move ideas section to monorepo, IDEAS.md
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Levi Neely
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## Ideas
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### Automation & Scripting
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**Scripting and automation** — shell scripts that submit prompts, poll `state` until idle, then read `chat` for the result. No SDK, no HTTP client, just file I/O. Works in any language that can write to a file.
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**Multiplexing sessions** — run several agents in parallel, each in their own session directory, and fan work out to them from a shell script. Coordinate by watching their `state` files.
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**Hooks and watchers** — poll `state` in a loop to trigger actions when the agent finishes, or use `tail -f chat` to react to new output. Build lightweight event-driven pipelines with standard shell tools.
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### Tooling & Integration
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**Unix tools** — pipe `chat` into `grep`, `awk`, `sed`. Diff two sessions' outputs. Log conversations with `cp`. Search history across sessions with `grep -r`.
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**Editor integration** — any editor that can read/write files gets agent access for free. In `acme` or `sam`, you write to `prompt` and read back `chat` with no plugin required. Fits naturally into the Plan 9 workflow.
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**Lightweight TUI alternatives** — `watch cat state` as a status bar, `tail -f chat` in one pane, prompt submission in another. Compose a working interface entirely from standard tools.
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### Network & Remote Access
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**Remote access** — 9P is a network protocol. Export the namespace over the network and access agent sessions from another machine using the same file interface, with no additional daemon or API layer.
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### Multi-Agent Workflows
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Each session exposes a `reply` file containing only the assistant text from the most recently completed turn. Writing to `prompt` clears `reply` as a side-effect. Together these make agent-to-agent handoffs expressible as plain file operations — no message queue, no orchestration framework, no shared memory.
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A shell script moves text between sessions. The agents communicate only through what the script explicitly passes between them, so information flow is fully under your control. Each agent can use a different backend, model, or tool configuration. A human can intervene at any point by writing directly to a session's `prompt`.
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#### Example: develop → review → test
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Three sessions with specialized agent configs run a feedback loop. The reviewer ends its response with `LGTM` (proceed) or `PTAL` (revise). The tester ends with `Approved` (done) or `Rejected` (revise). A shell script reads the verdict with `grep` and routes accordingly.
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```sh
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#!/bin/sh
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workdir=${1:?usage: $0 <workdir>}
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cd ~/mnt/ollie
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# Count existing sessions so we can identify the newly created ones by offset.
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n=$(ls s/ | wc -l)
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echo "new agent=developer workdir=$workdir" > ctl
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echo "new agent=reviewer workdir=$workdir" > ctl
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echo "new agent=tester workdir=$workdir" > ctl
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dev=$(ls s/ | sort | sed -n "$((n+1))p")
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rev=$(ls s/ | sort | sed -n "$((n+2))p")
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tst=$(ls s/ | sort | sed -n "$((n+3))p")
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wait_reply() {
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while [ "$(wc -c < s/$1/reply)" -eq 0 ]; do sleep 1; done
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}
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echo "implement a function that parses a JSON config file" > s/$dev/prompt
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while true; do
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wait_reply $dev
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code=$(cat s/$dev/reply)
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# review phase
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printf "Review the following code. End your response with LGTM if it is ready, or PTAL if it needs revision.\n\n%s" "$code" > s/$rev/prompt
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wait_reply $rev
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if ! grep -qi "LGTM" s/$rev/reply; then
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{ echo "revise this code based on the feedback below."
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echo "--- code ---"; echo "$code"
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echo "--- feedback ---"; cat s/$rev/reply
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} > s/$dev/prompt
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continue
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fi
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# test phase
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printf "Test the following code. End your response with Approved if all tests pass, or Rejected if they do not.\n\n%s" "$code" > s/$tst/prompt
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wait_reply $tst
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if grep -qi "Approved" s/$tst/reply; then
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echo "done."
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echo "$code"
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break
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fi
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{ echo "fix the failures reported below."
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echo "--- code ---"; echo "$code"
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echo "--- test report ---"; cat s/$tst/reply
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} > s/$dev/prompt
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done
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```
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Each agent only sees what the script explicitly sends it. Each can use a different backend, model, or tool configuration. A human can intervene at any point by writing directly to a session's `prompt`. The boundary between fully automated and human-in-the-loop is just whether the script pauses to ask.
