All models
agent
Research agent
research-agent
Web-search + synthesis agent for open-ended research and comparisons.
Decision and plan target (no provider attached) PII blocked by policy Tool calling
- Price per 1M tokens
- $6.00
- $0.0060 per 1k
- Requests · 30d
- 0
- no traffic
- Latency
- 8000 ms
- declared
- Success rate
- n/a
- quality prior 0.80
Traffic over 30 days
Requests per day this target answered on this deployment, with the cost line.
No requests yet. Route something from the playground to see usage here.
- Tokens
- 0
- Spend
- $0
- Previous window
- 0 req
Routed for
Domains the signal layer detected on requests that ended here.
No routed requests yet. Declared domains: General, Finance, Marketing, Travel, Legal.
Capabilities
DomainsGeneral, Finance, Marketing, Travel, Legal
ActionsReasoning, Planning, Summarize, Qa
Complexity band0.40 - 1.00
Languagesen
Context windowundeclared
Streamingyes
Policy constraints
Hard stops enforced before scoring.
Data boundaryPublic
Regionsanywhere
PIInot allowed
Max input tokensunlimited
Tenantsall
Representative prompts
Examples the similarity strategy matches against.
- Research the latest papers on LLM routing and summarize the key approaches.
- Compare the top three CRM vendors for a 50-person startup.
Route to it
This agent is not executed by the platform. Ask the router for a decision restricted to kinds=["agent"] and dispatch the winner in your own stack; send feedback afterwards so the learners improve.
curlbash
curl https://api.opensmartroute.ai/api/v1/route \
-H "Authorization: Bearer $OSR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"text": "Research the latest papers on LLM routing and summarize the key approaches.", "kinds": ["agent"], "top_k": 3}'
# Returns the chosen target, confidence, the ranked alternatives and the
# per-strategy trace. Execution of agent targets happens in your stack.httpxpython
import httpx
r = httpx.post(
"https://api.opensmartroute.ai/api/v1/route",
headers={"Authorization": "Bearer osr_live_..."},
json={"text": "Research the latest papers on LLM routing and summarize the key approaches.", "kinds": ["agent"]},
)
d = r.json()
print(d["target"]["id"], d["confidence"])
print([t["id"] for t in d["ranked"]]) # e.g. research-agent first when it fits