| name | swarmrecall-learnings |
| description | Error tracking, correction logging, and pattern detection via the SwarmRecall API. Tracks agent mistakes, corrections, and discoveries to surface recurring issues and promote learnings into actionable rules. |
| metadata | {"openclaw":{"emoji":"💡","requires":{"env":["SWARMRECALL_API_KEY"]},"primaryEnv":"SWARMRECALL_API_KEY","privacyPolicy":"Learning data is stored on SwarmRecall servers (swarmrecall-api.onrender.com). Data is scoped per agent and owner. The agent must have user consent before storing personal or sensitive information.","dataHandling":"All data is transmitted over HTTPS. Learnings are stored in PostgreSQL with pgvector embeddings. Data is tenant-isolated by owner ID and agent ID."}} |
| version | 1.1.0 |
| author | swarmclawai |
| homepage | https://www.swarmrecall.ai |
| tags | ["learnings","ai-agents","error-tracking","pattern-detection","persistence"] |
SwarmRecall Learnings
Error tracking, correction logging, and pattern detection via the SwarmRecall API.
Auto-Registration
Before making any API calls, check for a SWARMRECALL_API_KEY environment variable:
- If
SWARMRECALL_API_KEY is set, use it for all requests as a Bearer token.
- If
SWARMRECALL_API_KEY is not set, self-register by calling:
POST https://swarmrecall-api.onrender.com/api/v1/register
Content-Type: application/json
{ "name": "<your-agent-name>" }
- The response returns
{ "apiKey": "...", "claimToken": "..." }. Save the apiKey to the SWARMRECALL_API_KEY environment variable for all subsequent requests. Do NOT write the key to disk or any file without user consent.
- Tell the user: "SwarmRecall is set up! To manage your agent's data, visit swarmrecall.ai/claim with code:
<claimToken>"
Authentication
All API requests require:
Authorization: Bearer <SWARMRECALL_API_KEY>
API Base URL
https://swarmrecall-api.onrender.com (override with SWARMRECALL_API_URL if set)
All endpoints below are prefixed with /api/v1.
Privacy & Data Handling
- All data is sent to
swarmrecall-api.onrender.com over HTTPS
- Learning data (errors, corrections, discoveries) is stored server-side with vector embeddings for semantic search
- Data is isolated per agent and owner — no cross-tenant access
- Before storing user-provided content, ensure the user has consented to external storage
- The
SWARMRECALL_API_KEY should be stored as an environment variable only, not written to disk
Endpoints
Log a learning
POST /api/v1/learnings
{
"category": "error", // error | correction | discovery | optimization | preference
"summary": "npm install fails with peer deps",
"details": "Full error output...",
"priority": "high", // low | medium | high | critical
"area": "build",
"suggestedAction": "Use --legacy-peer-deps flag",
"tags": ["npm", "build"],
"metadata": {},
"poolId": "<uuid>" // optional — write to shared pool
}
Search learnings
GET /api/v1/learnings/search?q=<query>&limit=10&minScore=0.5
Get a learning
GET /api/v1/learnings/:id
List learnings
GET /api/v1/learnings?category=error&status=open&priority=high&area=build&limit=20&offset=0
Update a learning
PATCH /api/v1/learnings/:id
{ "status": "resolved", "resolution": "Added --legacy-peer-deps", "resolutionCommit": "abc123" }
Get recurring patterns
GET /api/v1/learnings/patterns
Get promotion candidates
GET /api/v1/learnings/promotions
Link related learnings
POST /api/v1/learnings/:id/link
{ "targetId": "<other-learning-id>" }
Behavior
- On error: call
POST /api/v1/learnings with category: "error", the summary, details, and the command/output that failed.
- On correction: call
POST /api/v1/learnings with category: "correction" and what was wrong vs. what is correct.
- On session start: call
GET /api/v1/learnings/patterns to preload known recurring issues. Check GET /api/v1/learnings/promotions for patterns ready to be promoted.
- On promotion candidates: surface candidates to the user for approval before acting on them.
Shared Pools
- The
POST /api/v1/learnings endpoint accepts an optional "poolId" field.
- When
poolId is provided, the learning is shared with all pool members who have learnings read access.
- The agent must have readwrite access to the pool's learnings module to write shared learnings.
- Search (
GET /api/v1/learnings/search) and list (GET /api/v1/learnings) results automatically include data from pools the agent belongs to.
- Pool data in responses includes
poolId and poolName fields to distinguish shared data from the agent's own data.
Dreaming Integration
Learnings benefit from dream-time promotion:
- Promotion candidates: The existing
GET /api/v1/learnings/promotions endpoint surfaces patterns meeting promotion criteria (3+ recurrences, 2+ sessions, within 30 days). During a dream cycle, the agent reads each candidate, synthesizes a best-practice learning, and creates it via POST /api/v1/learnings with category: "best_practice" and status: "promoted".
- Pattern consolidation: Related learnings are already linked via
POST /api/v1/learnings/:id/link. During dreaming, the agent can review patterns and archive individual learnings that are fully subsumed by the promoted best practice.
