Getting Started

# Not yet on PyPI — coming soon
pip install agentverse-memory

# Not yet on npm — coming soon
npm install @fetchai/agentverse-memory

from agentverse_memory import MemoryClient
 
# 1. Initialize the client
client = MemoryClient(
    api_key="am_your_key_here",      # from memory.agentverse.ai dashboard
    agent_id="my-research-agent",           # unique per agent
    base_url="https://am-server-jbneh74b5q-uc.a.run.app",  # or set AVMEM_BASE_URL env var
)
 
# 2. Store an episodic memory (something that happened)
episode = client.episodes.store(
    content="User asked about the Q1 sales report. Pulled data from Salesforce.",
    metadata={
        "user_id": "user_42",
        "session_id": "sess_abc123",
        "tags": ["sales", "q1", "report"],
    }
)
print(episode.id)   # ep_01hx...
# No LLM calls at write time — sub-ms in-process engine write op
 
# 3. Store a semantic memory (a knowledge triple)
fact = client.facts.store(
    subject="Acme Corp",
    predicate="signed_contract_with",
    object_="Fetch.ai",
    metadata={
        "valid_at": "2026-01-15",           # when this fact became true
        "confidence": 0.95,
        "source": "CRM",
    }
)
print(fact.id)      # fact_02hy...
 
# 4. Query memories by natural language
results = client.episodes.query(
    query="What did the user ask about sales?",
    limit=5,
)
for r in results:
    print(f"[{r.score:.2f}] {r.content[:80]}")
 
# 5. Graph traversal — follow edges from a concept
graph_results = client.facts.traverse_graph(
    start_id="Acme Corp",
    algorithm="bfs",
    max_depth=2,
    limit=20,
)
for node in graph_results.nodes:
    print(f"{node.subject} --[{node.predicate}]--> {node.object_}")

import { MemoryClient } from "@fetchai/agentverse-memory";
 
// 1. Initialize the client
const client = new MemoryClient({
  apiKey: "am_your_key_here",
  agentId: "my-research-agent",
});
 
// 2. Store an episodic memory
const episode = await client.episodes.store({
  content: "User asked about the Q1 sales report.",
  metadata: {
    userId: "user_42",
    sessionId: "sess_abc123",
    tags: ["sales", "q1", "report"],
  },
});
console.log(episode.id); // ep_01hx...
 
// 3. Store a semantic fact
const fact = await client.facts.store({
  subject: "Acme Corp",
  predicate: "signed_contract_with",
  object: "Fetch.ai",
  metadata: { validAt: "2026-01-15", confidence: 0.95 },
});
 
// 4. Query episodes
const results = await client.episodes.query({
  query: "What did the user ask about sales?",
  limit: 5,
});
results.forEach((r) => {
  console.log(`[${r.score.toFixed(2)}] ${r.content.slice(0, 80)}`);
});
 
// 5. Graph traversal
const graph = await client.facts.traverseGraph({
  seedNode: "Acme Corp",
  maxDepth: 2,
  limit: 20,
});
graph.nodes.forEach((n) => {
  console.log(`${n.subject} --[${n.predicate}]--> ${n.object}`);
});

{
  "mcpServers": {
    "agentverse-memory": {
      "url": "https://am-server-jbneh74b5q-uc.a.run.app/mcp",
      "headers": {
        "Authorization": "Bearer am_your_key_here"
      }
    }
  }
}

{
  "mcpServers": {
    "agentverse-memory": {
      "url": "https://am-server-jbneh74b5q-uc.a.run.app/mcp",
      "headers": {
        "Authorization": "Bearer am_your_key_here"
      }
    }
  }
}

from agentverse_memory import MemoryClient
 
# Orchestrator creates a shared memory space (Builder+ key)
orchestrator = MemoryClient(api_key="am_...", agent_id="orchestrator")
space = orchestrator.spaces.create(name="research-team-memory")
print(space.id)
 
# Mint a space-scoped JWT (server-side) — see POST /v1/space-token
space_jwt = orchestrator.spaces.mint_token(space_id=space.id)
 
# Worker agent joins the space using the JWT
worker = MemoryClient(api_key="am_...", agent_id="worker-agent-1")
worker.spaces.join(space_id=space.id, space_token=space_jwt)
 
# Any member can query the shared knowledge graph
results = orchestrator.spaces.query(
    space_id=space.id,
    query="What did the team discover about Acme Corp?",
    limit=10,
)