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OpenPlanter is a project-described AI investigation agent with a desktop interface and a Python command-line interface. Its documentation says it can work across mixed datasets, resolve entities, and present connections with supporting evidence. Those are documented capabilities, not independently verified accuracy or performance results.
What OpenPlanter is designed to do
The OpenPlanter README calls it “a recursive-language-model investigation agent with a desktop GUI and terminal interface.” The project describes a workflow in which an investigator supplies heterogeneous data, the agent looks for entities and relationships across it, and findings are presented with links to evidence.
Examples of relevant material in the README include corporate registries, campaign-finance records, lobbying disclosures, and government contracts. These are examples of dataset categories, not confirmation that OpenPlanter ships a built-in connector for every registry or records system. The README also lists web search and URL-fetching tools, but source access depends on the specific data and configuration available to an investigation.
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How the desktop investigation workflow is presented
The project documents a three-pane desktop layout:
- Sidebar: investigation sessions and provider or model settings.
- Chat pane: the investigation objective and the agent’s tool calls.
- Knowledge graph: entities and relationships, with interactive layouts and filters described by the README.
The documentation also describes a source drawer containing rendered wiki documents, saved sessions, and a background wiki curator. This design aims to let an investigator move from a proposed relationship to the material associated with it. The existence of those documented features does not establish that entity matching is correct or that every displayed link is adequately supported; those require case-by-case review.
#1 Best Overall
What the agent can access—and why scope matters
The README enumerates 19 agent tools spanning workspace file operations, shell execution, web retrieval, planning, delegation, and artifact handling. File tools include listing, searching, mapping, reading, and editing workspace files. The project says recursive mode is the default and describes delegation for tasks such as entity resolution, cross-dataset linking, and building evidence chains.
File and shell access are consequential: the investigator should understand which workspace is mounted or selected, what files it contains, and what actions the configured tools can perform before running a task. The README documents capabilities, but it does not establish an independent security assessment or a privacy guarantee. Do not assume that choosing a local model makes every part of a workflow local: the project also documents hosted model providers and separate external services for search and embeddings.
Setup paths and model choices
The project documents a Python CLI that can run independently, including a single headless task, as well as a Tauri desktop application. It also describes Docker usage with a workspace directory mounted into the container. The documented desktop distribution formats are macOS DMG, Windows MSI, and Linux AppImage. Check the repository’s current installation instructions and release assets before choosing a path; requirements and availability can change.
The README lists these model-provider options and related services:
| Choice | Documented role |
|---|---|
| OpenAI, Anthropic, OpenRouter, Cerebras | Hosted model-provider options named by the project. |
| Ollama | The local-model option named by the project. |
| Exa | Additional service key for web search. |
| Voyage | Additional service key for embeddings. |
Provider support, default models, service requirements, and upstream availability are version-sensitive. Confirm the current repository instructions and the relevant provider’s own documentation; do not treat a listed option as proof that a particular model or service remains available.
What the demos do—and do not—establish
OpenPlanter’s demo scenarios are project-authored illustrations of possible investigations, not independent tests. One scripted prompt asks whether politicians receive donations from people who own shell companies. The demo also narrates hypothetical counts and risk scores, including 14,203 entities and a composite score of 87/100. These figures describe the scenario, not measured OpenPlanter performance, a real investigation, or a validated risk assessment.
Rank #4
The reviewed project material does not establish independently measured accuracy, speed, usability, or security. It also does not provide a third-party privacy analysis or validation of entity resolution. Treat any proposed connection as a lead to verify against original records, and retain enough source context to check names, dates, aliases, and the basis for each relationship.
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The official releases page’s latest visible entry is v0.1.1 and gives a release line of March 6 without stating the year. That partial date is not enough to infer a full release date or ongoing maintenance. Check the current release page and repository before installing.
Best Value
- Confirm the release asset and installation requirements for your operating system.
- Decide whether to use a hosted model or Ollama, and identify which external search or embedding services your workflow requires.
- Limit the workspace to files appropriate for the investigation, and understand the configured file and shell tools.
- Test the workflow on material whose provenance and relationships you can independently verify.
- Review source documents before treating graph links or summaries as findings.
OpenPlanter may be worth evaluating when an investigation spans mixed local material and web sources and a graph-based view is useful. Its README explains the intended workflow; it does not by itself demonstrate that the agent reliably resolves entities or produces defensible conclusions.
Quick Recap
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