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Usage

Server

Start and stop

olliesrv                # start the server (foreground)
# Stop by sending SIGTERM or via the supervisor (e.g. rc-service ollie stop)

By default the server mounts at $HOME/mnt/ollie. Override with -mount:

olliesrv -mount /tmp/ollie

TCP listener

To also accept connections over TCP (e.g. for remote access):

olliesrv -tcp :9564

Debug logging

Log level is controlled by OLLIE_LOG (default: warn). Set to debug for verbose output:

OLLIE_LOG=debug olliesrv

Valid levels: debug, info, warn, error. Per-subsystem overrides follow the pattern OLLIE_<TAG>_LOG=debug where <TAG> matches the logger's tag (e.g. OLLIE_SESSION_LOG).

The rendered context window for any session is readable at s/{id}/context — the exact message array sent to the backend on the last turn. Combined with s/{id}/systemprompt, this gives full visibility into what the model sees.

Examples below assume $OLLIE is set to the ollie mount point (default: $HOME/mnt/ollie). Scripts under s/ and u/ are available directly via the mounted filesystem after running just.

One-shot generation

The generate file provides stateless prompt→response without creating a session:

echo 'summarize this repo' | ollie-9p rdwr generate
cat main.go | ollie-9p rdwr generate    # pipe content as the prompt
echo '{"prompt":"explain recursion"}' | ollie-9p rdwr generate  # JSON form

For programmatic use from any language, write a prompt to the generate file and read back the response.

Code completion

The complete file provides fill-in-the-middle code completion:

echo '{"file":"main.go","prefix":"func ","suffix":""}' | ollie-9p rdwr complete

Routing

The route file selects the best backend+model for a task description:

echo 'implement login page' | ollie-9p rdwr route
# backend=anthropic model=claude-sonnet-4-20250514

Use this to dispatch work to the appropriate tier without hardcoding model names.

s/cleanup

Remove all idle sessions:

$OLLIE/session/cleanup

Schedule regular cleanup with a cron job:

*/15 * * * * /path/to/mnt/ollie/session/cleanup

Raw s/ access

cat $OLLIE/session/new                           # show the spec template
printf 'cwd=%s\n---\nSummarize this.\n' "$PWD" > $OLLIE/session/new
cat $OLLIE/s/<session-id>/cfg              # state, backend, model, agent, cwd, params
cat $OLLIE/s/<session-id>/chat
rm -r $OLLIE/s/<session-id>               # cancel and remove

Valid spec keys: name (auto-generated if omitted), cwd (required), agent, backend, model.

Generation parameters

Parameters are set in the agent config JSON and can be overridden at runtime by writing key=value lines to s/{id}/cfg. Runtime writes take precedence over the agent config. All are optional; omitted values use the backend's default.

Key	Type	Description
`maxTokens`	int	Max output tokens (0 = no limit)
`maxCompletionTokens`	int	OpenAI o-series alternative to maxTokens
`temperature`	float	Sampling temperature
`topP`	float	Nucleus sampling threshold
`topK`	int	Top-K sampling (Anthropic, Ollama)
`minP`	float	Min-P sampling (Ollama)
`topA`	float	Top-A sampling (Ollama)
`frequencyPenalty`	float	Frequency penalty
`presencePenalty`	float	Presence penalty
`repetitionPenalty`	float	Repetition penalty (Ollama)
`reasoning`	int	Thinking budget in tokens (Anthropic); 0 = disabled
`reasoningEffort`	string	`low`, `medium`, `high` (OpenAI o-series)
`includeReasoning`	bool	Include reasoning in response (OpenRouter)
`responseFormat`	string	`json_object`, `text`, etc. (OpenAI)
`stop`	string	Comma-separated stop sequences
`verbosity`	string	Output detail level (internal, not sent to API)

Example agent config:

{
  "prompt": "You are a helpful assistant.",
  "temperature": 0.7,
  "topP": 0.95,
  "reasoning": 10000,
  "stop": ["\n\n"]
}

Example runtime override:

echo "temperature=0.3" > $OLLIE/s/mysession/cfg
echo "topP=0.9" > $OLLIE/s/mysession/cfg

Sandbox

Every shell invocation (and promoted tool call) runs inside a Landlock sandbox configured by ~/.config/ollie/sandbox/<name>.yaml. The sandbox wraps each invocation as a new command, so config changes (e.g. granting access to an additional directory) take effect on the next call without restarting the server or session.

