Agent Sleep

Persistent Experience Consolidation & Decision Support for AI Agents.

A lightweight, framework-agnostic Python library and MCP server that provides persistent experience consolidation and decision-support signals that a host agent can use to adapt across sessions — inspired by how the biological brain uses sleep cycles to consolidate waking experiences into lasting procedural rules and lessons.

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The Problem: "Agent Amnesia"

Every modern AI agent framework (LangChain, AutoGen, CrewAI, OpenAI Assistants) suffers from Agent Amnesia:

• Every new chat or subagent run starts completely from scratch.
• When an agent hits an error or discovers a codebase convention on Monday, it repeats the exact same mistake on Tuesday.
• Vector DBs (RAG) only search static documents — they do not learn from runtime experience.

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MCP Quick Start — 10 seconds

This is the primary usage path. agent-sleep ships as an MCP server, so any agent that supports MCP (Antigravity, Claude Desktop, Cursor, Cline) can use it without writing any code.

Step 1 — Install and generate your config

# Option A: zero-install (recommended)
uvx agent-sleep-mcp

# Option B: install first, then run the init helper
pip install "agent-sleep[mcp]"
agent-sleep init     # prints the correct config snippet for your platform

agent-sleep init auto-detects your OS and prints the JSON snippet to paste into your MCP client's config file. No hand-editing required.

Step 2 — Paste the config snippet

The init command prints exactly what to paste and where. Example output for Claude Desktop on macOS:

{
  "mcpServers": {
    "agent-sleep": {
      "command": "uvx",
      "args": ["agent-sleep-mcp"]
    }
  }
}

Paste that into ~/Library/Application Support/Claude/claude_desktop_config.json, restart Claude, and you're done.

Step 3 — Ask your agent to use it

"Before we start, check your memory for anything relevant to this task."
"Record that we use pytest fixtures — not unittest — in this project."
"Run a sleep consolidation so you remember today's lessons next session."

Memory is automatically stored in .agent_sleep/memory.db in your project directory (gitignored by default).

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Inspect what's stored — CLI

You don't need to go through an LLM to see what your agent has learned:

# See all memories and rules for the current project
agent-sleep show

# Clear a project's memory (with confirmation prompt)
agent-sleep reset

# Target a specific scope or DB
agent-sleep show --scope my_api --db /path/to/memory.db

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How It Works: The 3-Phase Pipeline

          [ ONLINE EXECUTION PHASE ]
            Agent executes tool calls
                       │
                       ▼
┌──────────────────────────────────────────────┐
│  1. EPISODIC RECORDING                       │
│     memory.record_episode(...)               │  Fast, minimal overhead.
│     Records goal, action, outcome, errors.   │  Stores execution events.
└──────────────────────┬───────────────────────┘
                       │
             (Session ends / Agent idle)
                       │
                       ▼
          [ OFFLINE SLEEP CONSOLIDATION ]
┌──────────────────────────────────────────────┐
│  2. SLEEP CONSOLIDATOR (8-Stage Pipeline)    │
│     SleepConsolidator.run(session_id)        │
│                                              │
│     • Priority Replay (prediction error)     │
│     • Deterministic Episodic Distillation    │  Grounding first:
│     • Procedural Recipe Extraction           │  distills facts & lessons
│     • How-Memory Trajectory Abstraction      │  before optional LLM
│     • Behavioral Rule Promotion (seen ≥2x)   │  generalization passes.
│     • Epistemic Status (observed vs verified)│
│     • Episodic Compression over time         │
│     • Self-Competence EMA Tracking           │
└──────────────────────┬───────────────────────┘
                       │
              (Next session / New task)
                       │
                       ▼
          [ ONLINE SELECTIVE RECALL ]
┌──────────────────────────────────────────────┐
│  3. SELECTIVE SEMANTIC RECALL                │
│     memory.recall(new_task)                  │  Pre-computed vector BLOBs.
│     Returns only relevant lessons & rules    │  Prevents prompt dilution.
│     filtered by project scope & relevance.   │
└──────────────────────────────────────────────┘

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Key Features (v0.1.2-alpha)

• Pre-Computed Vector BLOBs: Embeds the query once and compares it against pre-computed stored vectors, eliminating repeated text embedding during recall.
• Epistemic Memory Lifecycle: Tracks memory progression through stages (RAW → OBSERVED → REPEATED → VERIFIED → ACTIVE), automatically quarantining contradictory or high-failure memories.
• Verifiable Causal Attribution & Utility Feedback: Evaluates whether retrieved memories actually helped future execution via structured evidence records (retrieval → action change → outcome attribution).
• Evidence Diversity Causal Hypotheses: Distills recurring failures into causal mechanisms using evidence diversity scaling across independent sources and environments.
• Bayesian Self-Competence Model: Estimates domain competence and Bayesian Beta-distribution uncertainty across composite domains to provide adaptive decision support (verification intensity, retry budgets) for host agents.
• First-Class Rule Specificity Engine: Resolves rule conflicts through hierarchical precedence (specific verified > general verified > specific candidate > general candidate) and dynamic exception suppression.
• Scope & Project Isolation: Multi-tier namespaces (scope="repo_a", scope="global"). Project-specific knowledge is strictly isolated, while universal idioms and tool failure modes can optionally be shared via global.
• Zero Mandatory Heavy Dependencies: Works out-of-the-box using standard SQLite and a deterministic hashed bag-of-words fallback. Seamlessly upgrades to sentence-transformers (all-MiniLM-L6-v2) when installed.

