Simple Harness

1. What It Does

A terminal AI assistant that can actually work on your machine: read and write files, run commands and answer their prompts, search the web, and undo what it did. It runs against a local Ollama model or a hosted one (Anthropic, OpenAI, Gemini) with the same tools and the same safety prompts either way.

It is built for the local case first. A 4B model cannot reliably escape a source file into a JSON string, so it is never asked to; much of what is here exists to make small models genuinely usable rather than nearly usable.

2. Features

Any Provider: /connect points the harness at Ollama, Anthropic, OpenAI (or anything OpenAI-compatible), or Google Gemini. No vendor SDKs - four wire formats normalised into one event shape.
Two Tool Protocols, One Tool Table: A model with a real function-calling interface gets the tools through it; one without gets them as <tool_call> text with a JSON repair engine behind it. For Ollama this is decided per model. Both come from the same table, and both end up as the same call.
Deepthink: /deepthink on turns one request into plan → argue with the plan → build → review the real diff → run it. The planning stages cannot edit, the harness reports what the final check actually ran, and a check that says the work is not done sends the whole chain back to the plan.
Undo: every file an AI tool changes is committed on its own, so /undo takes it back. It commits only what the tool named, and refuses to undo over work it did not create.
Auto-verify: a turn that changes a file has the project's own check run against it - pytest, npm test, cargo test, go test - and a failure goes straight back to the model as the error it has to fix. Only a check the project already declares is ever run, four edits in one reply are one run of the suite, and after three failures in a row the harness stops guessing and asks the model to explain. This is most of the difference between a small model that needs checking and one that tells you when it is wrong.
Several harnesses in one project: people run three of these at once and, until now, none of them knew the others existed - two would read the same file and the second write would silently throw the first away. The instances working in one project now share a board: they can see each other, message each other, and a file one of them is in the middle of changing is refused to the others by name. See The Agent Channel.
Sub-agents: spawn_agent hires a second model for one self-contained job. It works in its own context and hands back only its report, so a twenty-tool-call search never enters the conversation.
Crash-safe writes: sessions, memory, permission rules and saved API keys are written to a temporary file and renamed into place, so being killed mid-write cannot empty one.
ANSI Terminal User Interface: Provides an ANSI-colored TUI with streaming text responses, live token-per-second (TPS) calculation, custom spinner animations, markdown rendering, syntax code blocks, and ASCII tables.
Interactive Action Approval: Security layer that prompts the user for confirmation prior to running shell commands, editing/writing files, or sending network API requests.
Dynamic Context Compression: Monitors active token counts and conversation length to automatically condense conversation history when nearing model limits, tailored to model size.
Persistent Memory Storage: Long-term key-value memory storage system backed by memory.json to store user preferences, facts, and instructions across sessions.
Session & History Management: Save, list, load, record, and export conversation transcripts in JSON or Markdown format.
Named Sessions: Sessions are filed under a readable title instead of a timestamp. The model names each new session after its first exchange (/autotitle off to stop it), /title <name> renames it by hand, and /load accepts either the title or the id.
Resuming from the command line: simple-harness --resume <id or title> reopens a saved conversation, and -c reopens the newest one you were last working on in this directory - a session records where it was worked, so -c in a project picks up that project's thread rather than whatever you did most recently anywhere.
@ file attachments: Typing @ opens a list of what is in the directory you are standing in - arrow keys to move, Tab to insert, / to descend into a folder. @src/main.py sends that file with your message instead of spending a round trip on the model asking for it. Directories arrive as their listing, a path that does not exist is reported without stopping the turn, and one mention cannot swallow the context window (MENTION_MAX_CHARS).
! shell escape: A line starting with ! runs as a shell command - yours, not the model's, so no approval prompt - and its output joins the conversation, so the next question can be about what it printed.
Enhanced Terminal Shell: Input autocompletion for slash commands and persistent input history across restarts powered by prompt_toolkit.
Hashline Line-Level Hashing: File reading returns every line as LINE_NUM:HASH|content, and edit_file takes that row back as the whole edit: 38:ff7|print() means line 38 becomes print(). No second block, no retyping the old line, no matching. The hash is checked against the file first, so an edit made against a stale reading is refused rather than landing a few lines off.
Multi-Source Web Search: Queries several keyless sources (DuckDuckGo, Wikipedia, Stack Exchange, GitHub, optional self-hosted SearXNG), reads the actual pages, ranks passages locally with BM25, and reports "no relevant results" rather than returning off-topic pages.
Agent Skills: Folder-based instruction packs (skills/<name>/SKILL.md) that the model loads on demand. Only each skill's name and description sit in the system prompt, so a large library stays cheap until a skill is actually needed.
Tool Permissions: Rules in .permissions.json decide what runs without asking and what never runs at all, filling the gap between prompting for everything and /automode allowing everything. Answering a at any approval prompt saves a rule.
Reasoning Model Support: A model's <think> blocks (and Ollama's separate thinking field) are kept out of the answer, off the screen by default, and out of the conversation history - so scratch work never eats the context budget.
MCP Servers: Any Model Context Protocol server declared in .mcp.json is started with the app, and its tools join the built-in ones as mcp__<server>__<tool>. Local subprocesses (stdio) and remote endpoints (streamable HTTP, legacy SSE) are all supported, with the same approval prompt guarding every call.

3. Setup

Prerequisites

Python: Version 3.10 or higher
Ollama: Installed and running locally (default endpoint: http://localhost:11434). Only needed for the local case - /connect reaches Anthropic, OpenAI and Gemini without it.

What the model has to be able to do

The harness is built so a small model is usable, not so any model is. The thing that decides it is not size but whether the model can be made to emit a tool call at all - and having done the work to make 4B models usable, here is where that actually lands.

	Runs	Notes
Recommended	`gemma4:e4b`, or any model Ollama reports `tools` for at 8B+	Native tool calling, ~5-9GB. Reliable in testing
Workable	A 12B-class model without `tools`, e.g. `gemma3:12b`	Text protocol. 11/15 tool calls landed in testing
The floor	A 4B-class model, e.g. `gemma3:4b`	Roughly a coin flip. Fine for one-shot edits, not for `/deepthink`
Below that	1-3B	Not recommended. Expect it to describe a tool call rather than make one

Across five tool-calling tasks - one call, using the result, exact arguments, two calls in order, and correctly calling nothing - gemma4:e4b passed all five.

Two things matter more than the parameter count:

Whether Ollama reports tools for it. That is per model, not per family - gemma4:e4b has it and gemma3:12b does not. A model that has it goes through the real function-calling interface, gets a 12KB smaller prompt, and is markedly more reliable. /connect status shows which protocol is in use.

Context window. NUM_CTX defaults to 65536. A model that cannot hold that will have its conversation compressed early and often; 32k is workable, below 16k is not really.

Measured on this project's own test tasks - creating a file, and fixing a function halfway down a 5,700-character file - on the machine it was written on. Sample sizes are small (6-15 runs); treat them as the difference between "works" and "does not", not as a benchmark.

Nothing here applies to /connect anthropic|openai|gemini. Those all support native tool calling, and the floor is whatever that provider's smallest model is.

Installation Steps

Install it:
```
git clone https://github.com/minjun1177/simple_harness
cd simple_harness
pip install -e .
```
Or, to run it straight from the checkout without installing:
```
pip install -r requirements.txt
```
get_code_skeleton and query_ast_node need Tree-sitter, which is ten grammar wheels for two tools and so is opt-in: pip install -e ".[ast]". Everything else runs without it.
Pull an Ollama Model:
```
ollama pull gemma4:e4b
```
Launch it:
```
simple-harness            # if you installed it
python -m simple_harness   # if you did not
```
To carry on where you left off instead of starting fresh:
```
simple-harness -c                     # the newest session worked on in this directory
simple-harness --resume <id or title>  # a particular one, by either name
```
-resume and -continue are accepted too. Both stop with an error rather than opening a blank session when there is nothing to resume, and --resume lists the candidates instead of choosing when a name matches more than one. /sessions inside a session shows the ids.

Running the tests

No framework - each file is a script that prints [ok] / [FAIL] and exits non-zero on failure. They need no Ollama daemon and no network:

for t in tests/*.py; do python "$t" || echo "FAILED: $t"; done

tests/test_platform.py is the one worth running on any new machine, and especially on Windows: it checks what that machine can tell you about a command waiting for input. tests/test_vm.py starts and kills real Python processes, so it is the slowest of them - about ten seconds, most of it waiting out a deliberate VM_TIMEOUT.

4. Tool Capabilities

The client equips the model with 34 tools. They are listed in one table in toolspec.py, from which both the system prompt and the dispatcher are generated - so this list cannot quietly drift from what actually runs.

Tool Call Format

A model with a real function-calling interface just calls the tool, and none of this section applies to it - skip to How tools are asked for under Providers.

Everything below is the text protocol, used for models that have no such interface. The model emits a <tool_call> block. Anything a parameter can hold in one line goes in the JSON; a file body does not:

<tool_call>
{"name": "write_file", "arguments": {"filepath": "game.py"}}
<content>
import random

print("Guess the number!")
</content>
</tool_call>

Escaping a whole source file into a JSON string is the single thing small local models get wrong most often - a bare quote inside print("hi"), a lost backslash before a line continuation, one uncounted brace - and any of them used to throw the entire generation away. A raw block removes the requirement: the text is written exactly as it belongs on disk, with no escaping at all. <content> feeds write_file and run_python; <old_content> and <new_content> feed edit_file; <stdin> feeds run_cmd, send_input and run_python.

Editing by hashline anchor

read_file returns every line with a prefix:

50:1fa|    print(answer)

50 is the line number and 1fa is a three-character fingerprint of that line's exact content. Both used to be decoration - edit_file stripped the prefix off and matched what was left as literal text, so to change one line the model still had to reproduce it perfectly: every space of indentation, every quote, every backslash. That is what a small model gets wrong most often. And a line that appears twice anywhere in the file could not be edited at all, because the snippet was ambiguous and the edit was refused.

