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pawbot

A self-hosted AI agent for your browser, terminal, and chat apps.

Choose your interface: run pawbot for the WebUI, or run pawbot agent for the native terminal UI (TUI).

Give pawbot a task and it can read and write files, run commands, search the web, call MCP tools, remember conversations, and run scheduled work. Use the WebUI when you want a visual workspace, the terminal when you want speed, or a chat app when you want your agent to be available wherever you are.

The feature that makes pawbot different is Record & Replay: record one real agent turn, then replay it offline while you change the code — without another model request, another token bill, or real tool side effects.

Start here

What can pawbot do?

• Work with files, shell commands, web search, web fetching, documents, images, and other tools.
• Connect to MCP servers and load extensions without changing the core agent.
• Keep session history and long-term memory across conversations.
• Edit global personal instructions and manage saved memories separately in Settings → Personalization; use /memories to override memory behavior for the current chat.
• Save explicit global or workspace preferences immediately with /remember, independently of Dream or context compaction.
• Run long tasks and scheduled automations.
• Use Anthropic, OpenAI-compatible endpoints, local models, fallbacks, and model presets.
• Reach the same agent from the WebUI, CLI/TUI, API, or supported chat channels.

For durable preferences, use /remember global reply_language=zh-CN or save a user message as memory from the WebUI. Natural-language remember requests are extracted into reviewable candidates first; Dream suggestions remain candidates until you confirm them.

• Expose a Python SDK and an OpenAI-compatible API for your own applications.

How a turn works

Every entry point reaches the same turn pipeline. The important boundary is between the orchestration loop and the provider/tool rails: the former can be budgeted, cancelled, checkpointed, and replayed without making the provider or real tools part of a regression test.

flowchart LR
    A[WebUI / CLI / API / chat channels] --> B[Gateway / Message Bus]
    B --> C[AgentLoop turn pipeline]
    C --> C0[Load personalization + project guidance]
    C0 --> C1[Restore session]
    C1 --> C2[Compact context]
    C2 --> C3[Dispatch command]
    C3 --> C4[Build provider request]
    C4 --> Q[Apply personalization + memory policy]
    Q --> D[AgentRunner ReAct loop]
    D --> E[Provider response]
    D --> F[Tool Registry]
    F --> G[Batch planner]
    G --> H[Call executor]
    H --> I[Tool observation]
    D --> J[Budget + checkpoint]
    J --> K[Session recovery]
    E --> L[LLM response rail]
    I --> M[Tool observation rail]
    L --> N[Record & Replay]
    M --> N
    N --> O[Offline replay + structural diff]
    D --> R[Trace + rolling replay evidence]
    R --> N
    D --> P[Turn delivery / UI events]

The result is one execution unit with explicit resource limits, recovery checkpoints, tool side-effect boundaries, and an offline evidence trail.

Reliability boundaries

The current runtime also makes three boundaries explicit:

• Memory provenance: confirmed preferences, Dream candidates, and external tool/web content have different trust levels and source references.
• Personalization boundary: user-authored instructions, project guidance, confirmed memories, and automatic candidates remain separate and can be disabled at the global or current-chat level.
• Task assertions: TaskContract can express must, must_not, and partial ordered constraints, returning passed, failed, or not_evaluable per assertion.
• Gateway recovery: negotiated WebSocket protocol v1 adds event sequence numbers, replay after a gap, idempotent WebUI mutations, and pawbot doctor diagnostics. Older clients remain compatible.

Why pawbot?

One agent, several ways to use it

The WebUI, terminal, API, and chat channels share the same conversations, tools, and configuration. Start in the browser and continue from the terminal; or keep the agent running behind a gateway and talk to it from a chat app.

Record an agent once, replay it as often as you need

LLM calls and external tools make agent bugs expensive and difficult to repeat. Pawbot records the model responses and tool observations of a turn. Replay then runs the current agent code against those recorded inputs:

• no new provider request;
• no new token cost;
• no network dependency;
• no real tool side effects;
• a structural diff when orchestration changes.

This is useful for debugging, regression tests, and safe refactoring of the agent loop.

Keep your data on your machine

Pawbot is designed for self-hosting. Sessions, configuration, workspaces, and recordings stay under your control. Shell commands, file access, network tools, and MCP servers are explicit capabilities with documented security boundaries.

Quick install

Published package

After the package is published, install and open the WebUI with one command:

macOS / Linux:

uv tool install --force --upgrade pawbot-ai && pawbot

After installation, update any existing Pawbot installation without repeating provider setup:

pawbot update

The update command upgrades the package through its detected installation manager and leaves provider credentials, channel settings, sessions, and the workspace untouched.

