Setting up OpenClaw automation using third-party AI agents according to the technical specifications.

Budget: 27000 UAH Deadline: 20 days

Greetings! The task of deploying a multi-agent system based on OpenClaw for automating project management and analytics is an excellent case for creating a full-fledged "AI office." I specialize in developing complex AI agents and automating workflows (over 30 projects). I have practical experience working with the OpenClaw architecture, particularly with configuring MCP (Model Context Protocol) and custom tools for integration with Jira and Slack. Below is my vision for implementing your ecosystem of 5 agents:

🛠 Technical implementation of agents
Agent 1 & 2. Jira & Team Load Assistant
Instead of two separate scripts, I propose using a single Jira Toolset within OpenClaw.
* Logic: The agent uses JQL (Jira Query Language) to collect tickets. To assess workload (%), we will implement an algorithm that matches Story Points or Original Estimate with a workday (8 hours).
* Slack Integration: Creating adaptive cards (Block Kit) for visualizing the Summary.

Agent 3. Research Agent (Perplexity)
* Implementation: Integration via the Perplexity API (sonar-reasoning model).
* Workflow: The agent receives a request from Slack, performs a web search, filters relevant links via BigQuery, and structures the response according to your template (Summary -> Links -> Key Ideas).

Agent 4. Meeting Agent (Fireflies)
* Implementation: Using Webhooks from Fireflies.
* Logic: Once the transcript is ready, OpenClaw retrieves the text, processes it through a prompt instruction to separate "Done" and "Action Plan," and pushes it to the appropriate Slack channel.

Agent 5. Multi-Agent Grooming (Orchestrator)
This is the "brain" of the system. In OpenClaw, we will set it up as a Supervisor Agent.
* Function: At the end of the day, it polls the logs of the previous 4 agents (via internal DB or session logs) and generates a meta-summary: "Agent 3 found 5 practices, Agent 1 identified 10 new bugs." This will allow seeing the overall picture of the system's effectiveness.

📋 Development plan for the test environment:
* Infrastructure: Deploying OpenClaw in a Docker container, configuring environment variables (API keys for Jira, Slack, Perplexity, Fireflies).
* MCP & Tools: Creating custom tools for reading Jira and sending messages to Slack.
* Prompt Engineering: Setting up system prompts for each agent to maintain the required summary format.
* Orchestration: Configuring Agent 5 for data aggregation.

⏱ Timelines and costs:
* Completion time: 14–20 working days (including testing all flows).
* Cost: $2,500 – $4,500 (depends on the depth of integration and the need for adjustments). Expand

Budget: 16000 UAH Deadline: 5 days

Good day
My name is Dmytro, from King Kong Lab. We have experience in building similar systems with AI agents, integrations (Jira, Slack, API), and automations, so we can effectively implement your case.
We can set up OpenClaw or propose a more stable architecture (through n8n / custom AI agents) if it will yield better results.
We implement all use cases:
- agent for Jira (task collection, statuses, summary formation in Slack)
- team loading agent (task analysis and % occupancy)
- Research agent (through Perplexity / LLM) with responses in Slack
- Meeting agent (Fireflies) with a clear summary + action plan
- multi-agent orchestration (overall summary, issues, recommendations)
We will create not just a "service connection," but a stable system with logic, error handling, and scalability.
We have practical experience working with LLM, AI agents, Slack, Jira, and similar automations.
We are ready to discuss the details, stack, and immediately connect to the test environment. Expand

Budget: 16000 UAH Deadline: 7 days

Good day. I reviewed the technical specifications, I will implement a multi-agent system (Jira, Slack, Perplexity, Fireflies): automation, summaries, RAG, synchronization. FastAPI + API integrations.

Budget: 999 UAH Deadline: 1 day

Hello!

