// 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.
Agent dreaming — memory consolidation, deduplication, pruning, contradiction resolution, and session summarization via the SwarmRecall API. Enables agents to optimize their memory store during idle periods.
Knowledge graph with entities, relations, traversal, and semantic search via the SwarmRecall API. Build and query structured knowledge graphs with vector embeddings for contextual entity discovery.
Conversational memory persistence with semantic search and session tracking via the SwarmRecall API. Stores and retrieves agent memories with vector embeddings for contextual recall.
Named shared data containers for cross-agent collaboration via the SwarmRecall API. Manage shared pools that let multiple agents contribute to and query from a common dataset across memory, knowledge, learnings, and skills modules.
Skill registry for tracking agent capabilities and getting contextual suggestions via the SwarmRecall API. Register, list, and query installed skills with semantic matching for task-relevant recommendations.
Use SwarmRecall when an AI agent needs persistent memory, a knowledge graph, learnings, a skill registry, shared pools, or background dream consolidation across sessions. Works via the SwarmRecall CLI (for stdio MCP) or directly over HTTP/SDK. Every module supports semantic search with vector embeddings and tenant-isolated storage.
| 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"] |
Error tracking, correction logging, and pattern detection via the SwarmRecall API.
Before making any API calls, check for a SWARMRECALL_API_KEY environment variable:
SWARMRECALL_API_KEY is set, use it for all requests as a Bearer token.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>" }
{ "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.<claimToken>"All API requests require:
Authorization: Bearer <SWARMRECALL_API_KEY>
https://swarmrecall-api.onrender.com (override with SWARMRECALL_API_URL if set)
All endpoints below are prefixed with /api/v1.
swarmrecall-api.onrender.com over HTTPSSWARMRECALL_API_KEY should be stored as an environment variable only, not written to diskPOST /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
}
GET /api/v1/learnings/search?q=<query>&limit=10&minScore=0.5
GET /api/v1/learnings/:id
GET /api/v1/learnings?category=error&status=open&priority=high&area=build&limit=20&offset=0
PATCH /api/v1/learnings/:id
{ "status": "resolved", "resolution": "Added --legacy-peer-deps", "resolutionCommit": "abc123" }
GET /api/v1/learnings/patterns
GET /api/v1/learnings/promotions
POST /api/v1/learnings/:id/link
{ "targetId": "<other-learning-id>" }
POST /api/v1/learnings with category: "error", the summary, details, and the command/output that failed.POST /api/v1/learnings with category: "correction" and what was wrong vs. what is correct.GET /api/v1/learnings/patterns to preload known recurring issues. Check GET /api/v1/learnings/promotions for patterns ready to be promoted.POST /api/v1/learnings endpoint accepts an optional "poolId" field.poolId is provided, the learning is shared with all pool members who have learnings read access.GET /api/v1/learnings/search) and list (GET /api/v1/learnings) results automatically include data from pools the agent belongs to.poolId and poolName fields to distinguish shared data from the agent's own data.Learnings benefit from dream-time promotion:
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".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.