QF-Mem logo QF-Mem Long-term memory for AI agents Request Technical Evaluation
Long-term memory for AI agents

Your AI agent forgets everything.
Every single session.

You explain the project. It makes good decisions. You close the session. Tomorrow it has no idea any of that happened. QF-Mem fixes that with structured, durable execution memory: decisions, requirement versions, focus, blockers, and next actions that survive across sessions.

We'll help you validate one workflow, define success criteria, and package the result for team rollout.

MCP-compatible AI agents Structured state, not prompt stuffing Built for technical champions proving team fit

Evaluating for a team rollout? See team version.

session_start response
❯ session_start { "focus": "Implement token refresh endpoint", "decisions": [ { "title": "Session tokens, not JWT", "status": "accepted" }, { "title": "Relational state store", "status": "accepted" } ], "next_actions": [ "Add refresh token rotation", "Write integration tests" ], "recent_progress": "Auth middleware done.", "blockers": [] }
How it fits

QF-Mem is the memory layer your agent reads from and writes to.

It usually sits behind an MCP connection. The agent uses it directly, and you feel the difference when the next session resumes with structured state instead of asking you to restate the project.

1. Connect the agent

MCP by default.

Wire QF-Mem into Claude Code, Codex CLI, Cursor, or your own orchestration so the agent can call the memory layer as part of normal work.

2. Persist state

Execution state becomes explicit.

Accepted decisions, requirement versions, focus, blockers, progress, and next actions persist as structured state instead of living in yesterday's prompt.

3. Resume fast

Next session starts from current truth.

The agent restores active context and continues. You spend less time rebuilding context and more time validating whether the workflow actually improves.

Default path: MCP-compatible agents. For custom work, the same memory layer can also be integrated directly into a customer application when the workflow calls for it.
Integration shape

What you actually connect

QF-Mem is infrastructure, not a second app to live in. Your agent connects to the memory layer, reads current state at session start, and writes durable state as work continues.

What gets persisted

  • Decisions so accepted directions do not get contradicted tomorrow.
  • Requirement versions so the agent can tell what changed and what superseded it.
  • Current focus so pauses and task switches do not wipe context.
  • Progress and blockers so the next session starts from real execution state.
MCP connection sketch
{
  "mcpServers": {
    "qfmem": {
      "transport": "streamable-http",
      "url": "https://api.qfmem.com/mcp",
      "headers": {
        "x-qfmem-api-key": "<issued-key>"
      }
    }
  }
}

Typical flow: the agent calls session_start, restores current decisions, focus, and next actions, does work, then records new progress and decisions as durable state.

You already know the pain

The operational gap starts with memory

These happen every day when your AI agent has no persistent execution state.

⚠

You re-explain the project. Again.

Every new session starts cold. Your agent re-reads docs, re-discovers the architecture, and wastes time before it is useful.

✕

It contradicts yesterday's decisions.

Last session you agreed on one direction. This session it recommends the opposite because it has no memory of what you already settled.

↻

Close the laptop, lose everything.

You build up context, decisions, and direction. The session ends. Tomorrow, the agent acts like none of it ever happened.

Workflow comparison

Same project. With and without memory.

This is what changes when your agent actually remembers.

Without memory
# New session — same project I don't have context about this project. Let me re-read the docs... [reads files, burns time] Recommends the wrong auth direction. # You already decided this yesterday. Needs you to explain where work left off.
With QF-Mem
# New session — same project Resuming. Focus: token refresh endpoint. Active decisions loaded. Recent progress restored. Next actions available. Ready to continue.

Same workflow. Less repetition.

Practical impact

What persistent agent memory changes in practice

Persistent execution memory improves continuity, consistency, and trust in daily technical agent work.

▶

Resume with current context

Agents start from current decisions, requirements, and recent progress instead of rediscovering the project from scratch.

⚓

Keep decisions and requirements visible

The agent can see what is accepted now, what changed, and what should guide the next step.

⇄

Make handoffs and pauses survivable

Pause one task, switch to another, and come back with full context instead of rebuilding it from memory.

◆

See how work happened

Durable state makes it easier to inspect what the agent relied on and what changed between sessions.

Why not just paste context?

Manual context stuffing does not scale.

It works for one session. It breaks down when work spans days, decisions, and handoffs.

Persistent agent memory needs more than a longer prompt window

You need execution state that survives and stays explicit:

  • Decisions that persist — not just for one session, but durably.
  • Requirements that are versioned — so the agent can tell what changed and what superseded it.
  • Focus you can stack — pause task A, work task B, return with context intact.
  • Progress that accumulates — tomorrow's session knows what today's session actually did.
  • Execution state that is explicit — active decisions, current focus, blockers, and next actions restored directly.

That is what QF-Mem gives technical teams: memory your agent should have had from day one.

How it works

Three steps to persistent memory

QF-Mem fits into existing agent systems through MCP by default, with direct integration available for custom work.

1

Connect agents

Add QF-Mem as the shared memory layer for the agents you already use.

2

Accumulate execution state

Decisions, requirements, progress, and focus persist as work continues.

3

Resume with confidence

Any agent can pick up with current context instead of asking you to restate the project.

Built under real long-running agent use

QF-Mem was developed in active agent systems, with thousands of timestamped events already recorded across ongoing work.

Durable persistence Deterministic Audit logging Drift detection Private VPC Any model

Execution memory used continuously over time, not a demo that resets every session.

QF-Mem was built under real usage, not as a thought experiment. In QuillForge and QF-Mem itself, it has carried long-running agent systems across months of work with 8,400+ timestamped events recorded as of March 2026.

It serves as the execution memory layer for QuillForge.ai — preserving governance and delivery history across long-running agent systems.

8,400+
Timestamped events
390+
Progress entries
150+
Tracked issues
Questions developers ask

What technical evaluators usually want to know

Yes. Private VPC deployment keeps agent memory inside your cloud boundary while preserving a managed product path.

No. RAG finds related material. QF-Mem restores what is currently true in execution: decisions, requirements, blockers, and next actions.

Most teams can tell within 48–72 hours whether continuity improves, with a meaningful pilot readout in 1–2 weeks.

Any MCP-compatible agent, including Claude Code, Codex CLI, Cursor, or custom orchestration.

Give your AI agent the memory it should have had from day one.

Tell us what agent system or workflow you are testing. We'll reply with a technical evaluation path, recommended first workflow, and rollout-readiness criteria.

No broad rollout required. Start with one workflow.

Evaluating for a team rollout? See team version.