Build AI Agents with Kimi K2.5: Tools, Swarms, and Workflows

Kimi K2.5 is Moonshot AI’s flagship open-weights model built for AI agents. It is made for coding, tool use, and multimodal work like understanding images.

In some setups, video understanding is available through the official API.

What makes Kimi K2.5 stand out is its Agent Swarm feature. It can spin up to 100 sub-agents and run up to 1,500 tool calls in one session.

This lets it split big tasks into parallel work, then merge the results into one final output.

In this guide, you will learn how to build agents with Kimi K2.5 step by step. We will start with a simple single-agent setup.

Then we will add tools, use “coding with vision,” and scale up to Agent Swarm for complex workflows.

What is the Right Kimi K2.5 Mode for Your Agent?

Kimi K2.5 comes in several modes, and each one is built for a different kind of agent work. If you pick the right mode early, your agent will be faster and cheaper.

It will also be easier to control.

K2.5 Instant: fast answers, low effort

Use K2.5 Instant when you need speed and simple results. It fits tasks like formatting text, extracting key points, or turning notes into a clean summary.

It is not the best choice for deep reasoning or long, complex plans.

K2.5 Thinking: careful reasoning for hard steps

Use K2.5 Thinking when your agent needs to plan, debug, or solve a tricky problem.

It is better for multi-step logic and careful decisions. Because it thinks more, it can be slower and use more tokens.

K2.5 Agent: built for deliverables

Use K2.5 Agent when you want the model to create a clear output like a website, slides, or a structured document.

This mode is meant for work that has a final deliverable, not just a chat answer. It is a good middle ground when you need both planning and production.

K2.5 Agent Swarm: parallel work for big tasks

Use K2.5 Agent Swarm when the job can be split into many parts. It can run multiple sub-agents in parallel and then combine their results.

This is useful for deep research, long reports, and big build plans.

A quick decision rule: If the task is small and repeatable, start with Instant. If one agent can solve it but it needs careful steps, use Thinking or Agent. If the task has many independent parts, move to Agent Swarm.

Also, even though the system can support up to 100 sub-agents, it may choose fewer agents for a task. It can decide how many sub-agents it needs based on the work.

So, your prompt should clearly describe parallel subtasks if you want more parallelism.

Your First Kimi K2.5 Agent (Single-Agent Baseline)

Before you use Agent Swarm, start with one agent that is easy to control. This makes results more reliable. It also helps you fix issues early.

Step 1: Pick one clear job for the agent

Choose a task with a clear “done” result. For example, the agent can create a research brief, write a build plan, or summarize findings from web searches. Keep it to one outcome per run.

Next, define three things in plain language. State the input, the output format, and what success means. This reduces confusion and cuts wasted tool calls.

Step 2: Write an output contract

An output contract is a short set of rules for the final answer. It tells the agent how to structure the response every time. This matters because agent runs can drift without a clear target.

Use a fixed structure. For example: Title, Summary, Steps, Risks, and Next Actions. If you need sources, require a short source list at the end.

Step 3: Add tool rules and stop rules

If your agent can use tools, define when it should use them. Also define when it should not. This prevents tool spam and keeps costs under control.

Add stop rules so the agent does not loop. For example: “If you cannot find a source after three searches, say what you tried and stop.” This keeps the agent predictable.

Step 4: Use a simple agent loop

Use a basic loop: plan, act, check, then finalize. This matches how agent workflows are shown in the Kimi demos. It is also the easiest loop to debug.

Ask for a short plan first. Then have it execute the plan with tools if needed. Finally, require a quick check against the output contract before it answers.

A starter prompt you can copy

System message

• You are an AI agent that produces: [one deliverable].
• Follow this output format: [your output contract].
• Tool rules: [when to use tools, and limits].
• Stop rules: [when to stop and report limits].

User message

• Task: [your task]
• Inputs: [data, links, files, or context]
• Constraints: [deadline, length, style, must-have points]

Next, add tools so the agent can search, calculate, and generate real deliverables. Start with a small tool set and clear limits. That is the easiest way to keep quality high.

Tooling That Makes Agents Useful (Plus Guardrails)

A chat-only agent can sound confident, but it cannot check much. Tools let a Kimi K2.5 agent search for sources, run calculations, and generate real files.

As a result, the agent becomes more reliable.

Start with a small, practical tool set

Start with tools you will actually use every day. Most agent builds need three basics. They are web access, a compute tool, and an output tool.

Use this starter set:

• Web search or browser access to find sources and verify facts.
• Python for calculations, parsing, and repeatable steps.
• Document tools when you need Word edits, PDFs, spreadsheets, or slides.

