Papez

The intelligence layer for AI memory.

Papez doesn't just remember what happened; it remembers why. A scoring engine + causal graph + lifecycle manager for AI agent memory. Speaks MCP natively.

LoCoMo benchmark (certified)

Self-reported vendor figures above ~90 use different answerers/judges and are not comparable — the oracle retrieval ceiling under this frozen protocol is 94.9. Reproduce it yourself: Astrix-Labs/locomo-harness · full methodology · per-run results.

Hosted product: papez.ai — your personal memory for AI, carried across ChatGPT, Claude, and every MCP app · Pricing · Developer docs · Benchmark methodology (85.55 on LoCoMo, certified over 10 runs, receipts published)

What is this

Papez is a scoring engine, causal graph, and lifecycle manager for AI memory. Memories are scored by a multiplicative formula (relevance × connectivity × reactivation), connected in a causal graph, and actively forgotten when they become irrelevant.

This package (papez) is the core library: an in-memory causal graph engine with optional JSON persistence, plus a stdio MCP server. It has no database dependency and no REST API. A hosted product built on top of this library — with Postgres, additional storage backends, and a REST/HTTP MCP API — is available separately at api.papez.ai; it is not part of this package.

Why

• Flat memory doesn't scale. Dumping everything into a vector store gives you recall with zero understanding. The 500th memory buries the 5 that matter.
• No forgetting = no intelligence. Real memory systems forget. Without active pruning, your AI drowns in stale context.
• No causal reasoning. Vector similarity can't answer "why did I choose X?" — you need a graph.

Your AI remembers everything but understands nothing. Papez fixes that.

Quick Start

Requires Python 3.11 or newer.

Install the package. The base install has zero database dependencies — state lives in memory and is optionally persisted to a JSON file.

pip install papez

Optional extras:

pip install 'papez[openai]'      # OpenAI embeddings
pip install 'papez[local]'       # Local embeddings (sentence-transformers, no API key)
pip install 'papez[anthropic]'   # LLM-based causal inference (consolidation, contradiction detection)

Run the stdio MCP server directly:

python3 -m papez

From source

git clone https://github.com/Astrix-Labs/papez.git
cd papez
pip install -e '.[dev]'
pytest tests/

Connect to your AI

Claude Code

claude mcp add papez -- python -m papez

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "papez": {
      "command": "python",
      "args": ["-m", "papez"]
    }
  }
}

Reliability & retries

The stdio server is a single local process. Under load — or during a restart or redeploy of a hosted transport in front of it — a tool call can transiently fail or the connection can briefly go unresponsive. Memory writes and reads are not worth crashing an agent turn over, so clients should degrade gracefully rather than treat a memory call as fatal:

• The server degrades gracefully too: a tool exception (or a missing required argument) is returned as a structured {"error": "...", "retryable": bool} payload instead of a protocol-level MCP failure, so a memory hiccup never crashes the transport. The retryable flag encodes the guidance below — true only for read tools.
• Retry idempotent reads (memory_recall, memory_search, memory_traverse, memory_explain, memory_stats) with a short bounded backoff (e.g. 2–3 attempts). These have no side effects worth worrying about beyond reactivation bookkeeping.
• Do not blindly retry memory_store / memory_amend on an ambiguous timeout — a silent success followed by a retry creates a duplicate node. Prefer to continue the turn and reconcile on the next memory_recall, or pass a stable source_session so duplicates are easy to spot.
• Treat memory as best-effort context, not a hard dependency. If a call fails, proceed with whatever context you already have and try again next turn rather than aborting. The graph is durable; a missed write is recoverable, a crashed agent turn is not.

MCP Tools

Writer-specified edges & corrections

memory_store's related argument lets the writer set edge semantics instead of guessing. Each entry is {"id": "<node-id>", "type": "<edge-type>"}, directed new_node --type--> target (so supersedes means the new node supersedes the target). Invalid types are rejected before the node is created — explicit writes never half-succeed. related_to still exists but always creates caused_by; prefer related.

To correct a fact, use memory_amend(node_id, content, reason=...): it stores the new version, links it SUPERSEDES → old, and keeps the old memory for audit. Recall automatically deprioritizes superseded hits and tags them with superseded_by.

When you memory_store something that lexically disagrees with an auto-link candidate (a changed number, a negation), the result may include possible_conflicts — heuristic hints, not verified contradictions, and never materialized as edges. Use them to decide whether to memory_amend.

Concise recall

memory_recall(query, verbosity="concise") skips the causal-chain enrichment and returns only id / summary / status / score / activation / is_core (plus superseded_by when set) per hit — much cheaper on tokens for high-frequency lookups. verbosity="full" (the default) is unchanged. Reactivation writes still occur in both modes (they are governed by read_only, not verbosity).

See docs/scoring.md for what activation / decay_score actually mean — in short, it is a retention weight that rises when a memory is recalled, not a countdown to deletion.

How it works

Every memory is scored by three forces multiplied together:

decay_score = relevance × connectivity × reactivation

• Relevance decays over time. Old memories fade unless reinforced.
• Connectivity rewards memories with many causal links. Hub memories survive.
• Reactivation boosts memories that keep getting recalled. Frequency matters.

Because the formula is multiplicative, a memory must score on all three axes to survive. A highly connected but never-accessed memory still decays. A frequently recalled but causally orphaned memory still fades.