POST /agent

The /agent endpoint runs your prompt through SERV guided reasoning with access to web search, on-chain data, DEX quotes, and Solana tools.

The /agent endpoint runs your prompt through Solrouter's full SERV-guided agent pipeline. Unlike a direct chat completion, the agent has access to a suite of built-in tools — web search, on-chain data, DEX quotes, token prices, and more — and uses SERV (Structured Execution via Reasoning Virtualization) to walk a deterministic reasoning graph rather than letting the model make freeform structural decisions. The result is faster, cheaper, and more reliable than a standard agent loop, while still producing synthesis-quality answers for complex multi-step queries.

Endpoint

POST https://api.solrouter.com/agent

Request

Field	Type	Description
`prompt`	string (required)	The question or task you want the agent to work on. You can ask for research, comparisons, on-chain analysis, swap quotes, or any other task covered by the built-in tools.
`model`	string	The model used for synthesis at the end of the reasoning pipeline. All models run on Solrouter's self-hosted Nosana GPU network — no third-party APIs. `gpt-oss:20b` (default) — Apache-2.0 open weights, 20B parameters. `qwen3:8b` — open weights, 8B parameters.
`useTools`	boolean	Enable or disable tool calls during agent execution. When `true`, the agent can call any of its built-in tools to gather data before synthesising a final answer. Set to `false` to run the prompt through guided reasoning without external data retrieval. Defaults to `true`.

Example Request

curl -X POST "https://api.solrouter.com/agent" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Compare Marginfi vs Kamino lending on Solana",
    "model": "gpt-oss:20b",
    "useTools": true
  }'

Example Response

{
  "success": true,
  "reply": "## Marginfi vs Kamino Lending Comparison\n\n...",
  "toolCalls": [
    { "tool": "web_search", "args": { "query": "Marginfi vs Kamino lending Solana" } },
    { "tool": "token_price", "args": { "token": "MNDE" } }
  ],
  "iterations": 4,
  "skillGraph": {
    "nodesTraversed": ["defi-analysis", "liquidity-risk", "comparative-analysis"],
    "relevanceScore": 0.72
  }
}

Response Fields

Field	Type	Description
`success`	boolean	Whether the request completed successfully. `true` on success; `false` if an error occurred.
`reply`	string	The agent's final synthesised answer in Markdown. This is produced by the LLM synthesis step after SERV has collected all relevant data through tool calls.
`toolCalls`	array	The list of tools the agent called during execution, in order. Each entry contains a `tool` name and an `args` object with the parameters that were passed to that tool.
`iterations`	number	The number of reasoning iterations the SERV pipeline executed before producing the final answer.
`skillGraph.nodesTraversed`	array	The skill graph nodes that were activated for this query. Nodes represent structured domain knowledge (e.g. `defi-analysis`, `liquidity-risk`, `comparative-analysis`) that was injected into the synthesis context. Simple queries may return an empty array if skill-graph traversal was skipped.
`skillGraph.relevanceScore`	number	A score between 0 and 1 indicating how relevant the activated domain knowledge was to the query. Higher scores mean the skill graph contributed meaningfully to the final answer.

Available Tools

The agent has access to all built-in tools listed on the Agents Overview page, including web_search, scrape_url, crawl_url, solana_balance, token_price, swap_quote, trending_tokens, and deepwiki. Tool selection is handled automatically by the SERV reasoning pipeline — you don't need to specify which tools to use.

Tip

If you are using the @solrouter/sdk, you can reach the same SERV-guided agent pipeline by passing reasoning: 'braid' to client.chat(). This routes your request through /agent automatically and returns the same structured response, with the added benefit of client-side encryption if encrypted: true is set.