Real Dakera API calls in your browser. Chat sessions, hybrid search tuning, entity extraction, multi-agent sharing, knowledge graphs, guided scenarios, and LLM comparison.
What makes Dakera different: Every stored memory carries an importance score (0–1) that controls exponential decay — critical facts persist indefinitely while ephemeral context fades naturally. Recall combines vector similarity + importance weight + temporal recency into a single smart score, achieving 88.2% accuracy on the LoCoMo long-context memory benchmark.
88.2% LoCoMo accuracy·<10ms recall latency·9 live modes·5 languages·Chat sessions
Live sandbox — this code runs against our real Dakera API. No account, no setup. Click ▶ Run to execute it now.
playground.py
30 req/min · 50 mem/session · ⌘⏎ to run
0.80
output.jsonidle
// Press ▶ Run to execute…
✨ Live Visualization
// Run a scenario to see the visualization
Compare Recall Strategies
Same query, three depth levels. See how top_k shapes what your agent retrieves — breadth vs. precision.
Try a query preset:
Broad Recall
top_k=10 · all results ranked
Returns up to 10 memories ranked by smart score. Best for discovery — you see everything the agent knows about this topic, including loosely related facts. Use when you want maximum context.
discovery mode
▶ Click Compare to run
Balanced
top_k=5 · top half of results
Returns the top 5 most relevant memories. Filters out peripheral context while keeping all meaningful signals. The default sweet spot for most agent use cases — not too noisy, not too narrow.
default mode
▶ Click Compare to run
Precise
top_k=3 · highest relevance only
Returns only the 3 highest-scoring memories. Maximum signal-to-noise ratio. Use when your prompt window is tight or you need the agent to act on only its strongest beliefs — ideal for critical decisions.
precision mode
▶ Click Compare to run
Smart Score = vector similarity × importance weight × temporal recency factor — a single 0–100% relevance signal combining semantic match, how critical the memory is, and how recently it was stored or accessed.
Free-form API Explorer
Call any Dakera endpoint directly — craft your own request, see the raw response.
RequestPOST
Request Body (JSON)
Responseidle
// Response will appear here…
AI With Memory vs. Without
Ask the same question to an LLM with and without Dakera memory. See exactly how persistent context changes the answer — and which memories made the difference.
Powered by OpenRouter free models — Llama 4 Maverick or Gemma 3. Real LLM inference, real memory retrieval.
Try a preset scenario:
1Seed memories
2Recall context
3LLM inference
4Compare
🚫 Without Memory
LLM has no context about your data
Select a preset or type a question, then click Compare…
Generic response — no domain knowledge
✅ With Dakera Memory
LLM receives recalled memories as context
Select a preset or type a question, then click Compare…
Precise, contextual response — powered by recalled memories
🔍 Memories injected as context:
AI Chat with Memory
Chat with an AI that actually remembers you. Every message is stored via Dakera — the AI recalls prior context to give personalized, contextual responses.
Session not started
Raw API Responses
Hybrid Search Tuner
Adjust the balance between BM25 keyword search and vector semantic search. See how results change in real-time.
BM25 (keyword)Vector (semantic)
vector_weight: 0.50
Seed memories, then search to see results
Entity Extraction — Live
Paste or type text. Dakera extracts named entities (people, orgs, locations, dates) with highlighted spans.
Type text above and click Extract
Multi-Agent Memory Sharing
Three specialized agents share a memory namespace. Onboarding stores user context, Support recalls it for personalized help, Analytics surfaces insights. Zero re-introduction — instant cross-agent context via Dakera.
✓ Agent A stored context
▸
2 Agent B recalls
▸
3 Agent C analyzes
🤖
Onboarding Bot
Captures user preferences & context
0.85
➡
💬
Support Bot
Recalls shared context & generates response
➡
📊
Analytics Agent
Recalls all context & surfaces insights
Knowledge Graph Explorer
Seed org memories, extract entities, then visualize the knowledge graph with force-directed layout. Click nodes to see details, drag to rearrange, double-click to expand, Shift+click two nodes to find shortest path. Each mode uses its own isolated agent namespace so graph data stays separate from chat, hybrid search, and other modes.
Seed memories and build graph to explore
Node Details
Ready to Add Memory to Your Agent?
Self-hosted, open-core, one binary. From zero to recalled memories in under 5 minutes.
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