Acontext

Source#

• Repository: memodb-io/Acontext
• Last source update: 2026-04-05
• Last verified: 2026-04-05

Tags#

Integration notes#

Platform-level abstraction; use worker wrappers for strict I/O schemas and auditable retries.

worker.md example#

Starter worker.md contract mapped from this registry entry. Copy this file and adapt schemas, constraints, and statuses for your task.

---
id: acontext-repo-derived-worker
name: Acontext Repo-Derived Worker
version: 1.0.0
source_registry_url: https://worker.md/registry/acontext/
source_repository: https://github.com/memodb-io/Acontext
repository_default_branch: main
repository_language: TypeScript
repository_license: Apache-2.0
repository_updated_at: 2026-04-05
worker_mode: agent-orchestration-worker
derivation_method: github_repository_metadata_plus_raw_readme
derivation_confidence: 0.95
derived_on: 2026-04-05
tags:
  - agent
  - agent-development-kit
  - agent-observability
  - ai-agent
  - anthropic
  - context-data-platform
---

# Acontext Repo-Derived Worker

## Repo-derived summary
- Registry summary: Agent Skills as a Memory Layer
- Repository description: Agent Skills as a Memory Layer
- Stars (snapshot): 3,265
- Primary language: TypeScript
- Worker mode classification: agent-orchestration-worker

## Extracted from
- https://github.com/memodb-io/Acontext
- https://github.com/memodb-io/Acontext/blob/main/README.md
- https://acontext.io
- https://platform.openai.com/docs/guides/function-calling
- https://img.shields.io/pypi/v/acontext.svg

## Evidence notes (from repository text)
- README summary paragraph: Acontext is an open-source skill memory layer for AI agents. It **automatically** captures learnings from agent runs and stores them as **agent skill files** — files you can read, edit, and share across agents, LLMs, and frameworks.
- If you want the agent you build to **learn from its mistakes** and **reuse what worked** — without opaque memory polluting your context — give Acontext a try.
- - **Acontext builds memory in the agent skills format**, so everyone can see and understand what the memory actually contains.
- - **Skill is Memory, Memory is Skill**. Whether a skill comes from one you downloaded from Clawhub or one you created yourself, Acontext can follow it and evolve it over time.
- - **Plain file, any framework** — Skill memories are Markdown files. Use them with LangGraph, Claude, AI SDK, or anything that reads files. No embeddings, no API lock-in. Git, grep, and mount to the sandbox.
- - **You design the structure** — Attach more skills to define the schema, naming, and file layout of the memory. For example: one file per contact, one per project by uploading a working context skill.

## Installation hints found in README
- `pip install acontext`
- `pip install acontext"]`

## worker.md contract (derived starter)
Purpose: Execute one orchestrated agent task as a bounded worker step.

### Input schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "task",
    "context"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "task": {
      "type": "string"
    },
    "context": {
      "type": "object"
    }
  }
}
```

### Output schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "status",
    "result"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "status": {
      "type": "string",
      "enum": [
        "ok",
        "retryable_error",
        "invalid_request",
        "invalid_output"
      ]
    },
    "result": {
      "type": "object"
    }
  }
}
```

### Constraints
- timeout_seconds: 30
- max_attempts: 2
- idempotency_key: run_id
- status_enum: [ok, retryable_error, invalid_request, invalid_output]
- notes: adapt to concrete APIs/classes documented in this repository before production use

## How this should be used
1. Treat this file as a repo-derived starter profile, not a claim of an official repository API contract.
2. Replace schemas with exact interfaces from code/docs you adopt.
3. Keep execution bounded and auditable using worker protocol constraints.

How to use#

• Save this as a worker spec file (for example: acontext-my-task.worker.md).
• Replace the input/output schemas and purpose with your real bounded task.
• Enforce schema validation + timeout + retry policy in your runtime before production use.

Citation#

Reference URL: https://worker.md/registry/acontext/

Source URL: https://github.com/memodb-io/Acontext