Keep the first version small. Too many tools at once makes mistakes harder to diagnose. It can also increase wasted tool calls.

Write simple rules for tool use

Tool rules explain when the agent should use a tool and why. Without rules, it may browse for things it already knows. It may also call tools in loops.

Use rules like these:

• “Browse only when you need a source for a factual claim.”
• “Use Python only for math, parsing, or structured transformations.”
• “Do not browse for opinions. Browse for evidence and references.”

Also set a maximum tool-call count per task. Kimi K2.5 Agent Swarm can run up to 1,500 tool calls in one session. That power needs limits, or costs can grow quickly.

Add stop conditions and budgets

Stop conditions prevent endless retries. Budgets prevent runaway spending. Both make your agent predictable.

These limits work well for most tasks:

• Max web searches: 3 to 5
• Max Python runs: 3 to 10
• Max retries per step: 1 to 2
• Hard stop: “If the task cannot be finished, list what was tried and what input is missing.”

Budgets matter even more with swarms. A swarm can multiply tool calls across many sub-agents. So limits protect your time and your wallet.

Log what happened so you can improve it

Logging is how you make an agent better over time. Keep it simple and consistent. Save the same set of details after every run.

Log these items:

• Task goal and chosen mode (Instant, Thinking, Agent, or Swarm)
• Tools used and how many calls were made
• Sources found, if browsing was used
• Final output and any uncertainty

This log helps you spot patterns. For example, you can see if the agent is over-searching. You can also see which step causes failures.

Video support depends on how you run it

Kimi K2.5 can understand video in some setups. In many third-party deployments, video may not work. If you need video inputs, plan to use Kimi’s official API.

Building “Coding with Vision” Agents

Kimi K2.5 is built to work with images, not just text. This matters because many coding tasks start with a visual.

A screenshot shows layout, spacing, colors, and text hierarchy in a way a written brief usually does not.

Screenshot to website: a simple build pipeline

Start by giving the agent a clear screenshot of the UI. Then ask it to describe the layout in plain terms before writing code. This forces it to notice structure like spacing, sections, and components.

Next, have it create a component plan. The plan should list the page sections and reusable parts. After that, it can generate the code in one pass.

Finally, run a visual check loop. Ask the agent to compare the output to the screenshot and list the top differences. Then have it update the code to close those gaps.

Visual debugging: the loop that improves quality

Coding with vision works best when the agent can iterate. The loop is simple: render, look, fix, and repeat. This is how you get from “close enough” to “looks right.”

Keep each iteration small. Ask for a short list of changes, then apply only those changes. This avoids breaking things that already look correct.

Also ask for a final polish pass. That pass should cover mobile layout, spacing, and animations. It should also check basic accessibility like readable text and clear buttons.

Video to code: when motion matters

Sometimes a screenshot is not enough. A video shows how the UI behaves. It shows scroll, hover, timing, and interaction flow.

Use video when the task depends on motion. For example, scroll-triggered animations or multi-step workflows are easier to copy from video. Then ask the agent to describe the interaction steps before it writes code.

If video input is not available in your setup, use screenshots instead. Take frames of key moments and provide them in order. This still gives the agent the behavior you want it to copy.

Scaling Up With Agent Swarm

Some tasks are too big for one agent. They have many independent parts that can run at the same time. Agent Swarm is built for that kind of work.

When Agent Swarm is the right choice

Use Agent Swarm when the task can be split into parallel chunks. Good examples are deep research, large reports, and multi-step projects with many sources. It also helps when you need speed on tool-heavy workflows.

In Agent Swarm, Kimi K2.5 can spin up many sub-agents and coordinate them. The system supports up to 100 sub-agents and up to 1,500 tool calls in one session.

It can also reduce execution time by about 4.5 times compared to a single-agent setup, based on the claims shown in the demos.

How the swarm works

Agent Swarm uses an orchestrator. The orchestrator breaks the main task into smaller tasks. Then it assigns those tasks to sub-agents.

Each sub-agent can work in parallel. They can run searches, collect sources, write sections, or test ideas. After that, the orchestrator gathers the results and writes the final answer.

Kimi may choose fewer agents than the maximum. In demos, it sometimes spins up only a few agents even when the system can support more.

So your prompt should describe clear parallel subtasks if you want more parallel work.

Swarm patterns you can copy

A good swarm has roles. Each role has a narrow job. This keeps the work clean and reduces overlap.