The prefix is enough on its own. Hand the row back with different text after the |, and that is the entire edit - there is no old_content block:

<tool_call>
{"name": "edit_file", "arguments": {"filepath": "game.py"}}
<new_content>
50:1fa|    print("the answer was", answer)
</new_content>
</tool_call>

50:1fa says which line and proves it is the line that was read; everything after the | is what it becomes. One row per line changed, and the lines need not be next to each other:

<new_content>
12:a41|import sys
50:1fa|    print("the answer was", answer)
</new_content>

Each row replaces one line with one line, so nothing below moves and every other anchor the model is holding stays valid - which is what makes several edits in one call safe.

When the number of lines changes, or they are being deleted, that form cannot say it: then old_content names the lines and new_content is ordinary text.

`old_content`	Means
`50:1fa`	Replace line 50
`50:1fa\| print(answer)`	The whole row copied out of the listing - the text beside it is the old line, and is only used to confirm it
`50:1fa` / `51:9c0` / `52:aa4`, one per line	Replace that run of lines
`50:1fa-53:9c0`	Replace the span, both ends checked

An empty new_content there deletes the lines outright.

The hash is what makes it safe rather than merely convenient. A line number on its own would happily point at whatever has since moved into that position. So every anchor is checked against the file before anything is written, and a mismatch is refused with what is actually there:

[Error] Line 50 of game.py is not what 50:1fa says it is. It now reads
50:9c0|    print(result)
The file has changed since you read it, or the anchor was mistyped. read_file it
again and use the anchors from the new listing.

That is the case worth having: another agent edited the file, or the model's own earlier edit moved everything below it, and the edit lands in the wrong place with nothing to say so. It is refused instead. When the number of lines changes, the result says so too, so the next edit starts from a fresh read_file.

In the one-row form the hash is the only check there is - the text beside it is what the line is to become, not what it is now - so it is never waived. In the old_content form the row may also quote the line, and then the quote decides, in both directions. A hash that disagrees with an exactly-correct line beside it (50:abd| print(answer)) is a slip of three hand-copied characters and the line is believed. A hash that agrees while the quoted line does not is the more interesting case: an anchor points at a position, so a line that moved away and a different line that moved in collide once in 4096, and the quote is what catches it. That is refused.

A spelling that can only mean one thing is read, not refused. read_file prints a | after every anchor, so a model writes one after a span too, and 6:cae-9:964| used to fall through to text matching and come back as "old_content was not found" - which says nothing about the anchor being one character off. A local 4B model spent eight tool calls resending it. Now it resolves. So does a row that names a line and quotes it with no hash at all (50 print(answer)), but only when the quoted text really is that line - which is the same evidence that forgives a mistyped hash. When it is not, the content goes back to being matched as text.

What is never repaired is a row with nothing behind it. 50| pass in new_content names a line and says what it becomes, and nothing there shows the model has read what it is about to overwrite. That is refused - but the refusal hands back the lines it meant, in the shape read_file prints them, so the next call can be right without going to look:

[Error] Nothing was written. Those rows name lines but carry no hash, and the
text after the `|` is what the line is to become - so there is nothing here
that shows you have read what is already on it. game.py currently has:
  50:1fa|    print(answer)
Send it again with each anchor exactly as it appears above - 50:1fa|<the new
line> - or read_file for the rest.

The model is not offered a "confirm and proceed" instead. Being asked is not being stopped, and a 4B model says yes.

An edit hands back the lines around it, already anchored. Editing a line changes its hash, and changing the number of lines moves every anchor below it

so straight after an edit the model is holding anchors that are wrong, and its only recourse was to read the whole file again. That is a round trip, and the whole file back into a context that is usually small, to recover a few lines it already knows. The neighbourhood comes back with the result instead:

[Success] File edited: greet.py (line 3 replaced). One line became one line, so
nothing below moved and the rest of your anchors are still good.
The file now reads, around what you changed:
  1:eb1|def greet(name):
  2:cd7|    answer = "hi " + name
  3:3dd|    pass
  4:964|    return answer
  5:d41|

Five lines either side, merged when two edits are close and elided when they are far apart. Nothing is inferred - this is the file as it now stands. When the line count changed, the listing says so, because anchors outside it have moved and those still need a read_file.

An old_content that is not made entirely of anchors is matched as text exactly as before, and so is a new_content whose rows are not all anchored. Anchors have to be certain before they take over, because falling back is always safe and taking over wrongly is not. For the same reason, an old_content that names different lines from the ones new_content anchors is refused rather than half-obeyed.

Plain JSON still works. When it arrives damaged, the parser repairs what is unambiguously safe - unclosed brackets, parameters the model put beside name instead of inside arguments, a payload whose quotes broke the JSON around it - and says so. What it refuses to repair is a reply that stopped early: closing the brackets there would invent arguments that were never sent, and write_file would happily write the empty result over a real file. Those are reported, and the model is asked to send the call again.

The envelope is repaired too. A model with no tool-calling template of its own does not reach for <tool_call>; it reaches for the nearest thing it knows. Gemma writes a markdown fence, and it writes the name under tool_name, and it nests the whole call under a tool_call key:

```tool_call
{"tool_name": "write_file", "arguments": {"filepath": "hello.py"}}
```
<content>
print('hi')
</content>

The JSON inside is usually byte-perfect and the raw block is byte-perfect - only the wrapper is wrong. Reading only the literal tag found nothing there, so the turn ended with no tool run, no error and nothing said, which is the one failure this protocol exists to prevent. All three shapes are now read.

The refusals are what keep that honest. A fence is only read as a call when there is no <tool_call> anywhere in the reply, so a correctly formatted call is never second-guessed; and unless the fence says tool_call or tool_code outright, what it holds has to decode to a tool that actually exists. An ordinary ```json block in an answer stays an answer.

When a tool fails

A failure used to arrive as the failure and nothing else - [Error] Command failed (exit code 1). and a traceback. Which command? Which file? The model had to recall what it asked for, and a small one often recalls wrong and fixes the file it was thinking about instead of the one that broke. So the call comes with the error:

[Error] run_cmd(command='python3 boom.py'): Command failed (exit code 1).
Traceback (most recent call last):
  File "boom.py", line 2, in f
    raise RuntimeError("kaboom")
RuntimeError: kaboom

Arguments are shortened so a file body cannot push the error off the top, and the error's own text is kept whole - it is usually the only thing that says what to do next. FileNotFoundError: 'confg.py' is a typo you can see; "a file error occurred" is a turn spent running the command again to find out.

A long result is only shortened when the context actually needs the room, and then from the middle. It used to be cut to 3000 characters on every turn no matter how much room was left - so reading a 12,000-character file left a quarter of it - and the cut took the end, which for a traceback is the line that says what went wrong.

Web & Network Tools

search_web: Multi-source search with local relevance ranking (see Web Search below).
get_url: Fetch web page contents and strip raw HTML down to readable text.
call_api: Execute HTTP requests (GET, POST, PUT, PATCH, DELETE) with custom headers and JSON/text payloads.

File System & Workspace Tools

read_file: Read contents of a local file formatted with line numbers and line MD5 hashes.
write_file: Create new files or overwrite existing file content. The body comes in a <content> raw block.
edit_file: Replace part of a file, via <old_content> / <new_content> raw blocks. old_content either names the lines by hashline anchor (50:1fa) or quotes them as text - see Editing by hashline anchor.
delete_file: Remove a file from disk.
copy_file: Copy a file to a new location.
create_dir: Create a new directory path.
list_dir: Display directory contents.
search_in_file: Search workspace files for string or regex patterns (grep functionality).

System & Git Management

run_cmd: Execute system shell commands (requires approval). Stays connected to the command and reports when it is waiting for input.
send_input: Answer a running command's prompt and read what it prints next.
end_process: Stop a command left running by run_cmd.
run_python: Run Python in a scratch process that keeps what it defines between calls, and get back what it printed plus the value of the last line. See The Python VM below.
get_system_info: Retrieve system CPU, memory usage, disk statistics, and top memory-consuming processes.
git_status: Check current git repository status.
git_diff: View current git working directory modifications.

Memory & Interaction Tools

write_memory: Save key information to persistent JSON storage.
read_memory: Retrieve content of a specific stored memory item.
get_memory_list: List stored memory IDs with timestamp and preview.
edit_memory: Update content of an existing memory record.
delete_memory: Remove a memory entry from disk.
get_user_input: Ask the user one or more questions, each with its own list of options plus a free-text choice.

MCP Tools

Present only when an MCP server is attached (see MCP Servers below).

mcp__<server>__<tool>: Every tool each connected server exposes, with its own parameters. A server with more than a handful of tools is announced by name instead and its parameters arrive on request - see Big servers are announced, not described.
use_mcp_server: Hand over one announced server's tools, with their parameters. Called once per server, per conversation.
list_mcp_resources: List the resources the connected servers expose, with the URI needed to read each one.
read_mcp_resource: Read one resource by URI.

Agent Channel Tools

Present whenever more than one harness is running in the same project (see The Agent Channel below).

list_agents: Who else is working here right now, on what, and which files each is holding.
send_agent_message: Say something to one of them, or to all of them.
claim_files: Announce files you are about to change, so nobody else changes them meanwhile.
release_files: Hand them back.

Workflow Tools

use_skill: Load the full instructions of a skill listed in the system prompt.
submit_plan_for_approval: Present a task plan and diff blueprint for approval before executing (plan mode).
get_code_skeleton: Return a JSON outline of a source file's structure via Tree-sitter.
query_ast_node: Search a source file for Tree-sitter S-expression patterns.
spawn_agent: Hire a second model for one self-contained job. See below.

Sub-agents

spawn_agent starts a fresh conversation - its own system prompt, its own history, its own tool loop - gives it a written brief, and returns its final report as the tool result. The user never sees the sub-agent's working; the assistant never sees it either, only the report.

