dakera-go

Go client for Dakera AI — the memory engine for AI agents

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Why Dakera?

	Dakera	Others
LoCoMo accuracy	88.2% (1,540 Q standard eval)	60–92%
Deployment	Single binary, Docker one-liner	External vector DB + embedding service required
Embeddings	Built-in — no OpenAI key needed	Requires external embedding API
Search modes	Vector · BM25 · Hybrid · Knowledge Graph	Usually one or two
Dependencies	stdlib net/http only	Often pulls in gRPC or third-party HTTP clients

→ Full benchmark results · dakera.ai

---

Run Dakera

docker run -d \
  --name dakera \
  -p 3000:3000 \
  -e DAKERA_ROOT_API_KEY=dk-mykey \
  ghcr.io/dakera-ai/dakera:latest

curl http://localhost:3000/health  # → {"status":"ok"}

For persistent storage with Docker Compose:

curl -sSfL https://raw.githubusercontent.com/Dakera-AI/dakera-deploy/main/docker-compose.yml \
  -o docker-compose.yml
DAKERA_API_KEY=dk-mykey docker compose up -d

Full deployment guide (Docker Compose, Kubernetes, Helm): dakera-deploy

---

Install

go get github.com/dakera-ai/dakera-go@latest

Requires Go 1.21+. Uses only the standard library — no external runtime dependencies.

---

Quick Start

package main

import (
    "context"
    "fmt"
    dakera "github.com/dakera-ai/dakera-go"
)

func main() {
    client := dakera.NewClientWithOptions(dakera.ClientOptions{
        BaseURL: "http://localhost:3000",
        APIKey:  "dk-mykey",
    })
    ctx := context.Background()

    // Store an agent memory
    imp := float32(0.9)
    mem, _ := client.StoreMemory(ctx, "my-agent", dakera.StoreMemoryRequest{
        Content:    "User prefers concise responses with code examples",
        Importance: &imp,
    })
    fmt.Println("Stored:", mem.Memory.ID)

    // Recall memories (semantic search)
    resp, _ := client.Recall(ctx, "my-agent", dakera.RecallRequest{
        Query: "what does the user prefer?",
        TopK:  5,
    })
    for _, m := range resp.Memories {
        fmt.Printf("[%.2f] %s\n", m.Score, m.Content)
    }

    // Upsert vectors
    client.Upsert(ctx, "my-namespace", []dakera.VectorInput{
        {ID: "vec1", Values: []float32{0.1, 0.2, 0.3}},
    })

    // Hybrid search (vector + BM25)
    results, _ := client.HybridSearch(ctx, "my-namespace", nil, "completed task", &dakera.HybridSearchOptions{
        TopK:         5,
        VectorWeight: 0.7,
    })
    for _, r := range results {
        fmt.Println(r.ID, r.Score)
    }
}

Context-based cancellation

All methods accept a context.Context — use it for timeouts, deadlines, or cancellation:

ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()

resp, err := client.Recall(ctx, "my-agent", dakera.RecallRequest{Query: "preferences", TopK: 3})

Text auto-embedding

Send raw text without pre-computing embeddings — Dakera embeds server-side:

client.UpsertText(ctx, "my-namespace", []dakera.TextInput{
    {ID: "doc1", Text: "Agent completed onboarding flow successfully"},
})

---

Features

• Agent Memory — store, recall, search, and forget memories with importance scoring
• Sessions — group memories by conversation with auto-consolidation on session end
• Knowledge Graph — traverse memory relationships, find paths, export graphs
• Vector Search — ANN queries with metadata filters and batch operations
• Full-Text Search — BM25 ranking with stemming and stop-word filtering
• Hybrid Search — combine vector similarity with keyword matching
• Text Auto-Embedding — server-side embedding generation (no local model needed)
• Namespaces — isolated vector stores per project, tenant, or use case
• Feedback Loop — upvote/downvote/flag memories to improve recall quality
• Entity Extraction — GLiNER NER for automatic entity detection
• SSE Streaming — Server-sent event subscriptions for real-time memory updates
• Typed Filters — Eq(), Gt(), Contains(), ArrayContains() and more
• Context-Based — all methods accept context.Context for cancellation and timeouts
• Retry & Rate Limiting — built-in exponential backoff and rate-limit header tracking
• Zero Dependencies — standard library net/http client, no external runtime deps

---

Connect to Dakera

// Self-hosted
client := dakera.NewClientWithOptions(dakera.ClientOptions{
    BaseURL: "http://your-server:3000",
    APIKey:  "your-key",
})

// Cloud (early access)
client := dakera.NewClientWithOptions(dakera.ClientOptions{
    BaseURL: "http://<your-server-ip>:3000",
    APIKey:  "your-key",
})

// With custom retry config
client := dakera.NewClientWithOptions(dakera.ClientOptions{
    BaseURL:     "http://localhost:3000",
    APIKey:      "your-key",
    RetryConfig: &dakera.RetryConfig{MaxRetries: 5, BaseDelayMs: 200},
})

---

Examples

See the examples/ directory:

• basic/ — vectors, namespaces, queries, filters
• memory/ — store/recall memories, sessions, agent stats
• advanced/ — text embedding, full-text, hybrid search, analytics

---

Resources

Documentation	Full API reference and guides
Go SDK docs	pkg.go.dev reference
Benchmark	LoCoMo evaluation results
dakera.ai	Website and early access
GitHub Org	All public repos
dakera-deploy	Self-hosting guide

Other SDKs

SDK	Package
dakera-py	dakera (PyPI)
dakera-js	@dakera-ai/dakera (npm)
dakera-rs	dakera-client (crates.io)
dakera-cli	CLI tool
dakera-mcp	MCP server for Claude/Cursor

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dakera.ai · Docs · Benchmark · Request Early Access

_{Built with Rust. Single binary. Zero external dependencies.}