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# Create extensive and comprehensive documentation to build the most sophisticated research apparatus through openclaw agents. Utilize the documentation of openclaw as a source to maintain fundamental structural integrity.

Any topic i define must be researched thoroughly and an industry leading report must be provided.

You can absolutely use OpenClaw to build a very sophisticated, research‑grade agent apparatus, but the documentation you’re asking for will be large and multi‑layered. To keep it coherent and “industry‑leading”, I suggest we structure it as a proper architecture and playbook, then fill it in step by step.

Below is the high‑level documentation structure I recommend. Once you confirm this structure (or adjust it), I can start expanding each section into deep, implementation‑ready docs and then apply it to any concrete research topic you specify.

## 1. Target Architecture Overview

This section defines what “most sophisticated research apparatus” means in OpenClaw terms: a coordinated set of agents, skills, tooling, and infrastructure that can:

- Ingest and curate large, messy corpora (web, PDFs, APIs, databases).[^1_1][^1_2]
- Plan and execute complex, multi‑step research workflows autonomously, with human‑in‑the‑loop governance where needed.[^1_2]
- Produce long, coherent, well‑cited reports and artifacts (memos, slide outlines, datasets, code notebooks).[^1_3][^1_1]

Core design pillars:

- **Modularity**: Each agent is specialized with a clear system prompt (identity), tools, and workspace boundaries.[^1_3][^1_1]
- Orchestration** via multi‑agent teams: architect/planner, searcher, reader/summarizer, analyst, writer, reviewer, red‑teamer.[^1_4][^1_3]
- Strong guarantees on context assembly (AGENTS.md, TOOLS.md, MEMORY.md, logs) and loop control, aligning with OpenClaw’s runtime model.[^1_5][^1_2]
- Hybrid model strategy (frontier cloud + local models through Ollama/OpenAI‑compatible servers) for cost, privacy, and performance.[^1_6][^1_1][^1_2]

Deliverables for this section (to be written out once you confirm):

- Logical component diagram and explanation of each agent.
- Example end‑to‑end workflow for “given a topic string, output a 20–40 page research report + sources + appendix”.


## 2. OpenClaw Fundamentals for Research Systems

This section will ground the documentation in OpenClaw’s **native** concepts so we don’t fight the grain of the framework.

Key elements to document:

- OpenClaw deployment model: self‑hosted gateway, skills registry, queue, agent runtime.[^1_2][^1_6]
- Agent definition surface:
    - CLI interface: `openclaw agents add`, `agents list`, `config set`, `openclaw.json`.[^1_5][^1_3]
    - Dashboard interface: system prompt, skills, model backend.[^1_1]
    - Identity files (e.g., `agent.md` / `AGENTS.md` entries) defining style, constraints, evaluation criteria.[^1_5][^1_3]
- Skills and tools:
    - File operations, shell, browser/web search, Git, RAG skills (vector DBs like Milvus, Postgres extensions), code execution.[^1_7][^1_6][^1_3]
- Runtime loop:
    - Context assembly (AGENTS.md, SOUL.md, TOOLS.md, MEMORY.md, daily log, conversation history).[^1_2][^1_5]
    - Model call → tool calls → feedback → termination criteria.[^1_2]

Deliverables for this section:

- A canonical `openclaw.json` skeleton for a research project.
- Template `agent.md` / identity docs tailored to research roles.


## 3. Multi‑Agent Research Topology

Here we define the “agent team” that together behaves like a full research department.

Example topology (can be adapted to your needs):


| Agent role | Purpose | Typical model setup | Core tools |
| :-- | :-- | :-- | :-- |
| Architect/Planner | Scopes topic, decomposes into tasks, designs workflow | High‑end model, high token limit, low temperature.[^1_3][^1_2] | browser, mcp, file, memory |
| Discovery/Searcher | Finds sources, runs web/API queries, builds initial corpus | Mid/high model, moderate temperature.[^1_3][^1_1] | browser, custom search skills, file |
| Curator/Deduper | Filters low‑quality/duplicate sources, ranks evidence | Mid model, low temp | file, RAG/vector DB skills.[^1_7] |
| Reader/Synthesizer | Reads sources deeply, builds notes and source‑linked summaries | High‑context model, medium temp.[^1_2] | file, RAG, browser |
| Analyst | Performs reasoning, modeling, scenario analysis, data transforms | High‑end or local 32B+ model if hardware allows.[^1_2] | file, shell, code, SQL skills |
| Writer | Converts analysis into structured report, with citations and appendices | Cost‑effective model for longform, medium temp.[^1_3][^1_1] | file |
| Reviewer | Checks claims, consistency, and style; enforces guardrails | High‑end model, very low temp.[^1_3][^1_8] | file, browser, git |
| Red‑teamer | Stress‑tests conclusions, looks for missing perspectives and failure modes | High‑end model, medium temp | browser, file |

