Complete Agent Storage

Context Storage

Messages, files, and skills — all the storage your AI agents need in one platform

Multi-provider message formats, S3-backed disk storage with search, and reusable skill packages that agents can discover and use.

Get Started GitHub

Core Capabilities

Three storage pillars for complete agent state management — messages, files, and learned skills.

Messages

Multi-Provider Format

Store messages once, retrieve in OpenAI, Anthropic, or Gemini format. Switch providers without rewriting serialization logic.

Multi-format retrieval

# Store in any format
session.store_message(blob={...})

# Retrieve in OpenAI format
get_messages(format="openai")
# Retrieve in Anthropic format
get_messages(format="anthropic")

Edit Strategies

Manage context window size with composable edit strategies. Remove old tool results, trim by token limit, or apply custom rules.

Edit strategies

edit_strategies=[
  {"type": "remove_tool_result",
   "params": {"keep_recent_n": 3}},
  {"type": "token_limit",
   "params": {"limit_tokens": 30000}}
]

Session Summaries

Get token-efficient summaries of any session for prompt injection. Maintain context without blowing up your token budget.

Session summary

# Summarize recent turns
summary = client.sessions
  .get_session_summary(
    session.id, limit=5)

# Inject into new prompt
system_msg = f"Context: {summary}"

Disk Storage

File Upload & Download

Upload any file with paths and metadata. Generate secure, time-limited download URLs for sharing artifacts.

Upload a file

artifact = client.disks.artifacts
  .upsert(disk.id,
    file=FileUpload(
      filename="report.md",
      content=b"# Report"),
    file_path="/docs/")

Grep & Glob Search

Search file contents with regex via grep, or find files by path patterns with glob. Full codebase-style search for your agent's files.

Search files

# Regex search in contents
client.disks.artifacts
  .grep_artifacts(disk.id,
    query="TODO.*")

# Glob pattern matching
  .glob_artifacts(disk.id,
    pattern="**/*.md")

Agent Tools

Pre-built LLM function-calling tools (DISK_TOOLS) let your agents read, write, and search files autonomously.

Agent tools

from acontext.tools import (
  DISK_TOOLS)

# Give agent file access
tools = DISK_TOOLS(disk.id)
# read_file, write_file,
# search_files, list_files

Skill Storage

Skill Packages

Upload reusable skill packages as ZIP files with a SKILL.md, scripts, and resources. Agents discover and use them at runtime.

Upload a skill

skill = client.skills.create(
  file=FileUpload(
    filename="my-skill.zip",
    content=f.read()),
  meta={"version": "1.0"})

print(skill.name, skill.id)

Catalog & File Access

Browse the skill catalog, inspect file indexes, and read any file — text content returned inline, binary files via presigned URLs.

Browse & read skills

catalog = client.skills
  .list_catalog()

skill = client.skills.get(skill_id)
for f in skill.file_index:
  result = client.skills
    .get_file(skill.id, f.path)
  print(result.content.raw)

Skill Tools for Agents

Pre-built SKILL_TOOLS let LLMs read skill content via function calling, or mount skills in a sandbox to execute scripts directly.

Agent skill tools

from acontext.agent.skill \
  import SKILL_TOOLS

ctx = SKILL_TOOLS.format_context(
  client, skill_ids)
tools = SKILL_TOOLS
  .to_openai_tool_schema()

Context Storage

Context Storage Platform

Acontext Sessions

Store · Retrieve · Multi-Provider Format

Store and retrieve context in OpenAI, Anthropic, or Gemini format with one simple API.

from acontext import AcontextClient
 
client = AcontextClient()
 
# Create a session
session = client.sessions.create()
 
# Store messages (OpenAI format)
client.sessions.store_message(session.id, blob={"role": "user", "content": "Hello!"})
 
# Retrieve in any format: openai, anthropic, gemini
messages = client.sessions.get_messages(session.id, format="anthropic")

# Get token-efficient session summary for prompt injection
summary = client.sessions.get_session_summary(session.id, limit=5)
 
# Apply edit strategies to manage context window size
result = client.sessions.get_messages(
    session.id,
    edit_strategies=[
        {"type": "remove_tool_result", "params": {"keep_recent_n_tool_results": 3}},
        {"type": "token_limit", "params": {"limit_tokens": 30000}}
    ]
)
print(f"Tokens: {result.this_time_tokens}")

Reinvent the Wheel for Each Provider

Focus on creating, not adapting.

How It Compares

See how Acontext Context Storage compares to building it yourself or using other frameworks.

	DIY	LangChain Memory
Messages\|Multi-provider format
Token-aware retrieval
Edit strategies
Session summaries
Per-user isolation	Manual	Manual
Cloud-native / API-first
Disk\|S3-backed file storage	Manual
Grep & glob file search	Manual
Pre-built agent file tools
Skills\|Skill package storage (ZIP)
Skill catalog & file access
Pre-built agent skill tools

How It Works

Pick a storage type to see the workflow and code examples.

Create Session

Initialize a session to scope your agent's context.

Store Messages

Persist messages in any provider format.

Retrieve & Transform

Get messages in any format with edit strategies.

Optimize & Resume

Summarize, trim, and continue sessions.

store.py

from acontext import AcontextClient

client = AcontextClient()

# Create a session
session = client.sessions.create(
    project_id="my-project"
)

# Store messages (any provider format)
client.sessions.store_message(
    session.id,
    blob={"role": "user", "content": "Hello!"}
)
client.sessions.store_message(
    session.id,
    blob={"role": "assistant", "content": "Hi!"}
)

retrieve.py

# Retrieve in OpenAI format
messages = client.sessions.get_messages(
    session.id, format="openai"
)

# Retrieve in Anthropic format
messages = client.sessions.get_messages(
    session.id, format="anthropic"
)

# Apply edit strategies
result = client.sessions.get_messages(
    session.id,
    edit_strategies=[
        {"type": "remove_tool_result",
         "params": {"keep_recent_n_tool_results": 3}},
        {"type": "token_limit",
         "params": {"limit_tokens": 30000}}
    ]
)
print(f"Tokens used: {result.this_time_tokens}")