Memory That Survives.
Reasoning That Doesn't Hallucinate.
Logic is DataGrout's persistent symbolic memory for AI agents. Store facts in natural language or structured maps, query with Prolog precision, define constraints as business rules — all at sub-millisecond latency with zero LLM cost.
10 tools
remember · assert · query · constrain · hydrate · more
Sub-millisecond
Prolog symbolic layer — no LLM at query time
Cross-session
facts persist across all agent runs
Free to start · No credit card required
Ten Tools. One Persistent Knowledge Layer.
From natural language storage to raw Prolog queries, graph traversal, and business rule constraints — Logic handles the full symbolic memory lifecycle.
Natural Language Fact Storage
Store facts as natural language statements or structured key-value pairs. Each fact gets a unique handle for later retraction. Supports batch storage, tags for grouping, namespaces for isolation, and TTL for ephemeral facts.
Key capabilities
- Single statement or batch array — same call
- tag parameter groups related facts (e.g. 'project-alpha', 'contacts')
- namespace isolates fact spaces — facts never cross boundaries
- ttl_seconds for ephemeral facts that auto-expire
- mode: append (default), upsert, or preview (dry-run)
- Returns handles for targeted retraction later
What Agents Can Do When They Remember
Logic transforms stateless agents into systems that learn, accumulate context, and enforce policies — across every call and every session.
Cross-Session Agent Memory
LLMs forget everything between calls. Logic gives agents a persistent fact base that survives across sessions, restarts, and model updates — so agents accumulate knowledge over time instead of starting from scratch.
Related Solutions
Declarative Business Rule Enforcement
Define rules once ('enterprise customers must have a billing contact before invoicing') and flow.into enforces them automatically before executing matching steps — without modifying each individual workflow.
Related Solutions
Adaptive Policy Enforcement with Warden
Store user trust levels, verified contexts, and threat history as logic facts. Warden reads these to apply adaptive security policies — escalate scrutiny for flagged users, relax for verified internal agents.
Related Solutions
Multi-Agent Shared Knowledge Base
Facts stored in Logic are shared across all agents on the same server within a namespace. One agent discovers a contact, another agent can immediately query it — no re-fetching, no duplication.
Related Solutions
Goal-Driven Context Window Priming
Instead of stuffing a full fact base into every LLM call, use logic.hydrate to assemble only the relevant facts for the current goal — within your token budget, as prose or structured JSON.
Related Solutions
CRM & Relationship Graph Queries
Store entities and relations from CRM data, then traverse the graph with Prolog. Find all companies reachable from an acquisition chain, or all contacts attributed to an enterprise account — in milliseconds.
Related Solutions
Logic Gets Richer With Other DataGrout Tools
Logic is the shared knowledge layer — every other DataGrout tool can read from it or write to it.
Constraint-Driven Workflow Guardrails
Constraints stored in Logic are checked by flow.into before executing matching steps. Define business rules once — they apply across every workflow without modifying each plan. Declarative enforcement at the orchestration layer.
✦ Business rules that apply everywhere, written once
Context-Aware Adaptive Defense
Store user trust levels, verified contexts, and multi-turn threat history as logic facts. Warden's intent analysis reads these to apply adaptive policies — escalate scrutiny for flagged users, relax for verified internal contexts.
✦ Security policies that evolve with your agent's knowledge
Passive Fact Enrichment on Every Call
When a Governor session is active, every tool call automatically asserts facts into Logic — tool name, timestamp, success/failure, cache_ref. Over time, agents build a complete audit trail of everything they've done, queryable in real time.
✦ Zero-effort audit trail — built automatically as agents work
Natural Language Queries Over Fact Bases
Pipe logic.export or logic.query results through prism.refract or prism.analyze for natural language reasoning over your entire fact base — pattern detection, anomaly surfacing, and narrative summaries without writing Prolog.
✦ LLM reasoning over symbolic facts — the best of both worlds
Integrate Logic in Minutes
One import. Facts persist instantly — queryable from any agent, any session.
from datagrout.conduit import Client
async with Client(
"https://gateway.datagrout.ai/servers/{uuid}/mcp",
auth={"bearer": "your-access-token"}
) as client:
# Store a fact in natural language
await client.perform("data-grout@1/logic.remember@1", {
"statement": "The billing contact for Acme Corp is jane@acme.com",
"tag": "contacts"
})
# Store structured facts in batch — zero LLM cost
await client.perform("data-grout@1/logic.assert@1", {
"facts": [
{"type": "attribute", "entity": "Acme Corp", "attribute": "plan", "value": "enterprise"},
{"type": "metric", "entity": "Acme Corp", "metric": "arr", "value": 2500000},
],
"tag": "contacts"
})
# Natural language recall — works across sessions
result = await client.perform("data-grout@1/logic.query@1", {
"question": "Who is the billing contact for Acme Corp?"
})
print(result["results"][0]["statement"])
# → "The billing contact for Acme Corp is jane@acme.com"Also available in TypeScript, Rust, Elixir, and Ruby. View Conduit SDK →
Frequently Asked Questions
Everything developers ask before integrating Logic.
More questions? Read the full Logic documentation.
Your agents deserve a memory.
Logic is the only symbolic memory layer built for AI agents — persistent facts, Prolog reasoning, and business rule enforcement without a database, schema, or LLM call at query time.
Free to start · No credit card required