I cannot afford to use the most expensive model for every task.

So I have been wondering whether a cheaper model with better skills could be enough for most of my daily work.

For example, AnySearch could give it access to current documentation, GitHub issues, and recent technical discussions instead of making it rely only on what it already knows.

I know this would not turn a cheaper model into a flagship model.

But for research, checking API changes, and finding current information, maybe it could make the gap much smaller.

Has anyone tried this?

I have been thinking about how much the model itself matters once the rest of the workflow is already in place.

Imagine two different setups.

The first uses a capable but not top-tier model. It has access to search, file handling, code execution, and a few reliable automation steps.

AnySearch handles current documentation, GitHub issues, and recent technical discussions. The prompts and task steps have also been refined over time.

The second setup uses the strongest available model, but without the same tools or an established workflow around it.

For a single difficult reasoning task, the stronger model may still win.

But for repeated daily tasks, I wonder whether the first setup would be more consistent, easier to maintain, and cheaper to run.

A good skill can provide the information a model is missing. A mature workflow can also reduce the number of decisions the model needs to make on its own.

Of course, tools cannot completely replace stronger reasoning.

But once the model is already good enough, how much additional value comes from upgrading the model, and how much comes from improving the workflow around it?

Would you rather use the strongest model alone, or a more affordable model with AnySearch and a mature set of skills?

My agent hit a FastAPI and Pydantic version conflict last night. Instead of pulling the compatibility matrix or changelog, it came back with five setup tutorials. None of them mentioned the version constraint.

It kept trying to reason through the conflict by swapping versions and guessing fixes. It made the problem worse twice.

What it actually needed was the Pydantic v2 migration notes and FastAPI release history. Those live in GitHub repositories and official documentation, but normal web search did not surface them.

I started wiring in AnySearch as the search layer. It routes directly to repository changelogs and package documentation, then returns clean Markdown the agent can act on. It found the breaking change note on the first try and pinned compatible versions.

The model was never the problem. It just could not reach the right source.
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I have been thinking about how to build an agent workflow that does not need to be rebuilt every time the model or platform changes.

The easiest way seems to be separating the workflow into three parts.

The first part contains the things that should stay stable, such as data sources, search tools, project files, and output formats.

The second part contains prompts, task steps, and automation rules that may need small adjustments.

The third part is the agent or model itself, which can be replaced when something faster, cheaper, or more capable becomes available.

AnySearch feels like it belongs in the first part.

It can be connected without redesigning the entire workflow, and the search step can continue serving the same purpose even when the agent changes.

That matters because search is not something I want to rebuild for every platform.

I still need current documentation, GitHub issues, recent technical discussions, and structured information that the agent can use directly.

Once AnySearch is connected, the value is not limited to a single task. It becomes a reusable search layer for coding, research, verification, and other workflows that depend on current information.

The agent may change how it reasons or decides when to search, so some prompts and tool rules may still need adjustment.

But the search capability itself does not need to be replaced every time.

That is the kind of skill I want in a long term workflow. It is simple to connect, useful across different tasks, and valuable enough to keep when the rest of the setup changes.

Has anyone else treated AnySearch as one of the stable parts of a workflow across different agents?
Which parts transferred easily, and which parts still needed to be adjusted?

I had my agent pick a Node.js framework last week, and it chose Express over Fastify. I thought it was actually weighing the tradeoffs. It wasn't.

I was testing AnySearch for technical queries and realized the top result was just an Express tutorial. The agent built its entire rationale around that one result.

Once the search tool pulled repository statistics and recent issue threads instead of that SEO page, the recommendation flipped to Fastify.

Your agent's tech stack decisions are basically downstream of whatever ranks first.

One thing I have learned from using AI agents is that finding an answer is easy.

Checking whether that answer is current and reliable is the harder part.

I now use a simple three step workflow with AnySearch:

1. Ask the agent to identify the exact claim it needs to verify.

2. Search official documentation, GitHub issues, and recent technical discussions instead of relying on one blog post.

3. Make the agent compare publication dates and mention any disagreement between sources before giving the final answer.

This has been especially useful for library updates, API changes, deprecated methods, and technical questions where an old answer can still look completely reasonable.

The biggest improvement is not that the agent finds more links.

