pibox

Install, configure, or run pi-coding-agent through the pibox wrapper, or connect to its HTTP, MCP, Telegram, or cron surfaces.

pi-coding-agent inside an aicodebox container. One image, seven ways in: interactive shell, one-shot exec, HTTP REST API, OpenAI-compatible endpoint, MCP server, Telegram bot, cron scheduler.

You talk to pibox, pibox talks to pi, and Pi talks to the configured upstream LLM. Use PIBOX_PROVIDER_* for Pi's documented custom HTTP APIs, including LiteLLM and Anthropic-compatible endpoints. ANTHROPIC_* remains a compatibility shortcut for existing Anthropic Messages deployments.

For installation and configuration, see references/setup.md.

Agent execution

Use pibox when it is on PATH. Run it from the workspace the user named. Do not assemble a new docker run command for routine interactive or one-shot work. The wrapper owns the workspace mount, ~/.pi, aicodebox state, SSH state, image selection, and container lifecycle.

bash

pibox                                      # interactive Pi
pibox -p "inspect this workspace"           # one-shot work
pibox -p "review this change" --thinking high
PIBOX_FULL=1 pibox -p "run the full suite"

Configure the Pi upstream before the first run with PIBOX_PROVIDER_* or the supported ANTHROPIC_* compatibility variables. Use an HTTP or MCP endpoint only when the user asks for a service or provides an already-running remote URL. MCP plugins connect to a server. They do not replace the local wrapper.

Start a local API and MCP server only when the user asks for one. Set the actual model identifiers and distinct bearer tokens:

bash

PIBOX_DETACH=1 \
PIBOX_API_MODE=1 \
PIBOX_MCP_MODE=1 \
PIBOX_AVAILABLE_MODELS=your-model-id \
PIBOX_API_MODE_TOKEN=your-api-token \
PIBOX_MCP_MODE_TOKEN=your-mcp-token \
pibox

If pibox, codexbox, and claudebox were installed in the same command directory, a box can invoke a sibling command directly. The parent wrapper passes the real host paths and the sibling wrapper file. Do not set AICODEBOX_HOST_*, copy wrapper files, or manually mount another box's state directory. If the sibling command is absent, ask the user to install it or to choose another approach.

Security & safety

• No auth when PIBOX_API_MODE_TOKEN is unset. With it empty the REST/OpenAI-compatible API surface is UNAUTHENTICATED — anyone who can reach it gets full agent-execution and workspace file-read/write/delete access. NEVER expose such an instance on a network or to untrusted agents; set the token and bind to loopback / behind an authenticating proxy.
• No auth when PIBOX_MCP_MODE_TOKEN is unset. Same story for the MCP surface (/mcp/ or the sidecar) — empty token means unauthenticated run_prompt/file-tool access, and it does not fall back to PIBOX_API_MODE_TOKEN. Set it explicitly.
• Destructive & irreversible. DELETE /run/{id}, DELETE /files/{path}, and the MCP delete_file tool remove state with no undo (canceled runs can't be resumed; deleted files are gone). An agent must NEVER call these unless the user explicitly asked for that exact action; confirm the specific target first, scope it to the current task, and never enumerate-then-bulk-delete. On a shared/multi-tenant instance a deletion can destroy another caller's in-flight run or workspace file — treat these routes as admin-only.

When To Use

• Drive pi-coding-agent from a script/service instead of an interactive terminal (/run, /openai/v1/chat/completions, or MCP tools).
• Wire pi into an OpenAI-SDK-compatible client (LangChain, openai-python) via the /openai/v1/* surface.
• Give an MCP-aware agent (Claude, Cursor, OpenClaw) remote tool access to a pi-driven workspace.
• Chat with pi from Telegram, with per-chat model/effort overrides and file transfer.
• Schedule recurring pi runs (standups, digests, periodic maintenance) via cron mode.
• One-off container run for a single prompt (CI step, quick script) via one-shot exec.