It is for work whose output matters and whose process does not: finding where something is handled across a codebase, reading six files to answer one question, checking a list of URLs. Twenty tool results that will never be needed again fill the sub-agent's context instead of the conversation's.

spawn_agent(task="Find every place a session file is written, and report the
                  file and line of each.",
            context="I already know session.py:save_session is one of them.",
            model="qwen3:8b")        # optional - a cheaper model for a long search

What it may do:

every tool the assistant has, except get_user_input, submit_plan_for_approval and spawn_agent itself. It has nobody to ask, no plan to submit, and hiring chains have unbounded cost;
nothing the assistant could not have done. Its tool calls go through the same permission rules and raise the same approval prompts. A sub-agent is not a way around a deny rule;
at most SUBAGENT_MAX_TURNS turns (12 by default), after which it is asked for a report from what it has rather than being cut off mid-search.

Starting one asks for approval, like running a command does - it costs a stretch of time, and on a hosted model real money, before it reaches its first tool. Allow it permanently with an allow rule for spawn_agent.

The Agent Channel

A sub-agent is one you hired. This is about the ones you did not: the other terminals you have open on the same project, each running its own harness, each with its own conversation and no idea the others exist.

That is how people actually use this - one window planning, one writing tests, one chasing a bug - and it has a failure mode nobody sees happen. Two agents read the same file. Each writes back its own idea of it. The second write throws the first one's work away, and neither transcript contains anything to say so.

So every harness started in a project joins one board:

❯ /agents

  Agents in /home/you/proj
  ────────────────────────
  a1 (you)     gemma4:e4b - Fixing the CSV parser
               started 20m ago, holding parser.py
  a2           claude-opus-5 - Writing tests for the parser
               started 4m ago, holding nothing

  Recently said
     2m ago    a2 → everyone: I am only touching tests/, parser.py is yours

Where the board is. ~/.localchat/channel/<project>-<digest>.json, one file per workspace, never in the project itself - it is a note about who is running right now, not something to commit. The workspace is the git working tree, so a terminal opened in src/ and one opened at the top are the same workspace and see each other.

Talking. send_agent_message posts to one agent or to all of them. The message reaches the other agent at the start of its next turn, and reaches the person in front of that terminal as soon as their prompt is free - it is printed above whatever they are typing, so a question asked while they are idle does not sit unread until they press Enter. You can join in yourself with /agents say <text>.

It is a message, not a call: nothing blocks waiting for a reply. Say what you need, carry on with something else, and the answer arrives on a later turn.

And a question that goes unanswered is chased, once. Two gemma4:e4b instances were run against each other to see whether any of this holds up. It did, until the last step: asked "are you finished with shared.py?", the holder released the file and then wrote its reply into its own answer - "you have my agreement for a1 to proceed" - addressed to the other agent and delivered to nobody, while the asker sat waiting for a reply it had said it would wait for. Asking the model in the note to use send_agent_message is not enough, for the same reason asking is never enough here. So when a turn ends with a direct message unanswered, the harness says so once:

[System] a2 sent you a message and is waiting on an answer. Nothing you write
here reaches them - your reply goes to the user. Call send_agent_message to
answer a2, then carry on. If you have nothing to say, send them that.

Once, not until it complies - a model that ignores the second one would ignore the fourth. Run again with this in place, the same model answered properly and the loop closed: A was refused, asked, B released the file and replied, A wrote. A broadcast is not chased; it is news, and news needs no answer.

Not conflicting. Talking is not enough, for the same reason nothing else in this harness relies on the model behaving: a model asked to coordinate will sometimes just edit the file. So the board is enforced.

claim_files takes the files you are about to change. While you hold them, any write_file, edit_file, delete_file or copy_file onto them from another session is refused before it runs, with your id and your stated reason in the refusal. release_files hands them back.
A claim is taken automatically by whichever agent writes a file, so the protection does not depend on anybody having remembered to ask for one. Those last CHANNEL_WRITE_TTL seconds (5 minutes); an explicit claim lasts CHANNEL_CLAIM_TTL (30).
Claims die with the agent. Leaving normally releases them; a terminal that is killed outright is noticed by its pid, and the claim expires regardless.
The refusal is a [System] result, so an agent that keeps knocking on the same closed door ends its turn after three attempts rather than spending the whole budget on it.

run_cmd is deliberately not covered. What a shell command touches cannot be known from the call, and pretending otherwise would be a lock that reads as protection while protecting nothing.

Overruling it. The person is the only one here who can see both terminals, so they are the only one who can take a claim off somebody: /agents release <path>. No tool does it. /agents off takes this session off the board entirely, and CHANNEL_ENABLED = False in config.py never puts it on.

Working alone, none of this happens: the board is empty, nothing is claimed, nothing is delivered, and the only cost is four extra tools in the prompt.

5. Providers

The harness starts on Ollama and stays there until told otherwise. /connect moves it:

/connect                     pick a provider, then a model from its own list
/connect anthropic           pick a model from Anthropic
/connect openai gpt-4o       connect straight to a model
/connect status              every provider, and what each one still needs
/connect forget anthropic    delete the API key saved for a provider

Provider	Endpoint	Key from
`ollama`	local, `OLLAMA_HOST` or a `base_url`	none needed
`anthropic`	`api.anthropic.com`	`ANTHROPIC_API_KEY`
`openai`	`api.openai.com/v1`, or any compatible `base_url`	`OPENAI_API_KEY`
`gemini`	`generativelanguage.googleapis.com`	`GEMINI_API_KEY` or `GOOGLE_API_KEY`

Because base_url is settable, the openai entry also reaches anything that speaks the same protocol - a local vLLM or llama.cpp server, OpenRouter, Groq, Together.

Keys are read from the environment first. A key typed at the /connect prompt is written to ~/.localchat/providers.json - never into the project directory, which is a place people commit from. On Linux and macOS the file is owner-only (0600) from the moment it is created. Windows has no POSIX mode bits, so there the file takes whatever ACL its directory gives it; %USERPROFILE% is per-user, but if that matters to you, keep the key in the environment instead.

Prompt caching

Every request re-sends the same ~6,000 tokens of system prompt and tool schemas. Against Ollama that is re-counted but not re-computed - llama.cpp reuses the KV cache for an unchanged prefix, so a 6,000-token prefix costs about 0.05s to "process" the second time. Against a hosted API it is billed every time, and a turn that takes sixteen tool calls pays it sixteen times.

The three hosted providers split two ways, and only one needed code:

Provider	Caching	What the harness does
Anthropic	Explicit - nothing is cached without a `cache_control` breakpoint	Marks two: the end of the system prompt, and the end of the conversation so far
OpenAI	Automatic above a per-model minimum	Nothing to send; reads `cached_tokens` back
Gemini	Automatic ("implicit caching") on 2.5 and newer	Nothing to send; reads `cachedContentTokenCount` back

Anthropic renders tools, then system, then messages, so the single breakpoint on the system block covers the tool schemas too - which is two thirds of the fixed cost. The second breakpoint sits on the last message, so the next request in a tool loop reads the whole conversation before it back out of the cache instead of paying for it again.

The reporting is the part that matters. Caching is a prefix match: one byte that moves invalidates everything after it, and it fails silently - as a bill, not an error. So /usage prints what actually happened:

  cache 12,200 prompt tokens read from Anthropic's cache - 63% of all input, billed at a fraction

...and says so when it is not working:

  cache nothing read from Anthropic's cache in 4 requests. Either the prompt is
        under that model's minimum, or something is rewriting the prefix

Nothing is claimed for Ollama, which reuses its prefix locally, charges nothing for it and reports nothing about it.

Two things in this harness rewrite the prefix and cost one miss each when they happen: the <SUMMARY> that compression writes into the system message, and context._trim_tool_results the first time a tool result exceeds its ceiling. Both converge - a result is not trimmed twice - so neither is a permanent miss.

/connect forget <provider> takes a saved key back out. /connect only ever asked for a key when there was none, so one pasted into the wrong provider, or one that has since been revoked, used to stay in that file with nothing in the program able to remove it. Only the key goes - the model beside it is not a secret, and keeping it makes reconnecting one step. A key coming from an environment variable is not touched, because this program cannot unset your shell's variables, and it says so rather than appearing to have done something.

How tools are asked for

Two protocols, chosen by who is answering:

	Tools are	Tool calls come back as
Anthropic, OpenAI, Gemini	sent with the request	the API's own tool-call events
Ollama, model supports tools	sent with the request	Ollama's own tool-call events
Ollama, model does not	listed in the system prompt	`<tool_call>` text, repaired if malformed

For Ollama this is decided per model, not per provider: whether a model can call a tool depends on the template it was built with, and Ollama says so outright. Of the twenty models installed on the machine this was written on, five have no tool support. Each one is asked once and the answer cached; a daemon that is down, or a model that cannot be asked, means the text protocol - which works everywhere.

That fallback is the point. A model whose template cannot format a tool call will simply never make one, and the text protocol plus its JSON repair engine is what makes those models usable at all. A model that can is more accurate through the real interface, and about 12KB of prompt cheaper per turn, because the tool list no longer has to be spelled out:

text protocol      system prompt 17.9KB   (tool schemas + <tool_call> rules)
native tool calls   system prompt 5.9KB   (schemas travel with the request)

Both protocols describe the same tools, from the same table in toolspec.py, so they cannot drift apart. Both end up as the same (name, arguments) pair, so dispatch, permissions, display, session files and context compression see no difference - the history stays plain text either way, and a session saved from one provider replays under another.

Set NATIVE_TOOLS = False in config.py to force the text protocol everywhere, which is what an OpenAI-compatible server without tool support needs. /connect status shows which protocol is in use.

Why this is still small. providers.py normalises four wire formats into one event shape - text, thinking, tool call, done - and everything downstream is untouched by which provider is answering. No vendor SDKs.

The shapes that do differ are handled in one place: Anthropic and Gemini take the system prompt as its own field rather than a message, Gemini calls the assistant role model, and both want consecutive same-role messages merged - which the harness produces constantly, since every tool result is its own user message.