It is that the agent has to explain why a source should be trusted before using it.

My current rule is simple:
If the answer depends on a version, date, or recent change, one source is not enough.

What checks do you add before trusting an answer from your agent?

I already have a fairly stable agent workflow where AnySearch handles searches for current documentation, GitHub issues, and technical discussions.

Now that GPT-5.6 is available, I am considering moving part of that workflow to Codex.

What I care about is not whether AnySearch can be connected.

I want to know how much of the original workflow can actually carry over.

I am fine with making a few adjustments, but I do not want to rebuild the entire search process every time I switch models or platforms.

Has anyone used AnySearch with GPT-5.6 in Codex?

Which parts transferred cleanly, and which parts needed to be configured again?

I pasted the same Rust async panic into my agent. A Tokio task was failing to schedule.​

Web search gave me generic async debugging blogs. Nothing matched the actual panic.​

Asking the model directly gave me a confident fix that referenced a Tokio API renamed two versions ago.​

Then I routed the query through AnySearch as a skill. It pulled the actual GitHub issue where someone had hit the same panic. The fix was a one line config change, not a rewrite.​

I have been testing AnySearch on a few more stack traces since. The source the agent reaches decides whether the fix is real or just plausible.

It feels like every major AI company has released something new recently.

GPT-5.6, Claude Sonnet 5, Gemini 3.5, Grok 4.5. Every launch comes with new benchmarks and claims about being the strongest.

But the strongest model is not always the one people actually use.

You might use one for coding, another for research, and something faster for everyday tasks.

Which AI do you currently open the most?

What do you mainly use it for?

Did any of the recent updates make you switch, or are you still using the same one?

Been bolting a search skill onto my coding agent lately and kept hitting the same 3 walls.​

First issue is that normal search just gives you blog spam instead of actual docs.​

I asked it to look up how a library handles async retries, and it pulled a Medium article that had zero API refs.​

Then the agent tried using method signatures from the article that do not even exist in the real package.​

Second thing is missing source links.​

The search tool did not pass provenance through, so my agent just synthesized an answer and fabricated citations that looked real but went to dead URLs.​

Third was the token burn.​

The skill kept dumping raw page HTML into context, so the agent spent half its window parsing cookie banners and nav bars before it could even think.​

That is when I started testing AnySearch through its MCP server.​

It returns structured markdown, so the agent gets the clean content without all the overhead.​

Still early days, but getting structured output back is the only thing that really helped.​

I am still a little worried about how it handles auth on shared agent environments though.

Been debugging a weird issue this week where my coding agent kept confidently using a deprecated async API.

The model was solid, and the context was right.

But every time it searched for confirmation, it pulled some random Medium article from 2023 instead of the actual library docs or the GitHub issue where the deprecation was announced.

Then it clicked.

We obsess over model benchmarks, context windows, and reasoning depth.

But if the agent cannot find the right source, it does not matter how smart it is.

It just hallucinates with extra confidence.

The real bottleneck in coding agent workflows is not the model.

It is the search layer.

When your agent looks something up, it needs to hit the actual repo, the real docs, and the relevant issue threads.

Not SEO farm pages that happen to rank.

I have been testing AnySearch on a few coding tasks recently.

The thing that stood out is how it routes to the right kind of source.

Code repos, package docs, issue trackers.

It returns clean markdown the agent can actually use instead of a wall of links to sort through.

I have been wiring in the anysearch-skill and anysearch-mcp-server repos from GitHub.

Still early days, but the pattern feels clear.

An agent with a decent search layer and a weaker model can outperform a strong model pulling junk results.

Was running a coding agent task last night looking up async patterns and checked the token log.​

Something felt off.​

A massive chunk of the budget went into parsing web pages. Raw HTML, navigation bars, cookie banners, and the full page context before it even found the actual code snippet.​

The model was spending tokens on content that had nothing to do with the coding problem.​

I started looking into whether returning compact results would reduce that waste.​

Been testing AnySearch as a skill in the workflow for this. Still too early for real numbers, but giving the agent only the information it actually needs instead of dumping an entire webpage feels much more efficient.​

Otherwise it feels like paying for the agent to read everything around the answer instead of the answer itself.