When NOT To Use

• Don't run two foreground modes together except Telegram+Cron (cron runs in-thread inside telegram) — API set alongside anything else wins and the others don't start.
• Don't expect /openai/v1/chat/completions to stream token-by-token when tools or a JSON schema is in play — those modes compute the full answer first and replay it as a single-shot SSE stream (still a valid stream, just not incremental). Plain chat streams incrementally.
• Don't rely on PIBOX_MCP_MODE_TOKEN falling back to PIBOX_API_MODE_TOKEN — MCP has its own bearer, no fallback.
• Don't point multiple concurrent runs at the same workspace — the API/OAI layer returns 409 "workspace busy" while a run is in flight in that workspace.

Interactive shell mode

Run pibox with no mode variables or arguments. The wrapper starts Pi in the current workspace and preserves its host state.

bash

pibox

Auth: none at the container boundary. Pi uses the upstream provider variables in references/setup.md.

One-shot exec mode

pibox -p "<prompt>" runs Pi non-interactively and writes the result to stdout, then exits. The wrapper forwards Pi flags unchanged.

bash

pibox -p "list the files in this workspace"

Any Pi CLI flag works here (--model, --thinking, --session, etc.). Auth: none at the container boundary. Pi uses the upstream provider variables in references/setup.md.

REST API mode

PIBOX_API_MODE=1. FastAPI server on :8080 (override PIBOX_API_MODE_PORT). Requires PIBOX_AVAILABLE_MODELS=<csv> — the server refuses to boot without it (no sensible default; pi can drive any provider's models).

bash

docker run -d --network host \
  -e PIBOX_API_MODE=1 \
  -e PIBOX_API_MODE_TOKEN=your-secret \
  -e PIBOX_AVAILABLE_MODELS=glm-4.6,glm-4.5-air \
  -e ANTHROPIC_AUTH_TOKEN=your-token \
  -e ANTHROPIC_BASE_URL=https://api.z.ai/api/anthropic \
  -e ANTHROPIC_MODEL=glm-4.6 \
  -v "$PWD/workspace:/workspace" \
  psyb0t/pibox:latest

Method	Path	What it does
GET	/healthz	liveness — {ok, adapter}, unauthenticated
GET	/status	in-flight runs + busy workspaces
POST	/run	run the agent — sync by default; body async or fireAndForget makes it fire-and-poll
GET	/run/result?runId=<id>	poll a run started with async/fireAndForget
DELETE	/run/{id}	cancel an in-flight run (kills the subprocess)
GET	/files	list the workspace root — {entries: [{name, type, size?}, ...]}
GET	/files/{path}	list a sub-directory, or stream a file's bytes
PUT	/files/{path}	upload — raw request body becomes the file contents; parent dirs auto-created
DELETE	/files/{path}	delete a file (refuses directories — 400)
POST	/openai/v1/chat/completions	OpenAI-compatible chat (see below)
GET	/openai/v1/models	model list, from PIBOX_AVAILABLE_MODELS
POST	/mcp/	MCP server (streamable HTTP) — mounted only when PIBOX_MCP_MODE=1. Slashless /mcp reaches the same handler

All /files/* paths resolve against the workspace root with traversal checking — .. segments that escape the root return 400. Every route except /healthz is gated by Authorization: Bearer <PIBOX_API_MODE_TOKEN> when that var is set; empty/unset token = no auth.

No auth when PIBOX_API_MODE_TOKEN is unset. With it empty the API is UNAUTHENTICATED — anyone who can reach it gets run-the-agent and workspace file-read/write/delete access. NEVER expose such an instance on a network or to untrusted agents; set the token and bind to loopback / behind an authenticating proxy.

Destructive & irreversible. DELETE /run/{id} kills the in-flight subprocess with no undo, and DELETE /files/{path} deletes a workspace file with no undo. An agent must NEVER call either unless the user explicitly asked for that exact action; confirm the specific target first; scope it to the current task; never enumerate-then-bulk-delete. On a shared/multi-tenant instance this can disrupt or destroy another caller's run or data — treat these routes as admin-only.

bash

# sync run
curl -s http://localhost:8080/run \
  -H "Authorization: Bearer your-secret" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "say HELLO", "workspace": "/workspace"}'