If a Windows installation from v0.4.1 reports os error 32, close Pawbot and run uv tool install --force --upgrade --refresh pawbot-ai once. Starting with v0.4.2, pawbot update hands the upgrade to a helper process so the running launcher can be released before it is replaced.

Windows PowerShell:

uv tool install --force --upgrade pawbot-ai; pawbot

The repository also includes isolated fallback installers. On a fresh desktop install they start the WebUI automatically; use pawbot agent when you want the terminal/TUI client explicitly:

• scripts/install.sh
• scripts/install.ps1

For a fresh macOS or Linux desktop, the installer can be run directly from GitHub:

curl -fsSL https://raw.githubusercontent.com/m2dumpling/pawbot/v0.7.3/scripts/install.sh | sh

If curl is not available, use wget instead:

wget -qO- https://raw.githubusercontent.com/m2dumpling/pawbot/v0.7.3/scripts/install.sh | sh

For native Windows PowerShell:

iex (irm https://raw.githubusercontent.com/m2dumpling/pawbot/v0.7.3/scripts/install.ps1)

The installer selects an active virtual environment, uv, pipx, or a dedicated ~/.pawbot/venv fallback, then opens the WebUI on a fresh desktop. Before creating the fallback environment it verifies that Python's venv and ensurepip support is available. If a minimal Debian/Ubuntu image is missing python3.x-venv, installation stops with the exact package command to run and does not print a misleading startup command. On success it runs pawbot --version; if the launcher is not on the current shell's PATH, it prints the verified launcher path and the command to use now. Configure the first Provider in Settings → Models before sending your first task. Quick Start and the WebUI probe a compatible provider's /models endpoint after credentials are entered, then enrich returned models with pawbot's capability table (context window and reasoning levels). If an endpoint does not publish a model list, the UI keeps manual model entry available.

From a source checkout

Requirements: Python 3.11+ and uv. Released packages download a checksum-verified native TUI for the current platform on the first pawbot agent launch; Bun is only needed when developing from a source checkout.

uv sync --all-extras --dev
uv run pawbot --help

Install optional channel dependencies when needed:

uv run --no-sync python -m scripts.install_channel_dependencies --all-channels

Quick start

WebUI

The browser workspace is the easiest first run:

uv run pawbot webui

Configure your first provider in Settings → Models; the model picker loads available models after credentials are saved. Start a new conversation and send Hello!. The first-run WebUI binds to localhost by default.

On a Linux server, localhost is intentionally private to the server. Bind the WebUI explicitly when you need to open it from another machine:

pawbot webui --remote --yes --no-open

Then open http://<server-ip>:8765 from your own computer and enter the channels.websocket.tokenIssueSecret value from the server's config file. Keep the gateway health port (18790 by default) private, allow only the WebUI port through the firewall, and use HTTPS/reverse proxy before exposing it to the public Internet. For a private setup, use an SSH tunnel instead:

ssh -N -L 8765:127.0.0.1:8765 <user>@<server>

Then open http://127.0.0.1:8765 on your own computer.

CLI

pawbot opens the WebUI. pawbot agent opens the native terminal UI (TUI). On a headless Linux server, pawbot keeps the WebUI on localhost and prints a ready-to-copy SSH tunnel command; use pawbot webui --remote --yes --no-open when you deliberately want to access it from another machine.

Run one request and exit:

uv run pawbot agent --message "Explain the top-level modules in this repository"

WebUI and the native TUI can attach to the same websocket:<chat-id> conversation. The terminal command and the complete session-boundary rules are documented in the usage guide.

Start the gateway directly when you want a long-running process:

uv run pawbot gateway

Keep the gateway in the background:

uv run pawbot gateway --background
uv run pawbot gateway status
uv run pawbot gateway logs

Record & Replay

Pawbot separates three jobs that are easy to confuse:

• Live execution records are created automatically for every turn. They show stages, timing, status, and failures without copying full prompts or tool results.
• Rolling replay evidence keeps a bounded recent window of full replay rails locally (20 turns per conversation by default). Failed, cancelled, budget- limited, or uncertain turns become candidate problem runs instead of being silently rotated away.
• Regression samples are saved manually when you want to keep one complete run. They include the request, model responses, tool calls, and tool results across every session until capture is stopped.
• Offline validation re-runs the current orchestration against a saved sample. It does not call the provider or execute real tools, and reports whether the recorded path still matches.

Save a real turn as a regression sample:

uv run pawbot agent \
  --message "Inspect the repository and summarize the agent loop" \
  --record .pawbot/blackbox/demo

Replay it offline:

uv run pawbot agent --replay .pawbot/blackbox/demo

The concise equivalent is:

uv run pawbot replay .pawbot/blackbox/demo

Add --benchmark to print provider-free local replay timing and message/diff counts.

