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Documentation Index

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Learning and memory endpoints use the /api/learning prefix, not /api/v1. 
https://api.burki.dev/api/learning
Authentication uses the standard flexible auth path: API key, JWT bearer token, or dashboard session. Results are scoped to the authenticated user’s organization.
Learning APIs may return customer conversation content, stable caller hashes, memories, prompts, and trace details. Treat these responses as sensitive operational data.

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Memories

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List Memories

GET /api/learning/memories
Authorization: Bearer YOUR_TOKEN
Returns memory objects:
Response
[
  {
    "id": 42,
    "fact": "Customer prefers morning appointment reminders",
    "memory_type": "preference",
    "memory_scope": "organization",
    "subject_type": "caller",
    "subject_id": "caller_hash_abc",
    "confidence": 0.91,
    "importance": 0.7,
    "provenance": {
      "source": "call_trace"
    },
    "created_at": "2026-04-30T10:00:00Z",
    "access_count": 3
  }
]

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Get, Create, Update, Delete

GET /api/learning/memories/{memory_id}
POST /api/learning/memories
PUT /api/learning/memories/{memory_id}
DELETE /api/learning/memories/{memory_id}
Create/update request fields include fact, memory_type, memory_scope, subject_type, subject_id, confidence, and optional provenance/edit metadata. Updating fact causes the backend to clear the old embedding so it can be regenerated.

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Search Memories

POST /api/learning/memories/search
Authorization: Bearer YOUR_TOKEN
Content-Type: application/json
Request
{
  "query": "What appointment reminder preferences should I know?",
  "caller_hash": "caller_hash_abc",
  "assistant_id": 123,
  "limit": 10
}
Response
{
  "memories": [
    {
      "id": 42,
      "fact": "Customer prefers morning appointment reminders",
      "confidence": 0.91,
      "source": "call_trace"
    }
  ],
  "intent": "preference_lookup",
  "traversal_path": [],
  "retrieval_ms": 12.4
}

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Memory Edges

GET /api/learning/memories/{memory_id}/edges
Returns graph relationships for a memory.

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Prompt Lifecycle

Prompt endpoints are scoped to an assistant: 
/api/learning/assistants/{assistant_id}/prompts
MethodPathPurpose
GET/api/learning/assistants/{assistant_id}/promptsList prompt versions
GET/api/learning/assistants/{assistant_id}/prompts/{version_id}Get one prompt version, including full system_prompt
POST/api/learning/assistants/{assistant_id}/promptsCreate a prompt version
POST/api/learning/assistants/{assistant_id}/prompts/{version_id}/approveApprove a version
POST/api/learning/assistants/{assistant_id}/prompts/{version_id}/start-canaryStart canary rollout
PATCH/api/learning/assistants/{assistant_id}/prompts/{version_id}/canaryUpdate canary percentage
POST/api/learning/assistants/{assistant_id}/prompts/{version_id}/promotePromote canary/current version
POST/api/learning/assistants/{assistant_id}/prompts/{version_id}/rollbackRoll back a version
GET/api/learning/assistants/{assistant_id}/prompts/{version_id}/gatesInspect promotion gates
Create request:
Request
{
  "system_prompt": "You are a helpful support assistant...",
  "change_summary": "Tighten escalation behavior"
}
Canary update request:
Request
{
  "percentage": 25
}

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Eval Datasets and Cases

/api/learning/assistants/{assistant_id}/eval-datasets
MethodPathPurpose
GET/api/learning/assistants/{assistant_id}/eval-datasetsList datasets
GET/api/learning/assistants/{assistant_id}/eval-datasets/{dataset_id}Get dataset
POST/api/learning/assistants/{assistant_id}/eval-datasetsCreate dataset
POST/api/learning/assistants/{assistant_id}/eval-datasets/importImport dataset with cases
POST/api/learning/assistants/{assistant_id}/eval-datasets/{dataset_id}/publishPublish dataset
GET/api/learning/assistants/{assistant_id}/eval-datasets/{dataset_id}/casesList cases
POST/api/learning/assistants/{assistant_id}/eval-datasets/{dataset_id}/casesAdd case
PUT/api/learning/assistants/{assistant_id}/eval-datasets/{dataset_id}/cases/{case_id}Update case
DELETE/api/learning/assistants/{assistant_id}/eval-datasets/{dataset_id}/cases/{case_id}Delete case
POST/api/learning/assistants/{assistant_id}/eval-datasets/{dataset_id}/cases/from-traceCreate case from call trace
Add case request:
Request
{
  "case_name": "Reschedule appointment",
  "conversation": [
    {
      "role": "user",
      "content": "I need to move my appointment"
    }
  ],
  "expected_response": "Ask for the customer's preferred new day and time.",
  "mock_tool_responses": {},
  "difficulty": "medium"
}

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Run Evals

POST /api/learning/assistants/{assistant_id}/eval/run
Authorization: Bearer YOUR_TOKEN
Content-Type: application/json
Runs an evaluation against a dataset/prompt configuration.

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Stats and Graph

GET /api/learning/assistants/{assistant_id}/stats
GET /api/learning/graph-stats
GET /api/learning/assistants/{assistant_id}/memory-graph
Assistant stats include memory counts, prompt versions, active/canary versions, eval datasets, and last eval pass rate.

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DSPy Optimization

POST /api/learning/assistants/{assistant_id}/dspy/optimize
Authorization: Bearer YOUR_TOKEN
Starts DSPy prompt optimization for an assistant. If dspy-ai is not installed in the backend environment, the endpoint returns 503 Service Unavailable.

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Call Traces

GET /api/learning/call-traces
GET /api/learning/call-traces/{trace_id}
List responses return shortened caller hashes. The detail endpoint can include full trace data, full caller_hash, turns, tool calls, and conversation content.
Response
{
  "id": 77,
  "assistant_id": 123,
  "call_sid": "CAxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  "caller_hash": "abc123...",
  "turns": [
    {
      "role": "user",
      "content": "Can I reschedule?"
    }
  ],
  "created_at": "2026-04-30T10:00:00Z"
}

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