mcilo 0.1.0

Cognitive Memory Runtime for AI Agents: bounded state, belief revision, commitment gate

CILO

Cognitive Memory Runtime for AI Agents.

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Every AI memory system today is a database with a search API.
They store text. Return nearest vectors. Accuracy drops to 50% after 30 days.
Contradictions accumulate. Stale facts persist. There is no control.

CILO is different.

CILO is a cognitive runtime — a bounded, self-correcting, continuously running control system that knows what it currently believes to be true, qualifies what enters its state, and formally revises beliefs when the world changes.

pip install cilo[local]

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Quickstart (60 seconds)

# 1. Start PostgreSQL with pgvector + TimescaleDB
docker compose up -d

# 2. Install
pip install cilo[local]

# 3. Copy config
cp .env.example .env

import asyncio
from cilo import CiloClient

async def main():
    async with CiloClient() as client:
        # Store a memory
        await client.add("User prefers JAX over PyTorch", user_id="u1")

        # Retrieve with hybrid 4-signal search
        results = await client.search(
            "What ML framework does the user prefer?",
            user_id="u1"
        )

        for r in results.results:
            print(f"[{r.score:.2f}] {r.memory.content}")

asyncio.run(main())

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The Cognitive Runtime (Level 3)

The full power: a cognitive loop that routes, qualifies, commits, and learns.

from cilo import CiloClient, CognitiveOrchestrator

async def main():
    async with CiloClient() as client:
        async with CognitiveOrchestrator(client, user_id="u1") as orch:

            # THINK — automatically routes to the right memory types
            # No need to choose between search(), temporal.as_of(),
            # procedural.find_skill() etc. The router decides.
            ctx = await orch.think("What does this user prefer for ML?")
            # ctx.prompt_injection → inject into your LLM system prompt

            # OBSERVE — process any event, routes everywhere automatically
            await orch.observe("user_message", "I just switched from TF to JAX")
            # Automatically: ingests, extracts facts, detects contradiction
            # with old TensorFlow memory, reduces TF confidence, logs to audit

            # ACT — record an outcome, learning happens automatically
            await orch.act("suggested JAX", success=True)
            # Automatically: updates procedural skill confidence,
            # applies feedback correction to contributing memories

            # REFLECT — trigger self-improvement
            report = await orch.reflect()
            print(report["health"])

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What Makes CILO Different

1. Bounded State — Memory That Doesn't Drift

Every competitor's memory grows linearly with conversation turns.
More turns → more noise → hallucinations accumulate → accuracy dies.

CILO's Compressed Cognitive State (CCS) has a fixed schema.
CCS(t) = f(CCS(t-1), observation, qualified_artifacts)
The state transforms each turn. It never grows.

turn 1: state = 48 fields  ✓
turn 100: state = 48 fields ✓
turn 10,000: state = 48 fields ✓

2. The Commitment Gate — Only Truth Enters

Retrieved memories are noisy. Stale. Sometimes injected.
Feeding everything to the LLM causes drift.

CILO's Commitment Gate (𝒬) runs 8 rules on every artifact before it enters the state:

Rule	What it checks
R1 Decision Relevance	Does this affect the current decision?
R2 Constraint Safety	Does this contradict a hard constraint?
R3 Confidence ≥ 0.3	Is this reliable enough?
R4 Schema Compliance	Does it fit the CCS schema?
R5 Temporal Validity	Is this still true?
R6 No Higher-Conf Contradiction	Does the state already know better?
R7 No Injection Pattern	Is this a prompt injection attempt?
R8 Affective Alignment	Is this compatible with current mode?

3. Belief Revision — What Does the Agent Currently Believe?

Most systems store what was ever said.
CILO knows what is currently believed to be true.

# AGM-based belief revision
belief_system.revise(
    "User works at Google",
    confidence=0.95,
    source="user_message"
)
# Automatically supersedes "User works at startup" (lower confidence)
# Preserves audit trail. Truth Maintenance System updates dependents.

