Skill: waydelyle/swarmrecall (version 1.2.0)

--- 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] --- # SwarmRecall 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. ## Setup Three ways to connect, in increasing order of flexibility: ### 1. MCP (recommended for Claude Desktop, Claude Code, Cursor) **Local stdio (default):** ```bash 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. ### 2. CLI ```bash 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. ### 3. Raw HTTP / SDK If the user is wiring a custom agent, install the SDK: ```bash npm install @swarmrecall/sdk ``` ```ts import { SwarmRecallClient } from '@swarmrecall/sdk'; const client = new SwarmRecallClient({ [REDACTED_SECRET] }); 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>`." ## Authentication All API requests require a Bearer token in the Authorization header: `Authorization: Bearer <SWARMRECALL_API_KEY>`. ## Privacy & Data Handling - All data is sent to `swarmrecall-api.onrender.com` over HTTPS. - Memories, entities, learnings, skills, sessions, and dream cycles are 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. - Store `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. --- ## Module 1: Memory Conversational memory with semantic search and session tracking. ### When to use - Storing user preferences, facts, decisions, and context. - Recalling relevant information from past interactions. - Managing conversation sessions end-to-end. ### MCP tools | 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. | ### Behavior - On session start: call `memory_sessions_current` to load context. If none, call `memory_sessions_start`. - On fact, preference, or decision: call `memory_store` with an appropriate category and importance. - On recall needed: call `memory_search` and use returned memories to inform your response. - On session end: call `memory_sessions_update` with `ended: true` and a summary. --- ## Module 2: Knowledge Knowledge graph with entities, relations, traversal, and semantic search. ### When to use - Storing structured information about people, projects, tools, and concepts. - Linking related entities together. - Exploring connections between concepts. ### MCP tools | Tool | Purpose | | --- | --- | | [REDACTED_SECRET] | Entity CRUD. | | [REDACTED_SECRET] | 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. | ### Behavior - When the user provides structured information: call `knowledge_entity_create`. - When linking concepts: call `knowledge_relation_create`. - When the user asks "what do I know about X?": `knowledge_search` then `knowledge_traverse` to explore connections. - Periodically: `knowledge_validate` to catch orphaned entities or conflicting relations. --- ## Module 3: Learnings Error tracking, correction logging, and pattern detection that surfaces recurring issues. ### When to use - Logging errors, corrections, discoveries, optimizations, or preferences. - Detecting recurring patterns across sessions. - Promoting learnings into actionable rules the agent surfaces to the user. ### MCP tools | 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. | ### Behavior - On error or correction: `learning_log` with the full error output / what was wrong vs. correct. - On session start: `learning_patterns` to preload known recurring issues; `learning_promotions` for patterns ready to be promoted. - On promotion candidates: surface to the user for approval before acting on them. --- ## Module 4: Skills Skill registry for tracking installed agent capabilities and getting contextual suggestions. ### When to use - Registering capabilities the agent acquires. - Listing what the agent can do. - Getting skill recommendations for a given task. ### MCP tools | 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. | ### Behavior - On skill install: `skill_register` with name, version, and source. - On "what can I do?": `skill_list`. - On task context: `skill_suggest` for relevant skill recommendations. --- ## Module 5: Shared Pools Named shared data containers for cross-agent collaboration. ### When to use - Sharing memories, knowledge, learnings, or skills between agents. - Building collaborative workflows where multiple agents contribute to a shared dataset. ### MCP tools | Tool | Purpose | | --- | --- | | `pool_list` | List pools this agent belongs to. | | `pool_get` | Pool details + members. | ### Behavior - Pool data returned in responses includes `poolId` and `poolName` to distinguish shared data from the agent's private data. - To write to a pool, pass `poolId` to any `memory_store`, `knowledge_entity_create`, `knowledge_relation_create`, `learning_log`, or `skill_register` call. - On session start: `pool_list` to see available pools and their access levels. --- ## Module 6: Dreaming Background memory consolidation — deduplication, pruning, contradiction resolution, and session summarization. ### When to use - Between sessions or during idle periods for memory maintenance. - When the user asks to "clean up", "consolidate", or "optimize" memories. - Periodically via auto-dream scheduling. ### MCP tools | 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. | | [REDACTED_SECRET] | Candidate primitives. | | `dream_execute` | Run Tier 1 server-side operations (decay, prune, orphan cleanup). | ### Behavior 1. Start a cycle: `dream_start`. 2. Run Tier 1 ops: `dream_execute` (decay, prune, orphan cleanup). 3. Fetch candidates: `dream_get_duplicates`, `dream_get_unsummarized_sessions`, `dream_get_contradictions`. 4. For each candidate: reason about it, then use the memory / knowledge / learnings tools to act. 5. Complete the cycle: `dream_complete` with the results. --- ## Resources Read-only MCP resources for clients that surface resources as inline context: - `swarmrecall://pools` — pools this agent belongs to - `swarmrecall://skills` — skills this agent has registered - `swarmrecall://sessions/current` — current memory session - `swarmrecall://dream/config` — dream configuration ## Pointers - <https://www.swarmrecall.ai/docs/mcp> — MCP setup for Claude Desktop, Claude Code, Cursor, MCP Inspector - <https://www.swarmrecall.ai/docs/api-reference> — raw HTTP endpoints - <https://www.npmjs.com/package/@swarmrecall/cli> — CLI source - <https://github.com/swarmclawai/swarmrecall> — source repository - `examples/quickstart.md`, `examples/memory-workflow.md`, `examples/knowledge-graph.md`, `examples/learnings-workflow.md` — workflow recipes - `references/commands.md`, `references/mcp-tools.md` — complete command and tool references - `TROUBLESHOOTING.md` — common auth and connectivity issues


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)