Potato Annotation Platform

A self-hosted workspace for multimodal annotation, agent evaluation, and qualitative research.

Stars
★ 429
Last updated
5d ago
License
GPL-3.0
Primary language
Python

At a glance

How it runs
CLISelf-hosted serviceWeb app
Works with
Universal · cross-platformOpenAI API · Claude APICodex · Claude Code (Partial support)
Cost
Free, no paid service needed
Setup effort
Medium · a few setup steps
You'll need
PythonFlaskYAML configurationOpenAI API (optional)Anthropic API (optional)Shell / CLINetwork accessLocal filesystemMCP Server
Typical use
An NLP research team configures classification, named-entity recognition, or coreference tasks and collects labels from multiple annotators.
Not a fit if
  • Teams seeking only a hosted annotation SaaS
  • Users who want evaluation without configuring an annotation task

What does this agent do, and when should you use it?

Potato is a free, self-hosted annotation platform for NLP, agent and generative AI evaluation, and qualitative research. Users configure tasks in YAML, start a Flask service locally or on a deployed server, and annotate text, audio, video, images, documents, and agent traces in a browser. It imports traces from multiple agent frameworks and supports evaluation at the trajectory, step, span, and pairwise-comparison levels. The platform also offers LLM label suggestions, active learning, quality controls, and qualitative analysis workflows. Annotations can be exported; deployment options include local use and the cloud and hosted platforms listed in the README.

Users prepare a YAML configuration and input data, run potato start or launch potato/flask_server.py from source, then open the service in a browser to annotate. potato.trace_converter converts traces from formats including OpenAI, Anthropic/Claude, ReAct, LangChain, and MCP. Reviewers can rate an overall task, individual steps, or text spans, and compare agents side by side. The platform connects to OpenAI, Anthropic, Ollama, vLLM, Gemini, HuggingFace, and OpenRouter for label suggestions; it can also reorder annotation queues using uncertainty, BADGE, BALD, and other strategies. It supports multiple annotators, adjudication, attention checks, gold standards, agreement metrics, and QDA workflows with a living codebook, memos, and cases; completed annotations can be exported.

  1. An NLP research team configures classification, named-entity recognition, or coreference tasks and collects labels from multiple annotators.
  2. An agent team imports tool-use traces to review action correctness, reasoning quality, and task success step by step.
  3. A RAG evaluator labels retrieval relevance, citation accuracy, and hallucinated answer spans.
  4. A speech researcher uses waveform and segment interfaces to label audio or imports transcripts, subtitles, and ELAN-style tiers.
  5. A qualitative researcher analyzes interviews or documents using an evolving codebook, memos, and cases.

How do you install or deploy this agent?

A Python environment and shell are required. The README documents installation from PyPI; the examples directory ships with the source repository, so clone the repository if you want to run that example. After starting the server, open its local address in a browser.

How do you use this agent?

The basic flow is to install the package, prepare a YAML configuration and data, start the server, annotate in the browser, and export results. The source-based startup example is:

What are this agent's strengths and limitations?

Pros
  • One platform covers text, audio, video, images, documents, and agent traces.
  • Imports multiple trace formats and supports evaluation by trajectory, step, span, or comparison.
  • Offers self-hosted use, configurable deployment targets, and quality controls for multi-annotator work.
  • Supports codebooks, memos, and cases, bringing QDA workflows together with conventional annotation.
Limitations
  • Users must prepare YAML task configuration and data; the first step is not simply opening a ready-made SaaS workspace.
  • Local use requires installing the Python package or source dependencies and starting a server; cloud deployment requires configuring the chosen platform.
  • LLM label suggestions depend on the selected provider and its service setup; local options such as Ollama and vLLM also need to be run by the user.
  • The README does not give the costs, resource requirements, or a single deployment recipe for every hosted target.

How does this agent compare with similar options?

The README positions Potato's QDA features alongside qualitative analysis tools such as NVivo, ATLAS.ti, and MAXQDA, emphasizing that Potato is self-hosted and free. It describes its agent evaluation capabilities without naming a direct alternative.

Key facts side by side with the most closely related agents.

Agent Source review Form / cost Stars Updated Language Full support on
Potato Annotation Platform This agent 59 · Major gaps CLIFree ★ 429 5d ago Python OpenAI API · Claude API
ClawBench Browser Agent Benchmark 48 · Major gaps CLIFree + model costs ★ 1k 1d ago Python Codex · Claude Code
Meta ARE 37 · Major gaps CLIFree + model costs ★ 566 12d ago Python —
MiroFlow Research Agent 53 · Major gaps CLIFree + model costs ★ 3.1k 8mo ago Python —

How does FollowAgents rate this agent?

