dank-py 1.0.0

Framework-agnostic Python agent containerization CLI for Dank Cloud

dank-py

PyPI · Dank Cloud

Framework-agnostic Python CLI for turning existing agent code into Dockerized HTTP microservices with a consistent runtime contract.

Turn existing Python agents into production-ready microservices in two commands, with no agent-code rewrites:

• no code rewrites required,
• standardized HTTP runtime contract,
• repeatable dependency locking + validation,
• built-in observability (status, metrics, logs, traces),
• optional multi-agent bundling for runtime efficiency.

If you want managed infrastructure instead of self-hosting containers, use Dank Cloud.
Dank Cloud can onboard directly from your GitHub repo and handles packaging, deploys, scaling, auth, logging/tracing & monitoring, and supporting infra (like hosted vector DB + MCP) for you.

In short:

• dank-py: local/self-hosted packaging and runtime control.
• Dank Cloud: managed microservice deployment and operations, built on top of dank-py.

Table of Contents

• Why dank-py
• In 2 Commands
• Architecture
• Install
• Quick Start
• Command Reference
• Configuration Reference (dank.config.json)
• Runtime API Contract
• Logging and Observability
• Environment and Secrets
• Build vs Build:Prod
• Agent Examples
• Troubleshooting
• Release

Why dank-py

• Works with existing agent code
  • Framework-agnostic support across LangChain, LangGraph, CrewAI, PydanticAI, LlamaIndex, or custom OpenAI/SDK implementations.
  • Auto-inspects entrypoints and I/O hints to bootstrap dank.config.json.
• Production-ready runtime out of the box
  • Consistent endpoints (/health, /prompt, /status, /metrics, /logs, /traces).
  • Input/output contract validation and strict output enforcement.
  • Native async/sync invocation handling for agent callables and methods.
  • Request tracing with trace IDs, per-agent log streams, and trace retrieval endpoints.
• Reliable build and dependency flow
  • Resolver-based lock generation (requirements.lock.txt).
  • Isolated validation in temporary virtual environments before build/run.
  • Build and production build workflows for local iteration and release pipelines.
• Flexible multi-agent deployment
  • Run each agent in its own container, or bundle agents into one container with header-based routing.
  • Reduce footprint for compatible agents while keeping per-agent observability.

In 2 Commands

dank-py auto-init --strict
dank-py run

That flow will:

1. inspect your project and generate dank.config.json,
2. infer and lock dependencies,
3. validate agents in an isolated environment,
4. build and run containerized agent service(s).

Architecture

flowchart LR
    A["Existing Python Agent Code"] --> B["dank-py auto-init"]
    B --> C["dank.config.json + requirements.lock.txt"]
    C --> D["dank-py run / build"]
    D --> E["Containerized Agent Microservice(s)"]
    E --> F["/prompt /health /status /metrics /logs /traces"]
    E --> G["Run locally or self-host (Docker/K8s)"]

Install

Requirements

• Python >=3.11 (supported host range)
• Python 3.12 is the target runtime/lock version
• Docker CLI + running Docker daemon

Install from PyPI

pip install dank-py

Editable install (local development)

pip install -e /absolute/path/to/dank-py

Both command names are available:

• dank
• dank-py

Quick Start

# 1) Scaffold config and ignore files
dank-py init

# 2) Inspect project and apply candidates
dank-py inspect

# 3) Resolve dependency lock
dank-py deps

# 4) Build and run
dank-py run

One command setup:

dank-py auto-init

Strict setup (includes full isolated validation):

dank-py auto-init --strict

Command Reference

Version

Quick version check:

dank-py -v
# or
dank-py --version
# or
dank-py version

What it shows:

• installed dank-py CLI version
• configured default base image reference
• Docker availability status
• installed local dank-py-base image tags (if any)

Flags for dank-py version:

Flag	What it does
--json	Outputs version/base-image details as JSON for tooling/scripts.

dank-py init

Scaffold dank.config.json and .dankignore.

dank-py init [name] [--force]

Arg/Flag	What it does
name	Optional target directory. If omitted, uses current directory.
--force	Overwrites existing scaffold files instead of leaving them untouched.

dank-py auto-init

Runs init + inspect apply + deps in one flow.

dank-py auto-init [name] [flags]