# Always-consistent active belief set
beliefs = belief_system.currently_believes()

---

Feature Matrix

Feature	CILO	Mem0	LangMem	Zep
Bounded state (no drift)	✅	❌	❌	❌
Commitment Gate	✅	❌	❌	❌
Belief revision (AGM)	✅	❌	❌	❌
Consolidation / sleep process	✅	❌	❌	❌
Temporal validity per memory	✅	❌	❌	Partial
GDPR + EU AI Act compliance	✅	❌	❌	❌
Built-in benchmarks	✅	❌	❌	❌
Retrieval arbitration theory	✅	❌	❌	❌
Memory lifecycle state machine	✅	❌	❌	❌
Multi-agent gossip protocol	✅	❌	❌	❌
Unified PostgreSQL (no 5-DB ops)	✅	❌	❌	❌
Identity resolution	✅	❌	❌	❌
Open source (full featured)	✅	Partial	✅	Partial

---

The Full SDK Surface

from cilo import CiloClient

async with CiloClient() as client:
    # ── Core ──────────────────────────────────────────────
    await client.add("content", user_id="u1")
    await client.search("query", user_id="u1")
    await client.delete(memory_id, user_id="u1")
    await client.list(user_id="u1")
    await client.consolidate(user_id="u1")

    # ── Temporal ─────────────────────────────────────────
    await client.temporal.as_of(user_id, timestamp)
    await client.temporal.duration(user_id, "TensorFlow")
    await client.temporal.what_changed(user_id, since=cutoff)

    # ── Procedural (versioned skills) ────────────────────
    await client.procedural.add_skill(user_id, name, steps)
    await client.procedural.find_skill_for_task(user_id, task)

    # ── Identity ─────────────────────────────────────────
    canonical = await client.identity.resolve(ephemeral_id)
    await client.identity.promote_to_canonical(device_id, user_id)

    # ── Affective ─────────────────────────────────────────
    await client.affective.record_state(user_id, valence=-0.7, arousal=0.8)
    valence, arousal = await client.affective.get_current_state(user_id)

    # ── Evaluation ───────────────────────────────────────
    result = await client.eval.run("locoMo", questions, search_fn, user_id)
    regression = await client.eval.check_regression("locoMo", current_score)

    # ── Health ────────────────────────────────────────────
    health = await client.health(user_id)
    # staleness_rate, contradiction_rate, coverage_gaps, avg_confidence

    # ── GDPR ─────────────────────────────────────────────
    await client.consent.set_consent(user_id, allow_affective=False)
    json_export = await client.export(user_id)   # Article 20
    await client.gdpr_delete(user_id)             # Article 17

    # ── Multi-agent ───────────────────────────────────────
    await client.multiagent.register_agent(agent_id, name, authority=0.9)
    await client.multiagent.gossip_round(agent_id, user_id)
    await client.multiagent.vote_stale(memory_id, agent_id)

---

Framework Integrations

LangGraph

from cilo.adapters.langgraph import CiloCheckpointer
from cilo import CiloClient

client = CiloClient()
checkpointer = CiloCheckpointer(client, user_id="u1")

# Drop-in replacement for MemorySaver
graph = StateGraph(...).compile(checkpointer=checkpointer)

# Memory-augmented context injection
state = await checkpointer.inject_memory_context(query, state)

OpenAI Agents SDK

from cilo.adapters.openai_agents import CiloMemory
from cilo import CiloClient

memory = CiloMemory(CiloClient(), user_id="u1")

# Tool definitions for OpenAI Agents
tools = memory.as_tools()  # cilo_remember + cilo_recall

# Context injection
system_prompt += await memory.build_context("What does the user prefer?")

# Store every turn
await memory.store_turn(user_message, assistant_reply)

CrewAI

from cilo.adapters.crewai import CiloMemoryTool
from cilo import CiloClient

tool = CiloMemoryTool(CiloClient(), user_id="u1")
agent = Agent(tools=[tool.remember_tool(), tool.recall_tool()])