FollowAgents source review · FARS-2.1
Major gaps
59/ 100 5-point scale 3.0 / 5
Trust 13/29
Reliability 6/14
Adaptability 14/18
Convention 11/18
Effectiveness 10/13
Verifiability 5/8
Why each dimension lost points
Trust13 / 29 · 2.2/5

The security policy identifies deployment boundaries and says the dangerous pickle import is off by default, debug mode is for development, and trusted sandbox mode requires explicit acknowledgement. This supports partial credit for confirmation and credential handling. The supplied tests also show Docker execution configured with networking disabled, a read-only filesystem, resource limits, and workspace cleanup at session end. I deduct because the files do not fully describe data flows or permissions across all components, and provide little evidence of dependency security; rollback guidance is limited, and publisher identity is unverified.

Reliability6 / 14 · 2.1/5

README, LICENSE, and package.json disagree about the license (GPLv3+ versus MIT), so self-consistency scores low. requirements.txt distinguishes core and optional dependencies, but uses broad lower bounds and the supplied materials show no lockfile, limiting confidence in dependency availability. The proxy test file includes error handling for timeouts, planner failures, invalid actions, and missing Docker, supporting a moderate failure-message score; this is static evidence, not a claim about execution.

Adaptability14 / 18 · 3.9/5

The materials cover NLP, agent traces, multimodal annotation, QDA, and several deployment and collaboration scenarios, with examples and configuration guidance, so audience and scenario coverage is strong. Security and configuration notes define some boundaries, but the broad capability claims are not individually corroborated by the supplied code. Configuration-driven workflows and multiple installation and deployment targets support moderate trigger precision and environment fit; this review does not infer runtime compatibility beyond the evidence provided.

Convention11 / 18 · 3.1/5

The README has clear feature sections, installation steps, a documentation index, and many examples. The security policy lists supported versions, a reporting path, and several limitations, supporting these areas. I deduct for installation guidance being spread across sources, the package name and license metadata conflicting with the README, and no clear changelog in the supplied materials. The security policy gives a maintenance and vulnerability-reporting path, but publisher identity is unknown, limiting how verifiable maintenance responsibility is.

Effectiveness10 / 13 · 3.8/5

The tool targets annotation and agent evaluation, with multiple annotation modes, trace conversion, quality controls, and export-related capabilities. Structured examples and configuration support usable outputs, so the potential utility is high. Marginal value and cost-benefit receive partial credit because the supplied implementation evidence does not substantiate each broad README claim, and the materials do not quantify the cost of choosing among optional dependencies and deployment approaches.

Verifiability5 / 8 · 3.1/5

The README links many capabilities to specific documentation, examples, and research papers; the security policy names concrete configuration settings and scope; and the test source supports some proxy behavior. These provide moderate traceability and corroboration. I deduct because the supplied evidence is mostly README claims, a limited test excerpt, and policy text, while the license conflict weakens consistency. The assessment distinguishes file-stated facts from unverified capability claims, but does not independently execute or validate them.

Risks and how to mitigate them
  • The README, LICENSE, and package.json conflict on the license; verify the applicable terms before adoption or redistribution.
  • The security policy calls for a TLS-terminating reverse proxy, debug mode off, and password authentication beyond local pilots; it says passwordless login does not verify usernames.
  • The README describes many agent, AI, and deployment capabilities. This static review of the supplied files cannot confirm their behavior in a target environment.
Evidence confidence: Low Reviewed Oct 13, 2026 Reviewed revision b7f652efe4ea
See the full review method →

FAQ

Does Potato cost money to use?
Potato is described as free software under GPLv3+. External LLM providers may charge for their services; the README does not list their prices.
Do I have to use OpenAI or Anthropic?
No. Label suggestions support OpenAI, Anthropic, Ollama, vLLM, Gemini, HuggingFace, and OpenRouter. Annotation and quality-control features can also be used without an LLM.
Can I use it only for agent evaluation?
Yes. Import traces from multiple frameworks and evaluate trajectories, steps, spans, or side-by-side comparisons. You still need to configure a task and run Potato.
Can a team make Potato available online to annotators?
Yes. The README lists multiple deployment targets and describes HTTPS certificates, authentication options, and annotation backups for some hosts whose disks do not persist across restarts.
View on GitHub ↗ Install ↓

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