Cognos Session Memory MCP server

CognOS Session Memory

mcp-name: io.github.base76-research-lab/cognos-session-memory

Verified context injection via epistemic trust scoring for LLMs.

Solves session fragmentation by maintaining verified, high-confidence session context between conversations.

Problem

Large language models suffer from session fragmentation: each new conversation starts without verified context of previous work. This forces repeated explanations, loses decision history, and breaks long-running workflows.

Existing solutions (persistent memory systems, vector retrieval) either:

• Lack trust scores before injection → hallucinations propagate
• Don't audit which context was injected → compliance gaps
• Treat all past information equally → noise overwhelms signal

Solution

A plan-mode gateway that:

1. Extracts structured context from 3-5 recent traces
2. Scores context quality via CognOS epistemic formula: C = p · (1 − Ue − Ua)
3. Injects as system prompt only if C > threshold
4. Flags for manual review if C < threshold
5. Audits every context injection with trace IDs → EU AI Act compliance

Architecture

recent_traces (n=5)
    ↓
extract_context() → ContextField + coverage
    ↓
compute_trust_score(p, ue, ua) → C, R, decision
    ↓
if C > threshold:
    system_prompt ← inject
else:
    flagged_reason ← manual review

Core Formula

C = p · (1 − Ue − Ua)
R = 1 − C

where:
  p   = prediction confidence (coverage of required fields)
  Ue  = epistemic uncertainty (divergence between traces)
  Ua  = aleatoric uncertainty (mean risk in traces)

Action Gate

R < 0.25       → PASS      (inject without review)
0.25 ≤ R < 0.60 → REFINE   (inject with caution)
R ≥ 0.60       → ESCALATE  (flag for manual review)

API

POST /v1/plan

Extract and score context.

Request:

{
  "n": 5,
  "trust_threshold": 0.75,
  "mode": "auto"
}

Response (if injected):

{
  "status": "injected",
  "trust_score": 0.82,
  "confidence": 0.82,
  "risk": 0.18,
  "decision": "PASS",
  "context": {
    "active_project": "CognOS mHC research",
    "last_decision": "Verify P1 hypothesis",
    "open_questions": ["How does routing entropy scale?"],
    "current_output": "exp_008 complete",
    "recent_models": ["gpt-4", "claude-3", "mistral"]
  },
  "system_prompt": "## CognOS Context...",
  "trace_ids": ["uuid-1", "uuid-2", ...]
}

Response (if flagged):

{
  "status": "flagged",
  "trust_score": 0.45,
  "decision": "REFINE",
  "flagged_reason": "Trust score 0.45 below threshold 0.75. Manual review recommended.",
  "trace_ids": [...]
}

Modes

• auto (default) — inject if trust_score ≥ threshold, else flag
• force — always inject (for testing)
• dryrun** — compute score but never inject

Claude Code Integration

As a /compact replacement

# In any Claude Code session:
/save

Claude writes a structured summary, trust-scores it, and persists it to SQLite. Next session: automatically injected as SESSION_CONTEXT before your first prompt.

See docs/COMPACT_ALTERNATIVE.md for a full comparison.

As an MCP server

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "cognos-session-memory": {
      "command": "python3",
      "args": ["/path/to/cognos-session-memory/mcp_server.py"]
    }
  }
}

Tools exposed:

Quick Start

Installation

git clone https://github.com/base76-research-lab/cognos-session-memory
cd cognos-session-memory
pip install -e .

Run Gateway

python3 -m uvicorn --app-dir src main:app --port 8788

Test /v1/plan (dry_run)

Shortened. The full README is on GitHub.