Is There a Community Edition of Palantir? Meet OpenPlanter: An Open Source Recursive AI Agent for Your Micro Surveillance Use Cases

The balance of power in the digital age is shifting. While governments and large corporations have long used data to track individuals, a new open-source project called OpenPlanter is giving that power back to the public. Created by a developer ‘Shin Megami Boson‘, OpenPlanter is a recursive-language-model investigation agent. Its goal is simple: help you keep tabs on your government, since they are almost certainly keeping tabs on you.

Solving the ‘Heterogeneous Data’ Problem

Investigative work is difficult because data is messy. Public records are often spread across 100 different formats. You might have a CSV of campaign finance records, a JSON file of government contracts, and a PDF of lobbying disclosures.

OpenPlanter ingests these disparate structured and unstructured data sources effortlessly. It uses Large Language Models (LLMs) to perform entity resolution. This is the process of identifying when different records refer to the same person or company. Once it connects these dots, the agent probabilistically looks for anomalies. It searches for patterns that a human might miss, such as a sudden spike in contract wins following a specific lobbying event.

The Architecture: Recursive Sub-Agent Delegation

What makes OpenPlanter unique is its recursive engine. Most AI agents handle 1 request at a time. OpenPlanter, however, breaks large objectives into smaller pieces. If you give it a massive task, it uses a sub-agent delegation strategy.

The agent has a default max-depth of 4. This means the main agent can spawn a sub-agent, which can spawn another, and so on. These agents work in parallel to:

Resolve entities across massive datasets.

Link datasets that have no common ID numbers.

Construct evidence chains that back up every single finding.

This recursive approach allows the system to handle investigations that are too large for a single ‘context window.’

The 2026 AI Stack

OpenPlanter is built for the high-performance requirements of 2026. It is written in Python 3.10+ and integrates with the most advanced models available today. The technical documentation lists several supported providers:

OpenAI: It uses gpt-5.2 as the default.

Anthropic: It supports claude-opus-4-6.

OpenRouter: It defaults to anthropic/claude-sonnet-4-5.

Cerebras: It uses qwen-3-235b-a22b-instruct-2507 for high-speed tasks.

The system also uses Exa for web searches and Voyage for high-accuracy embeddings. This multi-model strategy ensures that the agent uses the best ‘brain’ for each specific sub-task.

19 Tools for Digital Forensics

The agent is equipped with 19 specialized tools. These tools allow it to interact with the real world rather than just ‘chatting.’ These are organized into 4 core areas:

File I/O and Workspace: Tools like read_file, write_file, and hashline_edit allow the agent to manage its own database of findings.

Shell Execution: The agent can use run_shell to execute actual code. It can write a Python script to analyze a dataset and then run that script to get results.

Web Retrieval: With web_search and fetch_url, it can pull live data from government registries or news sites.

Planning and Logic: The think tool lets the agent pause and strategize. It uses acceptance-criteria to verify that a sub-task was completed correctly before moving to the next step.

Deployment and Interface

OpenPlanter is designed to be accessible but powerful. It features a Terminal User Interface (TUI) built with rich and prompt_toolkit. The interface includes a splash art screen of ASCII potted plants, but the work it does is serious.

You can get started quickly using Docker. By running docker compose up, the agent starts in a container. This is a critical security feature because it isolates the agent’s run_shell commands from the user’s host operating system.

The command-line interface allows for ‘headless’ tasks. You can run a single command like:

The agent will then work autonomously until it produces a final report.

Key Takeaways

Autonomous Recursive Logic: Unlike standard agents, OpenPlanter uses a recursive sub-agent delegation strategy (default max-depth of 4). It breaks complex investigative objectives into smaller sub-tasks, parallelizing work across multiple agents to build detailed evidence chains.

Heterogeneous Data Correlation: The agent is built to ingest and resolve disparate structured and unstructured data. It can simultaneously process CSV files, JSON records, and unstructured text (like PDFs) to identify entities across fragmented datasets.

Probabilistic Anomaly Detection: By performing entity resolution, OpenPlanter automatically connects records—such as matching a corporate alias to a lobbying disclosure—and looks for probabilistic anomalies to surface hidden connections between government spending and private interests.

High-End 2026 Model Stack: The system is provider-agnostic and utilizes the latest frontier models, including OpenAI gpt-5.2, Anthropic claude-opus-4-6, and Cerebras qwen-3-235b-a22b-instruct-2507 for high-speed inference.

Integrated Toolset for Forensics: OpenPlanter features 19 distinct tools, including shell execution (run_shell), web search (Exa), and file patching (hashline_edit). This allows it to write and run its own analysis scripts while verifying results against real-world acceptance criteria.

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Disclaimer: MarkTechPost does not endorse the OpenPlanter project and provides this technical report for informational purposes only.