# async run, then poll
RUN_ID=$(curl -s http://localhost:8080/run \
  -H "Authorization: Bearer your-secret" -H "Content-Type: application/json" \
  -d '{"prompt": "long task", "async": true}' | jq -r .runId)
curl -s "http://localhost:8080/run/result?runId=$RUN_ID" \
  -H "Authorization: Bearer your-secret"

# cancel it
curl -s -X DELETE "http://localhost:8080/run/$RUN_ID" \
  -H "Authorization: Bearer your-secret"

# upload / download / list / delete a workspace file
curl -sS -X PUT -H "Authorization: Bearer your-secret" \
  --data-binary @local.txt http://localhost:8080/files/notes/hello.txt
curl -sS -H "Authorization: Bearer your-secret" \
  http://localhost:8080/files/notes/hello.txt
curl -sS -H "Authorization: Bearer your-secret" \
  http://localhost:8080/files/notes | jq
curl -sS -X DELETE -H "Authorization: Bearer your-secret" \
  http://localhost:8080/files/notes/hello.txt

POST /run body fields: prompt (required), workspace, model, systemPrompt, appendSystemPrompt, jsonSchema, noContinue, resume, timeoutSeconds, thinking, noTools, toolsAllowlist, extraArgs, async, fireAndForget, includeRaw. With jsonSchema set the response is verbose: text, json (schema-validated), events, sessionId, usage, attempts (per-retry breakdown, up to 3 self-correction retries on parse/validation failure). Without jsonSchema the response is lean: {runId, workspace, exitCode, text}.

OpenAI-compatible endpoint mode

Same PIBOX_API_MODE=1 server, POST /openai/v1/chat/completions and GET /openai/v1/models. Drop-in for any OpenAI-SDK client — point the base URL at http://host:8080/openai/v1 and set the model to one of PIBOX_AVAILABLE_MODELS.

bash

curl -s http://localhost:8080/openai/v1/chat/completions \
  -H "Authorization: Bearer your-secret" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-4.6",
    "messages": [{"role": "user", "content": "say HELLO"}]
  }'

Streaming ("stream": true): plain chat streams incrementally, one SSE chunk per token delta. Requests carrying tools or a JSON-schema constraint can't stream token-by-token (the answer only exists once fully computed) — they're buffered: the whole response is computed, then replayed as a single-shot SSE stream (role chunk → one content/tool_calls delta → finish chunk → [DONE]). The client's streaming parser is satisfied either way.

Tools / tool_choice (OpenAI client-executed tool calling): send tools (OpenAI function-schema array) and optionally tool_choice (auto/none/required/{"type":"function","function":{"name":...}}). pibox's own internal tools (bash, file edits) default OFF while in tool mode so the model behaves as a pure function-calling LLM — override with header x-aicodebox-no-tools: 0 to re-enable the hybrid. The response comes back as tool_calls + finish_reason: "tool_calls", exactly like OpenAI; you execute the tool client-side and send the result back in the next message round.

response_format / JSON-schema: standard OpenAI response_format body field —{"type": "text"} (default), {"type": "json_object"} (force parseable JSON, no shape constraint), or {"type": "json_schema", "json_schema": {"name": ..., "schema": {...}}} (schema-validated, with up to 3 self-correction retries on failure → 422 if still invalid). tools and response_format compose in one request: a tool-call turn returns tool_calls (not schema-checked); the model's final answer (no more tool calls) is what gets schema-validated.

Extra x-aicodebox-* headers (workspace pinning, session continuation, extra args, timeout, tools allowlist) are documented in references/setup.md. Upstream provider errors (auth failure, rate limit, content-safety rejection) surface as HTTP 400, not a silent empty response.

