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VeraMem

by Thebinary110
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Python
Local

VeraMem

Long-term memory for AI agents that you can verify, that uses task outcomes to rank memories, and that is safe to share.

  • One SQLite file, no servers to run.

  • Works fully offline with Ollama, or with OpenAI / Anthropic.

  • Python SDK, REST API with a web UI, and an MCP server for Claude Desktop, Cursor and Claude Code.

pip install "veramem[mcp]"
veramem init          # finds Ollama / OpenAI / Anthropic and sets everything up
veramem add "I'm vegetarian and I moved to Lisbon last week"
veramem ask "where do I live?"
veramem serve         # web UI on http://127.0.0.1:8765
from veramem import Memory

mem = Memory()  # auto-detects your model provider
mem.add("I moved to Lisbon last week", user_id="priya")
mem.answer("Where does Priya live?", user_id="priya")
# Answer(answer='Lisbon', citations=['mem_…'], abstained=False, trace_id='tr_…')

Why another memory system?

Today's memory layers (Mem0, Letta, Graphiti/Zep, MemOS, TencentDB-Agent-Memory) have three gaps. VeraMem fills each one with techniques from recent research:

The gap

What VeraMem does

A

Memories are kept or dropped by heuristics. Whether a memory actually helped is never recorded.

Outcome-informed ranking: feedback(trace_id, reward, cited=...) records which memories an agent used and how it went. Query-aware utility re-ranks similar future searches. A learned ranker, deployed only when it beats retrieval with 95% confidence, learns non-retrieval signals such as source authority from feedback and transfers them to unseen tasks (Hit@1 0.14 → 1.00 on held-out attributes). On LoCoMo, a tuned static weighting beats it (+3.4 vs +2.1 pts). Write decisions are prompted, not learned. Evidence and negative results. (Memory-R1, AgeMem)

B

Extracted "facts" lose the link to what was said. A tool's claim quietly becomes "the user's preference".

Every memory points to a verbatim, verified quote in an immutable, hashed source message. Each carries a source-authority label (user / assistant / tool) that consolidation can never raise. Updates are versioned and time-travel queryable. Every write goes into a hash-chained ledger. Answers cite or abstain, and check_action() blocks low-authority memories from driving payments, bookings or sends. (Agent Zero Memory, AMA, AuthMem-Bench)

C

Sharing is all-or-nothing. Private data leaks through shared pools, dedup, or other users' preferences.

Access rules are compiled into SQL before ranking. Revocable user↔agent and agent↔resource grants apply retroactively. Preferences can never be shared. Experiences are shared through a two-stage pipeline with de-identification plus a deterministic PII/canary gate. Erasure is physical. (ShareMem, Collaborative Memory, AIM)

The paper-by-paper mapping is in docs/RESEARCH.md, and the design is in docs/ARCHITECTURE.md.

Related MCP server: CP Memory

Benchmarks

Measured head-to-head on the same machine, with the same local model (qwen3.6:35b, thinking off, temperature 0) and the same embedder (nomic-embed-text) for every system. The full method and raw outputs are in docs/BENCHMARKS.md.

Head-to-head with mem0 2.2.1 (its best OSS setup), both on the same local model. Raw results: docs/results/.

What is measured

VeraMem

mem0

Cross-user leakage over 336 adversarial queries

0%

0%

Tool, document or assistant claims stored as the user's own (all 7 AuthMem-Bench transitions)

0.0%

2.9%

Those claims still retained, with attribution (Ret⁻) / user statements retained (Ret⁺)

100% / 94%

97% / 89%

Machine-checkable authority to block risky actions

yes

no

Current value ranked first after three changes

75%

50%

Wrong top answer fixed by feedback within 5 rounds (synthetic)

0% → 100%

no feedback API

Feedback improves retrieval on unseen task families (LoCoMo, 5 seeds, placebo-controlled)

+2.1 pts Hit@3 [+1.1, +3.1]; a tuned static weighting gets +3.4 without feedback

no feedback API

Feedback teaches source authority on unseen attributes (synthetic, 5 seeds)

Hit@1 0.14 → 1.00

no feedback API

Revoking an agent's access hides its memories

yes

not supported

Tampering with stored memories detected

yes

not supported

User's secret left in plaintext on disk after deleting the user

no

yes (history.db)

Search latency p50

4 ms

40 ms

Status: these are the guarantee tests in benchmarks/guarantees.py, which follow the protocols of the cited papers on synthetic data. Accuracy on the standard LoCoMo and LongMemEval benchmarks is being measured and will be published here, wins or losses included.