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### Sub-Agents
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A session can spawn ephemeral child sessions to handle a focused task, then tear them down when done. The parent uses `execute_code` to write `new` to `ctl`, send an initial context and task to the child's `prompt`, poll until `reply` is ready, read the result, and write `kill <id>` to `ctl`. From the child's perspective it is just a normal session; it has no knowledge of its own ephemerality.
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**Sub-agents as function calls** — the parent primes the child with exactly the context it needs (excerpts from its own `chat`, relevant beads, local file contents) and receives a single focused reply. Failures are isolated; a child that errors or stalls can be killed and retried without affecting the parent. The pattern composes naturally: a sub-agent can itself spawn sub-agents, building a call tree bounded only by the number of sessions open at once.
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`spawn_subagents.py` reads a JSON spec from stdin, fans out the work concurrently, and returns a JSON array of results — a single `execute_tool` call from the parent's perspective. Note: this requires adding Python language support to `execute_code`, `execute_tool`, and `execute_pipe`, which is low effort.
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```python
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#!/usr/bin/env python3
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"""spawn_subagents.py — fan out tasks to ephemeral olliesrv sub-agents.
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Input (stdin): JSON array of {"task": str, "agent": str, "context": str}
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Output (stdout): JSON array of {"agent": str, "reply": str}
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Install in ~/.local/share/ollie/tools/ to make available via execute_tool.
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"""
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import json, os, sys, threading, time
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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BASE = Path.home() / "mnt" / "ollie"
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POLL = 0.5
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_spawn_lock = threading.Lock()
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def session_ids():
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return {e.name for e in (BASE / "s").iterdir() if e.is_dir()}
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def spawn_session(agent="default", workdir=""):
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with _spawn_lock:
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before = session_ids()
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(BASE / "ctl").write_text(f"new agent={agent} workdir={workdir}\n")
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while True:
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new = session_ids() - before
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if new:
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return new.pop()
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time.sleep(0.1)
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def kill_session(sid):
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(BASE / "ctl").write_text(f"kill {sid}\n")
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def wait_reply(sid):
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path = BASE / "s" / sid / "reply"
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while True:
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if path.stat().st_size > 0:
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return path.read_text().strip()
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time.sleep(POLL)
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def run_subagent(spec):
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agent = spec.get("agent", "default")
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workdir = spec.get("workdir", "")
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context = spec.get("context", "")
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task = spec.get("task", "")
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sid = spawn_session(agent, workdir)
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try:
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prompt = f"{context}\n\n{task}".strip() if context else task
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(BASE / "s" / sid / "prompt").write_text(prompt)
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return {"agent": agent, "reply": wait_reply(sid)}
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finally:
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kill_session(sid)
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def main():
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specs = json.load(sys.stdin)
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results = [None] * len(specs)
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with ThreadPoolExecutor(max_workers=len(specs)) as pool:
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futures = {pool.submit(run_subagent, s): i for i, s in enumerate(specs)}
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for f in as_completed(futures):
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results[futures[f]] = f.result()
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json.dump(results, sys.stdout, indent=2)
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print()
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if __name__ == "__main__":
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main()
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```
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The parent agent constructs the spec and makes a single tool call:
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```json
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[
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{ "task": "review this code for correctness", "agent": "reviewer", "context": "..." },
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{ "task": "check this code for security issues", "agent": "security", "context": "..." }
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]
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```
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Results come back as:
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```json
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[
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{ "agent": "reviewer", "reply": "..." },
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{ "agent": "security", "reply": "..." }
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]
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```
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### Self-Generating Workflows
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Since agents have access to `execute_code`, a session can write and execute a workflow script without any human involvement. Given a task and knowledge of the filesystem layout, an agent can decompose the work, spawn sessions, write the coordination script, and run it — all in a single turn. The README you are reading is essentially its system prompt. Conductor functionality, for free.
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### Persistent Memory
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**Task tracking** — Agent sessions are ephemeral by nature: context windows fill, processes crash, work gets lost. [9beads](https://github.com/lneely/9beads) addresses this by exposing a structured, version-controlled task database as a 9P filesystem. Any session can read the task list, claim work, track dependencies, and mark completion through ordinary file operations. If a session is interrupted, another can pick up exactly where it left off. Task lists are scoped per-project, keeping them focused and bounded. Combined with olliesrv, agents get both a communication surface and a memory layer, with no special integration required between them.
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