6. Web Search

A single general search engine answers the entity in a query and drops the term that matters. Asked for ollama num_ctx meaning it returns ollama.com, the Windows download page, and install blogs - none of which contain num_ctx. The old implementation passed those straight to the model, which then answered confidently and wrongly.

websearch.py fixes that in three stages:

Candidates from complementary sources, run concurrently. A general web index is weak on code identifiers, so Stack Exchange and GitHub are queried for those and Wikipedia for concepts. Keyword APIs receive a distilled query (ollama num_ctx meaning becomes ollama num_ctx); web engines get the original. If a source fails or times out, the rest still return.
The pages are read, not just their snippets, and split into passages ranked by BM25 whose IDF comes from the candidate pool itself - so a term in every candidate scores near zero and a rare one dominates. No model, no corpus, no network.
A relevance floor. The query's discriminative terms - explicit identifiers, or the rarest term the pool exposes - must actually appear in a passage. If nothing clears it, the tool reports that the search found nothing and names the pages it rejected, instead of handing over the closest junk.

Everything is free and keyless. To make candidate generation fully local too, run a SearXNG instance and point config.SEARXNG_URL at it (e.g. "http://localhost:8080"); it is then used as the primary source and the public ones stay as backup.

Tuning knobs live in config.py: SEARCH_CANDIDATES, SEARCH_FETCH_PAGES, SEARCH_PASSAGE_CHARS, SEARCH_RESULT_CHARS, and the three timeouts.

7. Skills

A skill is an instruction pack stored on disk that the model pulls in only when it is relevant. This keeps the system prompt small no matter how many skills exist: only name and description are always loaded, and the body arrives when the model calls use_skill.

skills/
  git-commit/
    SKILL.md          <- required
    types.md          <- optional bundled files, listed with absolute paths
  quick-skill.md      <- one-file skill

Skills are searched in ./skills/ first, then ~/.localchat/skills/; the first match on a name wins, so a project skill overrides a personal one.

SKILL.md opens with YAML frontmatter:

---
name: git-commit
description: Use when the user asks for a commit message. Triggers on "commit", "커밋".
allowed-tools: git_status, git_diff, run_cmd
---

Instructions in plain markdown.

name — optional; the folder or file name is used when it is missing.
description — the only thing the model sees before loading, so write it as when to use this and include the words a user would actually type.
allowed-tools — optional; shown to the model as the tool set the skill expects. Advisory, not enforced by the harness.

Two skills ship with the repo: git-commit and code-review. See skills/README.md for the full format reference.

They are in the repository, not in the installed package - skills are looked for in the working directory and in ~/.localchat/skills/, never next to the code, so that a project's own skills win and an install cannot quietly add instructions you did not write. From a pip install, copy the two you want:

git clone https://github.com/minjun1177/simple_harness
cp -r simple_harness/skills/* ~/.localchat/skills/

8. MCP Servers

MCP is the standard way to hand an assistant tools it did not ship with - a filesystem browser, a database, an issue tracker. Declare a server once and its tools appear alongside the built-in ones.

Declaring a server

Servers are read from ./.mcp.json first, then ~/.localchat/mcp.json; a project entry wins over a personal one with the same name. Copy .mcp.json.example to get started.

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "D:/work"]
    },
    "github": {
      "type": "http",
      "url": "https://api.githubcopilot.com/mcp/",
      "headers": {"Authorization": "Bearer ${GITHUB_TOKEN}"}
    }
  }
}

Key	Meaning
`command`, `args`, `env`, `cwd`	Start a local server over stdio.
`url`, `headers`	Talk to a remote server over streamable HTTP.
`type`	`stdio`, `http`, or `sse` for the deprecated HTTP+SSE transport. Inferred from `command`/`url` when omitted.
`disabled`	`true` keeps the entry but does not start it.
`timeout`	Seconds to wait for a tool call from this server.
`autoApprove`	Tool names on this server that may run without an approval prompt.
`trust`	`true` auto-approves every tool on this server.

${VAR} and ${env:VAR} anywhere in a server entry are replaced with the matching environment variable, so tokens stay out of the file.

How it behaves

Every enabled server is started when the app launches, in parallel. Its tools are fetched and written into the system prompt as mcp__<server>__<tool>, with each tool's JSON Schema flattened into the same parameter list the built-in tools use. A server that fails to start is reported and skipped - the app runs without it. Server-declared instructions are passed through to the model, and readOnlyHint / destructiveHint annotations are surfaced in the tool description.

Calls go through the same approval prompt as run_cmd and file edits, so nothing runs on an attached server without a y - unless /automode on, autoApprove, or trust says otherwise.

Big servers are announced, not described

A server's tools are written out in full on every request - into the system prompt over the text protocol, into the request's own tools field over a native one. Measured against a real @playwright/mcp server with 24 tools:

	tokens, every request
in the system prompt (text protocol)	3,549
in the `tools` field (native)	4,637
its name and its tools' names	130

That is paid whether or not the conversation has anything to do with a browser, and attaching three such servers spends most of a 65,536 context on tool descriptions nobody asked for.

So a server with MCP_LAZY_MIN_TOOLS tools or more (6 by default) is announced rather than described:

### MCP SERVERS (attached, tools not yet loaded):
- playwright (24 tools): browser_close, browser_resize, browser_navigate,
  browser_click, browser_type, browser_snapshot, browser_evaluate, …

Names only - a name is what tells the model whether a server does the thing it wants. When it decides it does, use_mcp_server("playwright") hands over the parameters, and from then on those tools are in the list like any other. It is the shape use_skill already has, for the same reason: the prompt should carry what is needed to choose, not everything that might be used.

Three things keep it from being a trap:

Loading is about what the model is shown, never about what it may do. A call to an unloaded server's tool still resolves and still runs - a context optimisation must not be able to break a call. And calling one loads that server, so the next call is not a guess. (Over a native interface a provider cannot emit a call whose schema it was never given; that is a protocol limit rather than a rule here, and it is what the announcement exists to work around.)

A load that the compressor dropped is a load that ended. The loaded set is read back out of the conversation, exactly as LOADED_SKILLS is, so the model is never told a server is "already loaded" after the message carrying its tools was pruned away.

A small server is not worth a round trip. Below MCP_LAZY_MIN_TOOLS the announcement costs about what the schemas cost, so those servers are shown outright. MCP_LAZY_TOOLS = False restores the old behaviour exactly.

Tuning knobs live in config.py: MCP_ENABLED, MCP_LAZY_TOOLS, MCP_LAZY_MIN_TOOLS, MCP_STARTUP_TIMEOUT, MCP_CALL_TIMEOUT, MCP_HTTP_TIMEOUT, MCP_RESULT_CHARS, MCP_MAX_TOOLS_PER_SERVER, MCP_TRUSTED_SERVERS, and MCP_AUTO_APPROVE_READONLY.

Inspecting and controlling

/mcp shows every configured server, its transport, what it exposes, and the error behind any failure. /mcp tools expands the tool list, /mcp resources lists readable resources, /mcp reload re-reads the config files and reconnects, and /mcp prompt <server> <name> key=value runs a prompt template the server offers as your next message.

The protocol client is a self-contained JSON-RPC implementation in mcp_client.py - no SDK dependency, and no new packages to install.

9. Running Commands

run_cmd captures the command's output, so nothing the command prints reaches the user's screen while it runs - including a prompt. An interactive program therefore used to hang the whole app: it sat waiting on stdin with its question captured and invisible, and there was no way to tell what it wanted or to answer it.

The fix is not to deny it stdin. The model answers the prompt. The command keeps a live pipe, and when it goes quiet its output so far is handed over with a session id - see below. A command that never finishes is stopped at config.CMD_TIMEOUT (120s) along with everything it started, and whatever it printed first is kept.

Answering a program while it runs

run_cmd does not wait for the command to finish. It stays connected, draining output as it appears, and when the command is waiting the output so far comes back with a session id:

추측:

[Waiting] 'python3 game.py' is still running and has printed nothing for 0.6s,
so it is most likely waiting for input. Answer it with send_input:
    {"name": "send_input", "arguments": {"session": "s1"}}

The model reads the prompt, answers it with send_input, and gets whatever the program prints next - one exchange per turn, until the program exits. That is how it tests something it has just written: it plays through the program itself. end_process stops one that will not exit. The first few answers can also be sent up front in a <stdin> block on run_cmd.

Telling "waiting" from "busy". Going quiet proves nothing on its own - a program that is merely computing looks identical from outside. The best signal each platform offers is used, strongest first:

Signal	Where	What it proves
The output ends without a newline (`추측:` )	everywhere	It is shaped like a prompt
`/proc/<pid>/syscall` says a thread is parked in `read()` on fd 0	Linux	It really is waiting on stdin - and a "no" is trusted too
The process tree has burned no CPU	Windows, macOS	It is idle, but sleeping and waiting look the same
Silence lasting `CMD_WAIT_TIMEOUT`	everywhere	Nothing better was available

On Linux the /proc answer is exact in both directions, so a sleep 3 is simply waited out. Windows and macOS have no equivalent, so idle CPU only shortens the wait to CMD_IDLE_GRACE instead of ending it - a long silent pause may be offered to the model as a prompt, and an empty send_input picks the output back up when it turns out not to be one.

To see what a given machine can actually manage:

python tests/test_platform.py

It prints which signals are available there, then runs a prompt, a silent sleep, a busy loop and a runaway command through run_cmd. On Linux it does the whole thing twice, the second time with /proc switched off - which is exactly the code Windows and macOS run, so the fallback can be checked without leaving Linux. It exits non-zero if anything fails.

A command that never stops printing is killed at CMD_TIMEOUT along with everything it started, and at most CMD_MAX_SESSIONS live commands are kept.

Text that is not ASCII. A command's output is decoded as UTF-8 first, because UTF-8 is the only candidate that can report that it is wrong - almost any byte is legal cp949, so guessing a code page first would silently turn good text into mojibake. If the bytes turn out not to be UTF-8, which is what a Python older than 3.15 printing to a pipe on Korean Windows produces, the console code page takes over for the rest of that command - both for what it prints and for what send_input sends back to it.

