# AI use cases for Octop Harness (TencentCloud/octop-harness) Repo: [TencentCloud/octop-harness](https://github.com/TencentCloud/octop-harness) | Use cases: 5 | Page: https://dedupedai.com/use-cases/tencentcloud-octop-harness/ octop-harness is a Python library that wraps deepagents into a production-oriented agent runtime with model routing, memory, tools, multi-agent registry, storage backends, and a CLI. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Run a production agent with swappable model providers: https://dedupedai.com/use-cases/tencentcloud-octop-harness/run-production-agent-with-model-routing/ - Build an assistant that remembers users across sessions: https://dedupedai.com/use-cases/tencentcloud-octop-harness/build-assistant-with-persistent-memory/ - Let an IDE or terminal AI drive your agent over ACP: https://dedupedai.com/use-cases/tencentcloud-octop-harness/let-ide-ai-drive-your-agent-via-acp/ - Run an unattended team of agents that message each other: https://dedupedai.com/use-cases/tencentcloud-octop-harness/run-unattended-agent-team-with-peer-messaging/ - Add image and video generation to an agent with Volcengine Ark: https://dedupedai.com/use-cases/tencentcloud-octop-harness/generate-images-and-videos-with-volcengine-ark/ ## How to run a production agent with swappable model providers with Octop Harness Repo: [TencentCloud/octop-harness](https://github.com/TencentCloud/octop-harness) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Build one deepagents-based agent that can switch between OpenAI, Anthropic, Bedrock, and preset providers. **Who it is for:** A Python developer who needs an agent in production and wants to change model providers without rewriting the agent. **The problem:** Wiring a model, tools, and checkpoints into a deployable agent takes many decisions, and locking into one provider makes switching costly. **The outcome:** A working HarnessAgent that answers prompts through a model chosen by configuration, with failures raised as exceptions for the host to handle. ### Steps 1. Confirm Python 3.12 or newer is installed. 2. Install the package with `pip install octop-harness`. (inferred, check the README) 3. Export your provider API key as an environment variable in your shell. 4. Run `octop-harness init` to create an agent workspace. (inferred, check the README) 5. Create a HarnessAgent through HarnessAgentManager, send a prompt with call(), and wrap the call in exception handling. ### Prompt to paste into your agent ```text In the TencentCloud/octop-harness repo, create a HarnessAgentManager with one agent that uses the model provider . Send the prompt through call() and return the reply. Verify the reply is non-empty and that exceptions from call() are caught. ``` ### Caveat The README does not give the exact pip install command, so the install step above is inferred and should be checked against the project's install docs. Risk, credentials: Model provider API keys are read from your environment, so keep them out of source code and shared workspace files. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/tencentcloud-octop-harness/run-production-agent-with-model-routing/ ## How to build an assistant that remembers users across sessions with Octop Harness Repo: [TencentCloud/octop-harness](https://github.com/TencentCloud/octop-harness) | Difficulty: Intermediate | Time: 45 minutes | Basis: From the README Keep strong memory and a user profile that persist across sessions and can be saved on request. **Who it is for:** A team building a support or personal assistant that must recall user preferences and facts from earlier conversations. **The problem:** Chat assistants lose context between sessions, so users repeat preferences and the agent cannot build on past work. **The outcome:** An agent that loads MEMORY.md and USER.md at session start, saves explicit remember requests with memory_save, and retrieves past history with memory_search. ### Steps 1. Install Octop Harness and create a workspace with `octop-harness init` (inferred command). (inferred, check the README) 2. Open MEMORY.md and USER.md in the workspace and add your own notes, since manual edits apply to new sessions. 3. Start an interactive chat with the CLI (inferred: `octop-harness chat`). (inferred, check the README) 4. Ask the agent to remember a preference, which it saves through memory_save. 5. Start a new session and ask a question that depends on that preference, then check that the answer uses the saved fact. ### Prompt to paste into your agent ```text In the TencentCloud/octop-harness repo, set up a workspace at and add the user preference to USER.md. Start a new session and ask . Verify the reply uses the saved preference. ``` ### Caveat The README says agent file tools cannot edit MEMORY.md or USER.md, so changes to those files must be made by you or a host-provided editor. Risk, credentials: Memory captures eligible conversation content, so avoid saving secrets or private identifiers into MEMORY.md, USER.md, or chat. