// 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.
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.
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.
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.
| name | swarmrecall |
| description | 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. |
| version | 1.2.0 |
| metadata | {"openclaw":{"requires":{"anyBins":["swarmrecall"]},"install":[{"id":"node","kind":"node","package":"@swarmrecall/cli","bins":["swarmrecall"],"label":"Install SwarmRecall CLI (npm)"}],"mcp_servers":[{"id":"swarmrecall","label":"SwarmRecall (local stdio)","command":"swarmrecall","args":["mcp"],"env":{"SWARMRECALL_API_KEY":"${env:SWARMRECALL_API_KEY}"}},{"id":"swarmrecall-remote","label":"SwarmRecall (remote HTTP)","transport":"streamable-http","url":"https://swarmrecall-api.onrender.com/mcp","headers":{"Authorization":"Bearer ${env:SWARMRECALL_API_KEY}"}}],"emoji":"🧠","homepage":"https://www.swarmrecall.ai/docs/mcp","privacyPolicy":"All 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. Data is stored in PostgreSQL with pgvector embeddings. Data is tenant-isolated by owner ID and agent ID across all modules.","primaryEnv":"SWARMRECALL_API_KEY"}} |
| author | swarmclawai |
| homepage | https://www.swarmrecall.ai |
| tags | ["memory","knowledge-graph","learnings","skills","pools","dreaming","consolidation","mcp","ai-agents","persistence"] |
Persistent memory, a knowledge graph, learnings, a skill registry, shared collaboration pools, and background "dream" consolidation — for any AI agent — via the SwarmRecall API at https://swarmrecall-api.onrender.com.
For onboarding, examples, command references, or troubleshooting, read the bundled README.md, examples/, references/, and TROUBLESHOOTING.md before improvising workflow advice.
Three ways to connect, in increasing order of flexibility:
Local stdio (default):
npm install -g @swarmrecall/cli
swarmrecall register --save # mints an API key and stores it in ~/.config/swarmrecall/config.json
swarmrecall mcp # runs the MCP server over stdio — point your client at this command
Remote HTTP (no install):
URL: https://swarmrecall-api.onrender.com/mcp
Auth: Authorization: Bearer ${SWARMRECALL_API_KEY}
Transport: streamable-http
Both transports expose the same 52 tools and 4 resources. See https://www.swarmrecall.ai/docs/mcp for client-specific configuration snippets.
npm install -g @swarmrecall/cli
swarmrecall register --save
swarmrecall memory store "<content>" --category fact --importance 0.8
swarmrecall memory search "<query>"
swarmrecall knowledge create --type person --name "Alice" --props '{"role":"engineer"}'
swarmrecall learnings log --category error --summary "<summary>"
swarmrecall dream start
See references/commands.md for the full command surface.
If the user is wiring a custom agent, install the SDK:
npm install @swarmrecall/sdk
import { SwarmRecallClient } from '@swarmrecall/sdk';
const client = new SwarmRecallClient({ apiKey: process.env.SWARMRECALL_API_KEY! });
await client.memory.store({ content: 'User prefers dark mode', category: 'preference', importance: 0.8 });
If SWARMRECALL_API_KEY is not set and a hosted agent needs one, self-register with no account:
POST https://swarmrecall-api.onrender.com/api/v1/register
Content-Type: application/json
{ "name": "<agent-name>" }
Response returns { "apiKey": "...", "claimToken": "..." }. Tell the user: "SwarmRecall is set up! To manage your agent's data, visit https://swarmrecall.ai/claim with code: <claimToken>."
All API requests require a Bearer token in the Authorization header: Authorization: Bearer <SWARMRECALL_API_KEY>.
swarmrecall-api.onrender.com over HTTPS.SWARMRECALL_API_KEY as an environment variable or in ~/.config/swarmrecall/config.json (created by swarmrecall register --save). Do not check it into source control.Conversational memory with semantic search and session tracking.
| Tool | Purpose |
|---|---|
memory_store | Store a memory with category, importance, and tags. |
memory_search | Semantic search over memories. |
memory_get / memory_list | Fetch a specific memory or filtered list. |
memory_update / memory_delete | Update metadata or archive a memory. |
memory_sessions_start | Start a new memory session. |
memory_sessions_current | Get the active session. |
memory_sessions_update | Append state, summary, or mark ended. |
memory_sessions_list | List sessions. |
memory_sessions_current to load context. If none, call memory_sessions_start.memory_store with an appropriate category and importance.memory_search and use returned memories to inform your response.memory_sessions_update with ended: true and a summary.Knowledge graph with entities, relations, traversal, and semantic search.
| Tool | Purpose |
|---|---|
knowledge_entity_create/get/list/update/delete | Entity CRUD. |
knowledge_relation_create/list/delete | Relation CRUD. |
knowledge_traverse | Walk the graph from an entity, filtered by relation and depth. |
knowledge_search | Semantic search over entities. |
knowledge_validate | Check graph constraints. |
knowledge_entity_create.knowledge_relation_create.knowledge_search then knowledge_traverse to explore connections.knowledge_validate to catch orphaned entities or conflicting relations.Error tracking, correction logging, and pattern detection that surfaces recurring issues.
| Tool | Purpose |
|---|---|
learning_log | Log a learning with category, summary, priority, area. |
learning_search/get/list/update | Retrieve and update. |
learning_patterns | List recurring patterns. |
learning_promotions | List promotion candidates. |
learning_resolve | Mark resolved with a resolution + optional commit SHA. |
learning_link | Link two learnings for pattern detection. |
learning_log with the full error output / what was wrong vs. correct.learning_patterns to preload known recurring issues; learning_promotions for patterns ready to be promoted.Skill registry for tracking installed agent capabilities and getting contextual suggestions.
| Tool | Purpose |
|---|---|
skill_register | Register a new skill. |
skill_list/get/update/remove | Manage registered skills. |
skill_suggest | Get skill suggestions for a task context. |
skill_register with name, version, and source.skill_list.skill_suggest for relevant skill recommendations.Named shared data containers for cross-agent collaboration.
| Tool | Purpose |
|---|---|
pool_list | List pools this agent belongs to. |
pool_get | Pool details + members. |
poolId and poolName to distinguish shared data from the agent's private data.poolId to any memory_store, knowledge_entity_create, knowledge_relation_create, learning_log, or skill_register call.pool_list to see available pools and their access levels.Background memory consolidation — deduplication, pruning, contradiction resolution, and session summarization.
| Tool | Purpose |
|---|---|
dream_start | Start a dream cycle. |
dream_get/list/update | Cycle management. |
dream_complete/fail | Cycle completion. |
dream_get_config / dream_update_config | Configuration. |
dream_get_duplicates/unsummarized_sessions/duplicate_entities/stale/contradictions/unprocessed | Candidate primitives. |
dream_execute | Run Tier 1 server-side operations (decay, prune, orphan cleanup). |
dream_start.dream_execute (decay, prune, orphan cleanup).dream_get_duplicates, dream_get_unsummarized_sessions, dream_get_contradictions.dream_complete with the results.Read-only MCP resources for clients that surface resources as inline context:
swarmrecall://pools — pools this agent belongs toswarmrecall://skills — skills this agent has registeredswarmrecall://sessions/current — current memory sessionswarmrecall://dream/config — dream configurationexamples/quickstart.md, examples/memory-workflow.md, examples/knowledge-graph.md, examples/learnings-workflow.md — workflow recipesreferences/commands.md, references/mcp-tools.md — complete command and tool referencesTROUBLESHOOTING.md — common auth and connectivity issues