octop-harness is a production-grade agent runtime built on the harness engineering theory. At its core it is a thin, battle-tested encapsulation and engineering layer around deepagents — we take the elegant create_deep_agent primitive from deepagents and package it with everything you need to run agents in production: multi-provider model routing, persistent memory, browser/search tools, a multi-agent registry, pluggable storage backends, and a terminal CLI.

💜 A tribute to deepagents. octop-harness stands on the shoulders of deepagents (by the LangChain team). Our HarnessAgent ultimately delegates to deepagents.create_deep_agent, and everything else — model routing, backends, tiered memory, the agent registry, CLI, and ACP — is the engineering we added on top. deepagents gives us the agentic foundation; Harness gives it a home for production.

What is Octop Harness?

	Feature	Description
🧩	Built on deepagents	A faithful, production-focused facade over `deepagents.create_deep_agent` — kudos to the deepagents project
🔀	Model routing	`ChatModelFactory` with OpenAI, Anthropic, AWS Bedrock, and 17 editable provider presets
🧠	Persistent memory	octop-memory middleware with tiered L0→L3 distillation
🛠️	Rich toolset	Browser automation, web search, file ops, sub-agents, and MCP tools out of the box
🗂️	Multi-agent registry	`AgentManager` runs many isolated agents in one process
💾	Pluggable backends	Local disk, S3, COS, or PostgreSQL as the agent workspace
🔌	ACP integration	A stdio ACP server so IDE / terminal AIs can drive your agent
🔒	Safety built-in	Tool guardrails, filesystem permissions, and PII redaction
💬	Teams	Peer inbox; `peer_invoke_mode` selects sync / async / both
⌨️	Terminal CLI	Interactive chat, provider/skill config, and agent management

Why Octop Harness?

octop-harness is a library (not an app) that turns deepagents into a deployable runtime. A single HarnessAgentManager owns a registry of agents; each agent wires together a model, a set of tools/skills, a memory backend, and a LangGraph checkpoint — all assembled through the harness layering discipline (L0 I/O helpers → L1 workspace facade → L2 business logic → L3 assembly).

Harness Agent's design goal: let you build a production-grade agent from deepagents in a few lines of code, while keeping model choice, memory, storage, and safety swappable without rewriting your agent.

Core Technology

Layer	Technology
Language	Python 3.12+
Agent core	deepagents (`create_deep_agent`)
Graph runtime	LangGraph + SQLite checkpoint
Model routing	`ChatModelFactory` (OpenAI / Anthropic / Bedrock + 17 presets)
Memory	octop-memory middleware
Browser / search	octop-browser + web search
Workspaces	`BackendWorkspace`: local / S3 / COS / PostgreSQL
ACP	agent-client-protocol
Build / quality	hatchling · ruff · mypy · pytest

Features

Agent runtime

HarnessAgentManager — a registry that creates, lists, and streams many agents in one process.
HarnessAgent — exposes call, stream, stream_events, aget_history, aappend_messages, and cancel.
Checkpointing via LangGraph AsyncSqliteSaver (or a octop-memory saver) for resumable chats.

Model routing & providers

ChatModelFactory resolves a model name to the right client.
First-class providers: OpenAI (incl. OpenAI-compatible via base_url), Anthropic, and AWS Bedrock (optional [bedrock] extra).
17 user-editable provider presets, including Hunyuan, Kimi, GLM, DeepSeek, MiniMax, and Moonshot.

Memory (tiered)

Each turn is captured as L0 raw events, then distilled asynchronously into L2 atoms (AtomCards) and L3 entity pages.
Powered by the octop-memory middleware; memory travels with the workspace.
Recall is frozen once per user turn and appended to the model-facing user message, keeping the system prompt stable. Checkpoints preserve recall snapshots for exact replay while chat history and capture retain the original user text.

Tools & skills

Built-in tools: browser (octop-browser), web search, files, ask_user_question for respond-only human decisions, optional provider-neutral image/video generation, sub-agents, and MCP tools. Set ask_user_enabled=False for unattended agents.
Skills come from the deepagents skills middleware; manage them per agent via the CLI.
Optional system_files_path (e.g. .octop) keeps skills, sessions, .env, and sqlite under a workspace subdir while persona markdown stays at the root.

Enable provider-neutral generate_image and generate_video tools with Volcengine Ark:

from octop_harness import HarnessAgentConfig, MediaGenerationConfig

config = HarnessAgentConfig(
    # ...providers/default_model...
    media_generation=MediaGenerationConfig(api_key_env="ARK_API_KEY"),
)

Generated files are stored under generated/images/ and generated/videos/ in the agent workspace. The tools are deferred by default and become visible through the configured tool-search strategy only when needed. Expected provider failures return a structured, model-visible error envelope with retry guidance instead of terminating the agent stream.

For separate image and video routes, configure named providers. Built-in adapters cover Volcengine Ark (Seedream/Seedance), Alibaba Cloud Model Studio (Wan), and MiniMax (image-01/H3):

from octop_harness import HarnessAgentConfig, MediaGenerationConfig, MediaProviderConfig

config = HarnessAgentConfig(
    # ...providers/default_model...
    media_generation=MediaGenerationConfig(
        providers=[
            MediaProviderConfig(
                id="ark-images",
                provider="volcengine",
                api_key_env="ARK_API_KEY",
                video_enabled=False,
            ),
            MediaProviderConfig(
                id="minimax-videos",
                provider="minimax",
                api_key_env="MINIMAX_API_KEY",
                image_enabled=False,
            ),
        ],
        default_image_provider="ark-images",
        default_video_provider="minimax-videos",
    ),
)

Advanced hosts can inject a MediaManager into build_media_generation_tools and register a constrained custom adapter without adding arbitrary protocol or authentication fields to declarative configuration.