The Python VM

run_python is a Python process kept alive beside the harness, for working something out before committing to it. It is aimed squarely at the small local model this harness is built around: gemma4:e4b cannot hold an intermediate result in its head, cannot reliably do arithmetic in prose, and will state what a regex matches rather than find out.

<tool_call>
{"name": "run_python", "arguments": {}}
<content>
import statistics
scores = [88, 92, 79, 95, 61]
print("mean", statistics.mean(scores))
sorted(scores)[-2]
</content>
</tool_call>

[Success] ran in 0.04s.

mean 83

=> 92

(kept for your next run_python call: scores, statistics)

Four things it does that run_cmd python3 -c "..." does not:

The code does not have to survive a JSON string. It arrives in a <content> block, byte for byte, exactly as a file body does. Escaping a snippet into a shell argument inside a JSON value is two levels of quoting, and it is the single thing a 4B model gets wrong most often.
It remembers. One process holds its globals across calls, so the model can compute, look at the answer, and compute again. The result names what it just defined, so the model knows what it still has. reset empties it.
The last expression is answered. 2 ** 10 on its own prints nothing under python -c; here it comes back as => 1024. A model reaches for a calculator far more readily when the calculator answers.
It is a scratchpad, not the project. The process runs in ~/.localchat/vm, so a stray open(..., "w") lands there rather than in the repository - and never in an auto-commit. The project is still on its PYTHONPATH, so a function that has just been written can be imported and tried; nothing is written back to it, not even a __pycache__.

Answers for anything the code reads with input() go in a <stdin> block, one per line, so a prompt can be tried without the run_cmd / send_input dance.

It is isolation from mistakes, not from a hostile program. The code runs as you, with your files and your network, and there is no pretence otherwise - which is why run_python still goes through the approval prompt, and why it counts as changing the world, so a read-only deepthink stage refuses it exactly as it refuses run_cmd. What the separate process does buy is that a runaway loop, a 40GB allocation or a hard crash takes down the scratch process and not the harness.

The wall-clock kill is the guarantee; the memory cap is an optimisation on top of it. On Linux the process is additionally held to VM_MEMORY_MB of address space, so an over-large allocation comes back as a MemoryError the model can read and the VM survives. macOS accepts that setrlimit call and does not enforce it, and Windows has no resource module at all - on both, the same allocation runs until VM_TIMEOUT stops it and the VM is restarted. The runaway is contained everywhere; only Linux turns it into a tidy error. VM_FILE_MB is enforced wherever RLIMIT_FSIZE is.

Any of the three ways it can die - the VM_TIMEOUT kill, a crash, an os._exit() - loses the namespace, and the result says so in as many words. Silence there would leave the model referring to variables that no longer exist, and the next error would be a NameError explaining nothing.

10. Deepthink

Off by default. /deepthink on turns one request into six turns:

1  Plan      work out what it takes - read the files, change nothing
2  Check     argue against that plan and settle every assumption
3  Implement carry out the plan as it now stands
4  Review    read the diff of what actually changed, and list what is wrong
5  Revise    fix what the review found, and nothing else
6  Verify    run it, check it against the plan, report what really came back

All six are one conversation, so each stage sees everything the ones before it did. What changes is the instruction at the top of each turn.

Asked to implement something, a model goes straight at it. It writes code from what it remembers of a file rather than what the file says, and when it is done it reports success without running the thing. Both come from the same place - one pass, with no step whose only job is to find fault.

Finding and fixing are two stages, not one. Review used to do both, and a stage that is allowed to fix stops looking as soon as it has something to fix - so the rest of its own list went unread. Review is now read-only and its whole output is a numbered list of what is wrong; stage 5 turns the tools back on and works through that list, and is told not to widen it, because a change nobody reviewed is a change nobody checked. An empty list means stage 5 changes nothing, which is a result rather than an idle turn to fill.

Three of the six carry the mode. Stage 2 is the only one asked to prove the plan wrong, and a plan nobody argued with is usually the one that fails. Stage 4 is handed the real git diff rather than being asked what it changed: reviewing from memory finds nothing, because the memory is of the intention, not of the code. Without git - no repository, or /autocommit off - it is told to re-read the files instead. Stage 6 goes back to the plan and checks it item by item, because code that runs and is not what was agreed is still not finished.

The stages that are meant to think cannot edit. Not "are asked not to" - the tools that change things are switched off in stages 1, 2 and 4, and a model that tries one is told to say what it would change instead. Telling a model to hold off does not hold it off; a local 4B model tried to edit fifteen times in the planning stage before this was enforced.

It stops early when there is nothing to build. A question costs one turn, not six: the plan stage marks it, and if the model forgets to, the plan itself is read back in one short call to decide. Anything unclear counts as work to do. A build stage that changed nothing also ends the chain rather than reviewing and verifying work that was never done.

And it goes round again when six turns were not enough. A stage 6 that finds half the plan undone used to have nowhere to put that finding - the chain ended and handed the report back as the answer. Now it says so, with a marker or in a short read-back of its own report, and the chain starts over at stage 1: never in the middle, because what is left after a failed pass is a different piece of work and planning it is the part that was missing. The next pass is told to finish what the report named and not to widen it, and it ends after one turn if there is nothing left after all. DEEPTHINK_MAX_PASSES (3) is the ceiling. This gate leans the opposite way to the one above: anything unclear counts as finished, because a chain that sets itself off again on a maybe does not end.

/deepthink            the stages, and whether it is on
/deepthink on|off     turn it on or off
/set DEEPTHINK_MAX_PASSES 3    how many times it may start over

Deepthink supersedes plan mode while it is on, so /planmode injects nothing - two sets of planning instructions only contradict each other.

11. Undoing AI Edits

Every file an AI tool changes is committed on its own, under a message naming the tool that did it:

ai(edit_file): greet.py
ai(write_file): parser.py

/undo takes the newest one back:

/undo             put the last AI edit back the way it was
/autocommit       whether this is on, and the recent AI commits
/autocommit off   stop committing (edits still happen, they are just not committed)

An assistant that edits files is only as useful as its undo. Without one the honest advice is "commit before you let it touch anything", which nobody follows, and a wrong edit three tool calls ago is gone.

Two rules keep this from being a nuisance:

It commits only what the tool named. Whatever else you have staged or changed is left exactly as it was - git commit is given those paths explicitly rather than being allowed to sweep up your index.

Undo refuses rather than destroying work it did not create. It will not touch a commit you wrote, and if a file in the AI's commit has changed since, it stops and says which one:

'ai(write_file): shared.py' touched files that have since changed: shared.py.
Commit or discard those first - undoing now would take them with it.

Outside a git repository, and on a machine with no git installed, nothing is committed and nothing breaks. Set GIT_AUTO_COMMIT = False in config.py to default it off.

12. Checking the Work

A model that has just written a file tells you it is done. It has not run anything. On a local 4B model this is not laziness - it genuinely does not occur to it, and telling it to check its work in the system prompt is forgotten by the third tool call. So the file is wrong, the turn ends, and you find out by running the program yourself, copying the traceback, and pasting it back in.

That round trip is now the harness's job.

When a turn changes a file, the harness works out what kind of project the file belongs to, runs that project's own check once the turn's tool calls are all done, and if it fails, puts the failure in front of the model:

› fix the off-by-one in slice_window

  ▸ edit_file(filepath='context.py')
  ⎇ committed 4f1c2ae   /undo to take it back
  ⟳ auto-verify: python -m pytest -x -q -l   in chat
  ✗ python -m pytest -x -q -l failed   2.4s

  ▸ read_file(filepath='context.py', start=180, end=205)
  ▸ edit_file(filepath='context.py')
  ⎇ committed 9b70dd1   /undo to take it back
  ⟳ auto-verify: python -m pytest -x -q -l   in chat
  ✓ python -m pytest -x -q -l passed   2.6s

Fixed. `slice_window` was dropping the last message when the budget landed
exactly on a turn boundary; the bound is now inclusive.

Reacting to a concrete traceback is the kind of work a small model is good at - much easier than noticing unprompted that something might be wrong. This is the feature that moves a 4B model from "usually needs checking" to "tells you when it is wrong".

It only runs a check the project already declares. No check is invented:

The project has	It runs	Not a failure
`pyproject.toml`, `pytest.ini`, `tox.ini` or `setup.cfg`	`python -m pytest -x -q -l`	exit 5 - a project with no tests yet
`package.json` with a real `test` script	`npm test --silent`
`Cargo.toml`	`cargo test --quiet`
`go.mod`	`go test ./...`

The check is picked by the extension of the file that changed, not by whichever marker turns up first, so a .py file and a .ts file in the same repository get the right one each. A file none of them cover - a README.md, a config file - runs nothing at all. Neither does a project whose runner is not installed, or one whose package.json still has the placeholder test script npm init writes.

It runs once per turn, not once per edit. Four files written in one reply are one run of the suite, against the state the model meant to leave them in rather than three states it was halfway through.

For Python it also hands over the variables. The pytest check runs with --showlocals, so a failure arrives as the state that produced it rather than a line number to reason backwards from:

>       assert lookup(records, user_id) == prefix + "c"
prefix     = 'user-'
records    = {1: {'name': 'a'}, 2: {'name': 'b'}}
user_id    = 3
E       KeyError: 3

Working backwards from a line number to what was in scope is the thing a small model is worst at; reading a value off the page is the thing it is best at. pytest cuts a long repr down itself, so this costs a few hundred characters, not the whole budget. The other three runners have no equivalent - a Go panic and a JS stack trace do not carry locals - so this is Python only.

It is bounded, and it gives up. The check gets VERIFY_TIMEOUT seconds with no stdin, and only the tail of the output - which is where a failure is written down - is shown to the model. A suite that runs past the timeout turns itself off for the rest of the session rather than costing that after every edit; /autoverify on tries it again. And when the same failure comes back three times, the harness stops feeding it back:

Auto-verify has come back with the same failure 3 times, so it is off for the
rest of this turn. Stop editing. Tell the user which check is failing, what
you changed, and what you think is wrong - a fourth guess is worth less to
them than an honest description. They can take your changes back with /undo.

