Dashr: RLM Plugin for DSH

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⚡ Quick Install

curl -fsSL https://raw.githubusercontent.com/fgm-builds/dashr/main/install.sh | bash

Alternative: DSH Plugin CLI (NPM)

dsh plugin --profile web add --config.auto-install-peers=false dsh-rlm-mode
# then copy the preset files (install.sh does this for you):
#   <profile>/node_modules/dsh-rlm-mode/preset/rlm-mode/*  →  ~/.dsh/.agent-presets/rlm-mode/

After installation, launch dsh web and select the RLM Mode agent preset.

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📖 Overview

DeepSeek Harness (dsh): Everything is a plugin (Cordis framework).
Prime Agent: Context is variable (RLM paradigm).
Why not both? That's dsh in RLM mode — that's Dashr.

Dashr is an open-source plugin for the DeepSeek Harness (dsh) agent runtime. It brings RLM (Recursive Language Models) and the "Context is Variable" paradigm to dsh, registering a dedicated rlm-mode agent preset upon installation.

Instead of paying massive token costs on every round-trip tool call in standard multi-turn chat, Dashr equips the agent with a stateful, persistent Python kernel. The agent writes self-contained Python programs per cell, manipulating context, tools, and memory as native variables.

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💡 RLM

Reference: arXiv:2512.24601

1. Context is Variable (Stateful Kernel)

In standard agent loops, reading large files or computing complex payloads dumps raw output directly into the conversation history. In Dashr:

• State and computation persist inside a live IPython kernel session.
• Intermediate variables survive across cells without re-entering the prompt.
• Tools are exposed as first-class Python functions (tools.<name>()). Intermediate execution data never round-trips through the prompt.

2. Recursive Sub-Agents (rlm())

The core mechanism of Recursive Language Models:

• For token-heavy or exploratory subtasks, the agent spawns child agents (handle = rlm("Investigate repository history")).
• Sub-agents operate recursively in their own isolated context loops.
• When finished, rlm_await(handle) collects only the final distilled summary back into the parent kernel.

3. Sliding Context Window

• Even without spawning sub-agents, Dashr maintains a bounded sliding context window over recent turns.
• Prevents context degradation and eliminates context window saturation on long workflows.

4. Compaction & Summarization

• Earlier turns that fall outside the active sliding window are automatically compressed into structured summaries (compact()).
• High-level progress, key decisions, and operating guidance are preserved in a dynamic harness (refine()) and reinjected into the prompt.

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✨ Features

• 🐍 Persistent IPython Kernel — One stateful kernel session per conversation. Variables, imports, and connections persist across cells.
• ⚡ Dynamic Tool Binding — Zero hardcoded tool adapters. At startup, Dashr dynamically binds all tools registered in the dsh host (bash, web_search, file operations, workflows, skills, etc.) into type-safe Python SDK functions under tools.*.
• 🔀 In-Kernel Recursive Sub-Agents — Call rlm(task) to spawn parallel sub-agents and rlm_await(id) to collect results inside Python code.
• 🧠 Dynamic Harness & Compaction — Built-in refine() for operating memory and compact() for context reduction under pressure.
• 💾 State Snapshot & Revival — Save and restore the kernel namespace across sessions.
• 🔄 Upstream-Proof Preset — The rlm-mode agent preset dynamically includes dsh's standard composition, staying compatible whenever upstream dsh introduces new capabilities.

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🔒 Security Model

• Tool Governance: Calls to tools.* run through dsh's host tool pipeline, where approval and sandbox policies apply normally.
• Kernel Code Execution: Python code inside cells executes with the permissions of the local user running dsh. Run Dashr in environments where you trust the agent's code execution against your user account (or run dsh within a container).

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📚 References & Academic Credit

The design of Dashr builds upon groundbreaking research in recursive agent execution and persistent prompt harnesses:

1. Recursive Language Models (RLM)
   Recursive Language Models, 2025.
   Paper: arXiv:2512.24601
   Establishes the recursive decomposition and sub-agent execution paradigm for ultra-long context and bounded prompt management.

2. Continual Harness & Prompt Refinement
   Continual Harness for Autonomous Agents, 2026.
   Paper: arXiv:2605.09998
   Formulation for dynamic prompt refinement and in-loop compaction.

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🙏 Acknowledgements & Attribution

Dashr is built as an open-source plugin for DeepSeek Harness (dsh).

While Dashr's codebase was developed independently from scratch for the dsh plugin ecosystem, the core design and philosophy are deeply inspired by the pioneering work of Prime Agent by Prime Intellect. We pay tribute to their introduction of the "Context is Variable" paradigm and the Recursive Language Model (RLM) execution model, which inspired us to bring these breakthrough capabilities to the dsh agent community.

⚖️ License & Compatibility

Both Dashr and upstream inspiration Prime Agent are licensed under the permissive MIT License. Dashr is fully open-source and license-compliant without IP or licensing conflicts.

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📄 License

This project is licensed under the MIT License.