logicmem 0.1.0

AI agent memory infrastructure — persistent memory, A2A sharing, reasoning engine

🧠 LogicMem — AI Agent Memory Infrastructure

Persistent memory, A2A sharing, reasoning engine, and immutable audit trail for AI agents via the Model Context Protocol.

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The Problem

AI agents are stateless by design. Every session starts from scratch:

Session 1                              Session 2
──────────                             ──────────
User: "I'm building a SaaS"     →     User: "How's my SaaS coming?"
Agent: "Tell me more..."              Agent: "I don't know anything
...                                   about your SaaS"
[Session ends]                        
                                      Agent forgot EVERYTHING.

This is fine for demos. It's catastrophic for production AI workflows.

The Solution

LogicMem gives your AI agent persistent memory — connect any MCP client and get:

• 🔍 Persistent Memory — Store and search memories across sessions
• 🧠 Reasoning Engine — Multi-step reasoning that consults memory
• 🔗 A2A Memory Sharing — Agents share context in real-time
• 📋 Immutable Audit Trail — Cryptographically verifiable history
• 🎙️ Voice Memory — Caller history for VAPI, Retell AI, Bland AI

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Open Core Model

This repo is the LogicMem SDK — the open-source client for connecting AI agents to the LogicMem memory fabric. The SDK is fully open (MIT licensed). The reasoning engine and audit chain run on LogicMem's private server.

	Open Source (This Repo)	LogicMem Pro / Enterprise
SDK / Client	✅ Fully open (MIT)	✅ Included
Persistent Memory	✅ Up to 1,000 ops/mo (free tier)	✅ Unlimited
A2A Memory Sharing	✅ Basic	✅ Advanced governance + cross-org
Reasoning Engine	✅ API call (server-powered)	✅ Deep / Exhaustive modes
Audit Trail	✅ API call (server-verified)	✅ Tamper-evident ledger + CNSA 2.0
Voice Agent Memory	✅	✅
Deployment	Cloud (logicmem.io)	Cloud, on-prem, or air-gap
Support	Community	Dedicated + SLA

Why this model? The SDK gives developers the steering wheel. The LogicMem server is the engine. You get a great developer experience — and your AI gets production-grade memory infrastructure without building it yourself.

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Install

# Install the Python SDK (library only)
pip install --break-system-packages git+https://github.com/LogicMem/LogicMem-mcp-.git

# Install with CLI tools (includes logicmem-server for OpenClaw MCP):
pip install --break-system-packages "logicmem[cli] @ git+https://github.com/LogicMem/LogicMem-mcp-.git"

# Linux/Ubuntu (no flag needed):
pip install "logicmem[cli] @ git+https://github.com/LogicMem/LogicMem-mcp-.git"

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Quick Start (< 5 minutes)

1. Get an API Key

Sign up at logicmem.io → Settings → API Keys → Create Key.

Free tier: 1,000 memory operations/month.

⚠️ macOS users: If you see a PEP 668 error during install, rerun with --break-system-packages flag (see Install section above).

2. Use the Python SDK

from logicmem import LogicMem

# Initialize the client
memory = LogicMem(api_key="lm_your_api_key")

# Store a memory
memory.log(
    text="User prefers urgent messages via Telegram, not email.",
    category="preference",
    importance=8,
)

# Search memories
results = memory.recall(query="user communication preferences")
print(results[0]["text"])
# → "User prefers urgent messages via Telegram, not email."

# Store a task with context
memory.log(
    text="Review Q3 proposal by Friday. Priority: cost breakdown first, then timeline.",
    category="task",
    importance=9,
)

# Session briefing — full context at start of session
brief = memory.session(client_id="ed_creed")
print(brief["confidence"])   # How confident is the agent about this user?
print(brief["relationship_trend"])  # improving / declining / stable

3. Reasoning Engine

# Multi-step reasoning with memory at each step
answer = memory.reason(
    question="Should we prioritize the mobile app or web dashboard first?",
    context="User is a solo founder with limited engineering bandwidth.",
    mode="deep",  # fast / deep / exhaustive
)
print(answer["answer"])
print(answer["confidence"])