The count is for a model that is stuck, not for a turn that has several things wrong with it. A run against gemma4:e4b hit three different failures - a module that was not callable, a bug that was already in the tree, then a brace it had just deleted - and was stopped on the third, one edit after being handed exactly what it needed. A failure that reads differently is progress, so the budget starts again.

That last sentence is why this ships after /undo (§11) rather than before it. Every retry is its own commit, so a loop that went the wrong way is undone one step at a time.

/autoverify        whether it is on, which checks exist, and any turned off here
/autoverify off    stop running anything after an edit

Set AUTO_VERIFY = False in config.py, or /set AUTO_VERIFY off, to default it off.

Lock the test, and "make it pass" means what it says. Ask a model to make a failing test pass and it will often make the test pass - loosening the assertion until it is true, or deleting it. Telling it not to does not work, for the same reason it does not work in a planning stage: a small model that is stuck takes the opening it is given. /tdd closes the opening:

/tdd make test_window_slicing pass

For that one request the project's test files are refused to edit_file, write_file, delete_file and copy_file at the dispatcher - through the same permission rules as everything else (§13), held in memory and never written to your .permissions.json. The model is told so up front, so it spends its calls on the code rather than discovering the wall. Auto-verify gets six tries instead of three, because staying in that loop is the whole point.

It lifts itself when the turn ends, including when the turn failed or you interrupted it - a lock nobody remembers turning on is worse than no lock. /tdd on its own arms it for your next message; /tdd off lifts it early.

The model is given one way out: if it concludes the test itself is wrong, it is asked to say so and stop rather than work around it. Sometimes the test is wrong, and six attempts at satisfying a bad test is not a better answer than saying so.

What this cannot cover is run_cmd - what a shell command writes is not knowable from the call, which is the same limit the agent channel's file claims have.

13. Tool Permissions

Until now the only gate was the approval prompt, and /automode on turned it off for everything at once - including run_cmd and delete_file. Rules give the middle ground.

Rules are read from ./.permissions.json and ~/.localchat/permissions.json; rules from both files apply. Copy .permissions.json.example to start.

{
  "allow": ["run_cmd(git status)", "mcp__filesystem__*"],
  "deny":  ["delete_file", "run_cmd(rm *)", "write_file(*/.env)"]
}

A rule is a tool name, optionally followed by a pattern in parentheses matched against the call's main argument - the command for run_cmd, the path for a file tool, the URL for a network tool. Both halves accept * and ?. A pattern with no wildcard also covers <pattern> <anything>, so run_cmd(git status) already allows git status --short.

deny does not run and does not ask. The tool's handler is never reached, and the model is told it is blocked so it stops retrying.
allow runs without a prompt.
Everything else asks, exactly as before - an empty rule set changes nothing.

What a rule does not cover

A rule is matched against the call's argument as text, and it is worth being plain about what that does and does not buy you.

An allow rule stops at the command it names. run_cmd runs its command through a shell, so git status && rm -rf ~ starts with the text run_cmd(git status) allows. It is not allowed: an operator the rule itself does not contain - ; && || | ` $( ${ > < - means the command does more than the rule accounts for, and it falls through to the approval prompt instead. A rule that asks for a pipeline outright, such as run_cmd(git log * | grep *), still gets one.

A deny rule is a stop sign, not a sandbox. It matches text, and text can be rewritten: run_cmd(rm *) denies rm -rf x and does not recognise sh -c 'rm -rf x' or /bin/rm -rf x. Those fall through to the approval prompt, so the prompt is still between the model and the command - but with /automode on there is no prompt, and then a deny rule is only as good as the spelling the model happened to use. Deny is for the mistakes you expect, not for an adversary.

A pattern is never empty. write_file() reads as "calls with no arguments" and would have meant the opposite, so it is refused at load time and named in /perms. Write the bare tool name, write_file, when you mean every call.

run_python takes no pattern at all. A rule matches one argument as text, and the argument here is a program - run_python(import *) would mean nothing useful and would read as though it meant something. So the only rule is the bare run_python, which allows every snippet, and the prompt shows the code before it runs. Allowing it is worth roughly what allowing run_cmd outright is worth; the difference is that the prompt is showing you exactly what will run.

At an approval prompt the choices are now [y/n/a], where a allows this exact call from now on and appends the rule to .permissions.json. /perms lists the active rules, /perms allow <rule> and /perms deny <rule> add one by hand, and /perms reload re-reads the files.

14. Reasoning Models

Reasoning models (qwen3, deepseek-r1, gpt-oss) emit their scratch work before the answer - either wrapped in <think> tags in the content stream, or in Ollama's separate thinking field. It is not the answer, so:

it is not printed (/think on shows it dimmed if you want to watch),
it is never stored in the conversation history, which matters most: on a local model the context budget is small, and reasoning is often longer than the answer it produces.

Set config.STORE_THINKING = True to keep it in history anyway, or config.SHOW_THINKING = True to have it shown from startup.

The same stream filter hides the <tool_call> tag, so a model that explains itself and then calls a tool shows only the explanation.

15. Configuration

Most behaviour is reachable from a slash command, and those changes last for the session. /set changes what it starts as, without editing any source.

/set                      every setting, with the changed ones marked
/set NUM_CTX 32768        change one - for this session and the next
/set NUM_CTX default      put it back to what config.py says

What was changed is written to ~/.localchat/settings.json and applied over config.py at startup. Only the deviations are recorded, so a default that improves in a later version still reaches you if you never overrode it - writing all sixty out would freeze this release's values the first time you changed one.

A setting is any UPPER_CASE name in config.py holding a number, a switch, a string or a list, so a setting added there is settable the moment it exists. What is not settable is named rather than listed, and it is a short list: the system prompt and the model (they have commands of their own), live state such as the session title, facts about the machine, the two tool-result markers (invariant 5.9 - a protocol, not a preference), and the paths under ~/.localchat, which LOCALCHAT_HOME moves together.

Values are checked against the type the setting already has - on/off for a switch, a number for a number, commas for a list - and a negative number is refused, because none of them mean anything below zero and one fails much later and somewhere else. Nothing above zero is second-guessed: this file was always editable by hand, and the same latitude belongs here. A settings.json that will not parse is ignored entirely and the harness starts on the defaults.

config.py is still where the defaults live, and where each one is commented. The settings worth knowing:

Setting	Default	What it does
`MODEL`	`gemma4:e4b`	The Ollama model used until `/connect` says otherwise
`NUM_CTX`	65536	Context window asked of Ollama
`NUM_PREDICT`	6144	Output cap. Must be an int - every hosted API rejects a float
`NATIVE_TOOLS`	`True`	`False` forces the `<tool_call>` text protocol everywhere
`MAX_TOOL_CALLS`	10	Tool calls per turn before asking whether to continue
`AUTO_ALLOW`	`False`	`True` is `/automode on` from startup - no approval prompts
`PERMISSIONS_ENABLED`	`True`	Whether `.permissions.json` rules are consulted at all
`GIT_AUTO_COMMIT`	`True`	A commit per AI edit, so `/undo` has something to take back
`AUTO_VERIFY`	`True`	Run the project's own check after a turn changes a file
`VERIFY_TIMEOUT`	90	Seconds one check gets before it is killed and turned off
`VERIFY_OUTPUT_CHARS`	2000	Of a failing check, how much of the tail the model is shown
`DEEPTHINK`	`False`	Start with the six-stage chain on
`DEEPTHINK_MAX_PASSES`	3	Times the chain may start over when the final check says it is not done
`SUBAGENT_MAX_TURNS`	12	Turns a sub-agent gets before it must report
`SUBAGENT_MAX_DEPTH`	1	1 means sub-agents cannot hire sub-agents
`SHOW_THINKING`	`False`	Show a reasoning model's scratch work
`STORE_THINKING`	`False`	Keep it in the history too. Expensive on a local model
`CHANNEL_ENABLED`	`True`	Join the board other harnesses in this project share
`CHANNEL_CLAIMS`	`True`	Refuse an edit to a file another agent is holding
`CHANNEL_CLAIM_TTL`	1800	Seconds a `claim_files` claim lasts
`CHANNEL_WRITE_TTL`	300	...and one taken automatically by writing a file
`CHANNEL_STALE`	120	Heartbeat age past which an agent is presumed gone
`CHANNEL_POLL_SECONDS`	2	How often an idle prompt looks for a new message
`MENTION_MAX_CHARS`	`40000`	Ceiling on what one `@path` may add to the context
`AUTO_TITLE`	`True`	Let the model name each new session
`SAVE_CHAT_HISTORY`	`True`	Write session files at all
`CMD_TIMEOUT`	120	Seconds before a runaway command is killed
`CMD_WAIT_TIMEOUT`	8	Silence before a command is called "probably waiting"
`VM_TIMEOUT`	20	Seconds a `run_python` snippet gets before the VM is killed and restarted
`VM_OUTPUT_CHARS`	4000	Ceiling on what one snippet may print back; the middle is dropped
`VM_MEMORY_MB`	512	Address space the VM may take. Enforced on Linux; accepted and ignored on macOS, absent on Windows. 0 for no limit
`VM_FILE_MB`	64	Largest file the VM may write. POSIX only; 0 for no limit
`MCP_ENABLED`	`True`	Attach MCP servers on startup
`MCP_LAZY_TOOLS`	`True`	Announce a big MCP server by name; send its tools when asked
`MCP_LAZY_MIN_TOOLS`	6	Tools a server needs before it is announced rather than described
`SEARXNG_URL`	`""`	A self-hosted search instance to prefer over the public sources

The rest are tuning knobs for search, MCP and command sessions; they are documented in the sections above, commented where they are defined, and all of them are listed by /set.

State that outlives a session lives outside config.py:

Path	Holds
Everything about you lives in one directory, `~/.localchat`. Everything about
a project is read from that project's own directory first, and from
`~/.localchat` second - so a repository can carry its own rules, servers and
skills, and they win.