MCP server mode

PIBOX_MCP_MODE=1. Exposes an MCP (streamable HTTP) surface with 5 tools: run_prompt, list_files, read_file, write_file, delete_file. Coexists with any foreground mode:

Foreground	MCP placement
API mode (PIBOX_API_MODE=1)	mounted at /mcp/ on the API port — no extra process
Telegram / Cron / passthrough / none	sidecar uvicorn on its own port, PIBOX_MCP_MODE_PORT (default 8081), mounted at the port root

bash

claude mcp add --transport http pibox http://localhost:8080/mcp/ \
  --header "Authorization: Bearer your-mcp-token"

Raw JSON-RPC (debugging, non-MCP-aware callers):

bash

curl -s http://localhost:8080/mcp/ \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "Authorization: Bearer your-mcp-token" \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}'

run_prompt(prompt, workspace?, model?, system_prompt?, append_system_prompt?, no_continue=true, resume?, thinking?, json_schema?) invokes the agent and returns its text. list_files/read_file/write_file/delete_file operate on workspace-relative paths with the same traversal guard as the REST /files endpoints.

Destructive & irreversible. delete_file removes a workspace file with no undo. An agent must NEVER call it unless the user explicitly asked for that exact action; confirm the specific target first; scope it to the current task; never enumerate-then-bulk-delete. On a shared/multi-tenant instance this can destroy another caller's workspace data — treat it as admin-only.

Auth: PIBOX_MCP_MODE_TOKEN=<token> — bearer via Authorization: Bearer …, or ?apiToken=… query param for clients that can't set headers. Empty = no auth. No fallback to PIBOX_API_MODE_TOKEN.

No auth when PIBOX_MCP_MODE_TOKEN is unset. With it empty the MCP surface is UNAUTHENTICATED — anyone who can reach it gets run_prompt (arbitrary agent execution) and workspace file read/write/delete access. NEVER expose such an instance on a network or to untrusted agents; set the token and bind to loopback / behind an authenticating proxy.

MCP keeps DNS rebinding protection enabled. Loopback hosts and origins work by default; a reverse proxy, tunnel, or public DNS name needs its exact Host and browser Origin values allowed with PIBOX_MCP_MODE_ALLOWED_HOSTS and PIBOX_MCP_MODE_ALLOWED_ORIGINS, or MCP calls through it fail.

Telegram bot mode

PIBOX_TELEGRAM_MODE=1 + PIBOX_TELEGRAM_MODE_TOKEN=<token from @BotFather>.

• Text in → pi runs → Markdown→HTML rendered response back.
• File uploads land in the chat's workspace. [SEND_FILE: path] in pi's output delivers workspace files as Telegram attachments.
• Per-chat overrides: /model, /effort (maps to pi's --thinking levels), /system_prompt, /append_system_prompt. Persisted across restarts.
• /cancel kills the in-flight run. /reload re-reads config. /config dumps merged settings. /status shows in-flight state. /fetch <path> downloads a file. /start, /help for basics.
• Replies to cron messages inject the job's instruction + result so pi has full context for follow-ups.

Config at $HOME/.aicodebox/telegram.yml (override via PIBOX_TELEGRAM_MODE_CONFIG):

yaml

allowed_chats: [-100123, 42]
default:
  model: glm-4.6
  workspace: shared
chats:
  -100123:
    workspace: alpha
    allowed_users: [10, 20]

Auth: chat/user allowlisting via the config yaml (allowed_chats, per-chat allowed_users) — no separate bearer token, the bot token itself gates who can even message it.

Cron scheduler mode

PIBOX_CRON_MODE=1 + PIBOX_CRON_MODE_FILE=/path/to/cron.yaml. 6-field cron schedules via croniter. Each job fires pi with the given instruction on schedule.

yaml

jobs:
  - name: morning-standup
    schedule: "0 0 9 * * 1-5"
    instruction: |
      Summarize what changed in /workspace since yesterday.
      Be brief. One paragraph max.
    workspace: myproject
    telegram_chat_id: -100123
    model: glm-4.6
    thinking: low

PIBOX_CRON_MODE_HISTORY_DIR (default $HOME/.aicodebox/cron) is the whole cron state root. Each run gets a history dir at <root>/history/<workspace>/<timestamp>-<job>/ with meta.json, stdout.log, stderr.log, result.txt. If telegram is also configured, telegram.json lands there too and the next run's prompt gets a "prior run" hint so pi can reference its own history without you wiring it up. A per-job summary jsonl is appended at <root>/<job>.jsonl. Running alongside Telegram mode (PIBOX_TELEGRAM_MODE=1 + PIBOX_CRON_MODE=1) runs cron in-thread inside the telegram process — the only foreground-mode pairing allowed.

