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, and the harness reports what the final check actually ran.
• 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.
• 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.
• Enhanced Terminal Shell: Input autocompletion for slash commands and persistent input history across restarts powered by prompt_toolkit.
• Hashline Line-Level Hashing: File reading appends 2-character MD5 hashes to line numbers (LINE_NUM:HASH|) allowing the agent to target precise code locations when making edits.
• 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.

Installation Steps

1. Install it:

   git clone https://github.com/minjun1177/simple_harness
   cd chat
   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.

2. Pull an Ollama Model:

   ollama pull gemma4:e4b
   

3. Launch it:

   simple-harness            # if you installed it
   python -m simple_harness   # if you did not
   

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.

4. Tool Capabilities

The client equips the model with 28 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; <old_content> and <new_content> feed edit_file.

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.

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: Find and replace specific content snippets within an existing file, via <old_content> / <new_content> raw blocks.
• 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.
• 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, listed in the system prompt with its own parameters.
• 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.

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.

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

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.

How tools are asked for

Two protocols, chosen by who is answering:

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 17.8KB   (tool schemas + <tool_call> rules)
native tool calls   system prompt 5.1KB   (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:

1. 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.
2. 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.
3. 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}"}
    }
  }
}

${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.

Tuning knobs live in config.py: MCP_ENABLED, 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:

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.

10. Deepthink

Off by default. /deepthink on turns one request into five 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 fix what is wrong
5  Verify    run it, and report what really came back

All five 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.

Two of the five 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.

The first two stages cannot edit. Not "are asked not to" - the tools that change things are switched off until stage 3, 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 five: 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.

/deepthink            the stages, and whether it is on
/deepthink on|off     turn it on or off

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. 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.

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.

13. 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.

14. Configuration

Most behaviour is reachable from a slash command, and those changes last for the session. config.py is where you change what it starts as. The settings worth knowing:

The rest are tuning knobs for search, MCP and command sessions; they are documented in the sections above and commented where they are defined.

State that outlives a session lives outside config.py:

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.

15. Slash Commands

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

16. 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.
• 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 five-stage chain - the stage instructions, what each stage may do, and when the chain stops early.
• 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.
• 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, 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_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_tool_parsing.py: Every shape a model wraps a tool call in, and every shape that must not be read as one.
• 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.
• .chat_history: History file managed by prompt_toolkit for command history recall across terminal runs.

17. License

Apache License 2.0 - see LICENSE.

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.