We are Devoxen, and we have extensive experience in similar projects, including setting up AI automations, multi-agent scenarios, and integrations with Slack, Jira, meeting services, and external research tools. Based on your description, the task is quite feasible in a test environment: we can build separate agent pipelines for Jira summary, team loading, research requests from Slack, meeting summaries, and an orchestration layer that will synchronize the agents with each other, collect daily reports, problem areas, and recommendations. We can also help assess where OpenClaw is truly optimal and where it would be better to enhance the solution with additional AI agents and integration logic.

I suggest moving to private messages for a more detailed discussion of the technical specifications, solution architecture, deadlines, and costs.

Budget: 11111 UAH Deadline: 30 days

Hello, Andrey!

The task is clear — it is not to "write a single bot," but to assemble a system of AI agents that interact with each other and provide a manageable result in Slack.

Judging by the description, you already have an understanding of the roles (Research / Meeting / Summary / loading by people), but now this needs to be gathered into a unified logic so that it is not just a collection of disparate tools.

I would implement this as a bundle:

— Slack as the main interface
— agent orchestrator (via OpenAI / Perplexity / Fireflies API)
— centralized logic (Node.js / Python)
— storage of history and states (so that agents "remember" the context)

As I see it for the agents:

Research Agent

Request from Slack → Perplexity / LLM →
output: summary + links + key ideas
(with a proper structure, not a "sheet of text")

Meeting Agent (Fireflies)
We collect data from meetings →
divide into:
— what has been done
— action plan
— risks / blockers

Tracking (Roman / Egor)
We form a clear summary:
— what they are working on
— % workload
— current tasks

Multi-agent orchestration (key block)
We aggregate everything into one daily summary:
— who did what
— where the problems are
— recommendations

Here the main risk is not in the API, but in **the logic of aggregation and the quality of the summary**.
If this is not thought out from the start — it will just be "noise from the data."

I have worked with:
— AI integrations (LLM + API)
— automation through Slack
— multi-agent logic (aggregation, pipelines)

Deadline: 4 weeks
Cost: 1000 USD

If you use OpenClaw — we can either integrate into it or propose an alternative architecture (sometimes this gives a more stable result).

We can discuss the details and build a system that will really help, not just generate texts 👍 Expand

Budget: 7000 UAH Deadline: 18 days

Good day, Andrey. I can implement the Openclaw logic for you in a Python script in which we will describe:
1. API Token.
2. Slack App.
3. We will add the necessary workspace.
I am fully familiar with your task and ready to start working with you. I will also add a configuration that you will need in the next stage.

Budget: 1000 UAH Deadline: 3 days

Good day!

I have practical experience in setting up complex automations with OpenClaw, integrating third-party AI agents. I am ready to implement the described use cases for Jira, Slack, Perplexity, Fireflies, and Google BigQuery API.

Message me privately, and we will clarify the details.

Budget: 27000 UAH Deadline: 30 days

Great idea, it can be implemented and I can do it for you, maximizing automation even with my additional ideas, but I want to understand right away what budget we are talking about, and whether it will be interesting. Thank you.

Budget: 2000 UAH Deadline: 3 days

Hello! I have reviewed the project and am ready to start working. I am sure you will be satisfied with the result.

Budget: 15000 UAH Deadline: 10 days

Hello, Andriy!

I am ready to take on the setup of OpenClaw with the described use cases. I have experience with multi-agent systems, integrations with Jira API, Slack, Fireflies, Perplexity API, and orchestration of AI agents.

What I will do:

Agent 1 (Jira) — connection to Jira REST API, gathering tasks, parsing statuses, forming summaries, and sending to Slack
Agent 2 (Team summary) — aggregation by team members displaying scope/progress/availability
Agent 3 (Research) — integration with Perplexity API for searching best practices, returns summary + links
Agent 4 (Meeting) — Fireflies API → parsing meeting protocols → action items + done in Slack
Agent 5 (Multi-agent) — orchestrator that synchronizes all agents and forms a consolidated report
I have worked with similar pipelines in Python + LLM (OpenAI API, RAG). I also have experience with n8n/Make for automations, if a hybrid approach is needed.

I communicate via text and am available every day. Expand