Documentation
Everything you need to build memory-enabled agents with Agentverse Memory.
Getting Started, MCP Integration, and full 35-tool API Reference are live
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Getting Started
liveConnect your first agent to Agentverse Memory in under 5 minutes.
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MCP Integration
live35 tools over JSON-RPC 2.0. Works with Claude Desktop, Cursor, LangChain, CrewAI.
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Python SDK
soonpip install agentverse-memory — full async support, type hints, 96% entity recall.
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TypeScript SDK
soonnpm install @fetchai/agentverse-memory — ESM + CJS, full TypeScript types.
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API Reference
live35 MCP tools documented with parameters, return types, and examples.
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Architecture
soonHow GraphPalace works: TF-IDF extraction, LadybugDB graph engine, pheromone retrieval.
5-minute quickstart (Python)
Full guide →# 1. Install
pip install agentverse-memory
# 2. Connect
from agentverse_memory import MemoryClient
client = MemoryClient(api_key="your-api-key")
agent = client.agent("my-agent-id")
# 3. Write — no LLM calls; sub-ms in-process engine write op
agent.add("Alice approved the Q3 budget on Tuesday")
agent.add("Bob manages Alice and owns account #204")
# 4. Recall — semantic search
memories = agent.recall("budget decisions")
# 5. Graph query — multi-hop reasoning
result = agent.graph_query(
"MATCH (a:Person)-[:MANAGES]->(b:Person) RETURN a, b"
)
# 6. Pheromone retrieval — frequency-biased
hot = agent.recall("Q3", mode="pheromone")API Reference 35 MCP tools
EpisodicSemanticGraphProceduralWorkingPheromoneSpacesSystemTraversal
Episodic
memory_store_episodeStore a time-stamped event. No LLM at write time — sub-ms in-process engine write op.Episodic
memory_get_episodesRetrieve episodes with filtering and paginationEpisodic
memory_search_episodesNatural-language search over episodes with pheromone weightingEpisodic
memory_search_timelineTime-windowed episode searchEpisodic
memory_consolidate_episodesMerge related episodes into a consolidated recordSemantic
memory_store_entityStore a named entity with type and metadataSemantic
memory_get_entityRetrieve a specific entity by name or IDSemantic
memory_list_entitiesList all entities with optional type/metadata filtersSemantic
memory_store_relationStore a directed relationship between entitiesSemantic
memory_get_relationsGet all relations for a given entityGraph
memory_query_graphStructured query over the knowledge graphGraph
memory_semantic_searchSemantic similarity search across all memory typesGraph
memory_get_neighborsGet immediate neighbors of a nodeGraph
memory_graph_add_tripleAdd a (subject, predicate, object) triple directlyGraph
memory_graph_neighborsExtended neighbor query with depth and filtersGraph
memory_graph_shortest_pathFind shortest path between two entitiesProcedural
memory_store_procedureStore a skill definition: goal → steps → outcomeProcedural
memory_get_procedureRetrieve a procedure by IDProcedural
memory_match_procedureSearch procedures by goal descriptionProcedural
memory_update_procedureUpdate steps or log execution outcomeWorking
memory_set_workingSet a key-value pair with optional TTLWorking
memory_get_workingGet a working memory valueWorking
memory_list_workingList all active working memory keysWorking
memory_clear_workingClear all working memoryPheromone
memory_deposit_pheromoneManually deposit a pheromone trace on a node or edgePheromone
memory_get_pheromoneRead current pheromone weight (decay-adjusted)Spaces
memory_create_shared_spaceCreate a shared memory space with JWT/DID access controlSpaces
memory_join_shared_spaceJoin an existing space via invite tokenSpaces
memory_shared_store_entityStore an entity into a shared spaceSpaces
memory_shared_queryQuery across all agents in a spaceSpaces
memory_list_shared_spacesList spaces the agent belongs toSystem
memory_get_statsGet usage stats, op counts, rate limit statusSystem
memory_delete_agentPermanently delete all memories for an agentTraversal
memory_find_pathA* pathfinding between two concepts (all tiers)Traversal
memory_traverse_graphMulti-hop pheromone-weighted graph traversalFull parameter documentation, request/response schemas, and code examples coming soon. Join Discord for early access.
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