AI pipelines

generate composes naturally with Unix pipes:

echo "write a haiku about filesystems" | ollie-9p rdwr generate | wc -w
cat error.log | ollie-9p rdwr generate    # content IS the prompt

Chain multiple generation steps:

echo "list 5 blog post ideas" | ollie-9p rdwr generate | head -3 | ollie-9p rdwr generate

Code completion (acme)

u/complete provides AI ghost-text code completion for acme windows. It reads the text before and after the cursor (dot), sends it to a lightweight model, and opens the suggested insertion in a new window.

Setup

Two environment variables are required (set them in ~/.config/ollie/env or export them in your shell):

OLLIE_COMPLETE_BACKEND=ollama
OLLIE_COMPLETE_MODEL=qwen3:4b

Usage

From an acme window, middle-click u/complete in the tag (or add it to the global tag). The script:

Reads up to 4000 characters before the cursor and 1000 after.
Finds or creates a persistent copilot session for the current working directory.
Submits a fill-in-the-middle prompt to the configured model.
Opens the completion in a new ollie/complete window.

Override model or backend per invocation with flags:

u/complete -model phi-4-mini -backend ollama

Session reuse

One copilot session is maintained per working directory. The session ID is derived deterministically from the cwd, so concurrent completions in the same directory share a single session and never spawn duplicates. The conversation history is cleared after each completion to keep context fresh.

If a session enters a failed state, it is automatically killed and recreated on the next invocation.

Model recommendations

Completion models should be small and fast — the goal is insertion text, not reasoning. Good choices for OLLIE_COMPLETE_MODEL:

Model	Backend	Notes
`qwen3:4b`	ollama	Use with `/no_think` or a `PARAMETER stop <think>` Modelfile to disable reasoning
`phi-4-mini`	ollama	~3.8B, strong at code, no thinking mode
`llama3.1:8b`	ollama	Straightforward, no chain-of-thought overhead
`openai/gpt-4o-mini`	openai	Fast and cheap via OpenRouter

Avoid "thinking" models (Qwen3 8B+ with default settings, DeepSeek-R1) — they burn tokens on internal reasoning chains that add latency without improving completion quality.

Multi-agent workflows

Two patterns cover most multi-agent use cases: ephemeral subagent delegation for independent parallel subtasks, and persistent concurrent sessions for workflows that need coordination or long-lived state.

Subagent delegation

subagent_spawn is a tool script that forks N ephemeral agents with a shared prompt, waits for all to finish, and returns their results concatenated in submission order. It is the right tool when work can be split into independent subtasks that don't need to talk to each other.

The agent invokes it via shell:

{
  "steps": [{"tool": "subagent_spawn", "args": "-n 3 summarize this module"}]
}

Or from a shell step:

subagent_spawn -n 3 "review this diff for security issues"
subagent_spawn -n 1 -agent reviewer -cwd /path/to/repo "check for style violations"

Flags

Flag	Description
`-n N`	Number of subagents to spawn (default: 1)
`-agent A`	Agent config for each subagent
`-backend B`	Backend override
`-model M`	Model override
`-cwd DIR`	Working directory for subagents (default: `pwd`)
`-keep`	Do not remove sessions after collecting results

With -n 1 (the default) the result is just the single agent's output. With -n > 1 results are separated by === {id} === headers.

Concurrent sessions

For workflows where multiple long-lived agents work in parallel and hand off to each other, the mechanism is direct prompt passing via the filesystem. Each agent has a prompt file that accepts writes; writing to it queues a new turn, so a busy agent will not drop a message.