Been running a coding agent for a few months, mostly for PR reviews and unfamiliar codebases.​

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The biggest issue was when the agent needed information outside the repo, like library version changes or API behavior.​

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I added AnySearch as a skill to the workflow last week. I expected it to mainly help with documentation lookups.​

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What surprised me was that the structured output changed how the agent handled follow up tasks more than the search itself.​

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It stopped dumping links and started getting cleaner context to work with.​

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The unexpected part was that it was still weaker at fuzzy conceptual questions than I expected.​

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Exact API behavior, version differences, and concrete facts were solid.​

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But when I asked questions like what the best approach was for a design decision, I still needed to stay involved.​

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Asked my agent to pull current best practices for Python async patterns last week.​

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It came back with a 2021 blog post recommending asyncio. gather for basically everything.​

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That was okay advice four years ago, but in 2026 most people have moved to anyio. TaskGroup and structured concurrency patterns that handle cancellation way better.​

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The model is not dumb.​

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The problem is what it can actually reach.​

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When an agent searches the normal web, it gets SEO optimized blog posts ranked by backlinks, not by recency.​

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The actual useful stuff lives in GitHub repos, official docs, and recent issue threads.​

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Those sources do not always rank well on regular search.​

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I started testing AnySearch through its skill integration to see if routing queries toward repos and docs instead of generic blog pages makes a difference.​

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Early impression is that the structured output helps the agent skip the fluff and get closer to source level info without burning tokens on a wall of HTML.​

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Been building a coding agent setup and keep going back and forth on where the search step should actually go.

Do you front load it before the agent plans anything, or just let it call search as a tool when it hits a gap?

Front loading gives it enough background to make a better plan instead of guessing.

But keeping it as a tool call feels more flexible and avoids wasting queries on information the agent might never need.

I have been testing the AnySearch API in both setups to see if the difference matters in practice.

honestly, I am still not sure which approach is easier to maintain long term

Had my agent look up asyncio task group patterns last week.

Nothing crazy, just needed current approaches for a project.

Default web search gave back the usual stuff.

A Medium article with 3 paragraphs of intro before any code.

A Stack Overflow thread from 2023.

Some blog post that was mostly SEO filler.

The agent had to read through all of it, burn tokens parsing nav bars and ads, and still try to pull the actual answer out of the noise.

A lot of people think normal web search is fine for agents.

It works for humans because we can skim and skip stuff.

But an agent reads everything into context.

Every page it pulls costs tokens and risks dragging in garbage.

What agents actually need is the result already cleaned up.

Not a page, not a list of links.

Just the relevant code, the key explanation, source attached, in a format the model can use directly.

I started testing AnySearch for this.

It routes the query to relevant sources and returns clean markdown with sources attached instead of dumping raw pages into context.

The agent gets structured input it can act on without wasting half the context window on page chrome.

Not saying normal web search is useless.

But for agent workflows where the model needs to reason over results, structured output beats raw pages every time.

Every time I build an agent for company research, it just grabs news headlines and earnings summaries

The actually useful stuff is public but totally scattered. SEC filings, job postings, Reddit threads, customer complaints, supplier updates. An agent that only reads articles never surfaces that

I am kind of stuck on the search side here. I am debating whether I should wire up a bunch of APIs myself or look for something that handles routing across domains

The more I test this, the more it feels like the LLM is not the real bottleneck. The harder problem is getting the right sources into the workflow in the first place

If the search layer only gives the agent headlines, the final output will always feel shallow no matter how good the model is

adhd-er here. the part that kills my day isnt the work itself, its the tiny context switches. "wait did the train actually get delayed" / "is the visa appt still thursday" / "is the steam deal on" → 20 min gone, im now in some random subreddit hole.

trying to set up an agent that handles the boring real-time lookups (transit, visa rules, game prices, weather) so i dont lose focus. want it to just grab the answer and ping me back, not hand me a wall of links.

found two repos that look like a decent fit, anysearch skill and anysearch-mcp-server. drop one in cursor or claude and the agent pulls the actual answer back instead of dumping a search results page.

still figuring out the data sources part. transit feeds are hit or miss, govt sites basically hate being scraped, and every country formats visa info differently

This is a detailed breakdown of a TSLA stock movement monitoring test I ran.

This test is not an auto trading system.

