RLM

Recursive long-context analysis for Agent Zero, powered by alexzhang13/rlm.

The plugin keeps Agent Zero in control of the conversation while selectively offloading oversized external material to an RLM worker. Version 2.0.1 targets the stable upstream rlms==0.1.3 package and its answer["content"] / answer["ready"] completion contract. A narrow compatibility adapter treats nullable token-usage values from OpenAI-compatible OAuth proxies as unknown instead of allowing upstream cost tracking to abort an otherwise valid run.

What It Does

Automatic routing:

1. Estimates prompt pressure against the active chat model's context length.
2. Finds eligible large message fields and text attachments.
3. Replaces those fields with compact placeholders in the visible prompt.
4. Gives the structured visible context and offloaded blocks to upstream RLM.
5. Returns the completed assistant response or exact Agent Zero tool-call JSON.

Short conversations, prompts without eligible external blocks, and prompts that do not shrink enough stay on Agent Zero's normal model path. If dependency, provider, or execution readiness is blocked, automatic routing also falls back to the normal model without modifying Agent Zero core code.

The rlm tool provides an explicit route for recursive analysis over recent Agent Zero history.

Installation — Docker Desktop

1. In Agent Zero, open Plugins → Install Plugin → Git, paste https://github.com/a0-community-plugins/rlm, and install/enable RLM.
2. Open RLM Context Explorer. Under Set up Docker Desktop, select macOS / Linux or Windows, then click Copy Setup Command.
3. Paste it into your computer's Terminal or PowerShell and confirm. Keep Docker Desktop running. Do not use Agent Zero's terminal or the container's Exec tab.
4. Wait for RLM Docker setup passed, then reopen Agent Zero at the same address. The sandbox check runs automatically.

No Compose file, host Python installation, repository checkout, or hand-edited Docker paths are required. The copied command gets the setup files directly from your installed plugin. First setup downloads the Docker CLI and a small sandbox image; later runs reuse them.

Setup briefly restarts the selected container. It preserves its web port, data mounts, environment, networks, and installed software. It supports both /a0/usr and older /a0 mounts, including Docker named volumes. If data is only inside the container, setup preserves it in a new persistent Docker volume. The original container is kept stopped as a rollback copy, and a failed setup restores it automatically. See setup details and rollback.

Docker access lets Agent Zero manage Docker containers and access shared host files. The setup command explains this before asking you to continue. Docker cannot add a mount to an existing container from the plugin UI, which is why this one host-terminal step is necessary. Local snapshot images can contain private data; never publish them.

The plugin install hook automatically installs rlms==0.1.3 into Agent Zero's framework Python (/opt/venv-a0/bin/python3 in the standard image). If that step was interrupted, click Retry Setup in the Explorer. This button repairs the Python dependency; it cannot grant Docker access. Restart Agent Zero after upgrading a dependency that was already loaded. There is no separate Execute step.

For an existing installation, update RLM first to get these setup controls. Advanced users who manage Compose can use the optional Dockerfile and Compose override.

Execution Safety

auto and docker modes require Docker and never silently switch to local. Local mode executes generated Python in Agent Zero's own framework process and is an explicit opt-in for trusted development workloads only.

The Explorer reports Docker CLI, endpoint, daemon, and sandbox-probe status separately. A passing probe verifies shared files and the callback connection from an actual sandbox; a complete model run also needs a configured provider. RLM's shim joins the sandbox to Agent Zero's network, directs its callback to Agent Zero, and translates the workspace to the daemon's shared data path. Only the run's workspace is mounted in the sandbox, not the entire data volume.

Attachment ingestion is restricted to Agent Zero's usr/uploads directory and rejects symlink escapes. Other local paths are not read as attachments.

Configuration

• auto_enabled: allow selective automatic routing.
• manual_tool_enabled: expose the rlm tool.
• trigger_threshold_pct: context-pressure threshold for automatic routing.
• min_block_chars: smallest message field eligible for offloading.
• attachment_max_chars: per-text-attachment ingestion cap.
• environment_mode: auto, docker, or explicit local.
• docker_image: image used by the upstream Docker REPL.
• max_depth, max_iterations, max_timeout, max_budget, max_tokens, max_errors: upstream execution limits.
• max_concurrent_subcalls: bound parallel recursive fan-out.
• subcall_model_source: use Agent Zero's utility or root model for subcalls.
• persistence_enabled: save run summaries and trajectories locally.
• retention_count: number of persisted runs to retain.

Trajectory persistence is off by default because stored trajectories may contain prompt, model-output, or tool-output excerpts. When enabled, data stays under the plugin-owned data/runs directory and is ignored by Git.

Supported Provider Mapping

The integration maps Agent Zero model settings to upstream RLM clients for OpenAI-compatible providers (including OpenRouter), Anthropic, Azure OpenAI, Gemini, and Portkey. The explorer reports the active mapping and any readiness blocker before a run.

Verification

The repository's regression suite uses only the Python standard library:

python3 -m unittest discover -s tests -v
node --check webui/rlm-context-store.js

An end-to-end RLM completion additionally requires the upstream dependency, a configured provider credential, and the selected execution environment. For Docker mode, run the Explorer sandbox probe before treating the route as deployment-ready.

Upstream and License

This Agent Zero integration is independently maintained by a0-community-plugins. It depends on alexzhang13/rlm, created by Alex Zhang and distributed under the MIT License. This plugin is also distributed under the MIT License; see LICENSE.