Multi-agent & collaboration

Multiple isolated agents per process via AgentManager + registry.
Teams peer inbox for agent-to-agent messaging. peer_invoke_mode selects the ask_agent surface (sync / async / both); request-scoped overrides may tighten it but cannot add background dispatch.
ACP stdio server so external IDE / terminal AIs can invoke your agent.

Safety

SecurityPolicy with tool guardrails, filesystem permissions (FilesystemPermission), and PII redaction middleware.

CLI

octop-harness provides: init, chat, agent, config (e.g. config provider add), skill, and update.

How to Use

Prerequisites

Python 3.12+
A model provider API key (OpenAI / Anthropic / Bedrock / compatible)

1. Install

# Core SDK — agent runtime, model routing, tools, skills, backends
pip install octop-harness

# With the terminal CLI — interactive chat, config, skill management
pip install octop-harness[cli]

# Multiple extras — comma-separated inside one pair of brackets (quote for the shell):
pip install 'octop-harness[object-storage,desktop]'
pip install 'octop-harness[cli,all]'

Optional dependency extras (install only what you need; missing extras fail at use-time with an install hint):

Extra	What it adds
`cli`	Terminal CLI (`octop-harness`)
`bedrock`	AWS Bedrock provider
`object-storage`	Tencent COS + Alibaba OSS + Huawei OBS SDKs
`desktop`	Desktop screenshot / input (`mss`, `pynput`, `pillow`)
`web-search-all`	All web-search backends (Tavily / Brave / Google)
`remote-backends`	Postgres / upstream S3 via `deepagents-backends` (Python ≥3.12)
`observability`	Langfuse
`acp`	ACP agent runner
`all`	All library feature extras above (excludes `cli`; use `[cli,all]` for both)

2. Initialize & configure

octop-harness init
octop-harness config provider add   # choose a provider and paste your key

3. Chat

octop-harness chat

Programmatic use

from octop_harness import HarnessAgentManager, HarnessAgentConfig, ProviderConfig, ChatRequest

manager = HarnessAgentManager()
agent: HarnessAgentConfig = manager.create_agent(
    name="assistant",
    provider=ProviderConfig(name="openai", api_key="sk-..."),
    model="gpt-4o",
)
request = ChatRequest(message="Summarize the Harness theory in one paragraph.")
async for chunk in agent.stream(request):
    print(chunk.delta, end="")

Progressive tool loading

Large, low-frequency tool sets can be hidden until the model needs them. The default client mode registers an ordinary tool_search function, so it works with any Chat Completions-compatible model that supports function calling. A deferred tool remains visible by its real name and short description, while its parameter schema is replaced by a lightweight reference. A search result is appended to the conversation, the matched full schemas replace their references on the next model step, and the loaded set persists for the thread. The selected tool is still called by its real name through the normal DeepAgents ToolNode, security guard, and interrupt policy.

from octop_harness import HarnessAgentConfig, ProviderConfig

provider = ProviderConfig(
    id="openai",
    base_url="https://openai-compatible.example/v1",
    api_key="sk-...",
    protocol="openai",
)

config = HarnessAgentConfig(
    providers=[provider],
    default_model="openai/your-function-calling-model",
    tools=[generate_image, generate_video, generate_3d_asset],
    deferred_tools=frozenset(
        {"generate_image", "generate_video", "generate_3d_asset"},
    ),
    defer_mcp_tools=True,
    tool_search_mode="client",  # default; no Responses API required
)

Set tool_search_mode="native" to use OpenAI Responses hosted tool_search or Anthropic hosted defer_loading / tool_reference. Native mode additionally requires ModelConfig.native_tool_search=True; unsupported routed models use the configured tool_search_fallback. Set the mode to eager to disable progressive loading.

CLI reference

Command	Description
`octop-harness init`	Bootstrap a workspace and config
`octop-harness chat`	Interactive chat with an agent
`octop-harness agent`	Create, list, and manage agents
`octop-harness config`	Provider / model configuration (`config provider add`)
`octop-harness skill`	Enable / disable per-agent skills
`octop-harness update`	Check for and install updates

Project layout

src/octop_harness/
  agent.py          facade over deepagents.create_deep_agent
  manager.py        AgentManager — multi-agent registry
  config/           configs, provider & model presets
  llm/factory.py    ChatModelFactory — model routing
  backends/         BackendWorkspace — local / S3 / COS / Postgres
  builtin/          tools + skills + seed files
  middleware/       model_router, skill_filter, tool_guard, memory, pii, ...
  memory/           MemoryRuntime (octop-memory wrapper)
  protocols/        langgraph / openai / mcp streaming
  acp/             ACP stdio server
  teams/           peer inbox
  cli/             terminal CLI

Development

Prerequisites: Python 3.12+, uv

make install          # pip install -e ".[cli,dev]"
make all              # lint + typecheck + test

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Run make all before submitting
Open a Pull Request against main

Module boundaries and coding conventions: AGENTS.md.

See CONTRIBUTING.md for branching, PR, and release details (release/* → main auto-publishes to PyPI).

Project	Description
deepagents	The agentic foundation Harness Agent wraps — ❤️ tribute
octop-memory	Memory system behind the tiered recall
octop-browser	CDP browser automation used by the agent
octop-gateway	Multi-platform IM channel bridge
Octop	The self-hosted assistant that composes the Harness stack

📄 License

This project is licensed under the MIT License.

✨ Contributors

Thanks to all contributors:

README.md

Octop Harness