# Verify a claim against stored facts
verdict = memory.verify("User has a budget of $50k for this project")
print(verdict["verdict"])   # supported / contradicted / inconclusive
print(verdict["evidence"])  # supporting entries

# Self-critique before committing to an answer
review = memory.reflect(
    draft_answer="You should build the web dashboard first.",
    question="What should we prioritize first?",
    memory_query="user preferences priorities",
)
print(review["score"])      # 0-100
print(review["gaps"])       # weaknesses in the answer

4. Agent-to-Agent (A2A) Memory Sharing

from logicmem.a2a import A2AClient

# Agent A: Share a memory with Agent B
a2a = A2AClient(api_key="lm_agent_a_key", agent_id="agent-researcher")

# Register this agent
a2a.register(name="Researcher Agent", agent_type="agent", client_id="team-alpha")

# Share context with another agent
a2a.share_memory(
    target_agent_id="agent-executor",
    memory={"text": "User needs Q3 report by Friday. High priority."},
    category="task",
    importance=9,
)

# Check for new shared memories from other agents
shared = a2a.sync()
for entry in shared:
    print(f"From {entry['from_agent_id']}: {entry['text']}")

5. Verify Audit Chain

from logicmem.audit import AuditChain

audit = AuditChain(memory)  # pass LogicMem client

# Verify the audit chain has not been tampered with
result = audit.verify()
print(result["valid"])  # True if chain integrity is intact

# Log a correction (improves the model)
audit.log_correction(
    original="The user prefers email for urgent messages.",
    corrected="The user prefers Telegram for urgent messages, not email.",
    reason="User explicitly stated Telegram in call on 2026-06-10.",
)

# Check DPO training pipeline stats
stats = audit.dpo_stats()
print(f"Correction pairs ready: {stats['ready_count']}")

---

Architecture

┌──────────────────────────────────────────────────────────────┐
│                      Your AI Agent                           │
│              (Claude, GPT, Any MCP Client)                    │
└──────────────────────────────────────────────────────────────┘
                             │ MCP
                             ▼
┌──────────────────────────────────────────────────────────────┐
│                   LogicMem MCP Server                        │
│                 api.logicmem.io                              │
│  ┌────────────┐ ┌────────────┐ ┌────────────┐ ┌────────┐ │
│  │   Memory    │ │  Reasoning │ │    A2A     │ │ Audit  │ │
│  │   Tools     │ │   Engine   │ │   Relay    │ │ Chain  │ │
│  └────────────┘ └────────────┘ └────────────┘ └────────┘ │
└──────────────────────────────────────────────────────────────┘
                             │
           ┌─────────────────┼─────────────────┐
           ▼                 ▼                 ▼
    ┌────────────┐   ┌────────────┐   ┌────────────┐
    │  Memory    │   │   Memory   │   │   Audit   │
    │  Storage   │   │   Index    │   │   Ledger  │
    │(Supabase)  │   │ (Qdrant)   │   │(Hash Chain)│
    └────────────┘   └────────────┘   └────────────┘

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OpenClaw Integration

OpenClaw is the fastest-growing open-source AI agent framework (300K+ GitHub stars). LogicMem is fully compatible with OpenClaw.

Option A — Direct MCP Server URL (Simplest, 1 line of config)

Add LogicMem as a streamable-http MCP server in your OpenClaw config (~/.openclaw/openclaw.json):

{
  "mcp": {
    "servers": {
      "logicmem": {
        "transport": "streamable-http",
        "url": "https://mcp.logicmem.io/mcp",
        "headers": {
          "Authorization": "Bearer lm_YOUR_API_KEY"
        }
      }
    }
  }
}

Note: The streamable-http transport is the modern MCP standard (2024-11-05). The server at mcp.logicmem.io supports both streamable-http and legacy sse.

Option B — Local stdio Server (For power users with multiple MCP servers)

Some OpenClaw users have multiple MCP servers configured — a mix of stdio (local programs) and HTTP/SSE (remote servers). OpenClaw has a known limitation where it can't freely mix stdio and SSE servers in the same config.

