aidememo-agent-sdk 0.1.0

Code-first agent memory SDK for AideMemo (aidememo)

aidememo-agent-sdk

Agent-friendly memory SDK for code-executing agents, including Codex, Claude Code, Hermes, CI jobs, and local scripts.

The SDK uses aidememo-python when available and otherwise falls back to the aidememo CLI. This keeps the first-use path portable while still taking the fast in-process route in environments that install the native binding.

Use it when memory needs to be a programmable working set: fan out searches, dedupe rows, check coverage, aggregate exact counts/timelines, and batch-write facts without spending model-visible tool calls on every intermediate step.

Install

# From a checkout, until the PyPI release lands:
python -m pip install -e packages/aidememo-agent-sdk

# After the PyPI release:
python -m pip install aidememo-agent-sdk

The fallback path needs the aidememo CLI on $PATH. After aidememo-python is published, python -m pip install "aidememo-agent-sdk[binding]" enables the optional in-process binding fast path.

Quick Start

from aidememo_agent import Memory

mem = Memory.open(
    source_id="project:aidememo",
    actor_id="codex:account-a",
    storage_backend="libsqlite",
)

rows = mem.search_rows([
    "release preflight decisions",
    {"query": "Hermes source_id lock retry lessons", "topic": "Hermes"},
])
coverage = mem.coverage_by(rows, ["fact_type"])
timeline = mem.aggregate_many([
    {"query": "release preflight", "op": "timeline"},
])

mem.remember([
    {
        "content": "Lesson: use source-scoped SDK fanout for multi-agent memory checks.",
        "fact_type": "lesson",
        "entities": ["aidememo", "Codex"],
    }
])

pack = mem.client.workflow_start(
    "Fix Redis timeout in worker",
    source="github:org/app#123",
    source_id="project:aidememo",
    actor_id="codex:account-a",
)
canvas = mem.session_canvas(pack["session_id"], limit=20)
profile = mem.project_profile(limit=80)

mem.remember(
    [
        {
            "content": "Decision: Redis timeout follow-ups stay attached to the workflow session.",
            "fact_type": "decision",
            "entities": ["Redis"],
        }
    ],
    source_id="project:aidememo",
    actor_id="codex:account-a",
    session_id=pack["session_id"],
)

branch = mem.branch_push(
    "candidate-b",
    "./shared",
    base="./shared/backup-01...",
)
print(branch["records_exported"])

main = Memory.open(store_path="./main.sqlite", storage_backend="libsqlite")
main.branch_merge("./shared", branch="candidate-b")

Use MCP/tools for one-off model-visible calls. Use this SDK when the agent needs memory as code: fanout retrieval, dedupe, coverage checks, aggregation, artifact hydration, branch-log experiments, or session-aware batch writes without spending model turns on intermediate state. session_canvas(...) and project_profile(...) return read-only Markdown strings suitable for direct prompt injection before resuming long work.

source_id can be passed to Memory.open(...) or inherited from AIDEMEMO_SOURCE_ID, matching the MCP aidememo mcp-install --source-id <namespace> path. Use it for the project/upstream namespace. actor_id, inherited from AIDEMEMO_ACTOR_ID, records which account or agent wrote each fact without splitting shared project retrieval.

storage_backend is optional and matches the CLI/native binding selector: omit it or pass an empty string for the compiled default, pass "sqlite" or "libsqlite" for the default local SQLite backend, or pass "redb" when the installed native binding / CLI was built with the Cargo redb feature. The SDK passes the selector to both the aidememo-python fast path and the CLI fallback (aidememo --backend ...).

branch_push(...) and branch_merge(...) use the native binding for local paths when available. S3 branch URIs fall back to the CLI so the installed aidememo --features s3 binary owns AWS credentials and compression behavior.