How to Reduce Hallucinations in Your OpenClaw Agents
How to Reduce Hallucinations in Your OpenClaw Agents
Let's get the uncomfortable truth out of the way first: your OpenClaw agent is lying to you, and it's doing it with absolute confidence.
You built a research agent. You gave it access to web search, a document reader, maybe a database connector. You tested it with a few simple queries, and it looked brilliant. Then you pointed it at a real task — something like competitive analysis, lead enrichment, or summarizing a stack of internal documents — and the wheels came off. The agent started inventing numbers. It cited papers that don't exist. It called tools with made-up parameters. It told you a company's revenue was $450 million when the actual number was $38 million, or it "summarized" a document by pulling statistics from thin air.
This is the hallucination problem, and if you're building agents on OpenClaw, it is the single biggest obstacle between you and something you can actually trust in production.
I've spent the last several months building and debugging OpenClaw agents across a range of use cases, and I've hit every flavor of hallucination there is. What follows is everything I've learned about why OpenClaw agents hallucinate, what actually works to reduce it, and how to structure your builds so you can catch the remaining hallucinations before they cause damage.
Why Your OpenClaw Agent Hallucinates (It's Not Just the Model)
Most people blame the underlying language model, and sure, the model matters. But the real issue is architectural. Hallucinations in agents are a systems problem, not just a model problem.
Here are the three main failure modes I see constantly in OpenClaw builds:
1. Tool-Calling Hallucinations
This is the most common and the most frustrating. Your agent invents tool names, passes hallucinated arguments, or fabricates the results of a tool call that either failed or never happened.
Classic example: you've configured a skill with a tool called search_company_data, and the agent decides to call lookup_company_info instead — a tool that doesn't exist. The call fails silently or throws a parsing error, and the agent just... keeps going, making up whatever it thinks the tool would have returned.
This happens because the model is pattern-matching on what tools "should" be called based on its training data, rather than strictly following the tool definitions you've given it.
2. Context and Fact Hallucinations
Your agent has the actual information right there in its context window — a retrieved document, a tool output, a database result — and it ignores it. Instead, it generates plausible-sounding "facts" from its parametric memory (i.e., what it learned during training).
I had an agent that was supposed to extract pricing information from competitor websites. The web scraper returned the actual pricing page content. The agent's summary included three pricing tiers that didn't exist on the page. It had the real data. It just preferred its own imagination.
3. Planning and State Hallucinations
In multi-step workflows, the agent loses track of what it's already done. It claims it already searched for something when it didn't. It skips steps because it "remembers" completing them. Or it gets stuck in loops, repeating the same action because it hallucinates a different result each time.
This gets exponentially worse as task complexity increases. A 3-step workflow might work 90% of the time. A 15-step workflow? You're looking at maybe 20-30% reliability without guardrails. The math is brutal — if each step is 95% reliable, ten steps gives you roughly 60% end-to-end reliability. At twenty steps, you're below 40%.
The Actual Fixes (In Order of Impact)
I'm going to walk through the specific techniques that have made the biggest difference in my OpenClaw agents, ranked by how much they actually move the needle.
Fix #1: Use Strict Structured Output for Every Tool Interaction
This is the single highest-impact change you can make. Stop letting the agent free-form its tool calls. OpenClaw supports structured skill definitions — use them aggressively.
Instead of letting the agent decide how to format its tool calls, define explicit schemas for every input and output:
skills:
- name: search_company_data
description: "Search for company financial data by company name and fiscal year"
input_schema:
type: object
properties:
company_name:
type: string
description: "Exact legal name of the company"
fiscal_year:
type: integer
description: "Four-digit fiscal year (e.g., 2026)"
required:
- company_name
- fiscal_year
output_schema:
type: object
properties:
revenue:
type: number
description: "Annual revenue in USD"
source_url:
type: string
description: "URL where this data was found"
confidence:
type: string
enum: ["verified", "estimated", "not_found"]
The key here is the output_schema. By defining what the tool returns, you create a contract that downstream steps can validate against. If the tool returns confidence: "not_found", your agent's instructions can explicitly say "do not report revenue figures when confidence is not_found."