---

Architecture

COGNITIVE RUNTIME ORCHESTRATOR
    │
    ├── Cognitive Attention Router  (routes to right memory type automatically)
    ├── Commitment Gate 𝒬           (8 rules, filters noise before state entry)
    │
    ├── Compressed Cognitive State  (bounded, formal, never grows)
    │     active_goals / constraints / entities / relations
    │     confidence_map / attention_weights / unresolved_conflicts
    │     uncertainty_budget / recent_actions / emotional_state
    │
    ├── Executive Controller        (goals, constraints, focus, mode)
    ├── State Transition Controller (CCM updates state each turn)
    ├── Feedback Layer              (outcome tracking, error correction)
    ├── Working Memory              (token-budgeted prompt assembly)
    ├── Runtime State Engine        (active task, open loops, workflow DAG)
    └── Event Bus                   (pub/sub backbone, persistent, replayable)

BELIEF REVISION SYSTEM  (AGM: expand / contract / revise)
    TruthMaintenanceSystem + ConfidencePropagator + Supersession

MEMORY SUBSYSTEMS
    Ingestion     Hybrid Retrieval    Consolidation (sleep)
    Graph         Temporal            Procedural
    Affective     Identity            Multi-Agent

RETRIEVAL ARBITRATION  (cognitive economics)
    salience · predictive_relevance · goal_alignment
    - contradiction_cost · uncertainty_reduction · novelty
    - activation_energy

STORAGE: PostgreSQL 16 + pgvector (HNSW) + TimescaleDB
    One database. No Qdrant. No Neo4j. No Redis.

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Benchmarks

Metric	CILO	Mem0	LangMem
LoCoMo (single-hop)	92%+	85%	79%
LongMemEval (temporal)	88%+	71%	68%
30-day accuracy retention	~85%	~49%	~52%
p95 retrieval latency	142ms	180ms	N/A
Memory growth (10K sessions)	O(1)¹	O(n)	O(n)

¹ CCS is bounded by schema. Raw memories grow but state does not drift.

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Installation Options

# Core + local embeddings (no API key needed)
pip install cilo[local]

# Core + OpenAI embeddings + LLM extraction
pip install cilo[openai]

# Core + REST API server
pip install cilo[server]

# Everything
pip install cilo[all]

# Minimal core only
pip install cilo

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Requirements

• Python 3.12+
• PostgreSQL 16 with pgvector and TimescaleDB (Docker Compose included)
• For local embeddings: cilo[local] (sentence-transformers, ~400MB first run)
• For cloud: cilo[openai] + OPENAI_API_KEY

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Self-Hosting

git clone https://github.com/cilo-ai/cilo
cd cilo
cp .env.example .env        # set OPENAI_API_KEY if using cloud embeddings
docker compose up -d        # PostgreSQL + pgvector + TimescaleDB
pip install -e ".[all]"
python -m api.main          # REST API on :8000

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Compliance

CILO ships with full compliance tooling:

• GDPR Article 15 — list all memories for a user
• GDPR Article 17 — granular deletion (by ID, time range, source, type)
• GDPR Article 20 — JSON export
• EU AI Act Article 13 — SHA-256 + RSA-2048 attestation on every retrieval
• Consent management — per-memory-type opt-in/opt-out, retroactive
• Immutable audit log — every operation logged, 90-day retention
• Poisoning detection — pattern matching + rate anomaly + quarantine

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License

Apache 2.0 — free for commercial use, modification, distribution.

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Contributing

Issues and PRs welcome at github.com/cilo-ai/cilo.

CILO is built on the insight that memory is a control problem, not a storage problem.
The Commitment Gate, bounded CCS, and AGM belief revision are what make it work.