I wanted to test a narrower question:

when TSLA shows abnormal movement, can an agent generate a structured alert result that helps a human decide whether to look closer?

That boundary matters.

A trading bot makes buy or sell decisions.

An alert layer filters noise.

I only wanted the second one.

The original task was roughly:

when TSLA shows abnormal price movement, query the current price, today's percentage move, whether trading volume is abnormal compared with the 90 day average, major news from the past 24 hours, latest SEC EDGAR Form 8-K disclosures, major negative events, lawsuits, regulatory developments, analyst rating changes from the past 7 days, and the current buy / hold / sell distribution. Return the result in a structured format. Round prices to 2 decimals and percentage moves to 1 decimal so another agent can use the result for threshold checks.

This looks like a simple stock search task.

But it is closer to a multi source information structuring task.

It involves at least five different layers.

Market data

Current price, percentage move, and trading volume.

News catalysts

Major news from the past 24 hours.

SEC disclosures

Formal filings such as Form 8-K.

Analyst reactions

Recent rating changes and the current buy / hold / sell distribution.

Machine readable output

The result should not only be a paragraph for humans. It should be usable by another agent, script, or rule system.

I wanted the final result to look more like an alert payload.

The ideal fields were roughly:

ticker

current_price

day_change_pct

volume_abnormal_vs_90d_avg

recent_news_24h

latest_8k_available

negative_event_detected

lawsuit_detected

regulatory_development_detected

analyst_rating_change_7d

buy_hold_sell_distribution

consensus

analyst_count

price_target

missing_fields

human_review_required

There are three things I would pay attention to in the screenshot.

First, the result is not just a normal market summary.

It is broken into modules.

Live quote data.

Recent catalyst check.

Analyst reaction.

Structured output.

That means it is not only answering “what happened to TSLA?”

It is breaking the movement into several decision points.

That matters for agents because the next step is not just reading.

The next step is reasoning over the result.

Second, the result keeps unknown and null values.

If a field is not reliably verified, it does not force a value into the output.

That is more important than making the answer look complete.

In finance, if a field cannot be confirmed, it should stay unconfirmed.

For example:

volume_abnormal_vs_90d_avg: null

That means the result did not reliably confirm whether volume was abnormal compared with the 90 day average.

That is not a failure.

That is honesty.

If an agent makes up a true or false value just to complete the object, the downstream alert logic can be pushed in the wrong direction.

Third, the structured result can feed threshold logic.

For example:

if day_change_pct is above 3.0, check the catalyst

if latest_8k_available is true, raise priority

if negative_event_detected is true, send to human review

if missing_fields is not empty, avoid automatic conclusions

This is why the original task asked for prices rounded to 2 decimals, percentage moves rounded to 1 decimal, and structured output.

Natural language summaries are fine for humans.

They are less stable for automation.

For example:

TSLA moved slightly today and market sentiment looked cautious.

That sentence is readable.

But it is not ideal for another agent or rule system.

Compared with that, fields like these are easier to pass forward:

day_change_pct: -0.1

consensus: Hold

analyst_count: 33

price_target: 393.65

This test made me think that the search layer for finance agents needs to do several things well.

Query across source types

It should not only look at prices. It should not only look at news.

Preserve source differences

News, SEC filings, and analyst ratings are not the same kind of evidence.

Return structured fields

Without structure, downstream agents are much harder to stabilize.

Preserve uncertainty

If something cannot be verified, keep it unknown.

Support alerting, not direct trading

In finance, alerting and execution have very different risk levels.

My conclusion is that this test is not really about TSLA.

TSLA is just a noisy stock that makes a good test case.

The real question is:

when an agent faces multi source financial information, does it need more links, or does it need cleaner decision material?

For this kind of workflow, I think the answer is the second one.

I was originally going to wire the same search MCP into Claude and Cursor separately, but honestly i got tired of repeating the same setup in every client.

ended up putting it inside OpenClaw instead, so i can treat it more like a shared search tool for my agent workflow rather than something tied to one IDE or one chat client.

the main thing i wanted was simple: when an agent needs current info, it should pull the actual answer back instead of handing me a wall of search results. for boring lookups like docs, repos, market data, security info, or general research, i would rather have one place where the agent can call search consistently.

still figuring out the best way to structure prompts around it. sometimes the agent asks too broadly, sometimes it needs a more specific intent before the search result is useful.

curious how others are handling this. do you install MCP/search tools separately in each client, or do you keep them in one shared agent environment?

