Knowledge Graph
Explore shared memory with live node, relationship, and provenance filtering.
Human-agent collaboration requires trust.
Trust is built over time through shared memory and portable context —
so every human and every agent can move from the same source of truth.
Shared memory wherever work happens
Coming Q3 2026
Deepening workspace connectors for team context portability
Desktop app screenshots
Native desktop control plane workflows for shared memory, delegated execution, and multi-agent governance.
Explore shared memory with live node, relationship, and provenance filtering.
Main-agent planning with memory-aware context and inline recall.
Scoped subagent delegation, run inspection, and runtime telemetry.
Tool, skill, and MCP analytics with trust and cost visibility.
LittleGuy is the memory control plane for human-agent systems: one shared context layer where goals, decisions, and execution history stay portable across tools and time.
Operating philosophy
Every conversation should inherit decisions, commitments, and prior context. No more starting from zero for each agent or surface.
Scope is not optional. Agents get only the tools, memory slices, and permissions needed for the task at hand.
Captured context is typed, scored, and connected as it lands so retrieval stays high-signal as your system grows.
Delegated work can be parallel and short-lived while memory remains durable across web, desktop, and mobile clients.
Define objectives, constraints, and acceptance criteria. Humans decide what matters and approve what becomes durable memory.
Plans strategy, decomposes work, routes tasks, and enforces policy. It is the orchestrator, not the single executor.
Specialized workers with tight scopes. They execute delegated tasks, return evidence, and commit only what is approved.
The control plane keeps capture, delegation, and memory commit in one reliable loop, so teams can move quickly without losing traceability.
Human-agent trust is a systems property. These controls keep context relevant, constrained, and auditable at every step.
LittleGuy is where human intent, agent execution, and shared organizational memory meet. One control plane. Multiple teams. Many agents.
Persist architecture decisions, incident context, and implementation constraints so main agents and coding subagents execute from the same baseline.
Capture interview insights, market signals, and messaging hypotheses as typed memory, then reuse them across strategy, content, and sales workflows.
Turn tickets, runbooks, and postmortems into durable operational memory so response agents can act quickly without repeating discovery work.
Apply project and role boundaries to memory recall and tool access. Keep delegation observable while preserving least-privilege behavior.
Beyond chat history. Beyond personal notes. LittleGuy gives teams a shared, governed memory layer for multi-agent execution.
Connect Claude, ChatGPT MCP, Gemini, and internal agents to one memory plane. Query context, commit outcomes, and preserve continuity across every tool.
Main agents orchestrate strategy while subagents execute tightly scoped jobs with bounded tools and contextual slices.
Attach domain skills to agents, gate tools by responsibility, and keep capabilities explicit so delegation remains auditable.
Keep prompts lean by routing memory retrieval: fast paths for known patterns, deeper recall only when confidence or novelty requires it.
Every capture is typed and connected so agents retrieve usable context instead of raw logs. Scope follows project, role, and task intent.
Capture on mobile, orchestrate on desktop, monitor on web, and execute through MCP-enabled agents. Same memory graph, same context lineage, everywhere.
Control plane internals
LittleGuy is designed as a Memory Control Plane: structured memory, scoped execution, and portable context across web, desktop, mobile, and MCP clients.
Neo4j relationships + pgvector semantic recall in a dual-store architecture.
Decisions, tasks, people, events, documents, and more for higher-precision retrieval.
Token issuance, refresh, and revocation for secure agent-to-memory connectivity.
Deterministic, cached, and agentic retrieval modes keep token use and latency under control.
Parses raw capture into structured objects: entities, facts, commitments, and decisions.
Assigns node type and confidence so retrieval can route to relevant context classes.
Connects people, projects, and knowledge over time to maintain relationship-aware recall.
Reduces stale relevance unless reinforced, keeping memory fresh without manual cleanup.
Main agents delegate tasks to subagents with explicit boundaries for memory and tools, reducing accidental context bleed.
Skills and tool surfaces align to roles so each agent uses the right capabilities for the right class of work.
Recall policies prioritize high-signal context first, then expand only when required by novelty or ambiguity.
OAuth 2.0 + PKCE secures client connectivity while scoped tokens, revocation, and policy-aware tool surfaces keep memory access intentional. This is the foundation for reliable collaboration between humans, main agents, and delegated subagents.
Build a human-agent system your team can trust: shared context, scoped delegation, and durable memory that follows every workflow.