exemplar-harness-sdk

Python SDK for Exemplar: session ingest, long-term memory, skills, and prompts.

Quick start covers Agno, OpenAI SDK, Google ADK, and Claude Agent SDK. More frameworks — see Framework extras.

Related package: terminal CLI — exemplar-cli (docs, examples).

Install
Quick start
- Agno
  - Session ingest
  - Memory
  - Skills
  - Prompts
- OpenAI SDK
  - Session ingest
  - Memory
  - Skills
  - Prompts
- Google ADK
  - Session ingest
  - Memory
  - Skills
  - Prompts
- Claude Agent SDK
  - Session ingest
  - Memory
  - Skills
  - Prompts
Session ingest
- Example 1 — Framework helper (Agno)
- Example 2 — Direct ingest (no framework)
Memory
- Example 1 — Add and recall
- Example 2 — Search and update
Skills
- Example 1 — Create and list
- Example 2 — Get and search
Prompts
CLI (separate package)
Advanced usage
Framework extras
Reference
- Optional install extras
- Envelope v1

Install

pip install exemplar-harness-sdk

# One agent framework (pick what you use)
pip install "exemplar-harness-sdk[agno]"
pip install "exemplar-harness-sdk[openai]"
pip install "exemplar-harness-sdk[google-adk]"
pip install "exemplar-harness-sdk[claude-agent]"

# Everything
pip install "exemplar-harness-sdk[all]"

export EXEMPLAR_API_KEY="eis_your_org_api_key"

Licensed for non-commercial use only. Commercial use requires a separate license from Exemplar Dev LLC. See LICENSE.

Quick start

Pick your agent framework below. Each section shows session ingest, memory, skills, and prompts for that stack.

Reuse the same session_id for every turn in a conversation. Session helpers call Harness.ingest() for you (Google ADK: call ingest_adk_session after the run). Skills and prompts are shared platform APIs — wire them into your agent with the patterns below.

flowchart LR
  Agent[Your agent] --> Integration[SDK integration]
  Integration --> Harness[Harness.ingest]
  Harness --> API[Exemplar platform API]
  API --> Eval[Harness eval]

Agno

pip install "exemplar-harness-sdk[agno]"

Session ingest

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from exemplar_harness import Harness
from exemplar_harness.integrations.agno import harness_agno_post_hook

harness = Harness.from_env()
agent = Agent(
    name="support-bot",
    model=OpenAIChat(id="gpt-4o"),
    post_hooks=[
        harness_agno_post_hook(
            harness,
            session_id="sess-abc",
            agent_id="support-bot",
            source_app="my-app",
        )
    ],
)
agent.run("Summarize our refund policy.")

Memory

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from exemplar_harness import Harness
from exemplar_harness.integrations.agno import harness_agno_post_hook
from exemplar_harness.integrations.memory.agno import (
    make_agno_memory_helper,
    harness_agno_memory_hooks,
)

harness = Harness.from_env()
mem = make_agno_memory_helper(
    harness, user_id="user-123", session_id="sess-abc", app_id="my-app"
)
pre, post = harness_agno_memory_hooks(mem)

agent = Agent(
    name="support-bot",
    model=OpenAIChat(id="gpt-4o"),
    pre_hooks=[pre],
    post_hooks=[
        post,
        harness_agno_post_hook(harness, session_id="sess-abc", agent_id="support-bot"),
    ],
)
agent.run("What do you know about my formatting preferences?")

Live example: examples/live/agno_memory_demo.py

Skills

Install the skill folder for Agent Skills–compatible runtimes (SKILL.md + files). Prompt-injection frameworks can also pass skill.instructions (markdown body only).

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from exemplar_harness import Harness

harness = Harness.from_env()
skills = harness.skills()

# Folder-first: dest/<name>/SKILL.md (+ references/, scripts/, assets/)
skills.install(".agents/skills", names=["refund-policy"])

skill = skills.get("refund-policy")
agent = Agent(
    name="support-bot",
    model=OpenAIChat(id="gpt-4o"),
    instructions=[skill.instructions],
)
agent.run("Can a customer return an item after 20 days?")

Live example: examples/live/agno_skills_demo.py

Prompts

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from exemplar_harness import Harness

harness = Harness.from_env()
built = harness.prompts().build("support-summary", variables={"topic": "returns"})

system = next((m["content"] for m in built.messages if m["role"] == "system"), "")
user = next((m["content"] for m in built.messages if m["role"] == "user"), "")

agent = Agent(
    name="support-bot",
    model=OpenAIChat(id="gpt-4o"),
    instructions=[system] if system else None,
)
agent.run(user)

OpenAI SDK

pip install "exemplar-harness-sdk[openai]"