Auth: none — this is a scheduled background job, not a request-driven surface. telegram_chat_id on a job routes its result through the (already-authenticated) Telegram bot if set; setting it to 0 opts that job out of an inherited default. A successful job with an explicit recipient notifies even when its result text is empty; a failed job sends a failure notice.

Auth (LLM upstream)

Use PIBOX_PROVIDER_* for Pi's documented custom HTTP APIs, including LiteLLM and Anthropic-compatible endpoints. ANTHROPIC_* remains a compatibility path for existing Anthropic Messages endpoints. The full configuration matrix and examples are in references/setup.md.

Var	Purpose
ANTHROPIC_AUTH_TOKEN	Bearer token (Z.AI, direct Anthropic, etc.)
ANTHROPIC_API_KEY	Same thing — pi reads both
ANTHROPIC_BASE_URL	Endpoint override (default https://api.anthropic.com)
ANTHROPIC_MODEL	Default model when the caller doesn't specify one

Z.AI's GLM models are a fast/cheap default: ANTHROPIC_BASE_URL=https://api.z.ai/api/anthropic + ANTHROPIC_MODEL=glm-4.6.

pi's thinking levels (--thinking): off, minimal, low, medium, high, xhigh. Exposed as /effort in Telegram mode and thinking in API/OAI requests.

Surface-level auth (separate from the LLM upstream) is per-mode: PIBOX_API_MODE_TOKEN gates the REST + OAI routes, PIBOX_MCP_MODE_TOKEN gates MCP (no fallback between the two), Telegram gates by bot-token possession + chat/user allowlist, cron and interactive/exec modes have no surface auth (container-boundary trust). Both PIBOX_API_MODE_TOKEN and PIBOX_MCP_MODE_TOKEN default to empty, which means no auth — see Security & safety above before exposing either surface beyond localhost.

Typical Workflows

One-off task from a script, no server:

bash

docker run --rm -e ANTHROPIC_AUTH_TOKEN=$TOKEN -e ANTHROPIC_BASE_URL=$BASE_URL \
  -e ANTHROPIC_MODEL=glm-4.6 -v "$PWD:/workspace" \
  psyb0t/pibox:latest -p "summarize the diff in /workspace"

Long-running server, drive it with curl:

bash

docker run -d --network host -e PIBOX_API_MODE=1 -e PIBOX_API_MODE_TOKEN=$SECRET \
  -e PIBOX_AVAILABLE_MODELS=glm-4.6 -e ANTHROPIC_AUTH_TOKEN=$TOKEN \
  -e ANTHROPIC_BASE_URL=$BASE_URL -v "$PWD/workspace:/workspace" psyb0t/pibox:latest
curl -s http://localhost:8080/run -H "Authorization: Bearer $SECRET" \
  -H "Content-Type: application/json" -d '{"prompt": "run the tests and report failures"}'

Structured extraction via JSON schema:

bash

curl -s http://localhost:8080/run -H "Authorization: Bearer $SECRET" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "extract TODOs from /workspace", "jsonSchema": {"type":"object","properties":{"todos":{"type":"array","items":{"type":"string"}}},"required":["todos"]}}'

Wire an MCP-aware agent (Claude Code, OpenClaw) to a running pibox:

bash

claude mcp add --transport http pibox http://localhost:8080/mcp/ \
  --header "Authorization: Bearer $MCP_TOKEN"

Chat + Telegram + scheduled digest, all on one box:

bash

docker run -d --network host \
  -e PIBOX_TELEGRAM_MODE=1 -e PIBOX_TELEGRAM_MODE_TOKEN=$BOT_TOKEN \
  -e PIBOX_CRON_MODE=1 -e PIBOX_CRON_MODE_FILE=/config/cron.yaml \
  -e ANTHROPIC_AUTH_TOKEN=$TOKEN -e ANTHROPIC_BASE_URL=$BASE_URL \
  -v "$PWD/workspace:/workspace" -v "$PWD/cron.yaml:/config/cron.yaml:ro" \
  psyb0t/pibox:latest