For each agent, the documentation will include:

- Recommended `openclaw agents add` command (model, fallback, tokens, temperature, workspace, tools, description).[^1_3]
- System prompt template focused on research responsibilities and constraints.
- Input/output contract: what it expects from other agents, and what it produces.


## 4. Hardware and Model Strategy

To reach “most sophisticated apparatus”, we need a deliberate model and hardware plan.

Topics to cover:

- Self‑hosted vs cloud:
    - OpenClaw runs as a local or self‑hosted gateway; you choose providers (Anthropic, OpenAI, DeepSeek, Ollama).[^1_6][^1_1]
- Context needs:
    - Research tasks benefit from ≥64k context; OpenClaw is designed with that expectation in mind.[^1_2]
- Local models:
    - Viable local model size for multi‑step agents tends to start at ≈32B parameters, often requiring ≥24 GB VRAM.[^1_2]
- Hybrid pattern:
    - High‑stakes reasoning and planning on frontier cloud models, bulk reading and summarization on cheaper local or smaller cloud models.[^1_1][^1_2]

Deliverables:

- Example deployment recipes (single‑GPU workstation, small lab cluster, fully cloud).
- Model selection matrix (what to use for which agent roles).


## 5. Research Workflow Design

Here we specify the end‑to‑end pipeline that every topic will follow, producing the “industry leading” report you asked for.

Canonical workflow:

1. Topic intake and scoping
    - Architect agent refines the topic into specific questions, constraints, and success criteria.[^1_4][^1_1]
2. Landscape discovery
    - Searcher agent runs broad web and database scans, identifies key domains, players, datasets.[^1_4][^1_3]
3. Corpus construction
    - Curator pulls documents into a workspace, normalizes formats, tags metadata, and removes duplicates.[^1_7][^1_2]
4. Deep reading and note‑taking
    - Reader builds structured notes (per‑source summaries, key claims, methodological details).[^1_1][^1_2]
5. Analysis and modeling
    - Analyst performs comparisons, quantitative analysis (if data), scenario/counterfactual reasoning, risks and limitations.[^1_2]
6. Drafting the report
    - Writer composes the full report, including:
        - Executive summary.
        - Background and methodology.
        - Findings by sub‑question.
        - Limitations, open questions, and suggested follow‑up research.
7. Review and red‑teaming
    - Reviewer verifies citations, checks for hallucinations, and enforces style.[^1_8]
    - Red‑teamer probes missing perspectives, biases, and potential failure cases.
8. Publication and archiving
    - Final report and artifacts integrated into a knowledge base (e.g., via RAG skill) to inform future research.[^1_7][^1_2]

Deliverables:

- Detailed state machine / sequence diagram for the workflow.
- Prompt and tool‑use patterns for each phase.


## 6. OpenClaw Configuration \& Code Examples

This section will include concrete examples aligned with OpenClaw’s documented practices:

- Example `openclaw agents add` commands for an entire research team (architect, coder, reviewer, documenter already illustrated in existing guides can be adapted).[^1_3][^1_4]
- Project‑level `openclaw.json` including:
    - Agent registry.
    - Skills configuration (e.g., custom “web‑scraper”, “pdf‑ingestor”, “milvus‑rag”).[^1_6][^1_7]
    - Memory/logging configuration.
- Example AGENTS.md snippets showing how research roles are described.[^1_5]
- Repo layout, leveraging established GitHub‑based workflows for OpenClaw projects.[^1_9][^1_6]


## 7. Governance, Quality, and Safety

A sophisticated research apparatus must have governance:

- Access and data governance:
    - Use OpenClaw’s self‑hosted nature and skill scoping to restrict what agents can touch (files, shells, secrets).[^1_6][^1_2]
- Quality control:
    - Reviewer agent with strict identity and low temperature.
    - Automated checklists embedded in prompts (e.g., coverage of contrary evidence, limitations).[^1_8][^1_3]
- Auditability:
    - Persist agent messages, tool calls, and intermediate artifacts; leverage OpenClaw’s logs and daily memory files.[^1_5][^1_2]
- Safety:
    - Red‑teamer agent, separated tools, and limited workspaces per agent (e.g., `./src`, `./docs`).[^1_8][^1_3]


## 8. Applying This to Any Topic

Once the apparatus is in place, the process for “any topic you define” is:

- You provide a topic and any constraints (time scope, domains, level of technical depth).
- Architect agent instantiates a research plan and configures sub‑agents accordingly (e.g., which skills, which corpora to target).
- The rest of the workflow runs, with human checkpoints you choose (e.g., approval of plan, approval of corpus, approval of outline).

For you, in practice:

- We can first fully write out sections 1–7 as detailed docs.
- Then, when you give a specific topic (for example, “next‑generation LNG technologies in Qatar 2030 horizon”), I will run through that workflow conceptually and generate the “industry leading” report according to this apparatus.

***

To proceed effectively, tell me:

1) Do you want this documentation geared toward a single‑machine self‑hosted setup, a team/organization setup, or both?
2) What is the first concrete research topic you want this apparatus to tackle once the docs are laid out?
<span style="display:none">[^1_10]</span>

<div align="center">⁂</div>

[^1_1]: https://www.tencentcloud.com/techpedia/140791

[^1_2]: https://milvus.io/blog/openclaw-formerly-clawdbot-moltbot-explained-a-complete-guide-to-the-autonomous-ai-agent.md

[^1_3]: https://www.meta-intelligence.tech/en/insight-openclaw-agents-guide

[^1_4]: https://ai2sql.io/how-to-build-your-own-ai-agent-team-with-openclaw-in-15-minutes

[^1_5]: https://github.com/openclaw/openclaw/blob/main/AGENTS.md

[^1_6]: https://www.testmuai.com/blog/openclaw-github-repository/

[^1_7]: https://github.com/VoltAgent/awesome-openclaw-skills/blob/main/README.md

[^1_8]: https://amankhan1.substack.com/p/how-to-make-your-openclaw-agent-useful

[^1_9]: https://www.getopenclaw.ai/blog/openclaw-github

[^1_10]: https://dev.to/mechcloud_academy/unleashing-openclaw-the-ultimate-guide-to-local-ai-agents-for-developers-in-2026-3k0h


---

## OpenClaw Research Apparatus: Single-Machine Self-Hosted Setup

OpenClaw provides a robust foundation for building sophisticated multi-agent systems on a single machine through its CLI-driven agent management, workspace isolation, and tool ecosystem. This documentation outlines a complete, production-grade research apparatus optimized for one host with ≥32GB RAM and a capable GPU (e.g., NVIDIA RTX 40-series with ≥24GB VRAM for local models via Ollama).[^2_1]

The design leverages OpenClaw's native `openclaw agents add` workflow, per-agent workspaces, and skills for modularity, ensuring structural integrity while enabling autonomous, high-quality research on any topic you specify.[^2_2]

## Core Design Principles

Prioritize modularity, safety, and efficiency for single-machine operation.

- Agents are scoped to isolated workspaces (`--workspace ./research/<role>`) to prevent cross-contamination and enable parallel execution.[^2_1]
- Hybrid models: Use cloud frontier models (e.g., Anthropic Claude Opus-4) for planning/review and local Ollama models (e.g., Llama 3.1 405B quantized) for bulk tasks to minimize latency and costs.[^2_2]
- Sandboxing: Enable `agents.defaults.sandbox.mode: "non-main"` for non-personal sessions, restricting tools like browser/shell to Docker per-session.[^2_1]
- Workflow orchestration via `sessions_*` tools (list, history, send) for agent-to-agent handoffs without external orchestration.[^2_1]
- Quality gates: Every output passes through reviewer/red-teamer agents with low temperature (0.1) for consistency.

This yields an apparatus capable of producing 20-50 page reports with citations, datasets, and appendices autonomously.[^2_2]

## Hardware and Software Prerequisites

Target a single workstation for self-hosting.

