anneal-memory

Living memory for AI agents. Episodes compress into identity.

Memory without grounding is amplification infrastructure.

Persistent user memory profiles increase agent sycophancy 16–45% across models (Gemini 2.5 Pro at 45%, others lower). Production deployments accumulate 97.8% junk entries within weeks. Clinical research documents memory scaffolding delusions across sessions. The failure mode here isn't memory. It's memory with nothing checking what gets kept.

anneal-memory adds structural defenses at the citation layer that the systems surveyed below don't ship. Patterns earn promotion through cited episode evidence with lexical-overlap explanation-grounding, fabricated citations get demoted, per-ID citation gaming surfaces a flag, replay attempts against stale episodes fail by construction, and the audit chain is SHA-256 hash-chained and tamper-evident. Stale patterns surface for the agent to act on; associations form through consolidation. These are narrow, structural primitives — not a complete defense against every form of memory drift. See Honest scope below for what these primitives catch and what they don't.

And it's memory you own and govern. A local store, zero dependencies, no vendor in the loop — you decide what graduates into long-term memory, every change is recorded in a tamper-evident chain (best-effort on the write side — see Hash-chained audit trail for what verify can and cannot see), and the consolidation step that rewrites an agent's identity is gated to a human, not run unbidden (the Single-consolidator gate below). Own the substrate; govern what enters it.

Four cognitive layers — episodic store, compressed continuity, Hebbian associations, affective state tracking — plus two sibling stores: prospective spores (what the agent intends to do next) and a crystallized pattern store (graduated wisdom held out of always-loaded context and recalled on cue, so a large body of proven knowledge stays effective without clogging attention). Together they implement Complementary Learning Systems — see The Memory Architecture below. Zero dependencies (Python stdlib only). Works with any agent framework.

Quick Start

pip install anneal-memory

Python Library

The library is the core product. Import it, use it in any framework or script.

from anneal_memory import Store, EpisodeType, prepare_wrap, validated_save_continuity

# Initialize (creates DB + continuity file automatically)
store = Store("./memory.db", project_name="MyAgent")

# Record episodes during work
store.record("Connection pool is the real bottleneck", EpisodeType.OBSERVATION)
store.record("Chose PostgreSQL because ACID outweighs speed", EpisodeType.DECISION)

# Recall before decisions
result = store.recall(episode_type=EpisodeType.DECISION, keyword="database")
for ep in result.episodes:
    print(f"[{ep.type}] {ep.content}")

# Compress at session end — this is where the cognition happens
wrap = prepare_wrap(store)  # fetches episodes, marks wrap in progress
if wrap["status"] == "ready":
    # Feed wrap["package"] to your LLM. Compression IS the cognition —
    # patterns emerge from the act of compressing, not from storage.
    compressed = your_llm.compress(wrap["package"])
    validated_save_continuity(store, compressed)  # full immune system pipeline
# "empty" status means no new episodes to wrap — skip

store.close()

See Library Quickstart for the full guide.

CLI

Inspect, debug, and manage agent memory from the command line — a full operator CLI with machine-readable --json output. Agents with shell access (Claude Code, Aider, etc.) can use the CLI directly for the full memory workflow.

# Initialize
anneal-memory init --project-name MyAgent

# Record and recall
anneal-memory record "Chose PostgreSQL for ACID" --type decision
anneal-memory search "database"

# Agent-driven compression (same workflow as library and MCP)
anneal-memory prepare-wrap           # Get compression package
# Agent compresses...
anneal-memory save-continuity out.md # Save with validation

# Operator commands (things MCP can't do)
anneal-memory stats                  # Detailed analytics
anneal-memory graph --format dot     # Association graph (Graphviz)
anneal-memory diff --wraps 5         # Wrap metric progression
anneal-memory audit --since 7d       # Read audit trail
anneal-memory export --format json   # Full store export

See examples/agent-instructions.lean.cli.example for the agent workflow snippet.

MCP Server

For MCP-capable agent harnesses — Claude Code, Codex, Gemini CLI, Cursor, Windsurf, and others.

The server is one command — anneal-memory --project-name MyProject serve — wired into the harness's MCP config. The serve subcommand starts the MCP server; --project-name (and optional --db) are global flags and come before serve. Each harness has its own config file and format:

Claude Code / Cursor / Windsurf — mcpServers JSON (the editor's MCP settings, or a project .mcp.json):

{
  "mcpServers": {
    "anneal_memory": {
      "command": "uvx",
      "args": ["anneal-memory", "--project-name", "MyProject", "serve"]
    }
  }
}

Codex — ~/.codex/config.toml (or a project .codex/config.toml):

[mcp_servers.anneal_memory]
command = "uvx"
args = ["anneal-memory", "--project-name", "MyProject", "serve"]

Gemini CLI — ~/.gemini/settings.json (or a project .gemini/settings.json):

{
  "mcpServers": {
    "anneal_memory": {
      "command": "uvx",
      "args": ["anneal-memory", "--project-name", "MyProject", "serve"]
    }
  }
}

Then add the agent-instructions snippet — agent-instructions.lean.example (the always-loaded baseline) or the .full.example reference — to the harness's instructions file (CLAUDE.md for Claude Code, AGENTS.md for Codex, GEMINI.md for Gemini CLI). It teaches the agent when and how to use the memory tools; without it the tools are available but the agent won't know the cognitive workflow. (See Claude Code / agent-harness adopters below for the lean/Skill/full layering.)

Pinned install: uvx fetches the latest published version on each run. For a pinned install, pip install anneal-memory, then set "command": "anneal-memory" with "args": ["--project-name", "MyProject", "serve"] — or point command at an absolute path to the installed binary.

All Three Paths, Same Cognitive Loop

CLI and MCP are thin transport adapters over the same library — not separate implementations. Every access pattern calls the same prepare_wrap(store) and validated_save_continuity(store, text) pipeline under the hood, preserving the same workflow: record episodes during work → compress at session boundaries → load continuity at session start. The agent that records is the agent that compresses; that compression can't be delegated.

Framework Integrations

anneal-memory works with any agent framework through the Python library. Each guide below shows where to call the four core functions — record(), recall(), prepare_wrap(), validated_save_continuity() — within the framework's lifecycle.