AgentDyne Docs

curl -X POST https://agentdyne.com/api/agents/AGENT_ID/execute \
  -H "Authorization: Bearer agd_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"input": "Summarise this: Q3 revenue grew 40% YoY…"}'

{
  "executionId": "exec_abc123",
  "output": {
    "summary": "Q3 revenue increased 40% year-over-year.",
    "keyPoints": ["Revenue up 40%", "Strong Q4 outlook"]
  },
  "latencyMs": 842,
  "tokens": { "input": 124, "output": 87 },
  "cost": 0.00312
}

Authorization: Bearer agd_YourApiKeyHere
# Also accepted:
X-API-Key: agd_YourApiKeyHere

// TypeScript — synchronous
const res = await fetch("https://agentdyne.com/api/agents/AGENT_ID/execute", {
  method: "POST",
  headers: {
    "Authorization": "Bearer " + process.env.AGENTDYNE_API_KEY,
    "Content-Type":  "application/json",
  },
  body: JSON.stringify({ input: "Summarise this email thread…" }),
})
const { executionId, output, latencyMs, tokens, cost } = await res.json()

// Streaming (server-sent events)
const res = await fetch("https://agentdyne.com/api/agents/AGENT_ID/execute", {
  method: "POST",
  headers: { "Authorization": "Bearer " + key, "Content-Type": "application/json" },
  body:   JSON.stringify({ input: "Write a blog post about AI agents", stream: true }),
})
const reader = res.body.getReader()
const dec    = new TextDecoder()
while (true) {
  const { done, value } = await reader.read()
  if (done) break
  const lines = dec.decode(value).split("\n")
  for (const line of lines) {
    if (!line.startsWith("data:")) continue
    const data = JSON.parse(line.slice(5))
    if (data.type === "delta") process.stdout.write(data.delta)
    if (data.type === "done")  console.log("\nDone in", data.latencyMs, "ms")
  }
}

# List active agents in the "coding" category, sorted by rating
curl "https://agentdyne.com/api/agents?category=coding&sort=rating&limit=10" \
  -H "Authorization: Bearer agd_your_key"

# Response
{
  "data": [{ "id": "…", "name": "…", "description": "…", "pricing_model": "free" }],
  "pagination": { "total": 284, "page": 1, "limit": 10, "pages": 29 }
}

curl -X POST https://agentdyne.com/api/agents/create \
  -H "Authorization: Bearer agd_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "name":          "Email Classifier",
    "description":   "Classifies support emails into categories",
    "category":      "customer_support",
    "pricing_model": "free",
    "system_prompt": "You are an email classifier. Return JSON: { category, priority }",
    "model_name":    "claude-sonnet-4-20250514",
    "temperature":   0.3,
    "max_tokens":    1024
  }'

# 1. Create a pipeline
curl -X POST https://agentdyne.com/api/pipelines \
  -H "Authorization: Bearer agd_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Research → Summarise → Email",
    "description": "Full research pipeline",
    "dag": {
      "nodes": [
        { "id": "n1", "agent_id": "RESEARCHER_AGENT_ID", "label": "Research" },
        { "id": "n2", "agent_id": "SUMMARISER_AGENT_ID", "label": "Summarise" },
        { "id": "n3", "agent_id": "EMAIL_DRAFT_AGENT_ID",  "label": "Email Draft" }
      ],
      "edges": [
        { "from": "n1", "to": "n2" },
        { "from": "n2", "to": "n3" }
      ]
    },
    "timeout_seconds": 120
  }'

# 2. Execute the pipeline
curl -X POST https://agentdyne.com/api/pipelines/PIPELINE_ID/execute \
  -H "Authorization: Bearer agd_your_key" \
  -H "Content-Type: application/json" \
  -d '{ "input": "Research recent AI breakthroughs in robotics" }'

# Response — per-node traces + final output
{
  "executionId":  "pex_abc",
  "status":       "success",
  "output":       "Dear team, here is the latest on robotics AI…",
  "node_results": [
    { "node_id": "n1", "status": "success", "latency_ms": 1240, "cost": 0.003 },
    { "node_id": "n2", "status": "success", "latency_ms": 820,  "cost": 0.002 },
    { "node_id": "n3", "status": "success", "latency_ms": 640,  "cost": 0.001 }
  ],
  "summary": { "total_latency_ms": 2700, "total_cost_usd": "0.006000" }
}

# 1. Create a knowledge base
curl -X POST https://agentdyne.com/api/rag/knowledge-bases \
  -H "Authorization: Bearer agd_your_key" \
  -d '{ "name": "Product Docs", "description": "Internal product documentation" }'
# => { "id": "kb_abc123", "name": "Product Docs", "doc_count": 0 }

# 2. Ingest a document (up to 100KB per request, 1000 docs per KB)
curl -X POST https://agentdyne.com/api/rag/ingest \
  -H "Authorization: Bearer agd_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "knowledge_base_id": "kb_abc123",
    "title":   "Getting Started Guide",
    "content": "## Installation\n\nnpm install @agentdyne/sdk…",
    "chunk_size": 1200,
    "chunk_overlap": 200
  }'
# => { "document_id": "doc_xyz", "chunks_indexed": 8, "status": "indexed" }

# 3. Attach knowledge base to your agent (via Builder Studio or API)
curl -X PATCH https://agentdyne.com/api/agents/AGENT_ID \
  -d '{ "knowledge_base_id": "kb_abc123" }'

# Semantic retrieval — called by the execute route automatically when
# the agent has a knowledge_base_id attached. You can also call it directly:
curl -X POST https://agentdyne.com/api/rag/query \
  -H "Authorization: Bearer agd_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "knowledge_base_id": "kb_abc123",
    "query":    "How do I install the SDK?",
    "top_k":    5,
    "threshold": 0.65
  }'
# Returns: { results: [{ content, document_title, similarity }], context_string }
# The context_string can be injected into an agent system prompt directly.

