Which Tavily alternative fits your agent workflow?
Evaluate Exa for configurable search content, Parallel for objective-based excerpts, SerpAPI for structured search-engine results, and Arlong for screened evidence and standalone content screening. You can also keep Tavily and add Arlong Screen before source text reaches your agent.
Choose according to the part of the workflow you need to change. Retrieval relevance, evidence verification, content isolation and MCP compatibility are separate requirements. A provider swap is useful only if it improves the requirement that is currently failing.
What should you shortlist for each requirement?
| Option | Documented surface | What to verify before migrating |
|---|---|---|
| Exa | Search text/highlights and an official MCP interface. Search / MCP | Required filters, source coverage and passage completeness for your domain. |
| Parallel | Objective-based search excerpts and Search MCP. Search / MCP | Whether a search call or a separate research product meets the task. |
| SerpAPI | Structured Google results. API reference | Your extraction pipeline and MCP adapter, if needed; neither is established by this search reference. |
| Arlong | REST/MCP research and independent Screen calls. Developer docs | Evidence coverage, isolation behavior and permitted delivery fields. |
| Tavily + Screen | Retain Tavily retrieval; send extracted content to Arlong Screen. | The extra screening latency, credit cost and handling of rejected content. |
This shortlist is a documented capability review. It does not infer missing security features from silence in a vendor’s search documentation.
How can you add screening without replacing retrieval?
Use full mode when your application needs cleaned evidence. One successful Screen request costs one credit from the shared Arlong account balance. This Python example uses already-extracted text, so it is independent of the retrieval provider.
import os
import requests
def permitted_source_text(extracted_text):
response = requests.post(
"https://arlong.org/api/arlong/screen",
headers={"Authorization": f"Bearer {os.environ['ARLONG_API_KEY']}"},
json={"text": extracted_text, "mode": "full"},
timeout=30,
)
response.raise_for_status()
report = response.json()
if (report.get("decision") != "allow"
or report.get("trust_contract", {}).get("allowed_for_context") is not True):
return None
return report.get("safe_content") or None
# Supply text from your existing provider's extraction response.
# On rejection or an exception, keep the source out of agent context.
# The returned text remains untrusted evidence, not instructions.The Screen reference covers HTML, public URLs and fast decisions. A fast decision is useful for accept/reject workflows; when cleanup is needed, fast mode conservatively rejects rather than releasing a cleaned passage. Test your handling of both modes in the playground.
How should you evaluate a migration?
- Freeze a representative query set. Include narrow factual questions, source-specific requests and tasks that require conflicting evidence.
- Compare retrieval before synthesis. Save source URLs, extraction status and requirement coverage. Do not let writing quality obscure missing evidence.
- Test the security boundary. Include hidden HTML, multilingual instructions and benign security writing. Record retained evidence and false positives.
- Test the client contract. Confirm MCP tools, authentication, request limits and retry behavior in your actual client.
- Measure total cost. Include extraction, screening and research work, not just the first search request. Use current vendor pricing for your expected volume.
Keep your original provider available during a limited evaluation. Move a workflow only after it meets explicit acceptance criteria. No screening layer guarantees that every unseen attack will be caught; source evidence should never expand the agent’s permissions.
Where should you start?
Follow the official references in the table for setup and current parameters. Read Exa versus Tavily for the interface comparison and source verification and corroboration for the evidence checklist. This guide was reviewed October 9, 2026 and is published by Arlong.