Create named sessions:

printf 'name=writer\ncwd=%s\n' "$PWD"   > $OLLIE/session/new
printf 'name=reviewer\ncwd=%s\n' "$PWD" > $OLLIE/session/new

Send a prompt and read the response:

# Queue a prompt on a named session
printf '%s\n' "Draft a design doc for the new cache layer." > $OLLIE/s/writer/prompt

# Wait for the turn to finish (statewait blocks until state changes)
until [ "$(cat $OLLIE/s/writer/statewait)" = "idle" ]; do :; done

# Read only the new output (offset marks the byte position after the user prompt)
tail -c +$(($(cat $OLLIE/s/writer/offset) + 1)) $OLLIE/s/writer/chat

Key files per session:

File	Mode	Description
`prompt`	write	Queue a new turn; writes are enqueued if agent is busy
`statewait`	read (blocking)	Blocks until state changes; returns new state
`state`	read	Current state: `idle`, `running`, `failed: <reason>`
`chat`	read	Full conversation transcript
`context`	read	Full message history as JSONL
`offset`	read	Byte position in `chat` immediately after the last user prompt
`plan`	r/w	Scratch space for agent planning
`prompt.prev`	read	The last submitted prompt
`env`	read	Session environment variables
`cost`	read	Cumulative cost in USD (if reported by backend)

Reading offset before writing prompt, then extracting chat[offset:] after the turn completes, isolates exactly the model's response for that turn.

Predefined workflows

When coordination logic is known in advance, a shell script can drive the entire workflow. Named sessions make routing unambiguous; statewait eliminates polling races. No external coordinator process is needed — the script is the coordinator.

The following example implements a write → review → test loop. The developer agent revises until the reviewer approves, then the tester validates:

#!/bin/sh
cwd=${1:?usage: $0 <cwd>}
cd $OLLIE

printf 'name=developer\nagent=developer\ncwd=%s\n' "$cwd" > s/new
printf 'name=reviewer\nagent=reviewer\ncwd=%s\n'  "$cwd" > s/new
printf 'name=tester\nagent=tester\ncwd=%s\n'      "$cwd" > s/new

# Send a prompt to a named session and return the model's response.
# Opens statewait before writing the prompt so the idle baseline is captured
# before the agent starts processing. offset marks the byte position after the
# user prompt in chat; everything from that position to EOF is the model response.
send() {
    sid=$1; shift
    exec 3< s/$sid/statewait
    printf '%s' "$*" > s/$sid/prompt
    while true; do
        read -r _ <&3 && { exec 3<&-; break; }
        exec 3<&-
        [ "$(awk -F= '/^state=/{print $2}' s/$sid/cfg)" != "idle" ] && break
        exec 3< s/$sid/statewait
    done
    until [ "$(awk -F= '/^state=/{print $2}' s/$sid/cfg)" = "idle" ]; do sleep 0.5; done
    tail -c +$(($(cat s/$sid/offset) + 1)) s/$sid/chat
}

reply=$(send developer "Implement a function that parses a JSON config file.")

while true; do
    review=$(send reviewer "Review the following code. Reply LGTM if ready, or PTAL with feedback.

$reply")

    if ! printf '%s' "$review" | grep -qi "LGTM"; then
        reply=$(send developer "Revise based on this feedback:

$review")
        continue
    fi

    result=$(send tester "Test the following code. Reply Approved if all tests pass, or Rejected with details.

$reply")

    if printf '%s' "$result" | grep -qi "Approved"; then
        printf '%s\n' "$reply"
        break
    fi

    reply=$(send developer "Fix the following test failures:

$result")
done

rm -r s/developer s/reviewer s/tester

The agents have no knowledge of each other — the script holds all the routing logic. A human can intervene at any time by writing directly to any session's prompt. Each agent can use a different agent=, backend=, or model= — set them in the s/new spec lines.

Self-generating workflows

Because agents have access to shell, a session can write and run a workflow script without any human involvement. Given a task description and knowledge of the filesystem layout, an agent can decompose the work, create the named sessions, write the coordination script to a temp file, and execute it — all in a single turn.

The architecture documents (ARCHITECTURE.md, USAGE.md) and tool descriptions (available via $OLLIE_TOOLS_PATH) are the agent's reference. No additional scaffolding is needed.

Store federation

OLLIE_TOOLS_PATH and OLLIE_TRANSCRIPT_PATH are ordinary filesystem paths. Because olliesrv speaks 9P and any remote instance can be mounted locally via 9pfuse, these vars can point at remote directories with no code changes — the OS handles the proxying transparently.