Arg/Flag	What it does
name	Optional target directory for scaffolding.
--force	Overwrites existing scaffold files.
--validate-dry	Runs isolated non-live validation after lock creation.
--validate-full	Runs isolated live validation (real env vars and non-mock outputs expected).
--strict	Alias for --validate-full.
--fallback-freeze	Allows lock fallback via pip freeze when resolver lock cannot be produced.
--no-discover-imports	Disables import discovery when no dependency metadata exists.
--install-tools	Auto-installs missing resolver tooling (pip-tools) in the active environment.
--no-install-prompt	Disables interactive prompts for tool installation.
--lock-python-version <ver>	Sets lock target Python version (default 3.12).
--include-lock-comments	Keeps resolver metadata comments in requirements.lock.txt.
--no-refresh-lock	Reuses existing lock file instead of regenerating it.

dank-py deps

Generates or refreshes requirements.lock.txt, optionally validates agents.

dank-py deps [flags]

Flag	What it does
--project-dir <dir>	Uses a specific project directory (defaults to current directory).
-c, --config <path>	Path to dank.config.json for validation modes.
--validate-dry	Runs isolated non-live validation.
--validate-full	Runs isolated live validation.
--fallback-freeze	Allows pip freeze fallback when resolver lock fails.
--no-discover-imports	Disables import-based dependency discovery.
--install-tools	Auto-installs pip-tools if missing.
--no-install-prompt	Disables prompt asking to install missing tools.
--lock-python-version <ver>	Sets lock target Python version (default 3.12).
--include-lock-comments	Keeps resolver metadata comments in lock output.
--no-refresh-lock	Reuses existing lock instead of regenerating.

dank-py inspect

Scans project files for likely agent entrypoints and model/schema hints.

dank-py inspect [flags]

Flag	What it does
--project-dir <dir>	Uses a specific project directory.
--json	Prints machine-readable inspect output only (no interactive prompts).
--interactive	Explicitly runs interactive apply flow (default behavior when not using --json).
--apply	Applies selected candidate(s) directly to config.
--candidate-index <N>	Chooses a specific 1-based candidate index when applying.
-c, --config <path>	Path to target dank.config.json.

dank-py build

Builds local target image(s).

dank-py build [selector] [flags]

Selectors (mutually exclusive):

Selector	What it does
--agent <id-or-name>	Builds one standalone agent target.
--bundle <bundle-name>	Builds one configured bundle target.
--bundle-agents <csv|all>	Builds one ad-hoc bundle from agent list or all agents.

Build flags:

Flag	What it does
-c, --config <path>	Path to dank.config.json.
--bundle-name <name>	Names an ad-hoc bundle created with --bundle-agents.
--prompt-routing required|default	Overrides bundle prompt routing mode.
--default-agent <id-or-name>	Sets default routed agent when using prompt-routing=default.
--tag <tag>	Sets output image tag (single target).
--base-image <image>	Overrides base image reference.
--pull-base	Forces pull of base image before target build.
--no-base-build	Skips local base-image build fallback if pull fails.
--force-base	Forces base image rebuild/pull path.
--verbose	Streams raw Docker build output.
--json	Returns structured JSON result.

Default behavior with no selector:

• no bundles configured: builds all agents separately
• bundles configured: builds configured bundles + unbundled agents separately

dank-py build:prod

Buildx-based production build flow.

dank-py build:prod [selector] [flags]

Selector flags are the same as build.

Production flags:

Flag	What it does
-c, --config <path>	Path to dank.config.json.
--bundle-name <name>	Names ad-hoc bundle from --bundle-agents.
--prompt-routing required|default	Bundle prompt routing override.
--default-agent <id-or-name>	Default agent for bundle prompt fallback.
--tag <tag>	Image tag (default latest).
--registry <host>	Registry host for push targets.
--namespace <prefix>	Repository namespace/prefix.
--tag-by-agent	Uses agent name as tag with shared namespace repo strategy.
--platform <platform|auto>	Buildx platform(s); auto chooses based on push/load mode.
--push / --no-push	Enables/disables push output explicitly.
--load / --no-load	Enables/disables local Docker load output explicitly.
--no-cache	Disables build cache.
--base-image <image>	Base image override.
--pull-base	Pulls base image before production build.
--force-base	Forces base image rebuild/pull path.
--output-metadata <file>	Writes build metadata JSON file.
--verbose	Streams raw buildx output.
--json	Returns structured JSON build result.

dank-py run

Builds (unless --no-build) and runs selected target container(s).

dank-py run [selector] [flags]

Selector flags are the same as build.