Session ingest

from openai import OpenAI
from exemplar_harness import Harness
from exemplar_harness.integrations.openai import HarnessOpenAICallback, sdk_chat_completion

harness = Harness.from_env()
callback = HarnessOpenAICallback(
    harness,
    session_id="sess-abc",
    agent_id="support-bot",
    source_app="my-app",
)
client = OpenAI()
sdk_chat_completion(
    harness,
    callback,
    client,
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarize our refund policy."}],
)

Memory

from openai import OpenAI
from exemplar_harness import Harness
from exemplar_harness.integrations.memory.openai import (
    make_openai_memory_helper,
    sdk_chat_completion_with_memory,
)

harness = Harness.from_env()
helper = make_openai_memory_helper(
    harness, user_id="user-123", session_id="sess-abc", app_id="my-app"
)
client = OpenAI()
sdk_chat_completion_with_memory(
    helper,
    client,
    model="gpt-4o",
    messages=[{"role": "user", "content": "What do you know about my formatting preferences?"}],
)

Live example: examples/live/openai_memory_demo.py

Skills

from openai import OpenAI
from exemplar_harness import Harness

harness = Harness.from_env()
skills = harness.skills()
skills.install(".agents/skills", names=["refund-policy"])

skill = skills.get("refund-policy")
client = OpenAI()
client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": skill.instructions},
        {"role": "user", "content": "Can a customer return an item after 20 days?"},
    ],
)

Live example: examples/live/openai_skills_demo.py

Prompts

from openai import OpenAI
from exemplar_harness import Harness
from exemplar_harness.integrations.openai import HarnessOpenAICallback, sdk_chat_completion

harness = Harness.from_env()
built = harness.prompts().build("support-summary", variables={"topic": "returns"})
callback = HarnessOpenAICallback(harness, session_id="sess-abc", agent_id="support-bot")
client = OpenAI()
sdk_chat_completion(
    harness,
    callback,
    client,
    model=built.model or "gpt-4o",
    messages=built.messages,
)

Google ADK

pip install "exemplar-harness-sdk[google-adk]"

Session ingest

import asyncio

from exemplar_harness import Harness
from exemplar_harness.integrations.google_adk import ingest_adk_session
from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

harness = Harness.from_env()
session_id, app_name, user_id = "sess-abc", "my_app", "user-1"

agent = LlmAgent(
    model="gemini-2.5-flash",
    name="support_bot",  # must be a valid Python identifier
    instruction="Answer concisely.",
)
sessions = InMemorySessionService()


async def run_and_ingest() -> None:
    await sessions.create_session(app_name=app_name, user_id=user_id, session_id=session_id)
    runner = Runner(agent=agent, app_name=app_name, session_service=sessions)
    message = types.Content(role="user", parts=[types.Part(text="Summarize our refund policy.")])
    async for _ in runner.run_async(user_id=user_id, session_id=session_id, new_message=message):
        pass
    session = await sessions.get_session(app_name=app_name, user_id=user_id, session_id=session_id)
    ingest_adk_session(
        harness,
        session.model_dump(mode="json", exclude_none=True),
        session_id=session_id,
        agent_id="support-bot",
        source_app="my-app",
    )


asyncio.run(run_and_ingest())

Memory

from exemplar_harness import Harness
from exemplar_harness.integrations.memory.google_adk import (
    make_google_adk_memory_helper,
    prepare_user_message,
    record_session_turn_with_memory,
)

harness = Harness.from_env()
mem = make_google_adk_memory_helper(
    harness, user_id="user-123", session_id="sess-abc", app_id="my-app"
)

question = "What do you know about my formatting preferences?"
user_text = prepare_user_message(mem, question)
# ... run your ADK agent with user_text, capture model_response ...
# record_session_turn_with_memory(mem, user_message=question, model_response=model_response)

Live example: examples/live/google_adk_memory_demo.py

Skills

from exemplar_harness import Harness
from google.adk.agents import LlmAgent

harness = Harness.from_env()
skills = harness.skills()
skills.install(".agents/skills", names=["refund-policy"])

skill = skills.get("refund-policy")
agent = LlmAgent(
    model="gemini-2.5-flash",
    name="support_bot",
    instruction=skill.instructions,
)

Live example: examples/live/google_adk_skills_demo.py

Prompts

from exemplar_harness import Harness
from google.adk.agents import LlmAgent
from google.genai import types

harness = Harness.from_env()
built = harness.prompts().build("support-summary", variables={"topic": "returns"})

system = next((m["content"] for m in built.messages if m["role"] == "system"), "Answer concisely.")
user = next((m["content"] for m in built.messages if m["role"] == "user"), "")

agent = LlmAgent(model="gemini-2.5-flash", name="support_bot", instruction=system)
message = types.Content(role="user", parts=[types.Part(text=user)])
# pass `message` into Runner.run_async(...), then ingest_adk_session as above

Claude Agent SDK

pip install "exemplar-harness-sdk[claude-agent]"

Session ingest

import asyncio

from exemplar_harness import Harness
from exemplar_harness.integrations.claude_agent import HarnessClaudeAgentHandler

harness = Harness.from_env()
handler = HarnessClaudeAgentHandler(
    harness,
    session_id="sess-abc",
    agent_id="support-bot",
    source_app="my-app",
)
asyncio.run(handler.run_query("Summarize our refund policy."))