- **Hardware**: macOS/Linux host with Node ≥22, ≥32GB RAM, ≥1TB SSD, NVIDIA GPU ≥24GB VRAM (for local inference).[^2_1]
- **Software stack**:
    - OpenClaw: Latest stable (`npm i -g openclaw@latest`).
    - Ollama: For local models (`curl -fsSL https://ollama.com/install.sh | sh`; pull `llama3.1:405b` or equivalents).[^2_2]
    - Docker: For sandboxing (`docker compose up` readiness).[^2_1]
    - Tailscale (optional): For remote access via Serve/Funnel.
- **API keys**: Anthropic/OpenAI env vars (`export ANTHROPIC_API_KEY=sk-...`).

Verify: `openclaw doctor` post-install.

## Installation and Base Configuration

Bootstrap the environment with OpenClaw's CLI.

Run in a dedicated project dir (`mkdir ~/research-apparatus && cd $_`):

```
openclaw onboard --install-daemon
openclaw config set agents.defaults.model.primary "anthropic:claude-opus-4" --global
openclaw config set agents.defaults.model.fallback "ollama:llama3.1:70b" --global
openclaw config set agents.defaults.temperature 0.3 --global
openclaw config set agents.defaults.maxTokens 128000 --global
openclaw config set agents.defaults.sandbox.mode "non-main" --global
openclaw config set browser.enabled true --global
openclaw gateway run --port 18789 --verbose
```

This sets global defaults, enables browser tool, and starts the gateway daemon.

## Agent Team Topology

Define 8 specialized agents via `openclaw agents add`. Each has a unique identity file (`agent.md`), workspace, tools, and model tuning.


| Agent | Role | Model (Primary/Fallback) | Temperature | Max Tokens | Workspace | Key Tools |
| :-- | :-- | :-- | :-- | :-- | :-- | :-- |
| architect | Scopes topics, plans workflows | claude-opus-4 / llama3.1:70b | 0.2 | 128k | ./agents/architect | file, shell, sessions_list |
| searcher | Web/API discovery, corpus building | claude-sonnet-4 / llama3.1:70b | 0.4 | 64k | ./agents/searcher | browser, file |
| curator | Dedupe, rank, tag sources | llama3.1:70b / llama3.1:8b | 0.1 | 64k | ./agents/curator | file, shell |
| synthesizer | Deep read, note-taking | claude-sonnet-4 / llama3.1:70b | 0.3 | 128k | ./agents/synthesizer | file, browser |
| analyst | Reasoning, modeling, sims | claude-opus-4 / llama3.1:405b | 0.2 | 128k | ./agents/analyst | file, shell, code (custom) |
| writer | Structured report drafting | claude-sonnet-4 / llama3.1:70b | 0.5 | 128k | ./agents/writer | file |
| reviewer | Fact-check, style, citations | claude-opus-4 / claude-sonnet-4 | 0.1 | 64k | ./agents/reviewer | file, browser |
| redteamer | Bias/risk probing | claude-opus-4 / claude-sonnet-4 | 0.3 | 64k | ./agents/redteamer | browser, file |

CLI to create (run sequentially):

```
openclaw agents add architect --model "anthropic:claude-opus-4" --fallback-model "ollama:llama3.1:70b" --temperature 0.2 --max-tokens 128000 --workspace ./agents/architect --tools "file,shell,sessions_list,sessions_send" --description "Research architect: decomposes topics into workflows"
# Repeat for others, adjusting params per table
```

Verify: `openclaw agents list --verbose`.

## Identity Templates (agent.md Files)

Place in each agent's `./agent.md`. These enforce role-specific behavior.

**Architect example** (`./agents/architect/agent.md`):

```
You are the Research Architect. Given a topic, output a JSON workflow: {questions: [...], agents: [...], handoffs: [...], artifacts: [...]}. Constraints: 5-10 sub-questions max, cite sources inline, plan for 24-48h execution. Always include red-team step.
```

**Analyst example**:

```
You are the Research Analyst. Receive notes from synthesizer; perform quantitative/qualitative analysis. Output: markdown with tables/charts (use code tool), scenarios, risks. Use low-temp reasoning chains.
```

Adapt for others: Synthesizer extracts claims; Writer structures into IMRaD (Intro/Methods/Results/Discussion); Reviewer verifies ≥90% claims.[^2_1]

## Research Workflow Protocol

Standard sequence for any topic, orchestrated via architect's plan + `sessions_send`.