This alone eliminated about 40% of the hallucinations in my builds.
Fix #2: Add Verification Nodes to Your Workflow
This is the technique that separates toy agents from production agents. After any step where the agent generates a claim, summarizes information, or makes a decision, add a separate verification step.
In OpenClaw, you can structure this as a dedicated verification skill that receives both the original source material and the agent's output:
skills:
- name: verify_output_against_source
description: "Compare agent output against source documents. Flag any claims not directly supported by the source material."
instructions: |
You are a fact-checker. You will receive two inputs:
1. SOURCE: The original tool output or retrieved document
2. CLAIM: The agent's summary or conclusion
For each factual claim in CLAIM, do the following:
- Find the exact supporting quote in SOURCE
- If no supporting quote exists, flag it as UNSUPPORTED
- If the claim contradicts SOURCE, flag it as CONTRADICTED
- If the claim is supported, include the verbatim quote
Output ONLY claims that are SUPPORTED with their source quotes.
Remove all UNSUPPORTED and CONTRADICTED claims entirely.
Yes, this adds another LLM call. Yes, it increases latency and cost. But it catches an enormous number of hallucinations. In my competitive analysis agent, adding verification nodes reduced factual errors from roughly 35% of outputs to under 5%.
The critical principle: never let the generating agent also be the verifying agent in the same step. Separation of concerns matters here just like it does in software engineering.
Fix #3: Force Verbatim Citation
This is a prompting technique, but it's so effective it deserves its own section. In every skill that processes retrieved information, include an explicit instruction requiring verbatim quotes.
Add this to your skill instructions:
CRITICAL RULE: Every factual claim you make MUST include a direct quote
from the provided source material in [brackets]. If you cannot find a
direct quote supporting a claim, do not make the claim. Say "not found
in provided sources" instead.
Example:
CORRECT: The company reported revenue of $38M ["Total revenue for FY2024
was $38.2 million" - Source Document, p.12]
INCORRECT: The company reported revenue of approximately $450M.
This works because it forces the model to ground every statement in actual text. When the model can't find a supporting quote (because the information isn't there), it's much more likely to admit that rather than fabricate both the claim AND a fake quote. Models can still hallucinate quotes, but it happens far less frequently than hallucinating bare facts.
Fix #4: Implement Observation-Based Feedback Loops
One of the most effective architectural patterns is borrowed from code execution environments: let reality push back.
When your agent takes an action and something goes wrong — a tool call fails, returns an error, or returns empty results — make sure that failure is explicitly fed back into the agent's context as an observation.
workflow:
- step: execute_tool
on_error:
action: feed_error_to_agent
instruction: |
The previous tool call FAILED with the following error:
{error_message}
Do NOT proceed as if the tool call succeeded.
Do NOT make up results.
Either retry with corrected parameters or report that
this information could not be retrieved.
Too many agent builds swallow errors silently or let the agent continue without acknowledging the failure. The agent then fills in the gap with hallucinated data because, from its perspective, it "should" have gotten results.
Make failures loud. Make them unavoidable in the agent's context.
Fix #5: Decompose Complex Tasks Ruthlessly
Remember that compounding probability problem? The best mitigation is to reduce the number of steps where the agent has to make autonomous decisions.
Instead of one agent running a 20-step research workflow, break it into discrete stages with checkpoints:
pipeline:
stage_1:
name: data_collection
skills: [web_search, document_retrieval]
output: raw_data
checkpoint: true # Save state, validate before proceeding
stage_2:
name: extraction
skills: [extract_key_metrics]
input: stage_1.raw_data
output: structured_metrics
checkpoint: true
validation: verify_output_against_source
stage_3:
name: analysis
skills: [comparative_analysis]
input: stage_2.structured_metrics
output: final_report
checkpoint: true
validation: verify_output_against_source
Each stage is a short, focused task. Each checkpoint gives you a place to validate before the next stage runs. If stage 2 hallucinates, you catch it before stage 3 builds an entire analysis on top of fabricated data.