// Agentic RAG — agent decides when to retrieve via tool calls
// Add this to your agent's system prompt:
const AGENTIC_SYSTEM_PROMPT = `
You are a knowledgeable assistant with access to a knowledge base tool.

When asked a question that requires specific factual information:
1. Call search_knowledge_base(query) to retrieve relevant context
2. Use the returned context to formulate an accurate answer
3. Cite the source document when referencing specific facts
4. If no relevant context is found, clearly state the knowledge base
   does not contain information on this topic

Always prefer retrieved context over your training data for domain-specific questions.
`

# Machine-readable agent discovery — "DNS for agents"
# Find all agents that can summarise text in under $0.01/call
curl "https://agentdyne.com/api/registry?capabilities=summarize&max_cost=0.01&prefer=cost" \
  -H "Authorization: Bearer agd_your_key"

# Response — machine-optimised (no UI cruft)
{
  "agents": [
    {
      "id":       "agent_abc",
      "endpoint": "https://agentdyne.com/api/agents/agent_abc/execute",
      "capability_tags": ["summarize","extract","classify"],
      "pricing":  { "model": "per_call", "price_per_call": 0.005 },
      "quality":  { "composite_score": 91.2, "accuracy_score": 94.1 }
    }
  ],
  "capability_graph": { "summarize": ["agent_abc", "agent_def"] },
  "composition_hints": [
    { "chain": ["agent_abc","agent_def"], "compatible_on": ["text"] }
  ]
}

# Full schema for a specific agent (input/output schemas, versions, seller)
curl "https://agentdyne.com/api/registry/AGENT_ID"

# Get current credit balance
curl https://agentdyne.com/api/credits \
  -H "Authorization: Bearer agd_your_key"
# => { "balance": 12.50, "hard_limit": 50, "low_balance": false }

# Purchase credits (creates Stripe Checkout session)
curl -X POST https://agentdyne.com/api/credits \
  -H "Authorization: Bearer agd_your_key" \
  -d '{ "package_id": "credits_20" }'
# packages: credits_5 ($5), credits_20 ($22), credits_50 ($57), credits_100 ($120)
# => { "url": "https://checkout.stripe.com/…" }

// Attach MCP tools to an agent via the Builder Studio or API:
// PUT /api/agents/AGENT_ID (PATCH the mcp_servers field)

// In your system prompt, reference the tools by name:
const SYSTEM_PROMPT = `
You have access to the following tools:
- GitHub: read repositories, create pull requests, manage issues
- Supabase: query tables, run SQL, fetch records
- Slack: send messages, read channels

When the user asks you to perform an action that requires one of these tools,
use the tool explicitly before responding. Always confirm the action was
completed successfully before reporting to the user.
`

// Available MCP servers in Builder Studio:
// databases: supabase, postgres, mysql, mongodb, redis
// communication: gmail, slack, twilio, discord
// productivity: google-calendar, notion, google-drive, linear, asana
// development: github, filesystem, browserbase, puppeteer
// cloud: aws, gcp, cloudflare, vercel
// ai: anthropic, openai, pinecone, qdrant
// finance: stripe, plaid
// marketing: hubspot, salesforce

// Register a webhook URL in Settings → Webhooks
// Every event is signed with HMAC-SHA256 in X-AgentDyne-Signature

// Next.js App Router handler:
export async function POST(req: Request) {
  const sig  = req.headers.get("x-agentdyne-signature") ?? ""
  const body = await req.text()

  // Verify signature (prevents spoofed events)
  const expected = await computeHmac(process.env.AGENTDYNE_WEBHOOK_SECRET!, body)
  if (sig !== expected) return new Response("Unauthorized", { status: 401 })

  const event = JSON.parse(body)
  switch (event.type) {
    case "execution.completed":  await onExecutionDone(event.data);  break
    case "execution.failed":     await onExecutionFailed(event.data); break
    case "agent.approved":       await onAgentApproved(event.data);  break
    case "payout.processed":     await recordPayout(event.data);     break
  }
  return Response.json({ received: true })
}

// Event types:
// execution.completed | execution.failed
// agent.approved | agent.rejected
// subscription.created | subscription.updated | subscription.canceled
// payout.processed | review.posted | credits.low

// Every error response has this shape:
{
  "error": "Human-readable message",
  "code":  "MACHINE_READABLE_CODE",   // e.g. QUOTA_EXCEEDED, INSUFFICIENT_CREDITS
  "retryAfter": 60                    // only on 429
}

// Common codes:
// QUOTA_EXCEEDED          — monthly call limit reached; upgrade plan
// INSUFFICIENT_CREDITS    — balance < agent price; top up at /billing
// SUBSCRIPTION_REQUIRED   — agent requires active subscription
// INJECTION_BLOCKED       — input rejected by security filter
// AGENT_NOT_CONFIGURED    — system prompt missing or too short