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Benchmarks & Evaluation

1. Controlled Transfer Simulation (benchmarks/run.py)

Evaluates memory consolidation, vector retrieval, and knowledge transfer across 12 sequential software tasks with recurring architectural traps:

Metric	Memory OFF	Memory ON	Improvement
Pass Rate (Pass@12)	67%	92%	+25 percentage points
Avg LLM Calls / Task	14.7	8.5	-42% (fewer calls)
Repeated Mistakes	8	2	-75% (fewer mistakes)

Note: The controlled transfer simulation evaluates the deterministic cognitive-control dynamics of memory retrieval and trap avoidance.

2. Canonical 6-Way Ablation Benchmark (benchmarks/agent_eval/runner.py)

Controlled sandbox evaluation of memory-driven agent-control dynamics across 8 standardized software engineering tasks:

Experimental Condition	Pass Rate (Zero-Shot)	Avg LLM Calls / Task	Repeated Traps	Memory Useful Rate
NO_MEMORY (Baseline Amnesia)	12.5%	3.6	4	0.0%
RAW_TRANSCRIPT (Unconsolidated)	12.5%	3.6	4	0.0%
VECTOR_RAG (Naive Semantic)	12.5%	3.6	4	0.0%
AGENT_SLEEP_CORE (Episodic Distillation)	25.0%	2.9	2	12.5%
AGENT_SLEEP_EPISTEMIC (Core + Provenance)	37.5%	2.5	1	25.0%
AGENT_SLEEP_FULL (Full Cognitive Architecture)	75.0%	1.4	0	75.0%

python benchmarks/agent_eval/runner.py

[!NOTE] Scientific & Backend Disclosure:

• The sandbox benchmark evaluates agent control dynamics, token efficiency, and error avoidance under controlled test suites.
• Embedding Backends: High-precision vector similarity relies on sentence-transformers (all-MiniLM-L6-v2). When dependencies are absent, the library automatically falls back to a deterministic hashed bag-of-words embedding.
• Full reproducibility protocols and metric logs are documented in benchmarks/agent_eval/results.json.

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Python Library Usage

If you prefer to drive the memory system from your own agent code rather than via MCP, the Python API is fully supported.

from agent_sleep import AgentMemory, SleepConsolidator

# 1. Initialize memory scoped to your project/repo
memory = AgentMemory(session_id="session_01", scope="payment_service")

# 2. Record actions and outcomes during your agent's loop
memory.record_episode(
    goal="Refactor payment processor to async",
    action="edit_file('processor.py', ...)",
    outcome="failure",
    failure_reason="SyntaxError: 'await' outside async function",
)

# 3. Trigger sleep consolidation when idle or at session end
consolidator = SleepConsolidator(scope="payment_service")
report = consolidator.run(session_id="session_01")
# -> {'episodes_processed': 1, 'memories_written': 1, 'rules_promoted': 0, ...}

# 4. Next session: recall relevant context before executing
context = memory.recall("Add Stripe webhook handler")
print(context)
# [MEMORY CONTEXT]
# Relevant past experience:
#   ⚠ [LESSON] Caution on task: Refactor payment processor to async:
#     A previous attempt failed: SyntaxError: 'await' outside async function.
# [END MEMORY CONTEXT]

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Installation

Quick install with MCP support:

pip install "agent-sleep[mcp]"

With full semantic embeddings (recommended):

pip install "agent-sleep[all]"

From GitHub (latest alpha):

pip install git+https://github.com/thevisionhub/agent-sleep.git

Editable install for development:

git clone https://github.com/thevisionhub/agent-sleep.git
cd agent-sleep
pip install -e ".[all]"

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MCP Tools Reference

Tool	When to call
agent_sleep_recall	Before planning or executing any non-trivial task — retrieves lessons, rules, causal traps, and self-competence directives
agent_sleep_record	During execution — after each tool failure or milestone
agent_sleep_consolidate	After a session ends or when the agent is idle
agent_sleep_status	Anytime — inspects memory health, epistemic breakdowns, and pending episodes
agent_sleep_feedback	After applying retrieved knowledge — records causal outcome attribution and updates utility scores
agent_sleep_specialize_rule	When discovering exceptions or boundary conditions for existing rules

All tools default scope to the current working directory name and db_path to .agent_sleep/memory.db in the project root. No configuration required for the common case.

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Run Tests

pytest tests/ -v

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Get Discovered — Registry Listings

Submitting agent-sleep to MCP registries takes about 5 minutes each and is the fastest way to reach developers looking for memory tools:

• Smithery — paste the GitHub URL, add a short description, done.
• modelcontextprotocol/servers — open a PR adding an entry to the README under "Community Servers".
• Cursor — also surfaces MCP servers; check their current docs for the latest submission process.

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License

MIT License — free for personal, commercial, and research use.