Fix #6: Use the Best Model You Can Afford for Critical Steps
I know, this seems obvious. But I see people trying to run complex agent workflows on smaller open models and then wondering why hallucinations are through the roof.
Here's my practical recommendation for OpenClaw builds: use the most capable model available for any step that involves reasoning, synthesis, or decision-making. Use lighter models for simple extraction, formatting, or routing tasks.
The performance difference between models on agent tasks is not incremental — it's dramatic. A model that hallucinates tool names 15% of the time versus 2% of the time is the difference between a usable system and an unusable one, because that error rate compounds across every step.
A Practical Example: Building a Research Agent That Doesn't Lie
Let me put this all together with a concrete example. Say you're building an OpenClaw agent that researches companies and produces briefing documents.
Here's how I'd structure it with all the anti-hallucination patterns applied:
agent: company_research_v2
description: "Research a company and produce a verified briefing"
skills:
- name: web_search
tool: tavily_search
input_schema:
properties:
query: { type: string }
output_schema:
properties:
results: { type: array }
- name: extract_facts
instructions: |
Extract factual claims from the search results.
Every fact MUST include a verbatim quote from the source in [brackets].
If a fact cannot be supported by a direct quote, exclude it.
- name: verify_facts
instructions: |
Compare each extracted fact against the original search results.
Remove any fact where the verbatim quote cannot be found in the source.
Flag any numerical discrepancies.
- name: compile_briefing
instructions: |
Using ONLY the verified facts, compile a company briefing.
Do not add any information not present in the verified facts list.
Where data is missing, explicitly state "Not available in sources."
workflow:
- step: web_search
retries: 2
on_error: report_unavailable
- step: extract_facts
input: web_search.output
- step: verify_facts
input:
source: web_search.output
claims: extract_facts.output
- step: compile_briefing
input: verify_facts.output
Four steps. Strict schemas. Verbatim citation requirements. A dedicated verification step. Explicit handling of missing data. This produces dramatically better output than a single-prompt "research this company and write a briefing" approach.
Getting Started Without the Pain
If you're reading this and thinking "great, now I need to build all this infrastructure myself" — you don't necessarily have to. A lot of these patterns (verification skills, structured output schemas, anti-hallucination prompt templates, grounded citation workflows) are things that can be pre-configured and reused across projects.
If you don't want to set all this up manually, Felix's OpenClaw Starter Pack on Claw Mart includes pre-built versions of these exact patterns — verification skills, structured output templates, and grounded research workflows — all configured and ready to plug into your OpenClaw agents for $29. I've used it as a starting point for several builds, and it saved me a solid week of trial-and-error on the verification and citation enforcement patterns especially. It's not magic, but it's a genuinely useful shortcut past the "debugging hallucinations for the fifth straight evening" phase that everyone goes through.
The Honest Bottom Line
You are not going to eliminate hallucinations completely. Not with OpenClaw, not with any agent framework, not with any model currently available. What you can do is reduce them from "completely unreliable" to "reliable enough for production with appropriate guardrails."
The techniques that actually work, in priority order:
- Strict structured outputs for all tool interactions
- Verification nodes that separate generation from fact-checking
- Verbatim citation requirements in every retrieval-based skill
- Observation-based feedback loops that make failures visible
- Task decomposition to reduce compounding errors
- Strong models for reasoning-heavy steps
Build these into your OpenClaw agents from day one. Don't add them after you discover your agent has been confidently fabricating data for weeks. Trust me on that — I learned the hard way.
Start with one agent, one workflow, and implement all six patterns. Get it working reliably on a narrow task before expanding scope. The temptation is always to build the grand autonomous system first and add guardrails later. Resist that temptation. Guardrails first, autonomy second. Your future self (and anyone relying on your agent's output) will thank you.