Centralized tool distribution

Host one authoritative tool set and point all machines at it:

# On each client machine:
olliesrv mount toolserver:9564 ~/mnt/toolserver
export OLLIE_TOOLS_PATH=~/mnt/toolserver/t

Every agent on every host now uses the same tool scripts. Update tools in one place; all clients pick up the change immediately.

Centralized transcript archive

Collect transcripts from all hosts in one place:

olliesrv mount archivehost:9564 ~/mnt/archive
export OLLIE_TRANSCRIPT_PATH=~/mnt/archive/tr

Sessions started after this change write their transcripts to the remote host. Existing sessions are unaffected.

Both vars can be combined. Set them in ~/.config/ollie/env for persistence:

OLLIE_TOOLS_PATH=~/mnt/toolserver/t
OLLIE_TRANSCRIPT_PATH=~/mnt/archive/tr

Markdown rendering of assistant responses
9P filesystem browser (navigate s/, a/, m/, sk/)
Two built-in themes: midnight (dark) and acme (Plan 9-inspired light); choice persisted to localStorage

System prompt override

The default system prompt is compiled into olliesrv. To replace it — for example, when using a model that needs its own identity preamble — set the systemPrompt field in an agent config JSON to a file path:

{
  "systemPrompt": "~/.config/ollie/prompts/SYSTEM_PROMPT_QWEN.md",
  "prompt": ["$OLLIE_CFG_PATH/prompts/agent-coding.md"],
  "temperature": 0.7
}

The file is read at session creation time. If it doesn't exist, the embedded default is used. The override replaces only the base system prompt layer; the operational model (9P/D-Bus documentation), environment block, and agent-specific prompt commands are still appended on top.

Model-specific prompts live at ~/.config/ollie/prompts/SYSTEM_PROMPT_*.md. Create an agent config per model family and point systemPrompt at the appropriate file.

Tool discovery

Tools are executables in $OLLIE_TOOLS_PATH (default: ~/.config/ollie/tools/). At session startup, all .meta sidecar files are scanned and a compact listing (name + one-line description) is injected into the system prompt.

Metadata format

Each tool has a .meta JSON sidecar file alongside the executable:

{
  "description": "Short description (used in system prompt listing).",
  "prompt": "## my_tool\n\nFull documentation shown when tool is loaded.\n\n**Args**: ...",
  "args": {"type":"object","required":["path"],"properties":{"path":{"type":"string","description":"File path"}}},
  "tier": "cold",
  "readOnly": true
}

Field	Purpose
`description`	One-liner shown in tool listing
`prompt`	Full documentation injected when tool is loaded
`args`	JSON Schema for tool input parameters
`tier`	Result cacheability: `cold`, `warm`, or `hot` (default: hot)
`readOnly`	Safe for parallel execution with other read tools

Runtime access

The full prompt for any tool is available on-demand via the /tools endpoint:

echo 'file_edit' | 9p rdwr ollie/tools   # returns file_edit's full documentation

The system prompt only includes names and one-line descriptions to save context. Agents query /tools when they need detailed usage for a specific tool.

Adding a tool

Drop an executable and its .meta file in $OLLIE_TOOLS_PATH. The next session created will pick it up automatically. No restart required.

Alternative front-ends

The 9P filesystem is the lowest level interface to ollie. All interfaces are wrappers around filesystem operations. Front-ends:

ellie — ellie.el is the Emacs front-end, just wire it into your init.el and M-x ellie
KDE — plasmoid, standalone GUI, Kate plugin, KRunner, system tray (see kde/)

See each repo's README for installation and usage.

17 KiB Raw Blame History

Usage

Server

Start and stop

TCP listener

Debug logging

One-shot generation

Code completion

Routing

s/cleanup

Raw s/ access

Generation parameters

Sandbox

AI pipelines

Code completion (acme)

Setup

Usage

Session reuse

Model recommendations

Multi-agent workflows

Subagent delegation

Concurrent sessions

Predefined workflows

Self-generating workflows

Store federation

Centralized tool distribution

Centralized transcript archive

System prompt override

Tool discovery

Metadata format

Runtime access

Adding a tool

Alternative front-ends

17 KiB

Raw Blame History