Comparative Test: OpenClaw vs. Manus vs. Other AI Agents on Telegram: Which One Suits You Better?
When using AI Agents in Telegram, the pain points you encounter most often are: unstable conversations, unreliable command execution, slow cross-lingual understanding, and unclear tool invocation chains. To answer these questions, we conducted a comparative test using the same set of tasks: the performance differences of OpenClaw, Manus, and other AI agents on Telegram will directly affect your efficiency in processing information, generating content, and distributing summaries in groups. This article focuses on the usability, response latency, and controllability of OpenClaw vs. Manus on Telegram, and provides a reproducible testing method.
Table of Contents
1. Test Objectives: What exactly are we comparing in Telegram?
2. Test Preparation: Same environment, same input, reproducible.
3. Core Comparison Table: OpenClaw vs. Manus vs. Other AI Agents.
4. Telegram Conversation Experience: Context, tool invocation, and message flow.
5. Tool Chain Performance: Stability from intention to action.
6. Implementation Steps on Telegram (including feature list).
7. Turrit Main Feature Highlights: Making AI agents easier to use on Telegram.
8. Feature Introduction and Download: How to get started quickly.
9. FAQ: Common Long-tail Questions.
1. Test Objectives: What exactly are we comparing in Telegram?
AI Agents on Telegram are not just about "whether they can answer," but whether they can complete tasks within real message flows. We break the comparative test into three types of observable indicators: Conversation Stability, Command Execution Reliability, and Cross-lingual and Information Retrieval Efficiency. These indicators affect your experience in organizing content in groups, summarizing long texts into action items, or distilling information from external links into key points.
The performance aspects we focus on observing
Context Retention: Whether constraints (e.g., tone, format, output length) are lost during multi-turn follow-up questions.
Tool Invocation Controllability: Whether it can select the appropriate tools and steps according to your instructions, rather than just "looking like it finished."
Latency and Interruption Handling: How soon the agent starts responding after a message arrives on Telegram; whether it provides actionable prompts when a failure occurs.
Message Summarization and Structured Output: Whether it can directly generate copyable action lists, tables, or field-based content.
Multi-lingual Understanding: Whether the agent can maintain terminology consistency and target language output when encountering mixed Chinese and English text.
2. Test Preparation: Same environment, same input, reproducible.
Comparative testing should be as fair as possible. We use the same input tasks, similar conversation lengths, the same Telegram session modes (tested once in both groups and private chats), and standardize the agent triggering methods: using the same command format, the same output requirements, and the same "citation required/not required" constraints. This will make it easier for you to get similar results when re-testing.
Test Task Design
Each agent will receive three types of tasks: ① "Summarize-Rewrite-Generate List" (to evaluate structured output), ② "Rewrite with Constraints" (to evaluate stability in rule following), and ③ "Read External Link Content and Provide Conclusion" (to evaluate information processing ability). We record the following for each response: time to first response, whether the final output is directly usable, and whether secondary correction is needed.
3. Core Comparison Table: OpenClaw vs. Manus vs. Other AI Agents.
The table below only compares the most common performance points in the Telegram scenario. You can treat it as a quick screening checklist for selection.
Evaluation Dimensions (for Telegram scenario) | OpenClaw | Manus | Other AI Agents (similar) |
|---|---|---|---|
Conversation Stability (Multi-turn follow-up) | Usually able to maintain constraints, but relies more on you to reiterate format requirements after long conversations. | Stronger adherence to format; tends to clarify first when encountering conflicting instructions. | Fluctuates significantly; commonly "answers correctly but doesn't follow your structure." |
Command Execution Reliability (Are tasks done correctly?) | Clear execution chain, but occasionally writes boundary conditions into the results rather than processing them. | Works well with step-by-step workflows and is suited to breaking tasks into smaller steps. | Some agents are more prone to providing advice rather than direct action outputs. |
Structured Output (Copyable) | Field-based and sectioned output feels more natural, making it suitable for a deliverable version. | Field-based and segmented structure is more natural, suitable for creating "deliverable versions." | Requires more secondary organization by you. |
Cross-lingual Understanding (Mixed Chinese/English) | Can output Chinese, but requires you to specify terminology consistency. | You more often need to organize the output further. | Commonly suffers from translation style drift or uses mixed Chinese-English terminology. |
Failure and Retry Prompts | Provides alternative solutions, but you need to understand its suggestions for the next step. | Prompts are more like "process guidance," suitable for beginners to follow. | Some simply stop at "unable to complete," lacking a path to continue. |
If you are doing group content production or information organization, structured output and retry prompts are more critical than "single-answer quality."
