SynapticAI

Verifiable State Plane for Autonomous Agents

"Remember less. Stay correct longer." Neuro-Symbolic Memory That Learns What to Forget.

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What is SynapticAI?

SynapticAI is not a memory database — it is the context control plane for long-running agents.

While other memory systems simply "retrieve and inject" context into the model, SynapticAI implements Recall → Verify → Commit: every memory entry passes through a verification gate, gets checked against existing beliefs via formal AGM belief revision, and is only committed if it passes quality checks.

The Problem

Current agent memory solutions fail because:

• Vector stores (Pinecone, Qdrant) — good retrieval, no conflict resolution, no staleness detection
• Graph databases (Zep/Graphiti) — powerful but ops-heavy, complex onboarding
• Session replay — expensive and unbounded (1M context = 1M tokens to parse)
• BigTech bundled memory (ChatGPT, Claude) — siloed, no cross-tool portability

The SynapticAI Solution

Bigger context kills weak memory products, not strong memory companies.

SynapticAI solves what context windows can't:

• ✅ Verification gate — "Is this memory still accurate?" before commit
• ✅ AGM Belief Revision — formal conflict resolution with mathematical guarantees
• ✅ State Plane — context-independent typed state (separate from model prompt)
• ✅ Budget-aware recall — learned allocation across memory types
• ✅ Learnable forgetting — biological decay curves, not static TTLs
• ✅ Cross-tool translation — Claude Code ↔ Cursor ↔ OpenClaw ↔ VS Code via MCP

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Architecture

┌──────────────────────────────────────────────────────────┐
│              OBSERVABILITY LAYER                          │
│  Memory Debugger │ Provenance Graph │ Drift Detection     │
├──────────────────────────────────────────────────────────┤
│             ADAPTIVE BUDGET LAYER                         │
│  ContextBudget RL │ Budget-Aware │ Compression            │
├──────────────────────────────────────────────────────────┤
│                 STATE PLANE (Core)                        │
│  Context-independent typed state storage                  │
│  Model prompt receives clean result vectors, not raw data │
├──────────────────────────────────────────────────────────┤
│           NEURO-SYMBOLIC MEMORY CORE                      │
│  System 1 (Neural/Fast) │ System 2 (Symbolic/Slow)        │
│  Episodic │ Procedural  │ Semantic │ Symbolic             │
├──────────────────────────────────────────────────────────┤
│           AGM BELIEF REVISION ENGINE                      │
│  EXPAND │ CONTRACT │ REVISE — formal guarantees           │
├──────────────────────────────────────────────────────────┤
│           LEARNABLE FORGETTING ENGINE                     │
│  Biological forgetting curves │ Importance-based retention│
├──────────────────────────────────────────────────────────┤
│               VERIFICATION GATE                           │
│  Recall → VERIFY (real-world check) → COMMIT / REJECT    │
└──────────────────────────────────────────────────────────┘

Key Components

1. Neuro-Symbolic Memory Core

• System 1 (Neural/Fast): Episodic + Procedural memory, sub-50ms retrieval via hybrid search
• System 2 (Symbolic/Slow): Semantic + Symbolic memory, formal reasoning via knowledge graph

2. AGM Belief Revision

• EXPAND: Add new information (may create inconsistency)
• CONTRACT: Remove information (maintain consistency)
• REVISE: Add new info + resolve conflicts (the key operation)

3. Verification Gate

• Recall → Verify → Commit pipeline
• Staleness detection (temporal + strength-based)
• Conflict detection against existing beliefs
• Confidence scoring with explainable rejections

4. Hybrid Retriever

• BM25 (term-frequency) + Vector similarity
• RRF (Reciprocal Rank Fusion) for combining methods
• Lightweight reranker with metadata boost
• Budget-aware allocation across memory types

5. ContextBudget RL

• Learned context allocation strategies
• Task-type specific budget distribution
• Epsilon-greedy exploration → PPO in Phase 2

6. Learnable Forgetting

• Biological decay curves (Ebbinghaus model)
• Importance-based retention
• Explainable: "Why was this forgotten?"

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Quick Start

Install

pip install synaptic-state

Basic Usage

from synaptic_state import StatePlane
from synaptic_state.core.models import MemoryType

# Initialize
plane = StatePlane()

# Commit with verification
plane.commit(
    key="user_pref",
    value={"editor": "cursor", "tabs": 2},
    memory_type=MemoryType.SEMANTIC,
    verify=True,
)

# Recall with budget
result = plane.recall(
    intent="editor configuration",
    budget_tokens=2048,
    strategy="rl_optimized",
)

# Explainable forgetting
explanation = plane.explain_forgetting("old_pref")

PostgreSQL Backend

plane = StatePlane(
    backend="postgresql",
    dsn="postgresql://user:pass@localhost/synaptic",
)

LangGraph Integration

from synaptic_state.integrations.langgraph import LangGraphCheckpointer
from langgraph.graph import StateGraph

plane = StatePlane()
checkpointer = LangGraphCheckpointer(plane)
graph = builder.compile(checkpointer=checkpointer)

MCP Server

from synaptic_state.mcp_server.server import create_mcp_server
from synaptic_state import StatePlane

plane = StatePlane()
server = create_mcp_server(plane)
# Now available as MCP tools: commit, recall, forget, explain, status

Cross-Tool Translation

from synaptic_state.integrations.cross_tool import CrossToolTranslator, ToolFormat
from synaptic_state import StatePlane

translator = CrossToolTranslator(StatePlane())

# Import from Claude Code
translator.import_from_tool("CLAUDE.md", ToolFormat.CLAUDE_CODE)

# Export to Cursor
translator.export_to_tool(".cursor/memory.json", ToolFormat.CURSOR)

# Universal MCP format
mcp_json = translator.export_to_tool("", ToolFormat.MCP)

CLI

synaptic commit --key my_pref --value '{"theme":"dark"}'
synaptic recall --intent "editor settings"
synaptic forget --key old_pref
synaptic explain --key my_pref
synaptic status

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Research Foundation

SynapticAI combines breakthroughs from multiple research papers:

Paper	ArXiv	Contribution
ContextBudget	2604.01664	RL-based context allocation (1.6x gain)
ACC/CCS	2601.11653	Compressed Cognitive State, bounded drift
D-MEM	2603.14597	Reward-gated memory consolidation
StatePlane	2603.13644	Context-independent state storage
Kumiho AGM	2603.17244	Formal belief revision for agents
Oblivion	2604.00131	Biological forgetting curves
S3-Attention	2601.17702	GPU-memory efficient retrieval
HippoRAG	2405.14831	Neuro-inspired RAG

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Roadmap

Phase	Features
v0.1.0 ✅	MVP: StatePlane, AGM, Verification, Hybrid Retriever, ContextBudget RL, Learnable Forgetting, LangGraph, MCP, CLI, PyPI
v0.2.0	Full PPO policy training, SQLite backend, OpenClaw integration
v0.3.0	Multi-agent memory consensus, OT/CRDT resolution, Cross-tool sync
v1.0.0	Production-ready: enterprise security, observability dashboard, formal eval suite

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Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

License

MIT

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SynapticAI: Memory is not storage. It is state.