Reproduce:

python -m benchmarks.guarantees --systems veramem,mem0
python -m benchmarks.locomo --systems veramem,mem0 --convs all

What you get

Feature

API

CLI

Remember chats, tool results, documents (role sets authority)

add(messages, user_id=…)

veramem add

Hybrid search (dense + BM25 + entity, routed by granularity)

search(q, user_id=…)

veramem search

Grounded answers with citations and abstention

answer(q, user_id=…)

veramem ask

Outcome-informed ranking

feedback(trace_id, reward, cited=…), train_ranker(), learn(task, trajectory, reward)

veramem feedback, veramem train-ranker

Evidence and version history

evidence(id), history(id), search(…, as_of=ts)

veramem history

Authority gate for actions

check_action([ids])

Teams, grants, revocation

join_team, grant_agent, revoke_agent, grant_resource

Safe sharing

set_shareable, share(user_id, team_id)

Tamper check

verify()

veramem verify

Right to be forgotten / data export

forget(user_id=…, everything=True), export(user_id)

veramem forget, veramem export

Training data for your own memory model

export_trajectories(path)

veramem export-trajectories

Maintenance (archive, dedupe, summarise)

consolidate()

veramem consolidate

Use it from any app

  • Python SDK: Memory and AsyncMemory.

  • TypeScript SDK: sdk/typescript, with no runtime dependencies; works in Node 18+ and browsers.

  • Agent frameworks: LangChain, LangGraph (long-term BaseStore), LlamaIndex and CrewAI adapters, plus plain tool schemas for OpenAI and Anthropic tool calling. See docs/guide/frameworks.md.

  • MCP (Claude Desktop, Claude Code, Cursor, VS Code): veramem mcp-config prints the config to paste. See examples/mcp_clients.md.

  • REST: veramem serve serves interactive docs at /docs. Set VERAMEM_API_KEY to require a bearer token; the CLI refuses to listen publicly without one.

  • Docker: VERAMEM_API_KEY=… docker compose up starts VeraMem plus Ollama, fully local.

  • Multi-user deployments: per-user API keys or OIDC/JWT, PostgreSQL for many replicas, rate limits, and a web UI for sharing, access and key management. See docs/guide/deployment.md.

Configuration

veramem init writes ~/.veramem/config.json. You can override any setting with an environment variable:

Variable

Meaning

VERAMEM_LLM

ollama · openai · anthropic · none

VERAMEM_LLM_MODEL, VERAMEM_LLM_BASE_URL

e.g. qwen3:8b, or any OpenAI-compatible URL (vLLM, LM Studio, Groq, Together)

VERAMEM_EMBEDDER, VERAMEM_EMBED_MODEL

ollama · openai · hash (no model at all)

VERAMEM_DB

Path of the SQLite file

VERAMEM_API_KEY

Bearer token for the REST API

With no model at all, VeraMem still works. It stores and searches raw messages, with the same access control and ledger.

Security and trust

  • No telemetry, ever. The only network calls go to the model provider you configure.

  • Releases are built in CI with SLSA provenance, signed with Sigstore, and published to PyPI with OIDC trusted publishing.

  • Contributions follow a vouched-contributor model: open an issue first. See CONTRIBUTING.md, SECURITY.md and GOVERNANCE.md.

License

Apache-2.0. Some prompts are adapted from mem0 and AMA (both Apache-2.0); see NOTICE.

This server cannot be deployed

Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

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