In the WebUI, the trajectory icon in the top-right of a conversation opens the current execution as it happens: stages, model requests, tool calls, timing, bounded previews, retries, approvals, and failures. Settings → Execution & regression keeps the cross-session sample and offline-validation workbench: use Save as regression sample, run tasks across as many chat sessions as needed, then click Stop saving. The capture window belongs to the agent, so all turns before Stop are stored in one sample. Incomplete samples stay visible with a reason and can be deleted from the UI instead of failing later.

For a running gateway, the same controls are available from the CLI:

uv run pawbot record start --name demo
uv run pawbot record status
uv run pawbot record stop
uv run pawbot record list
uv run pawbot trace list --filter errors
uv run pawbot trace show <trace-id>
uv run pawbot eval list
uv run pawbot eval run --json

You can review the same automatic evidence in the WebUI. Type /record candidates to list abnormal recent turns, /record keep <candidate-id> to keep one as a regression sample, or /eval run to run the provider-free task evaluation set.

After offline validation, expand a turn to see a readable execution trace: the user's request, model thinking when the provider returned it, model decisions, tool calls, tool-result previews, and the final answer in event order. Long values can be expanded in place. View raw record opens the complete JSON/JSONL turn data in a full-screen inspector; the green result only means that no observable difference was found under the recorded inputs and observations.

Pause after an iteration and inspect the reconstructed messages:

uv run pawbot agent \
  --replay .pawbot/blackbox/demo \
  --break-at 2

The recording contains the provider-response rail, the tool-observation rail, and the turn envelope. Replay checks tool ordering, result insertion, context governance, continuation, and the final message structure. Review prompts, tool results, and local paths before sharing a recording.

Verify completion and recover safely

Pawbot can evaluate whether a task actually reached its declared outcome instead of treating the model's final sentence as proof. A TaskContract can require specific final text, successful tools, tool-result content, files, file content, or a caller-owned synchronous validator:

from pawbot import Pawbot, TaskContract

result = await bot.run(
    "Create the release note and verify it.",
    task_contract=TaskContract(
        id="release-note",
        final_content_contains=("verified",),
        required_tools=("write_file", "read_file"),
        required_files=("CHANGELOG.md",),
    ),
)
print(result.task_evaluation)  # passed, failed, or not_evaluable

For a one-shot terminal run, the same declarative contract can be kept in a JSON file and passed with the --task-contract option:

{
  "id": "release-note",
  "final_content_contains": ["verified"],
  "required_files": ["CHANGELOG.md"]
}

pawbot agent --message "Create and verify the release note" --task-contract contract.json

When a check fails, the failed assertions are returned to the Agent as model-facing feedback and the loop can continue within its normal iteration and turn budgets. A task check is reported separately from replay consistency and from whether the original execution encountered a tool or provider error.

Every Tool also exposes an execution policy covering side-effect class, idempotency, reversibility, recovery strategy, and optional execution receipts. Read-only or explicitly idempotent operations may be retried after an interruption. Unknown, non-idempotent, or irreversible operations fail closed and require human confirmation; Pawbot does not pretend that a cancellation automatically rolled back an external side effect.

Agent Harness Benchmark

Record & Replay checks one saved execution. The Agent Harness checks a stable catalog of nine success, task, and failure scenarios against the real AgentRunner without calling a provider or touching the workspace. It covers normal tool use, two task workflows (change-and-verify and investigate-and- summarize), tool failure and recovery, provider errors, LLM timeouts, user cancellation, turn-budget boundaries, and human approval before a write-capable Tool runs.

Run it after an AI-assisted change:

uv run --no-sync pawbot harness run

The same behavioral check is part of the local quality gate:

uv run --no-sync python scripts/quality_gate.py

The Harness separates trajectory checks from simple task contracts such as final content and required successful tools. A green replay or Harness result is not a general model-quality score.

Agent Task Eval Set

The task evaluation set is a small, deterministic layer above the Harness. It checks six fixed task and boundary cases across three axes: task outcome, trajectory behavior, and execution status. It uses scripted providers and in-memory tools, so it is safe to run in CI without a network request or a real workspace side effect:

pawbot eval list
pawbot eval run --json

The report keeps task_passed, trajectory_passed, not_evaluable, tool failures, model requests, and elapsed time separate. This is a regression gate, not a general model-accuracy score. A real failure can be promoted from the rolling replay buffer into a permanent sample after reviewing its sensitive content.

