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python-agent-harness

A Python port of the Emacs gptel-agent-harness: a minimal-dependency coding agent designed for daily use and easy customization.

Features

  • Agent loop with completion supervision — the model is nudged (max 2) when it tries to stop before the task is complete; the nudge counter resets on tool calls; tool results are sanitized so a failed call never strands the loop.
  • Context management — CJK-aware token estimation, per-model context windows (deepseek-v4/glm-5.2 1M, gpt-5 400k, kimi-k2.7 256k, claude 200k, ...), self-calibrating estimates from API-reported input tokens, and automatic compaction at 70% usage that summarizes the conversation and resumes with the last user request.
  • Tools — Agent (sub-agents), TodoWrite, Glob (git-aware), Grep (git grep → rg → grep), Read, Insert, Edit (incl. unified diffs), Write, Mkdir, Bash, Skill, Question, and PlanExit (registered while in plan mode) — all OpenAI-compatible tool schemas. Every tool call issued in one round runs concurrently in a thread pool (up to PARALLEL_TOOL_MAX, default 8) — Agent calls included — with results delivered in the original call order.
  • Default agent prompts — the main agent and sub-agents each get a distinct default system prompt bundled with the package (prompts/agent.txt, prompts/subagent.txt), with YAML frontmatter stripped and the {{SKILLS}} placeholder filled from the discovered skill directory. The main prompt is prefixed with the project context files and the task-completion rules; sub-agents get only their own prompt. /init /review /explain and custom commands (prompts/commands/*.txt) run with their own prompt for that run.
  • Plan / Build modes — plan mode is read-only except the per-session plan file; PlanExit switches back to build with an "execute the plan" prompt; sub-agents in plan mode receive the read-only reminder.
  • Safety — forbidden paths (default /mnt/), catastrophic/destructive/ dangerous bash pattern tiers, per-session allow/deny memory, 300s command timeout, plan-mode read-only bash whitelist, and file snapshots with /undo /history.
  • Sessions — auto-saved after every response to ~/.local/share/python-agent-harness/sessions/, LLM-generated titles (one-shot per session, fired when the agent loop finishes; the file is renamed to <title>_<TS>.md), /restore (with --latest) and /sessions TUI commands.
  • Commands — /init (create/update AGENTS.md), /review (uncommitted changes / commit / branch / PR), /summary, /explain and custom commands from prompts/commands/*.txt — all TUI slash commands. Tool availability: /init//review may use all tools except PlanExit (the PlanExit tool is hidden for the run, including for spawned sub-agents); custom commands may use all tools including PlanExit; compact/summary run with no tools (a one-shot chat_sync call, like session-title generation).
  • Editing input — prompt_toolkit-backed multi-line editor with persistent history (Up/Down recall), Enter for a newline, Esc+Enter (or Alt+Enter) to submit, and Tab completion (Tab to complete, Shift+Tab to cycle backwards): the first token starting with / completes against the slash commands (builtins + custom prompts/commands/*.txt); after a slash command's space, Tab completes paths relative to the project dir (absolute and ~ paths work too; directories get a trailing / to keep drilling). In plain messages, any token containing / or starting with ~ (e.g. ~/wor, docs/) completes as a path the same way — ~ against $HOME, otherwise relative to the project dir — so ~/wor + Tab becomes ~/workspace/.
  • TUI — rich live interface with a pinned status bar (mode, context usage, spinner), streaming assistant output, tool-result previews, a pinned Todos panel (sub-agent lists shown with a sub: label), and numbered-choice questions for Bash approval / Question tool / PlanExit confirmations.
  • Diff rendering — Edit/Write tool calls capture a unified diff of the file change and render it inline (red/green) in the TUI, so file edits are visible without leaving the app.