1. **Intake**: Message architect: "Research [topic] with constraints [budget/deadline/etc]".
2. **Planning**: Architect outputs JSON plan, sends to relevant agents via `sessions_send`.
3. **Discovery**: Searcher builds `./corpus/raw/`; hands to curator.
4. **Synthesis**: Curator → `./corpus/curated/`; synthesizer → `./notes/`.
5. **Analysis**: Analyst processes notes → `./analysis/`.
6. **Draft**: Writer → `./reports/draft.md`.
7. **Review**: Reviewer + redteamer iterate → `./reports/final.md`.
8. **Artifacts**: Appendices in `./output/` (CSVs, images via code tool).

Human checkpoints: Architect plan approval via chat; final report delivery.[^2_1]

Custom skills (in `~/workspace/skills/research/SKILL.md`): Embed RAG (Milvus via shell), citation formatter.[^2_1]

## Single-Machine Optimization

Maximize throughput on one host.

- **Parallelism**: Agents run concurrently via separate sessions; limit via `gateway.maxConcurrentAgents: 4`.
- **Local models**: Ollama for 80% workload; fallback to cloud only on errors.
- **Storage**: Use `./agents/*/workspace` symlinks to RAM disk if ≥64GB RAM.
- **Monitoring**: `openclaw agents list --verbose`; tail gateway logs (`~/.config/openclaw/logs/`).
- **Costs**: ~\$5-20/report at scale (cloud bursts); zero with full local.

Test: `openclaw agent --agent architect --message "Plan research on Qatar LNG 2030" --thinking high`.

## Governance and Safety

Embed safeguards per OpenClaw best practices.

- **Tool restrictions**: Per-agent `--tools` limits (no shell for writer).
- **DM policy**: `channels.*.dmPolicy: "pairing"` for any exposed channels.
- **Audit**: All sessions logged; review via `sessions_history`.[^2_1]
- **Killswitch**: `openclaw gateway stop`; per-agent `openclaw agents disable <name>`.

This apparatus is now fully structured and ready. Provide a topic (e.g., "Qatar LNG innovations 2030") to generate your first industry-leading report.[^2_2]

<div align="center">⁂</div>

[^2_1]: https://github.com/openclaw/openclaw/blob/main/AGENTS.md

[^2_2]: https://www.meta-intelligence.tech/en/insight-openclaw-agents-guide


---

# When we talk about context length and the AI models assigned to each agent and sub-agent, each agent and sub-agent has their own amount of context length. In the context of conducting thorough research, is there a methodology that can be implemented where a main agent or main sub-agent takes on the primary research task and divides it into multiple sub-tasks? Currently, I am not sure what methodology can be used to divide the main research task into multiple sub-tasks. Once that is established, how can the research of the sub-tasks be conducted in an exhaustive manner? After the content has been gathered by these sub tasks (sub agents), how can the sub-tasks be consolidated by research sub-agents? Throughout this process, how would the context limitations of the AI models impact this workflow?

Assume the main research agent and sub agents have been give an ai model that has a context length of 250K.

Now say i have a research sub agent that is specialized in researching. My assumption and fear is that the 250K context length is insufficient to conduct a comprehensive research in an industry leading format and length.

I need clarity in the following matter:

1. If you sub divide tasks from research sub agent into multiple sub-sub-agents based on a predetermined research methodology.
1.1. Will the new sub sub agent have a fresh allocation of 250K context length or is the sub sub agent sharing context length with the research sub agent?

OpenClaw's architecture supports task decomposition via SubAgents (child agents), where a main research agent delegates subtasks to specialized sub-agents, each getting its own independent 250K context window—**not shared** with the parent.[^3_1][^3_2]

This enables exhaustive, industry-leading research by scaling horizontally across sub-agents while the main agent consolidates via summaries and artifacts.[^3_3][^3_1]

## Task Decomposition Methodology

Use OpenClaw's **SubAgent delegation** (parent-child model) for structured breakdown, ideal for research pipelines.[^3_1]

The main research agent (e.g., "architect") analyzes the topic and outputs a JSON plan:

```
{
  "subtasks": [
    {"id": "lit-review", "agent": "searcher", "prompt": "Find 50+ sources on [subtopic1] post-2020"},
    {"id": "data-analysis", "agent": "analyst", "prompt": "Extract metrics from [sources]; model trends"},
    {"id": "risks", "agent": "redteamer", "prompt": "Probe biases/gaps in [evidence]"}
  ],
  "consolidation": "Summarize outputs into master report"
}
```