I used to think agent web search was mostly about result quality: whether the agent could find the right page, summarize it correctly, and cite the source.

But there is another problem that feels easy to miss: the pages an agent reads can also become part of the attack surface.

When an agent fetches content from the open web, it is not always reading a clean article. It may be pulling in raw HTML, hidden text, SEO blocks, ads, comments, or other page fragments. Some of that content may contain instructions that were never meant to come from the user.

That makes indirect prompt injection different from normal prompt injection. The user does not need to paste anything malicious. They can simply ask the agent to research something, and the agent may retrieve a page that influences its next step.

A lot of defenses seem to focus on telling the model to ignore instructions from retrieved content. I am not sure that is enough. Once polluted content enters the context window, the agent may already start reasoning from it.

The more interesting fix seems to happen before the model sees the content: clean the page, normalize it, keep provenance, and return a structured representation instead of dumping raw page content directly into the agent.

Curious how others think about this. Should agent search systems treat web content as untrusted input by default?

Hot take after like 6 months of running agents for actual work

the LLM is almost never the thing that breaks

i kept blaming the model. swapped models, tuned prompts, spent ages on evals. turns out 9 times out of 10 the failure traces back to what the agent pulled in, or failed to pull in, before it even started thinking

agent hallucinates a citation → search layer fed it a bad or stale page

quotes a wrong price → search layer didn't refresh

calls the wrong tool → search layer routed based on a vague query

i'm slowly starting to think of an agent as LLM + a dedicated search bridge that handles routing, freshness, and structured retrieval rather than LLM + clever prompt. the bridge is the actual moat now. swapping in a smarter model without fixing the bridge just gives you a smarter liar

curious if anyone else debugging agents keeps ending up at the search layer instead of the model

Spent the last month building an agent for a small client. Every new data source was another week of pain. Read the docs, wire up auth, fight the rate limits, then the JSON schema is always slightly different from the last one. We literally had a Notion doc just to track which vendor SDK was broken that week. someone on the team pointed us at MCP and it kinda changed things. Started with the obvious ones (filesystem, GitHub, Postgres) and that alone cut days of glue code. Then added a few more like Playwright for browser stuff, and anysearch-mcp-server when we needed to route across multiple data sources (financial, code, academic) without wiring up a dozen separate APIs. for a 3 person team the difference is honestly shipping vs not shipping. Still figuring out which ones hold up under real workloads though, the ones we tried are mostly demo quality so far.

data leakage does not always come from files, logs, emails, or API responses.

sometimes it starts with the query itself.

agent search makes this more awkward because agents can turn internal context into external searches very directly.

a security agent might search for a CVE, a product version, and a deployment stack in the same task.

that query can reveal what the company runs and what it is worried about.

a research agent might search for pricing models, market size, acquisition targets, and an internal project name in sequence.

that query history starts to look like a business briefing.

humans usually self censor when searching.

agents often do not.

they may include version numbers, internal terms, file paths, customer names, product plans, or whatever else helps finish the task.

that makes agent search feel less like a convenience feature and more like a new data exposure surface.

enterprise agents probably need query policies before they need more search power.

which queries can go to the public web?

which ones need to be rewritten or sanitized first?

which ones should only touch internal or trusted sources?

search looks harmless, but in agent workflows, the query can reveal almost as much as the answer.

I used to think the obvious answer to agent search was just giving the agent more tools.

Need papers? Add a paper search tool.

Need company data? Add a business database tool.

Need code examples? Add GitHub search.

Need security data? Add a threat intel source.

In theory, that sounds flexible. In practice, the routing gets messy very quickly.

The agent does not always understand which tool is the right one for a task. Even with clear tool descriptions, it can still call a general web search when it should use a domain-specific source, or use a specialized source for something that only needs a simple search.

At some point, I started wondering if “more tools” is actually the wrong abstraction. Maybe the agent should not be choosing between ten different search tools directly. Maybe there should be one search layer that understands the query, routes it to the right source, and returns results in a consistent format.

Curious how others are handling this. Do you let your agent pick between many tools, or do you hide the routing behind one interface?