Path	Holds
`~/.localchat/providers.json`	The connected provider and any API keys typed at `/connect`. Owner-only on POSIX
`~/.localchat/sessions/*.json`	Conversation transcripts, named after the session title, each recording the directory it was last worked in so `-c` can find it
`~/.localchat/memory.json`	The long-term key-value memory
`~/.localchat/history`	Input history for the prompt
`~/.localchat/channel/*.json`	One board per project: which harnesses are running in it, what they have said to each other, and which files each is holding
`~/.localchat/vm/`	The `run_python` scratch directory - where the VM runs, and where anything it writes ends up
`~/.localchat/settings.json`	The settings `/set` changed - only those, never the whole table
`./.permissions.json`, then `~/.localchat/permissions.json`	Allow and deny rules
`./.mcp.json`, then `~/.localchat/mcp.json`	MCP server declarations
`./skills/`, then `~/.localchat/skills/`	Skills

Set LOCALCHAT_HOME to put that directory somewhere else - two profiles, or a throwaway one for trying something out.

Before 0.2.0 the sessions, the memory and the input history were written into whatever directory the harness started in. If you have those, they are not read any more and nothing has moved them; the harness names them at startup and prints the one line that moves them across.

16. Slash Commands

The interactive terminal supports special slash commands to control options and inspect state:

Command	Description
`/help`	Display the list of available commands
`/usage`	Token cost per turn, as an ASCII chart, plus the cumulative total and what every request pays before the conversation starts. One bar is one thing you asked for, tool calls included
`/model`	Show the connected provider and pick another of its models
`/models`	List the models the connected provider offers
`/clear`	Clear the terminal display and reset conversation history
`/sessions`	List saved conversation sessions, newest first, with their titles
`/load <id or title>`	Load and render a past conversation session, found by id or title
`/title`	Show the current session's title and id
`/title <name>`	Retitle the current session and rename its file to match
`/autotitle <on/off>`	Toggle letting the model name a new session after its first exchange
`/automode <on/off>`	Enable or disable approval prompts for tool execution
`/fullcontent <on/off>`	Toggle truncating large file displays
`/record <on/off>`	Toggle automatically recording chat history into session files
`/export [filename]`	Export current chat history into a Markdown file
`/system <prompt>`	Set a custom system persona or reset to default (`/system reset`)
`/planmode <on/off>`	Require a plan approval before file edits or complex work
`/skills`	List discovered skills with their descriptions and paths
`/skills reload`	Rescan the skill directories and refresh the system prompt
`/skill <name>`	Load a skill into the current conversation by hand
`/mcp`	Show every configured MCP server, its state, and what it exposes
`/mcp tools [server]`	Expand the tool list of one or every connected server
`/mcp resources [server]`	List the resources the servers expose
`/mcp reload`	Re-read the config files and reconnect every server
`/mcp connect <name>`	Reconnect a single server
`/mcp prompt <server> <name> [k=v]`	Run a prompt template the server offers
`/mcp <on/off>`	Attach or detach every MCP server for this session

Two prefixes act on the message itself rather than being commands:

Prefix	Description
`@<path>`	Attach a file (or a directory's listing) to this message. Typing `@` opens a completion menu of the current directory - arrows to move, Tab to insert
`!<command>`	Run a shell command yourself. It skips the approval prompt, because you typed it, and its output is added to the conversation
`/connect [provider] [model]`	Connect a provider, or pick one interactively
`/connect status`	Show every provider and whether it is usable
`/connect forget <provider>`	Delete the API key saved for a provider. An environment variable is left alone, and said so
`/perms`	Show the active tool permission rules
`/perms reload`	Re-read the permission rule files
`/perms allow <rule>`	Add an allow rule, e.g. `/perms allow run_cmd(git *)`
`/perms deny <rule>`	Add a deny rule
`/think <on/off>`	Show or hide a reasoning model's thinking
`/deepthink`	The plan-check-build-review-verify chain, and whether it is on
`/deepthink <on/off>`	Turn that chain on or off
`/agents`	Show the other harnesses running in this project, what they hold, and what has been said
`/agents say <text>`	Say something to all of them yourself
`/agents release <path>`	Take a file back from the agent holding it
`/agents <on/off>`	Whether this session appears on the board at all
`/vm`	Show the `run_python` scratch process: whether it is up, what it has run, and the directory it runs in
`/vm reset`	Throw away every variable the model left in it
`/vm stop`	End the process; the next `run_python` starts a fresh one
`/set`	Every setting that can be changed, and which ones you have changed
`/set <NAME> <value>`	Change one, e.g. `/set NUM_CTX 32768`. Saved for next time
`/set <NAME> default`	Put it back to what `config.py` says
`/undo`	Take back the last file change the AI committed
`/autocommit`	Whether AI edits are committed, and the recent AI commits
`/autocommit <on/off>`	Turn that on or off
`/autoverify`	Whether an edit is checked against the project's own tests, and any check turned off here
`/autoverify <on/off>`	Turn that on or off
`/tdd <request>`	Run one request with this project's test files locked
`/tdd`	Arm that for your next message
`/tdd off`	Lift it without sending anything
`/exit` or `/quit`	Exit the application

17. Architecture

The codebase is organized cleanly around the following components:

ARCHITECTURE.md: How the codebase is put together - the turn's control flow, the data shapes, the invariants, and what to touch for a given change. Read that before editing; read this to use it.
app.py: Event loop, slash command router, and system prompt composition.
llm_client.py: The conversation loop - streaming a reply, parsing the tool calls out of it, running them. Knows nothing about which provider answered.
tools.py: Tool implementations, and the table binding each one to its entry in toolspec.py.
toolspec.py: What every built-in tool is - name, description, parameters. The system prompt is rendered from it and dispatch binds arguments through it, so the two cannot drift apart.
channel.py: The board the harnesses running in one project share - who is here, what they have said, and which files each is in the middle of changing.
subagent.py: spawn_agent - a second model, hired for one self-contained job, working in its own context and handing back only its report.
skills.py: Skill discovery, frontmatter parsing, and on-demand loading.
providers.py: The provider abstraction - Ollama, Anthropic, OpenAI, Gemini - and the saved connection.
connect.py: The /connect flow.
sse.py: Reading server-sent events without waiting for data that has not been sent. Shared by the providers and the MCP client.
permissions.py: Permission rule loading, matching, and the allow/deny/ask decision.
shell_session.py: Live commands - output draining, waiting-for-input detection, and the session registry.
deepthink.py: The six-stage chain - the stage instructions, what each stage may do, when the chain stops early, and when it starts over.
git_ops.py: A commit per AI edit, and the undo that makes it worth having.
atomic.py: Writing a file so a crash cannot leave half of it behind. Used for sessions, memory, permission rules and the saved API keys.
terms.py: What the harness does to the machine it runs on, shown once before it does it.
tests/test_platform.py: Checks the waiting-for-input detection on the machine it is run on. Worth running on any new machine, and especially on Windows - see below.
tests/test_registry.py: Fails if the tool table, the system prompt and the handlers stop describing the same tools.
tests/test_durability.py: Atomic writes (including killing a writer mid-write) and the token estimate.
tests/test_deepthink.py: Stage sequencing, both early stops, the repeat pass and its ceiling, and that the planning stages really cannot edit.
tests/test_git_ops.py: Auto-commit and undo against real repositories - including that undo refuses when it would destroy something.
tests/test_native_tools.py: Each provider's tool-call wire format, and that both protocols end up in the same place.
tests/test_docs.py: Fails when README.md or ARCHITECTURE.md names something that is gone, or misses something that is new.
tests/test_compat.py: The public surface written down - fails when a slash command, a /set setting, a tool name or a file on disk is renamed or dropped.
tests/test_subagent.py: What a sub-agent may do, what it may not, and that only its report crosses back.
tests/test_permissions.py: What an allow rule covers - and that it stops at the command it names, rather than at whatever the shell was told to run next.
tests/test_paths.py: That nothing personal is written into whatever directory you started in, and that state from an older version is named rather than moved.
tests/test_terms.py: That the terms are shown before the harness can act, asked once, and never assumed from a pipe.
tests/test_tool_parsing.py: Every shape a model wraps a tool call in, and every shape that must not be read as one.
tests/test_resume.py: That --resume and -c open the conversation they name - and that neither hands back a blank one, or guesses, when they cannot.
tests/test_tool_reporting.py: That a tool result is judged by the marker it starts with, not one it happens to contain, and that no library writes an unasked-for paragraph to stderr while a tool is running.
tests/test_hashline_edit.py: That 38:ff7|print() reaches the line it names, that a stale or mistyped anchor is refused rather than applied a few lines off, and that everything which is not an anchor still behaves as it did.
tests/test_channel.py: That a file one harness is changing cannot be written from another, that the refusal names who to ask, that a claim dies with the terminal that took it, and that several processes writing to the board at once lose nothing.
tests/test_mentions.py: What @ attaches and what it must leave alone - an email address is not a file - and that the completion menu reads the real directory.
requirements-lock.txt: The exact dependency set the harness was tested against. requirements.txt gives the tested floors and a ceiling before the next breaking release.
mcp_client.py: MCP transports (stdio / streamable HTTP / SSE), the JSON-RPC session, tool and resource calls, and the prompt section they are advertised in.
websearch.py: Multi-source retrieval, page extraction, and BM25 reranking.
context.py: Token budgeting, tool-result trimming, and context compression. The token estimate is script-aware and calibrates itself against the counts each provider reports.
renderer.py / tui.py: Markdown rendering and the terminal chrome.
session.py: Session save/load/list and the persistent memory store.
systemprompt.py: The system prompt - the assistant's own instructions, plus the tool-protocol rules that subagent.py shares. The tool schemas themselves come from toolspec.py.
skills/: Project-level skills. Personal skills live in ~/.localchat/skills/.
.permissions.json: Project-level tool permission rules (see .permissions.json.example). Personal ones live in ~/.localchat/permissions.json.
.mcp.json: Project-level MCP server declarations (see .mcp.json.example). Personal ones live in ~/.localchat/mcp.json.
memory.json: Key-value JSON storage backing the long-term memory system.
sessions/: Session directory containing JSON transcript backups for conversation history. Each file is named after the session's title (slugified, e.g. 웹-검색-랭킹-개선.json); untitled sessions fall back to a timestamp until a title exists. Each also records the working directory it was last saved from, which is what -c matches against.
.chat_history: History file managed by prompt_toolkit for command history recall across terminal runs.