Run flags:

Flag	What it does
-c, --config <path>	Path to dank.config.json.
--bundle-name <name>	Names ad-hoc bundle from --bundle-agents.
--prompt-routing required|default	Bundle prompt routing override.
--default-agent <id-or-name>	Default bundle target when prompt header is omitted in default mode.
--tag <tag>	Uses specific image tag for build/run.
--base-image <image>	Base image override.
--pull-base	Pulls base image before building.
--no-build	Runs existing image without rebuilding.
-d, --detached	Starts container(s) in detached mode.
--foreground	Forces foreground attach mode.
--port <host-port>	Starting host port mapping (auto-increments to avoid collisions).
--force-base	Forces local base rebuild path.
--keep-build-context	Leaves generated .dank-py build context on disk for debugging.
--verbose	Streams raw Docker build logs.
--quiet	Reduces runtime request/startup logging.
--env-file <path>	Injects env file at runtime (repeatable).
-e, --env KEY=VALUE|KEY	Injects explicit env var (repeatable).
--no-auto-env-file	Disables automatic loading of project .env.
--json	Returns structured run result.

Run defaults:

• single target: foreground monitor mode
• multi-target: detached mode

dank-py logs

Reads logs from container targets and bundled agent runtime streams.

dank-py logs [target] [flags]

Arg/Flag	What it does
target	Container name, bundle name, or agent id/name.
-f, --follow	Follows logs live (streaming mode).
-t, --tail <N>	Number of lines/events to show (default 100).
--since <timestamp-or-duration>	Shows logs only since provided time point/window.

Routing behavior:

• bundled agent target + --follow uses websocket stream
• bundled agent target (non-follow) queries runtime logs endpoint
• container target uses Docker logs

dank-py stop

Stops running dank-py containers.

dank-py stop [selector] [flags]

Flag	What it does
-c, --config <path>	Path to dank.config.json.
--agent <id-or-name>	Stops one standalone agent target.
--bundle <bundle-name>	Stops one configured/named bundle container.
--bundle-agents <csv|all>	Stops one ad-hoc bundle computed from those agents.
--bundle-name <name>	Specifies ad-hoc bundle name when used with --bundle-agents.
--all	Stops all running dank-py containers.
--keep	Stops containers without removing them.

If no selector is provided, it stops all running dank-py containers.

dank-py status

Displays running/stopped container status and available agent images.

dank-py status [--json]

Flag	What it does
--json	Prints status payload as JSON for tooling.

dank-py clean

Removes generated resources.

dank-py clean [flags]

Flag	What it does
--project-dir <dir>	Project path used for .dank-py build-context cleanup.
--all	Cleans containers, images, and build contexts.
--containers	Cleans only dank-py containers.
--images	Cleans only dank-py agent images.
--build-contexts	Cleans only .dank-py build context directories.
--include-base	Also removes local base image when cleaning images.

Configuration Reference (dank.config.json)

Top-level fields

Field	Type	Required	Notes
name	string | null	no	Project label.
version	string	no	Defaults to "1".
agents	AgentConfig[]	yes	Must contain at least one agent.
bundles	BundleConfig[]	no	Defaults to empty list.

Validation rules:

• unknown top-level fields are rejected
• agent.name and agent.id must be unique
• missing agent.id is auto-derived from normalized agent.name

AgentConfig

Field	Type	Required	Notes
name	string	yes	Human-readable agent name.
id	string | null	no	Stable routing identity; defaults from name.
entry	EntryConfig	yes	Runtime invocation target.
io	IOConfig	no	Input/output contract definitions.

EntryConfig

Field	Type	Notes
file	string	Python file path.
symbol	string	Exported function/class/object.
method	string | null	Method name for object/class entry styles.
call_type	auto | callable | method	Invocation target resolution strategy.
call_style	auto | single_arg | kwargs	Request payload passing strategy.

IOConfig

Field	Type	Notes
input.model	module:Symbol | null	Optional typed input model reference.
input.schema	object | null	Optional JSON Schema input contract.
output.model	module:Symbol | null	Optional typed output model reference.
output.schema	object | null	Optional JSON Schema output contract.
strict_output	boolean	Defaults to true.

Runtime error classes:

• input contract failure: 422
• invocation parameter mismatch: 400
• invocation failure: 500
• strict output contract failure: 500

BundleConfig

Field	Type	Required	Notes
name	string	yes	Unique bundle identifier.
agents	string[]	yes	Included members by id or name.
prompt_routing	required | default	no	Defaults to required.
default_agent	string | null	no	Only valid with prompt_routing: default.