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/tencentcloud-octop-harness/build-assistant-with-persistent-memory/ ## How to let an IDE or terminal AI drive your agent over ACP with Octop Harness Repo: [TencentCloud/octop-harness](https://github.com/TencentCloud/octop-harness) | Difficulty: Advanced | Time: 60 minutes | Basis: From the README Expose your agent through the stdio ACP server so an external coding AI can invoke it. **Who it is for:** A developer who wants an IDE or terminal AI tool to call a custom agent built on Octop Harness. **The problem:** External AI tools cannot reach a custom agent unless it speaks a protocol they understand. **The outcome:** An external IDE or terminal AI sends requests to your agent through the ACP stdio server and receives replies. ### Steps 1. Install Octop Harness and configure at least one model provider with its API key. 2. Find the ACP stdio server entry point in the project docs, since the README names the server but not its start command. (inferred, check the README) 3. Start the ACP stdio server for your configured agent. (inferred, check the README) 4. Register the server in your IDE or terminal AI client as an ACP agent. 5. Send a test request from the client and confirm the agent returns a reply. ### Prompt to paste into your agent ```text In the TencentCloud/octop-harness repo, find the ACP stdio server start command in the docs and start it for the agent . Connect the client and send the request . Verify the client receives a reply from the agent. ``` ### Caveat The README mentions the stdio ACP server but does not show its start command or client configuration, so those steps need confirmation from the full docs. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/tencentcloud-octop-harness/let-ide-ai-drive-your-agent-via-acp/ ## How to run an unattended team of agents that message each other with Octop Harness Repo: [TencentCloud/octop-harness](https://github.com/TencentCloud/octop-harness) | Difficulty: Advanced | Time: 60 minutes | Basis: From the README Host several isolated agents in one process that call each other through the Teams peer inbox. **Who it is for:** A platform engineer running background agents that must hand work to each other without a human answering questions. **The problem:** Separate agent processes are costly to run, and background agents stall when they wait for a human to answer a question. **The outcome:** Multiple isolated agents run in one process, exchange messages through the peer inbox, and never block on ask_user_question. ### Steps 1. Install Octop Harness and configure the model providers each agent will use. 2. Create several agents through AgentManager and the registry, giving each its own workspace. 3. Set ask_user_enabled=False on each agent config so it does not wait for human answers. 4. Set peer_invoke_mode to sync, async, or both, based on how the team should call each other. 5. Trigger a task on one agent that asks a peer for help, then confirm the peer's reply reaches the first agent. ### Prompt to paste into your agent ```text In the TencentCloud/octop-harness repo, create three isolated agents with AgentManager and set ask_user_enabled=False and peer_invoke_mode to . Send the task to agent and verify it receives a reply from a peer. ``` ### Caveat The README says request-scoped overrides can tighten peer_invoke_mode but cannot add background dispatch, so plan the mode in the agent config. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/tencentcloud-octop-harness/run-unattended-agent-team-with-peer-messaging/ ## How to add image and video generation to an agent with Volcengine Ark with Octop Harness Repo: [TencentCloud/octop-harness](https://github.com/TencentCloud/octop-harness) | Difficulty: Intermediate | Time: 45 minutes | Basis: From the README Enable generate_image and generate_video tools that save output into the agent workspace. **Who it is for:** A content developer who wants an agent to produce images or short videos as part of a workflow. **The problem:** Adding media generation to an agent means integrating provider APIs, handling failures, and tracking output files. **The outcome:** An agent that calls generate_image or generate_video and saves files under generated/images/ or generated/videos/ in its workspace. ### Steps 1. Install Octop Harness and obtain a Volcengine Ark API key. 2. Export the key as ARK_API_KEY in your shell environment. 3. Add MediaGenerationConfig(api_key_env="ARK_API_KEY") to your HarnessAgentConfig. 4. Start the agent and ask it to create an image or video through the generation tools. 5. Check the generated/images/ or generated/videos/ folder in the workspace for the output file. ### Prompt to paste into your agent ```text In the TencentCloud/octop-harness repo, configure HarnessAgentConfig with MediaGenerationConfig using api_key_env="ARK_API_KEY". Ask the agent to generate an image of . Verify a new file appears under generated/images/ in the workspace. ``` ### Caveat The README says the media tools are deferred and only appear through the configured tool-search strategy when needed, so the agent may need a prompt that triggers them. Risk, credentials: The Ark API key grants paid provider access, so store it in an environment variable and never commit it to the config file. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/tencentcloud-octop-harness/generate-images-and-videos-with-volcengine-ark/