memledger 1.0.1

Memory governance and trust layer for AI agents — provenance, attribution, confidence gating

memledger

Memory governance and trust layer for multi-agent AI systems.

memledger adds attribution, provenance, confidence gating, and quality measurement on top of any vector-backed memory store. Every memory carries a source, a confidence score, a derivation chain, and an audit trail — so when agents share memory, trust transfers become legible instead of invisible.

✅ v1.0.0 — first stable release. API is stable; subsequent releases follow SemVer. See the CHANGELOG for the v1 contract.

Why memledger

Existing memory frameworks (Mem0, Zep, Letta, AgentCore Memory) optimize for recall quality — how accurately the system retrieves relevant memory. None of them address accountability: who wrote this memory, how confident were they, what did they derive it from, and how has it been used since.

In a single-agent system, the accountability gap is academic. In a multi-agent system where agents read and act on each other's beliefs, it is the fault line where systems fail at scale.

What v1 ships

• Attribution — every memory carries created_by, confidence, session_id, derived_from, supersedes, workflow_id, triggered_by, hedged, namespace
• Weakest-link provenance chain — derivation chains tracked across agent boundaries; effective confidence for retrieval is min(declared, chain.min_confidence) — a high-conf claim built on a low-conf ancestor cannot outscore its weakest link
• Confidence-gated retrieval — PASS / FLAG / FILTER policy applied at search time, against effective confidence
• Namespace RBAC — declarative per-agent access control over hierarchical namespaces; add() and search() both gate on it
• 3-tier evaluation suite — deterministic Memory Attribution Integrity scorer + RAGAS LLM-as-judge (provider-agnostic via LiteLLM) + (optional) AWS Bedrock AgentCore evaluator
• OpenTelemetry observability — every operation emits an OTLP span with memory trust attributes; tier-1 set to OpenInference span kinds so Arize Phoenix categorizes them out of the box
• Outcome feedback loop — record_outcome() updates memory confidence based on observed downstream outcomes
• MCP server — framework-agnostic adoption via Model Context Protocol (validated against LangGraph in v1; broader framework coverage in v1.5)

See docs/architecture.md for the full design.

Install

The OSS-default install needs no cloud credentials:

pip install 'memledger[local,pgvector]'

Other extras (combine as needed):

Extra	Use
[local]	Local ONNX embeddings via fastembed (default — no cloud keys)
[pgvector]	Postgres + pgvector backend (required for the default path)
[eval]	RAGAS LLM-as-judge evaluator
[telemetry]	Phoenix-OTEL helper on top of the core OTEL stack
[mcp]	MCP server for framework-agnostic adoption
[aws]	AWS path: Bedrock LLM/embedding, SigV4 OpenSearch, DynamoDB

Backend contract (v1): the only required backend is open-source PostgreSQL ≥ 14 with the pgvector extension ≥ 0.5. Aurora, RDS, Supabase, Neon — same backend, only the DSN changes. See Memory backend contract for verified Docker / Kubernetes / AWS deployment patterns.

Quick start (60 seconds)

# 1. Postgres + pgvector locally
docker run -d -p 5432:5432 -e POSTGRES_PASSWORD=postgres pgvector/pgvector:pg16

# 2. Install
pip install 'memledger[local,pgvector]'

# 3. Write + search one memory
import asyncio
from memledger import Memledger

async def main():
    ml = await Memledger.create(
        backend_name="pgvector",
        connection_string="postgresql://postgres:postgres@localhost:5432/postgres",
    )
    await ml.add(
        content="HikariCP maxPoolSize=50 fixes payment-service OOM",
        namespace="/ops/incidents/payment-svc",
        confidence=0.9,
        created_by="ops-agent",
    )
    results = await ml.search(
        query="connection pool fix",
        namespace="/ops/incidents/payment-svc",
    )
    for r in results.records:
        print(r.confidence, r.created_by, "::", r.content)
    await ml.close()

asyncio.run(main())

For a full multi-agent flow showing the weakest-link gate intercepting a contamination cascade, see the examples/multi-agent/ reference client — five LLM-driven agents over a shared memledger, with Phoenix observability and the deterministic + RAGAS evaluation tiers wired end-to-end.

CLI

memledger init                                  # write a starter memledger.yaml
memledger add "fact" --namespace /n --agent-id a --confidence 0.8
memledger search "query" --namespace /n --confidence-min 0.6
memledger get <memory-id> --chain
memledger eval <session-id>
memledger status

memledger --help for the full surface.

Roadmap

Version	What
v1.0 (in flight)	Stable API for everything in What v1 ships above
v1.5	Temporal decay wired into the gate; OTEL collector fan-out to Prometheus + Grafana; MCP server validated against CrewAI / AutoGen / LangChain; graph-native chain storage (Apache AGE / Neo4j) behind the existing ChainStore interface
v2	AWS-first integrations (AgentCore evaluations on CloudWatch + OTEL; Bedrock-native eval pipeline; first-class Aurora/OpenSearch backend tuning)
v3 candidate	Dreaming — async memory consolidation, reflection, adaptive forgetting, replay (the G4 "memory patterns" roadmap item)

Companion repositories

memledger lives across three repositories under the memledger-ai GitHub organization:

• memledger-core — Python SDK, MCP server, Helm chart (this repo)
• memledger-ui — Trust graph visualization (FastAPI + React)
• memledger-docs — Documentation site (Docusaurus)

Contributing

We accept issues and PRs. See CONTRIBUTING.md (lands in v1) and SECURITY.md for the disclosure policy.

License

Apache 2.0. See LICENSE.