Features SDKs Architecture GitHub
AI Agent Memory Infrastructure

The memory engine
for AI agents

Give your agents persistent, searchable memory that survives across sessions. They remember users, learn from mistakes, and get smarter over time. Built in Rust. Deploys in seconds.

Get Started See How It Works
Built in Rust Single binary Sub-10ms queries
agent memory
# Store agent memory
POST /v1/memory/store
{
  "agent_id": "assistant-1",
  "text": "User prefers TypeScript",
  "memory_type": "semantic",
  "importance": 0.9
}

# Recall by meaning
POST /v1/memory/recall
{
  "query": "language preferences",
  "top_k": 5
}

# → Result
{ "score": 0.97, "text": "User prefers TypeScript" }
0
p99 query latency
0
MCP tools built-in
0
Native SDKs
0
External dependencies
The problem

Your agents forget
everything they learn

Every session starts from zero. Thousands of interactions, zero retained knowledge. You're paying to re-teach your agents the same things over and over.

Sessions are isolated silos
Each conversation starts blank. Your agent can't recall what it learned yesterday, last week, or across 10,000 prior interactions.
Knowledge evaporates at scale
Insights from thousands of users vanish after each session. Your agent never compounds intelligence — it stays perpetually naive.
Context stuffing is a dead end
Cramming history into prompts burns tokens, inflates costs, and hits a hard ceiling. It's duct tape, not architecture.
agent session
agent.recall("user preferences")
Error: No memory found.
Context window empty.
agent.sessions
1,847 sessions completed
0 memories persisted
agent.monthly_cost
$4,200/mo on context stuffing
0 knowledge retained
0%
Retention
$50k
Wasted / year
Capabilities

Everything agents need
to remember

Semantic search, built-in embeddings, knowledge graphs, memory consolidation, and 45 MCP tools. All in a single binary you deploy once.

Vector + Hybrid Search
Find memories by meaning, not just keywords. Cosine, HNSW, and BM25 full-text combined with tunable hybrid weights.
Persistent Agent Memory
Store, recall, consolidate, and forget. Four memory types — episodic, semantic, procedural, working — with automatic importance decay.
Built-in Embeddings
Text is auto-embedded on store and query. No OpenAI calls, no external APIs. HuggingFace models ship inside the binary.
MCP Native (45 Tools)
Drop into Claude, Cursor, or Windsurf instantly. Memory, search, and knowledge graph exposed as 45 callable MCP tools.
Knowledge Graph
Automatically connects related memories into a queryable graph. Similarity edges, cluster summaries, and deduplication built-in.
Dashboard + CLI
Visual admin dashboard for exploring memories, running queries, and monitoring agents. Plus a full dk CLI for automation.
SDKs

Integrate in
minutes

Native SDKs for Python, TypeScript, Go, and Rust. Plus REST and gRPC for everything else. Five lines to first memory.

Store & Recall
Semantic memory with automatic embedding
Session Lifecycle
Context persists across every conversation
Multi-Agent Ready
Isolated namespaces for hundreds of agents
from dakera import DakeraClient

client = DakeraClient("http://localhost:3000")

# Store agent memory
client.memory_store(
    agent_id="assistant-1",
    text="User prefers TypeScript",
    memory_type="semantic",
    importance=0.9
)

# Recall by meaning
memories = client.memory_recall(
    agent_id="assistant-1",
    query="language preferences",
    top_k=5
)
import { DakeraClient } from "dakera"

const client = new DakeraClient("http://localhost:3000")

await client.memoryStore({
  agentId: "assistant-1",
  text: "User prefers TypeScript",
  memoryType: "semantic",
  importance: 0.9
})

const memories = await client.memoryRecall({
  agentId: "assistant-1",
  query: "language preferences",
  topK: 5
})
import "github.com/dakera/dakera"

client := dakera.NewClient("http://localhost:3000")

client.MemoryStore(ctx, &dakera.Memory{
    AgentID:    "assistant-1",
    Text:       "User prefers TypeScript",
    MemoryType: "semantic",
    Importance: 0.9,
})

memories, _ := client.MemoryRecall(ctx, "assistant-1", "language preferences", 5)
use dakera_client::DakeraClient;

let client = DakeraClient::new("http://localhost:3000");

// Store agent memory
client.memory_store(&Memory {
    agent_id: "assistant-1".into(),
    text: "User prefers TypeScript".into(),
    memory_type: "semantic".into(),
    importance: 0.9,
    ..Default::default()
}).await?;

// Recall by meaning
let memories = client
    .memory_recall("assistant-1", "language preferences", 5)
    .await?;
# Store memory
curl -X POST localhost:3000/v1/memory/store \
  -H "Content-Type: application/json" \
  -d '{"agent_id":"assistant-1","text":"User prefers TS","importance":0.9}'

# Recall
curl -X POST localhost:3000/v1/memory/recall \
  -d '{"agent_id":"assistant-1","query":"language preferences","top_k":5}'

# Text search with auto-embedding
curl -X POST localhost:3000/v1/namespaces/docs/query-text \
  -d '{"text":"semantic search systems","top_k":5}'
Architecture

Batteries included

Nine Rust crates compiled into a single binary. Every layer — from inference to storage — ships together. Nothing to install, configure, or manage.

Client
REST API
gRPC
MCP Server
Dashboard
CLI
Middleware
Auth & API Keys
Rate Limiting
Audit Log
Prometheus
Engine
Inference
Vector Search
BM25
Agent Memory
Knowledge Graph
Clustering
Storage
In-Memory
Filesystem
S3 / MinIO
How it works

Three steps to persistent intelligence

01
Store
Your agent stores conversations, decisions, and user preferences as semantically embedded memories with importance scores.
02
Recall
Before each response, the agent retrieves the most relevant memories via hybrid search. The right context, every time.
03
Learn
Over time, overlapping memories merge, patterns emerge, and your agent builds genuine, compounding knowledge.
Ready to start

Stop re-teaching your agents.
Give them memory.

Deploy in seconds. Your agents start remembering immediately. One binary, no infrastructure to manage, no vendor lock-in.

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Built in RustSingle binarySub-10ms queriesProduction ready