Screenshot

demo.png

Install

python -m venv venv
venv/bin/pip install rich httpx prompt_toolkit
venv/bin/pip install -e python-agent-harness

Configuration

LLM settings live in a JSON config file — no environment variables needed:

python-agent-harness config --init        # write ~/.config/python-agent-harness/config.json
python-agent-harness config               # show effective settings (API key masked)

Edit ~/.config/python-agent-harness/config.json:

{
  "llm": {
    "base_url": "https://api.deepseek.com/v1",
    "api_key": "sk-...",
    "model": "deepseek-chat",
    "reasoning_effort": "medium",
    "stream": true
  },
  "paths": {
    "context_path": null,
    "skill_path": null
  }
}

reasoning_effort is passed to the API as-is (omitted when unset), so you can use whatever your provider accepts ("low"/"medium"/"high" for OpenAI and compatible providers). Other optional keys: backend, temperature, max_tokens, timeout, stream (true by default; set false for non-streaming one-shot responses — python-agent-harness run --no-stream overrides it on the command line). The paths object (context_path, skill_path) overrides the context/skill directory discovery — defaults are <project>/contexts or ~/.emacs.d/contexts for context files, and <project>/skills or ~/.emacs.d/skills for skills.

Precedence: code defaults < config file < OPENAI_* environment variables (env still wins if you set them, but nothing is required). Use a custom file with --config PATH (also settable via PYTHON_AGENT_HARNESS_CONFIG).

LLM request/response bodies are logged as JSON to /tmp/python-agent-harness-<date>-<id>.json (override the directory with LLM_LOG_DIR); the path is printed at TUI startup.

Usage

python-agent-harness run [project-dir]   # interactive TUI agent

TUI slash commands: /plan /build /init /review /explain /compact /undo /history /save /summary /sessions /restore /clear /exit — /explain [project] [target] explains code and /summary appends a conversation summary (both TUI-only); /sessions lists saved sessions and /restore [path|title|--latest] restores one (/restore matches sessions by title substring). Custom commands from prompts/commands/*.txt are TUI slash commands too (TUI-only — no CLI subcommand is registered for them).

Input editing: type your message, press Enter for a new line, and Esc then Enter (or Alt+Enter) to submit. Up/Down recall previous inputs from ~/.local/share/python-agent-harness/input_history. Ctrl-D quits; Ctrl-C cancels the current input or agent run without leaving the app — the conversation history is preserved, so you can immediately ask a follow-up question; a cancelled worker can never clobber the next run's state (per-run cancellation identity).

Layout

python_agent_harness/
├── agent.py        agent loop (supervision, nudges, compaction)
├── client.py       OpenAI-compatible streaming client (httpx)
├── models.py       Message / ToolCall / ToolSpec data classes
├── token_estimator.py  CJK-aware token estimation + calibration
├── safety.py       path guards + bash policy tiers
├── undo.py         file snapshots / undo
├── planmode.py     build/plan mode + plan file lifecycle
├── prompts.py      prompt loading + system prompt assembly
├── session_store.py  session persistence + titles
├── agent_session.py  AgentSession (wiring hub)
├── subagent.py     sub-agent runner (error containment, plan reminder)
├── commands.py     init/review/custom command definitions
├── cli.py          argparse entry points
├── tui.py          rich + prompt_toolkit TUI
├── diffrender.py   unified diff generation + rich rendering
└── tools/          tool implementations + registry

Tests

venv/bin/python -m unittest discover -s tests -v

Python ≥ 3.11 is required (CI runs 3.11 / 3.12 / 3.13).

Verification checklist (ported semantics)

  • Nudge supervision with fail-closed dead-session budget
  • Tool-result sanitization (None → error placeholder)
  • Compaction: frame, resume last request
  • Plan mode: read-only + plan-file writes only
  • Bash tiers: catastrophic → plan gate → destructive → dangerous → run
  • Session metadata round-trip and title sanitization
  • One-shot LLM title generation after the agent loop finishes
  • Ctrl-C cancel: stale workers can't clobber the next run's history