18. How This Differs From Other Harnesses

Most terminal AI harnesses - Claude Code, Cursor, Aider, Continue, OpenHands - are built against one or two hosted, native-tool-calling models and treat anything smaller as an afterthought, if they support it at all. Simple Harness was built the other way: for a model too small to be trusted, with the hosted providers added on top of the same code path rather than the other way round. That ordering is where most of the differences below come from.

Feature	Simple Harness	Claude Code	Cursor	Aider
Text-protocol fallback for models with no native tool-calling	✅ per-model, automatic	❌	❌	❌ (assumes JSON tool-calls)
Raw content blocks instead of JSON-escaped file bodies	✅	❌	❌	❌
Anchor-based edits (line + fingerprint) instead of exact-text matching	✅ hashline	❌ (old/new text block)	❌ (old/new text block)	❌ (unified diff / search-replace)
Multiple instances in one project aware of each other	✅ shared board, file claims	❌	❌	❌
Cross-instance messaging between running sessions	✅	❌	❌	❌
Planning stage with tools disabled, not just discouraged	✅ dispatcher-level	❌ (plan mode still has tools)	❌	❌
Review stage fed the real `git diff` rather than the model's memory	✅	❌	❌	❌
Undo scoped to one AI edit, refuses if the file changed since	✅ per-commit	❌ (no built-in undo)	⚠️ full checkpoint/reset only	❌ (relies on your own git discipline)
The project's own tests run after an edit, with no setup and no flag	✅ detected from the project	❌ (only if you write a hook)	❌	⚠️ `--auto-test` + you supply the command
A failing check fed back as the next thing the model reads, capped	✅ 3 tries, then it must explain	❌	❌	⚠️ retries, no cap of its own
Crash-safe atomic writes for all state (sessions, memory, permissions, keys)	✅	⚠️ unclear/partial	⚠️ unclear/partial	❌
Single source of truth tying prompt, schema and dispatcher together (tested)	✅ `toolspec.py` + CI test	⚠️ unclear (closed source)	⚠️ unclear (closed source)	⚠️ unclear
Designed and benchmarked around small (4B-12B) local models	✅ primary use case	❌ hosted models only	❌ hosted models only	⚠️ connects, not tuned for it

"❌" means the feature was not found in that harness's documented behavior as of this writing, not that it is provably absent - Claude Code and Cursor are closed source, so this is based on published docs and observed behavior, and either could add any of these later.

It is the only one of these that makes a 4B local model genuinely usable, not just connectable. Aider and Continue can point at an Ollama endpoint, but they hand it the same prompt and the same JSON tool-call contract a hosted model gets, and a 4B model fails that contract constantly - a bare quote inside print("hi"), an uncounted brace, and the whole generation is thrown away. Simple Harness detects per-model whether Ollama actually reports a native tools interface and, when it does not, switches to a text protocol where a file body is a raw block (<content>...</content>) instead of a JSON string - the one thing small models get wrong most often stops being asked of them at all. No other harness in this space carries a second tool-call protocol just to keep weak models working; most only have the one.

Hashline editing removes the failure mode every other diff/patch format has. Claude Code, Aider and Cursor all ask the model to reproduce the exact old text it wants to change, then match that text back into the file - and a line that appears twice, or one reproduced with a stray space, makes the edit ambiguous or wrong. read_file here returns 50:1fa|<content>, a line number plus a fingerprint of that exact line, and an edit is just that row handed back with different text after the |. There is no old-text block to get subtly wrong, a stale anchor is refused rather than landing a few lines off, and a duplicated line is no longer ambiguous because its number disambiguates it. This is a correctness property, not a convenience one - it is what makes hashline edits reliable coming from a model too small to retype a line perfectly.

Multiple instances in one project actually know about each other. Run Claude Code, Cursor, and Aider in three terminals against the same working tree and none of them knows the others exist - two agents editing the same file is a silent last-write-wins. Simple Harness keeps a shared board per workspace (channel.py): every instance sees who else is running and what they are doing, can message them, and a file one instance is mid-edit on is refused to the others by name, with the holder named back. The claim is taken automatically on write, so protection does not depend on any model having thought to ask for one - and it expires with the process that took it, so a crashed terminal cannot lock a file for the afternoon.

Deepthink enforces the separation other "plan mode" features only ask for. Several harnesses offer a plan-then-execute mode, but the planning stage is still handed the tools and simply told not to use them - which a small model does not reliably respect (a local 4B model tried to edit fifteen times in planning before this was enforced here). Simple Harness's six-stage chain switches the editing tools off at the dispatcher level during plan, check and review stages, so "cannot" rather than "was asked not to." It also gives the review stage the real git diff of what changed rather than asking the model to recall its own edit, and finding problems (review) is a separate, read-only stage from fixing them (revise) - so review's list is not cut short by the model stopping to patch the first thing it finds.

Undo is per-tool-call and safe to use blindly, not a repo-wide reset. Cursor and Copilot's rollback (and a bare git reset) take back everything in the working tree since some point, which also erases whatever you changed by hand in between. Every AI edit here lands in its own commit named for the tool that made it, so /undo reverts exactly the last one - and it refuses outright if a file in that commit has since been touched by anything else, rather than taking that other change down with it.

The project's own tests run themselves, with nothing to configure. Aider has --auto-test, but you supply the command and turn the flag on; everywhere else, checking the work is a hook you write or a thing you remember to do. Here the check is detected - a pyproject.toml means pytest, a Cargo.toml means cargo, a package.json means npm if its test script is a real one - and it runs on its own after any turn that changed a file, once for the whole turn rather than once per edit. A failure becomes the next thing the model reads, which is the form small models handle best: fixing a traceback is pattern-matching, while noticing unprompted that something might be wrong is not. And it is capped where nothing else caps it - three failures in a row and the harness stops feeding them back and tells the model to explain what is broken instead, because a fourth guess is worth less to you than an honest description. It is safe to leave on precisely because /undo is per-edit: each retry is its own commit.

State survives being killed mid-write, everywhere, not just in the editor buffer. Sessions, memory, permission rules and saved API keys are all written to a temp file and renamed into place (atomic.py). A crash never leaves a half-written memory.json or a corrupt session transcript - a guarantee most terminal harnesses only apply, if at all, to the file the model is actively editing.

The tool table cannot drift from the prompt or the dispatcher, because there is only one table. toolspec.py is the single source both the system prompt and the dispatch logic are generated from, and test_registry.py and test_docs.py fail the build if a tool, a doc section, or a handler falls out of sync with it. Harnesses that hand-maintain a prompt description alongside a separate dispatch table can silently drift; this one cannot pass its own tests while doing so.

None of this makes the underlying model smarter - a 4B model is still a 4B model. What it changes is how much of that model's unreliability the harness absorbs before it reaches you: fewer thrown-away generations, edits that land where they were meant to, multiple terminals that do not overwrite each other, an edit that breaks the build saying so in the same turn rather than the next time you run anything, and a wrong edit that is always one /undo away rather than a reason to git stash before every session.

19. License

Apache License 2.0 - see LICENSE. It is provided "AS IS", without warranties or conditions of any kind, and its authors and contributors are not liable for any damage, data loss or other harm arising from its use; sections 7 and 8 of the licence are the ones that say this properly.

Because a licence file nobody opens is a poor way to tell someone that the program they just installed runs shell commands on their computer at a language model's suggestion, the first run says so on screen and asks. The answer is kept in ~/.localchat/accepted-terms.json and not asked again. Refusing starts nothing. With no terminal to ask - a pipe, a cron job, a container - it refuses rather than assuming, and SIMPLE_HARNESS_ACCEPT_TERMS=1 answers for it.

Agreeing adds nothing to the licence and refusing takes nothing away. What it adds is that the disclaimer is read.

pyproject.toml holds the packaging metadata under the name simple-harness. The modules live in simple_harness/, and the distribution installs that one package rather than twenty-two top-level modules - which would otherwise put config, tools and session in the importable root of every environment that took it.

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Release files / simple_harness-0.6.0-py3-none-any.whl

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Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing repository: github.com/minjun1177/simple_harness
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Release history Release notifications | RSS feed

This release

0.6.0 This release

Sep 8, 2026 2 release files Full provenance

simple-harness 0.6.0

Simple Harness

1. What It Does

2. Features

3. Setup

Prerequisites

What the model has to be able to do

Installation Steps

Running the tests

4. Tool Capabilities

Tool Call Format

Editing by hashline anchor

When a tool fails

Web & Network Tools

File System & Workspace Tools

System & Git Management

Memory & Interaction Tools

MCP Tools

Agent Channel Tools

Workflow Tools

Sub-agents

The Agent Channel

5. Providers

Prompt caching

How tools are asked for

6. Web Search

7. Skills

8. MCP Servers

Declaring a server

How it behaves

Big servers are announced, not described

Inspecting and controlling

9. Running Commands

Answering a program while it runs

The Python VM

10. Deepthink

11. Undoing AI Edits

12. Checking the Work

13. Tool Permissions

What a rule does not cover

14. Reasoning Models

15. Configuration

16. Slash Commands

17. Architecture

18. How This Differs From Other Harnesses

19. License

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

Provenance

License expression

Requires

Provides Extra

Tags

Classifiers

Metadata

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

Provenance

License expression

Requires

Provides Extra

Tags

Classifiers

Release files for simple-harness 0.6.0

Source distribution (sdist)

Built distribution (wheel)

Release files / simple_harness-0.6.0.tar.gz

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Release files / simple_harness-0.6.0-py3-none-any.whl

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