- Invoke via tools: `sessions_send` or native SubAgent spawn (`context_retention: "task"` for ephemeral kids).[^3_1]
- Methodology: **MECE framework** (Mutually Exclusive, Collectively Exhaustive)—decompose into orthogonal angles (background, players, trends, risks, forecasts).[^3_1]

This keeps the main agent's context lean (just plan + summaries).[^3_3]

## Exhaustive Sub-Task Execution

Each sub-agent starts **fresh** with its full 250K context, including:

- Workspace bootstrap (AGENTS.md ~20K chars max).[^3_4]
- Session history (compacted automatically).[^3_5][^3_3]
- Tools/files injected dynamically (cap via `bootstrapMaxChars: 12000`).[^3_4][^3_3]

**Exhaustive tactics** (250K is ample for 100+ docs summarized):


| Tactic | How | Context Impact |
| :-- | :-- | :-- |
| Iterative search | Sub-agent loops: search → read → summarize → refine query | Compaction (/compact) prunes old turns; keeps ~40K reserve.[^3_3] |
| RAG integration | Embed docs to vector DB (Milvus skill); query top-K | Injects only relevant chunks (~10-20K).[^3_4] |
| Multi-turn depth | 20-30 exchanges per sub-task | Window strategy: last 12-20 turns + key memories.[^3_5] |
| Parallel spawn | Main spawns 5-10 sub-sub-agents simultaneously | Each independent; no shared load.[^3_1][^3_2] |

250K handles ~200 pages of text (at 1.25 tokens/char); for deeper dives, sub-agents write artifacts (CSVs/notes) for parent review.[^3_6][^3_3]

## Consolidation Process

Main agent aggregates without overload:

1. Sub-agents complete → write structured outputs (`./subtask1/summary.md`, JSON claims table).[^3_1]
2. Main invokes `sessions_list` + `file` tool to ingest summaries (~5-10K total).[^3_4]
3. Synthesizer sub-agent (fresh 250K) merges: cross-references, resolves conflicts.[^3_1]
4. Writer finalizes report with citations.

**File-based handoff** ensures no context bloat—main sees digests, not raw data.[^3_6][^3_4]

## Context Limitation Mitigation

250K is **sufficient** for comprehensive research per sub-task; fears of insufficiency stem from misunderstanding independence.[^3_7][^3_3]


| Challenge | Impact on Workflow | Mitigation |
| :-- | :-- | :-- |
| Single sub-task corpus | 250K covers 150-300 sources summarized | Decompose further into sub-sub-agents (each fresh 250K).[^3_1] |
| Main agent overload | Tries to hold all raw data | Use summaries/artifacts only; compaction reserves 40K+.[^3_3][^3_5] |
| Long chains | Session history balloons | `/new` for reset; `context_retention: "task"` auto-cleans kids.[^3_1] |
| Bootstrap bloat | AGENTS.md etc. eat 20-50K | Concise files; tune `agents.defaults.bootstrapMaxChars: 12000`.[^3_4] |

Tune in `openclaw.json`:

```
{
  "session": {
    "compaction": {"reserve": 40000, "recent": 25000},
    "window": {"turns": 20}
  }
}
```

Workflow stays robust: horizontal scaling via sub-agents bypasses vertical limits.[^3_3][^3_1]
<span style="display:none">[^3_10][^3_8][^3_9]</span>

<div align="center">⁂</div>

[^3_1]: https://www.meta-intelligence.tech/en/insight-openclaw-multiagent

[^3_2]: https://lumadock.com/tutorials/openclaw-multi-agent-setup

[^3_3]: https://blog.laozhang.ai/en/posts/openclaw-context-length-exceeded

[^3_4]: https://github.com/openclaw/openclaw/issues/4561

[^3_5]: https://www.clawcloud.dev/news/openclaw-session-management-and-context-window-optimization

[^3_6]: https://tacnode.io/post/openclaw-and-the-context-gap

[^3_7]: https://www.reddit.com/r/AISEOInsider/comments/1rbvcdy/openclaw_1m_context_window_just_broke_the_limits/

[^3_8]: https://docs.openclaw.ai/concepts/multi-agent

[^3_9]: https://open-claw.bot/docs/cli/reference/session-management-compaction/

[^3_10]: https://www.youtube.com/watch?v=4s5uVzC0UC8