Rules:

• bundle names must be unique
• all agents[] references must exist
• default_agent must belong to its bundle

Example: Partial bundle + leftover separate agent

{
  "name": "multi-project",
  "version": "1",
  "agents": [
    { "name": "langchain-agent", "id": "langchain-agent", "entry": { "file": "a.py", "symbol": "run" } },
    { "name": "langgraph-agent", "id": "langgraph-agent", "entry": { "file": "b.py", "symbol": "run" } },
    { "name": "custom-agent", "id": "custom-agent", "entry": { "file": "c.py", "symbol": "run" } }
  ],
  "bundles": [
    {
      "name": "lang-bundle",
      "agents": ["langchain-agent", "langgraph-agent"],
      "prompt_routing": "default",
      "default_agent": "langchain-agent"
    }
  ]
}

Default dank-py run behavior for this config:

• one container for lang-bundle
• one separate container for custom-agent

Runtime API Contract

All runtime containers expose:

Endpoint	Purpose
GET /health	Container health and runtime mode metadata.
POST /prompt	Agent invocation endpoint (single or bundled routing).
GET /status	Container/runtime status summary.
GET /status/{agent_id}	Per-agent status in multi-agent runtime.
GET /metrics	Process/system metrics snapshot.
GET /logs	Buffered logs query.
GET /logs/stats	Log buffer stats.
WS /logs/stream	Live log stream.

Trace endpoints:

• GET /traces (default/only agent traces)
• GET /traces/stats (default/only agent trace stats)
• GET /traces/{agent_id}
• GET /traces/stats/{agent_id}
• GET /trace/{trace_id}

Query filters:

• GET /logs: startTime, endTime, minutesAgo, limit, offset, stream
• GET /traces, GET /traces/{agent_id}: startTime, endTime, minutesAgo, limit, offset
• GET /traces/stats, GET /traces/stats/{agent_id}: startTime, endTime, minutesAgo
• GET /logs/stats, GET /trace/{trace_id}: no query filters

Pagination defaults:

• trace list endpoints default to limit=100, offset=0
• use hasMore=true with offset to fetch additional pages

Per-agent live log stream:

• WS /logs/stream/{agent_id}

Bundle prompt routing

Header: x-dank-agent-id

• prompt_routing=required: header is required.
• prompt_routing=default: header is optional; runtime routes to default agent.

Single-agent containers:

• header optional
• mismatched header returns 400

Trace headers

• x-dank-trace-id
  • Optional request header.
  • If omitted, runtime generates one.
  • Always returned in /prompt response header and metadata.trace_id.
  • If provided and already present in in-memory trace/log history, runtime returns 409.
• x-dank-include-trace
  • Optional request header (true|1|yes|on).
  • When enabled, /prompt includes full grouped trace payload in metadata.trace.

Logging and Observability

Runtime keeps an in-memory log buffer and supports:

• query endpoints (/logs, /logs/stats)
• websocket streaming (/logs/stream)
• grouped trace retrieval (/traces*)
• bounded retention (DANK_LOG_BUFFER_MAX_SIZE, DANK_LOG_BUFFER_MAX_AGE_MS, DANK_LOG_BUFFER_MAX_BYTES)

/logs/stats includes both:

• bufferSize (entry count)
• bufferBytes (approx current bytes)

CLI support:

• dank-py logs
• dank-py logs --follow <target>

Environment and Secrets

Runtime env injection (recommended)

dank-py run --env-file .env
# or
dank-py run -e OPENAI_API_KEY=... -e OPENAI_MODEL=gpt-4o-mini

By default, run auto-loads project .env when --env-file is not explicitly provided. Use --no-auto-env-file to disable that behavior.

Build context safety

Use .dankignore to keep secrets out of image build context.

Recommended:

• keep .env and .env.* ignored
• inject secrets at runtime with --env-file / -e

Build vs Build:Prod

• build
  • local Docker image build
  • best for local development and quick iteration
• build:prod
  • buildx workflow for production tagging/loading/pushing
  • suited for CI/CD and registry publish workflows

If you only need local execution, use run (or build + run --no-build).

Agent Examples

See agent-examples/ for framework-diverse templates:

• 01-multi-agent-mixed-repo
• 02-crewai-kickoff-agent
• 03-pydanticai-structured-agent
• 04-llamaindex-query-agent
• 05-langgraph-websearch-agent

Troubleshooting

Docker not installed/running

docker version
docker info

dank-py run/build includes Docker availability checks and remediation prompts.

Platform mismatch on build:prod

Error like no match for platform in manifest usually means base image architecture mismatch.

Fixes:

• publish multi-arch base image (linux/amd64,linux/arm64), or
• set explicit compatible --platform.

Missing pip-tools

python -m pip install pip-tools
# or
dank-py deps --install-tools

Ambiguous logs target

Use exact container name from dank-py status.

Release

Release and publish workflow:

• RELEASE.md
• scripts/release.py

License

MIT