The fix: Use our local stdio server as a bridge. Install it via pip:

pip install --break-system-packages \
  "logicmem[cli] @ git+https://github.com/LogicMem/LogicMem-mcp-.git"

Then configure OpenClaw to use the local stdio command:

{
  "mcp": {
    "servers": {
      "logicmem": {
        "command": "logicmem-server",
        "env": {
          "LOGICMEM_API_KEY": "lm_YOUR_API_KEY",
          "LOGICMEM_CLIENT_ID": "your-client-id"
        }
      }
    }
  }
}

This approach:

• Works alongside any other MCP server (stdio or HTTP)
• No SSE/stdio mixing conflict
• Installs in seconds via pip

Quick Test — Verify Your Setup

After configuring, test that LogicMem is connected:

# Check if the MCP server is recognized
openclaw mcp list

# Or test directly in a conversation with your agent:
# "What is my name?" (should recall from memory if previously stored)

For Specific OpenClaw Agents

Agent	Recommended Setup	Config Type
Themis (any OpenClaw agent)	Direct URL	streamable-http config
Hermes	Direct URL or stdio pip	Same as above
Claude Code	Direct URL	streamable-http in Claude Code config
Custom OpenClaw agents	Direct URL	Same as above

Environment Variables

Variable	Default	Description
LOGICMEM_API_KEY	—	Your API key (lm_xxx) from logicmem.io/settings
LOGICMEM_SERVER_URL	https://api.logicmem.io	Point to self-hosted server if using logicmem-open
LOGICMEM_CLIENT_ID	default	Default client_id for memory operations
LOGICMEM_TIMEOUT	30	HTTP request timeout in seconds

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MCP Protocol Reference

The server accepts JSON-RPC 2.0 requests over HTTPS.

Base URL: https://api.logicmem.io

Authentication: Authorization: Bearer <api_key> header.

Core Tools

Tool	Description
logicmem_memory_log	Store a new memory with category, importance, tags
logicmem_memory_recall	Search memories with natural language
logicmem_memory_session	Get full context briefing for current session
logicmem_reason	Multi-step reasoning with memory consultation
logicmem_verify	Verify a claim against stored facts
logicmem_reflect	Self-critique — evaluate draft against memory
logicmem_audit_verify	Verify integrity of the audit chain
logicmem_a2a_share	Share memory with another agent
logicmem_a2a_receive	Receive shared memory from another agent

See MCP-PROTOCOL.md for the full protocol reference.

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Comparison

Feature	LogicMem	Mem0	Letta	Zep
MCP-native	✅ Full	⚠️	✅	⚠️
Reasoning engine	✅	❌	⚠️	❌
A2A memory sharing	✅	❌	⚠️	❌
Immutable audit trail	✅	❌	❌	⚠️
DPO training pipeline	✅	❌	❌	❌
Voice agent memory	✅	❌	⚠️	❌
Federated memory	✅	❌	❌	❌

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Security

• Encryption: AES-256-GCM at rest, TLS 1.3 in transit
• Compliance: CNSA 2.0 cryptography for defense/government workloads
• Audit: Every operation logged to immutable hash-linked chain
• API Keys: Per-agent keys with fine-grained permissions

See SECURITY.md for the full security model.

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Documentation

All documentation lives in the docs/ folder right here in this repo:

Doc	What You Need
📖 Start Here	Install + first 10 lines of code
🔌 MCP Protocol	Full protocol reference
🔗 A2A Sharing	Agent-to-agent memory
🔒 Security	Encryption, CNSA 2.0, audit
💻 Code Examples	All examples in one place

Links

• 🌐 logicmem.io — Product
• 💬 Discord — Community
• 📧 [e-mail]

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Contributing

Contributions welcome. Please see CONTRIBUTING.md.

We especially welcome:

• MCP client examples (more clients → more adoption)
• Framework integrations (LangChain, AutoGPT, CrewAI, etc.)
• A2A protocol extensions
• SDK implementations in other languages

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License

MIT License. See LICENSE.