; review the sandbox and connector permission model before production use.
- The version is 0.1.0 with no CHANGELOG, so interface and naming stability are unknown; pin versions and watch for breaking changes.
- Publisher identity is not verified by the FollowAgents curated enterprise registry, and maintenance responsibility and update path are unclear; confirm supply-chain provenance independently.
FAQ
What does self-hosting cost and which credentials are required?
What limits how many agents can run at once?
Does self-hosting mean no data leaves my infrastructure?
Is Community different from Enterprise?
Can I run the local development instance for my whole team?
tale deploy uses separate data volumes from the local development instance and points to production preparation (DNS, TLS, backups, access controls) before serving a team. Note too that bun run setup:check covers only part of the environment and does not prove databases, storage or a provider are configured.