4. Telegram Conversation Experience: Context, tool invocation, and message flow.
In Telegram, the message pace is fast: you might first collect information in a group, then ask the agent to generate a summary in a private chat. The conversation experience at this point depends on two points: context passing and message format consistency.
OpenClaw acts more like an "Execution Assistant" in Telegram
OpenClaw's advantage usually lies in converting instructions into executable steps, with output that is easier to copy into your workflow. However, when your input lacks boundary conditions, it might "write your requirements into the result" rather than actually using the requirements for processing. You can use clearer format constraints to reduce rework, such as requesting a title, the number of key points, tone, and whether field names are needed.
Manus acts more like a "Process Partner" in Telegram
Manus is friendlier regarding process and clarification. After you provide a goal, it tends to align on your constraints first before outputting results. This reduces the problem of "looking correct but not fitting the format you need." For technical users, this method also makes it easier to connect output directly to templates or scripts.
Common Problems with Other AI Agents
Similar agents sometimes exhibit two phenomena: ① the output content is correct but the structure is unstable; ② when a failure occurs, they do not provide a retry path that can be acted upon. The group chat environment of Telegram will amplify these problems because you often have to complete publication or delivery within a limited amount of time.
5. Tool Chain Performance: Stability from intention to action.
What really sets them apart is not "generating text," but whether the "intention-to-action" chain can be stably completed. We observed the agent's performance in the following scenarios:
From summary to action items: Whether the agent can break long text into executable tasks (assignee, deadline, priority).
From external link to conclusion: When the information in the link is dense, whether the agent can provide key points that can be verified.
From constraint to format: When you request tables/bullet points/word counts, whether it strictly adheres to them, rather than "trying to get close."
In the comparative test, Manus is more stable in "process alignment," while OpenClaw is more direct in "templated output." Other AI agents sometimes provide suggestive text in the middle of the tool chain, causing you to need additional follow-up questions to enter the actual action output.
If you use an agent as an assistant for automated writing and information processing, prioritize products that can provide clear retry steps when they fail.
6. Implementation Steps on Telegram (including feature list).
You don't need to change your usage habits on Telegram. As long as you make your "questioning method" a fixed template, you can significantly improve the usability of OpenClaw, Manus, and other agents in Telegram.
How to get an agent to follow instructions, step by step.
Give the goal first: Clearly state the result type you want (summary/rewrite/generate list/generate announcement) in one sentence.
Then provide constraints: Specify language, tone, length, fields, or format (e.g., title + +3 paragraphs + 5 bullet points per paragraph).
Then provide input source: Directly paste the original text, or provide an external link and explain what kind of information you want it to extract.
Finally, specify the delivery method: Tell it whether the output should be copyable, whether it needs to be a table, and whether numbered lists should be used.
Feature list: Capabilities you should prioritize using in Telegram
AI translation and message-level translation: Reduce comprehension bias in mixed Chinese/English text.
Page-level translation (external link content): Let external web page information directly enter your analysis process.
Cloud collection and file organization: Keep the results generated by the agent and materials in the same workspace.
Keyword filtering and anti-spam: Reduce group noise so the agent processes fewer irrelevant texts.
By doing this, you will find that the same agent can have vastly different results under different prompt templates.
7. Turrit Main Feature Highlights: Making AI agents easier to use on Telegram.
When you are doing an OpenClaw vs. Manus comparison, many "problems that look like AI issues" actually come from experience bottlenecks at the Telegram client level. Such as slow external link reading, incoherent translation, too much chat noise, and scattered files. The capabilities provided by Turrit can push down these bottlenecks, allowing you to focus more on the agent's task performance itself.