Pattern 1: Research Swarm

• Agent 1: Find sources and links
• Agent 2: Summarize each source with key claims
• Agent 3: Fact-check and flag weak claims
• Agent 4: Synthesize into one structured report

Pattern 2: Build Swarm

• Agent 1: Create a build plan and file structure
• Agent 2: Implement core features
• Agent 3: Write tests and check edge cases
• Agent 4: Write documentation and usage steps

Pattern 3: Office Swarm

• Agent 1: Draft or edit the document
• Agent 2: Build the spreadsheet model
• Agent 3: Create the slide deck
• Agent 4: Review formatting and consistency

Avoid messy results with a strong orchestrator

Parallel work can create repeated content. It can also create conflicting claims. To prevent this, give the orchestrator clear merge rules.

Tell the orchestrator to dedupe similar points. Tell it to resolve conflicts and explain which sources it trusted. Then require a final outline before it writes the full output.

Quality Control: Evaluation and Debugging

Agents fail in predictable ways. They can repeat themselves, miss key facts, or use tools too much.

A simple quality process keeps your Kimi K2.5 agent reliable.

Use a clear evaluation checklist

After every run, check the output against the same standards. This makes quality measurable. It also makes improvements easier.

Use this checklist:

• Does the output match the requested format and scope?
• Are the main claims supported by sources when sources are needed?
• Are the steps clear enough to follow without guessing?
• Did the agent avoid repeating the same point?

Also check what the agent did, not just what it wrote. Look at the number of tool calls and retries. Too many calls often means the prompt is unclear.

Add verification roles when accuracy matters

For important tasks, add a second pass. This can be a second agent, or a second step in the same agent. The goal is to challenge the first output.

Use these roles:

• Skeptic: challenges claims and asks “what evidence supports this?”
• Fact checker: verifies key numbers, names, and dates.
• Editor: improves structure, removes repetition, and fixes unclear language.

This works especially well in Agent Swarm. You can assign one sub-agent to each role. Then the orchestrator can merge their feedback into a final version.

Fix common failure modes with simple prompt changes

If the agent repeats itself, tighten the output contract. Require one point per bullet and no duplicates. Also tell it to merge similar ideas.

If the agent hallucinates sources, force it to separate “verified” and “not verified” claims. Require it to list sources at the end. If it cannot find a source, it should say so.

If the agent overuses tools, add budgets and stop rules. Then rewrite tool rules so tools are only used for specific steps. This keeps runs faster and cheaper.

Cost, Speed, and Deployment Choices

Agent building is not only about quality. It is also about cost and speed. If you manage both, your agents stay useful over time.

Control cost with simple budgets

Costs grow when an agent loops or overuses tools. This is even more important with Agent Swarm. Many sub-agents can multiply tool calls quickly.

Set budgets before you run. Limit web searches, Python runs, and retries. Also set a hard stop rule that ends the run with a short status report.

A smart pattern is to start cheap and scale only when needed. Use Instant for quick drafts and cleanup. Then use Thinking or Agent for the hardest step.

Improve speed with parallel work and clear scopes

Speed depends on how you structure the task. If your task has many independent parts, Agent Swarm can finish faster. It does this by running subtasks in parallel.

However, parallel work can create overlap. So give each sub-agent a narrow scope. Then tell the orchestrator to merge and dedupe before it writes the final output.

Choose how you will run Kimi K2.5

You can run Kimi K2.5 through an API. This is the simplest path for most teams. It also makes tool calling easier to manage.

You can also run open-weights models privately if you have the infrastructure. Kimi K2.5 is a very large MoE model, so serving it well can require serious GPU resources.

If you need private deployment, plan for that from the start.

Get Started With Helply Today For Production-Ready Agents

Kimi K2.5 is a strong model for building agents. Still, most teams get stuck on the “last mile.” That is where good demos turn into messy support experiences.

When you build your own agent, you usually end up owning the hard parts:

• Keeping answers accurate as docs change
• Preventing confident wrong replies
• Routing edge cases to humans with full context
• Measuring real resolution, not just “bot replies”

Kimi gives you the brain. However, it does not ship a complete customer support system by itself. You still need workflows, guardrails, and an accuracy loop to keep performance stable over time.

That is why teams use Helply, an AI support agent designed to resolve real requests end to end.

Helply is built around a measurable outcome: 65% AI resolution in 90 days, or you pay nothing.

Here is what Helply adds on top of “just a model”:

• Action-based support that completes workflows
• Hallucination-proof escalation into your help desk with transcript, citations, and customer context
• Gap Finder that surfaces missing or outdated docs from real conversations so accuracy improves over time
• Auto-training updates and auto-syncing knowledge so changes apply without manual rework

Want an AI agent you can measure and trust?

Start a free trial or book a demo and see how Helply takes actions, escalates safely, and works toward 65% resolution in 90 days.