Real-model evaluation now uses the pinned Promptfoo integration. It runs ten reviewed diagnostic/evidence tasks with local fixture tools, three independent trials per case, deterministic assertions, optional soft LLM Judge, and JSON/HTML reports. It does not require starting the Gateway:

cd evals/promptfoo
npm ci
npm run validate
npm run eval
npm run view

The cases are sanitized reconstructions and synthetic controls, not production success-rate evidence. Runtime Harness, TaskContract, Trace and Record & Replay remain; the self-built evaluation workbench, APIs and pawbot eval live were removed. See the integration README for Python setup, case boundaries, cost accounting, and comparable baseline/candidate runs.

When a WebUI chat uses Workspace access, write, execute, and network-capable Tools pause for an explicit approval. Full access remains the opt-in mode that runs those Tools directly. The same approval protocol is available to the native TUI.

When choosing a model in Settings → Models, pawbot also reads capability metadata from the provider's /models response when available. For known model IDs, the curated capability registry wins over stale or generic provider values; unknown models use provider metadata and remain manually editable. Context length and supported reasoning levels are shown before saving. The fallback registry is maintained in pawbot/providers/registry.py and exposed through the WebUI capability settings.

Inside the TUI, /model lists the current provider's discovered models and their known context/reasoning metadata. /model <model-id> pins a discovered or manually entered model to the current session; /model default returns to the configured default. The global configuration is not changed by a session pin.

Provider compatibility note

Pawbot includes a dedicated DeepSeek integration: the current DeepSeek Flash and DeepSeek V4 Pro capability table, 1M-token context metadata, reasoning controls, a 65,536-token DeepSeek output default, tool-call history normalization, streaming tool-call accumulation, retry/error metadata, and local provider contract tests are maintained together. This is the provider path with the deepest validation in the repository.

Other providers use the shared adapter and may work well, but their model catalogue, reasoning fields, streaming events, tool-call conventions, session headers, and error semantics can differ by endpoint or gateway. If you need more precise behavior for another provider, use the DeepSeek integration as a reference for a focused adapter and test suite, or open an issue or pull request with a sanitized reproduction so the support can be developed together.

For a busy self-hosted process, optional local guardrails are available through PAWBOT_MAX_CONCURRENT_REQUESTS, PAWBOT_MAX_CONCURRENT_PER_SENDER, PAWBOT_PROVIDER_MAX_INFLIGHT, and PAWBOT_PROVIDER_RPM (provider-specific overrides such as PAWBOT_DEEPSEEK_RPM take precedence). These limits are process-local safety controls, not a distributed quota service.

Channels and integrations

Pawbot can be used from its WebUI, terminal, OpenAI-compatible API, Python SDK, WebSocket channel, and supported chat channels. MCP servers and extension points let you add capabilities without hard-coding them into the agent loop.

Architecture

User message
    ↓
Channel / WebUI / CLI / API
    ↓
AgentLoop: prepare the conversation and run one turn
    ↓
AgentRunner: ask the model, call tools, add results, repeat when needed
    ↓
Provider + ToolRegistry + MCP
    ↓
Answer, saved session, and channel response

The core source is organized around:

• pawbot/agent/loop.py — turn orchestration;
• pawbot/agent/runner.py — the model/tool loop;
• pawbot/agent/turn/ — turn state and stages;
• pawbot/agent/blackbox/ — Record & Replay;
• pawbot/agent/tools/ — tool contracts and execution;
• pawbot/session/ — conversations, memory, and recovery;
• pawbot/providers/ — model adapters and retries.

Documentation

• Changelog
• Contributing
• Agent evaluation and observability
• Agent control flow and recovery
• Security policy

Security and privacy

Pawbot can run shell commands, access local files, call network tools, and connect to MCP servers. Read SECURITY.md before enabling them.

Never commit:

• config.json, .env*, provider credentials, or certificates;
• .pawbot/ recordings and session data;
• work/, sessions/, run/, SQLite files, or private logs;
• prompts, tool results, or local paths containing personal information.

Replay artifacts can contain sensitive prompts and tool output. Sanitize them before sharing; use tests/fixtures/blackbox/ for repository-safe examples.

Development

uv sync --all-extras --dev
uv run --no-sync python -m scripts.install_channel_dependencies --all-channels
uv run ruff check pawbot
uv run basedpyright
uv run pytest -q
uv run --no-sync python scripts/quality_gate.py

For WebUI changes:

cd webui
bun install --frozen-lockfile
bun run test
bun run build

Project status

Pawbot is an Alpha/Experimental Preview project. It is ready for personal self-hosting, development, testing, and small single-node deployments. It does not currently promise multi-instance session consistency, durable distributed execution, automatic failover, or enterprise high availability.

License

MIT — see LICENSE and THIRD_PARTY_NOTICES.md.