Build a private Twitter digest agent with OpenClaw and GLM-5

Why GLM-5 matters for agents

GLM-5 is an open-source model with benchmark scores competitive with frontier closed models, and you can run it privately through Venice. On the Artificial Analysis intelligence index it scored 50, against 53 for Opus 4.6 and 51 for GPT 5.2, while costing far less per million tokens (roughly 1.6 versus 10 for Opus).

Rather than using GLM-5 through a chat interface, this guide builds something with it: a research agent that monitors X for you. GLM-5 supplies the intelligence behind the agent.

The three-layer agent stack

The agent is built from three distinct layers, each with one job.

The brain is GLM-5 via Venice. It has a 200K context window and stays fully private, with no logs of your activity anywhere. It answers the judgment questions: is a post insightful or noise, what is the key takeaway, what are people asking in the comments, and what is the overall sentiment.

The orchestrator is OpenClaw. It schedules the research, manages the analyze-summarize-output workflow, and manages its plugins. The executor is the Camoufox browser, a plugin that actually browses X. It is valued for its anti-detection technology and token efficiency.

This guide assumes you already have OpenClaw installed and configured to use Venice as its provider. If you do not, watch the OpenClaw setup video on the Venice YouTube channel first.

Installing the Camoufox browser plugin

Many sites, including X, block automated access. Camoufox is an anti-detection browser server built for AI agents, and at the time of recording it is one of the best options for browsing sites that resist automation, including LinkedIn and Amazon.

Installing it is a single step. Copy the install command from the Camoufox browser GitHub (linked in the video description) and paste it to your OpenClaw bot. Installation can take a while depending on your setup, and OpenClaw works quietly in the background rather than narrating each step.

To confirm it has not frozen, open your OpenClaw dashboard and check that the process is running. If you run OpenClaw in a Docker container, you may need to manually restart the gateway after install; otherwise OpenClaw will tell you it restarted on its own.

One prompt to build the research assistant

With the plugin installed, a single prompt builds the whole agent. Ask OpenClaw to create a production-ready research assistant using the GLM-5, OpenClaw, and Camoufox stack. It will create an agent profile, configure Camoufox, and set up a workflow to search X for the topics you care about over the last 24 hours, then schedule that workflow to run daily and save a digest.

The prompt in the video does roughly this:

Create a production-ready research assistant using GLM-5 on Venice, OpenClaw,
and the Camoufox browser. Create an agent profile and configure Camoufox.
Build a workflow that searches Twitter for AI, browser agent, OpenClaw, and
Venice talks from the last 24 hours. Schedule it to run daily at 8 a.m. and
save a daily digest. Send me a daily digest every morning.

This build took about five minutes. OpenClaw creates a new folder structure for the agent, defines the skill, attaches the Camoufox plugin, and returns a configuration summary.

Adding API keys and Twitter cookies

The agent now needs credentials. You can set keys through the terminal, or simply hand them to OpenClaw in the conversation. Provide your Venice API key and ask OpenClaw to help generate the Camoufox API key.

After the first test run, expect Camoufox to fail to reach X, because it is not logged in. To authenticate, the agent acts as you using your browser cookies. Open Twitter, right-click, choose Inspect, go to the Application tab, and find the Cookies section. The fields the agent needs are:

auth_token
ct0
guest_id
personalization_id
twid
kdt

You can copy these fields manually and paste them to OpenClaw. A faster route is a "copy all cookies" browser extension that exports everything in the right format at once. If you use one, be careful to install a clean, well-reviewed extension, since these tools have access to your session data.

First run and your daily digest

Once the cookies are in place, the agent logs in and can see your home feed. Because it is operating inside your own account, it sees who you follow and what you are interested in, and it views the page from a browser perspective: the feed, the sidebar, and the surrounding UI, not just raw text.

Tell it to run the daily digest. GLM-5 filters the feed against your intent, stripping out FOMO and fear-mongering so you get signal rather than slop. The output is a ranked digest, for example the top five AI stories on X right now, each summarized with its key takeaway. You now have a working scraper that runs on schedule.

Private inference and DIEM economics

The combination that makes this work: GLM-5 is a powerful open-source agentic model, and it stays fully private when you run it through Venice. OpenClaw orchestrates the workflow and Camoufox handles the browsing that sites normally block.

Venice gives you access to premium models either anonymized or fully private. You can get recurring daily API access by buying and staking the DIEM token: one DIEM gives you 100 API credits each day, and the allowance refreshes daily. Share what you build in the Venice Discord.

Key takeaways

• A capable agent splits into three layers: GLM-5 as the brain, OpenClaw as the orchestrator, and Camoufox as the executor that browses.
• Camoufox installs as a single OpenClaw plugin and lets agents reach sites like X, LinkedIn, and Amazon that block automation.
• One well-structured prompt can build, configure, and schedule a complete research assistant in minutes.
• Authentication works by exporting your Twitter cookies (auth_token, ct0, guest_id, personalization_id, twid, kdt) so the agent acts as you, while GLM-5 on Venice keeps the whole workflow private and unlogged.