LOCAL-FIRST / BETA 2.6 · v0.2.6b1

Give your agentsa past they can trust.

MemCoder turns verified work into reusable context, known failure warnings, and safer next moves.

Provider-independentProof-gatedReversible
RUN 0184 VERIFIED
What survives the runadmission record
01Contextretrievedready
02Outcomecheckedholds
03Boundaryknown risknoted
04Changereversiblesafe
ADMITTEDusable in the next run

MemCoder keeps the evidence, not just the answer.

01 / Observe the work02 / Verify the outcome03 / Keep the proof04 / Reuse with context

THE COGNITION LAYER

Memory with a standard of proof.

MemCoder sits beside an agent’s existing workflow. It does not replace the model; it gives the workflow a dependable past.

01

Verified memory

Record experiences, principles, mistakes, and reflections only after the host has checked the result.

02

Failure frontiers

Keep a compact boundary around known failure modes so an agent can avoid repeating expensive mistakes.

03

Cognitive branches

Test a proposed change in isolation, attach proof obligations, then merge or roll it back.

04

Reusable skills

Promote patterns that survive verification into concise guidance for the next similar task.

ONE LOOP, MANY HOSTS

Small enough to disappear into the workflow.

Use the CLI, Python SDK, MCP server, or a host integration. The lifecycle stays the same wherever the work happens.

01Prepareretrieve context
02Worklet the agent solve
03Verifycheck the evidence
04Reusecarry forward what held

A REAL SESSION

Not another vector store.

Retrieval is only the beginning. MemCoder connects context to verification, quality feedback, and reversible learning.

Explore the implementation
memcoder / prepare
$ memcoder prepare "Fix webhook validation"

confidence  0.82
retrieved   verified validation pattern
warning     whitespace-only identifiers
next        test before recording

✓ guidance is evidence-backed

START HERE

One install. A better memory for every run.

MemCoder is provider-independent and local-first. Add it to an existing agent workflow without changing the model that powers it.

python -m pip install --pre memcoder

Then verify with python -m memcoder --help

Current release: 0.2.6b1 (Beta 2.6)

Python 3.10+ · MIT licensed · no model provider required