Translation Ability: Making cross-lingual tasks smoother
Turrit's AI Translation is geared towards Telegram message flows. You can translate before sending, or translate an entire conversation in the chat with one click; external link pages can also be translated directly into the language you need. Relevant features include: Translate before sending, Free Real-Time Translation, and Real-Time Page Translation. When you ask the agent to process multi-lingual content in Telegram, unify the language of the input first, and the agent will find it easier to maintain terminology consistency.
Download and Acceleration: Improving agent delivery efficiency
Upload and download acceleration and multi-item speed settings will save you time when processing tasks with materials, for example: letting you save files generated by the agent faster and return materials to subsequent processes.
Cloud Storage and Organization: Reducing the cost of "not finding results"
Unlimited Ultra cloud storage lets you centrally manage images, videos, music, and files. You will generate result versions repeatedly when conducting agent comparisons, and cloud organization allows you to locate output differences between the "OpenClaw version" and "Manus version" faster.
Privacy and Anti-Spam: Keeping group testing cleaner
Privacy Detection is used to detect the risk of number leakage; Keyword Blocking Settings and message filtering capabilities help you hide advertisements and spam messages first. For Telegram group testing, this reduces distracting text, making the agent's input closer to what you actually want to evaluate.
Multiple Accounts and Multiple Sessions: Suitable for long-term evaluation
If you manage multiple groups or multiple work domains at the same time, Turrit supports the ability to log in with more accounts (relative to the native limits of Telegram). This allows you to perform long-term re-testing of different agents under the same set of test templates.
For authoritative information regarding the Telegram platform and privacy/encryption, you can also refer to the official instructions and privacy-related documents of Telegram: Telegram Privacy.
8. Feature Introduction and Download: How to get started quickly.
You can treat Turrit as a tool to "enhance your Telegram workflow," using it to amplify the differences between OpenClaw and Manus at the agent level. After installation, first do three things: ① turn on translation-related settings; ② use page translation for external link tasks; ③ use cloud storage to save the output of different agent versions.
Recommended getting-started order
Enable translation: Confirm that message translation and page translation are available in the translation settings of Turrit.
Enable external link page translation: Allows for more unified input when the agent reads web page content.
Set up file organization: Get agent output and materials into the same cloud space to facilitate re-testing.
Turn on privacy detection and message filtering: Reduce group noise to keep inputs cleaner.
Download and installation portal: Turrit. After you complete the installation according to the page guidance, you can start using the same test template to compare the actual performance of OpenClaw, Manus, and other AI agents on Telegram.
9. FAQ: Common Long-tail Q&A
Q1: When testing OpenClaw vs. Manus in a Telegram group, how can I reduce the errors caused by "different inputs for the same task"?
You need to fix three things: ① the scope of the input content (only paste the same original text or the same announcement link); ② the output format (e.g., "title + +5 bullet points + action item table"); ③ language and terminology requirements (explicitly specify Chinese output or specify that key nouns should keep the original text). In addition, you can first use Turrit's Real-time Translation and Page Translation to unify the language, and then pass the unified text to the agent, which will significantly reduce differences caused by multiple languages.
Q2: Why does Manus sometimes seem better at tasks, but still requires rework when delivered in Telegram?
This is because in the Telegram message flow, you might have omitted "the form of result you want." Manus tends to align on processes first, it will provide answers that conform to logic, but if you haven't clearly written the field structure (e.g., needing a table, needing numbers, needing a specific word count), what you receive might be "high completeness but not directly usable." The solution is to supplement constraints using a stable template: goal, format, length, delivery method, and when necessary, request "only output the final result, no intermediate reasoning."
Q3: If I need the agent to process external link content, what is the actual relationship between Turrit's page translation and OpenClaw/Manus?
Page translation mainly solves "consistency between input language and comprehension." When you open an external link in Telegram and let the agent read the information, page translation first unifies the web page content to the language you set, thereby reducing deviations in the agent's comprehension of the context. The end result is: it is easier for the same agent to exhibit stable differences under the same task, rather than having the evaluation results affected by translation quality or language inconsistency.