Memory

import asyncio

from exemplar_harness import Harness
from exemplar_harness.integrations.claude_agent import HarnessClaudeAgentHandler
from exemplar_harness.integrations.memory.claude_agent import (
    make_claude_agent_memory_helper,
    prepare_prompt,
    record_agent_result_with_memory,
)

harness = Harness.from_env()
handler = HarnessClaudeAgentHandler(harness, session_id="sess-abc", agent_id="support-bot")
mem = make_claude_agent_memory_helper(
    harness, user_id="user-123", session_id="sess-abc", app_id="my-app"
)


async def run() -> None:
    question = "What do you know about my formatting preferences?"
    prompt = prepare_prompt(mem, question)
    result, _ = await handler.run_query(prompt)
    record_agent_result_with_memory(mem, prompt=question, result=result)


asyncio.run(run())

Live example: examples/live/claude_agent_memory_demo.py

Skills

import asyncio

from exemplar_harness import Harness
from exemplar_harness.integrations.claude_agent import (
    HarnessClaudeAgentHandler,
    merge_claude_agent_options,
)

harness = Harness.from_env()
skills = harness.skills()
# Claude Code also discovers folders under .claude/skills via CLI install/pull
skills.install(".claude/skills", names=["refund-policy"])

skill = skills.get("refund-policy")
handler = HarnessClaudeAgentHandler(harness, session_id="sess-abc", agent_id="support-bot")


async def run() -> None:
    from claude_agent_sdk import ClaudeAgentOptions

    options = merge_claude_agent_options(
        handler,
        ClaudeAgentOptions(system_prompt=skill.instructions),
    )
    await handler.run_query("Can a customer return an item after 20 days?", options=options)


asyncio.run(run())

Live example: examples/live/claude_agent_skills_demo.py

Prompts

import asyncio

from exemplar_harness import Harness
from exemplar_harness.integrations.claude_agent import (
    HarnessClaudeAgentHandler,
    merge_claude_agent_options,
)

harness = Harness.from_env()
built = harness.prompts().build("support-summary", variables={"topic": "returns"})
system = next((m["content"] for m in built.messages if m["role"] == "system"), "")
user = next((m["content"] for m in built.messages if m["role"] == "user"), "")
handler = HarnessClaudeAgentHandler(harness, session_id="sess-abc", agent_id="support-bot")


async def run() -> None:
    from claude_agent_sdk import ClaudeAgentOptions

    options = merge_claude_agent_options(
        handler,
        ClaudeAgentOptions(system_prompt=system or None),
    )
    await handler.run_query(user, options=options)


asyncio.run(run())

Session ingest

Base helper: Harness.ingest() / framework helpers above.

Example 1 — Framework helper (Agno)

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from exemplar_harness import Harness
from exemplar_harness.integrations.agno import harness_agno_post_hook

harness = Harness.from_env()
agent = Agent(
    name="support-bot",
    model=OpenAIChat(id="gpt-4o"),
    post_hooks=[harness_agno_post_hook(harness, session_id="sess-abc", agent_id="support-bot")],
)
agent.run("What is the return window?")

Example 2 — Direct ingest (no framework)

from exemplar_harness import Harness

harness = Harness.from_env()
harness.ingest(
    "generic",
    session_id="sess-abc",
    event="turns",
    data={
        "turns": [
            {
                "input": "What is harness eval?",
                "output": "Automated judge over agent sessions.",
                "model": "gpt-4o",
            }
        ]
    },
    agent_id="my-agent",
    source_app="my-app",
)

Parameter	Purpose
`session_id`	Groups turns into one eval session
`agent_id`	Agent id on the ingest body (`agentId`); set on `Harness(..., agent_id=...)` to also send `X-Harness-Agent-Id` on MCP tool calls
`source_app`	Your application name
`auto_judge_run`	Run harness judge after ingest (`True` / `False` / omit)

Memory

Base helper: harness.memory(...).

Example 1 — Add and recall

from exemplar_harness import Harness

harness = Harness.from_env()
memory = harness.memory(user_id="user-123", session_id="chat-abc", app_id="my-app")

memory.add("User prefers bullet-point answers.", memory_type="preference")
context = memory.recall("how should I format answers?")  # inject into system prompt

Example 2 — Search and update

results = memory.search("formatting preferences", top_k=5)
listed = memory.list(limit=20)
record = memory.get(listed[0].memory_id)
memory.update(record.memory_id, content="User prefers numbered lists.")
memory.delete(record.memory_id)

Skills

Base helper: harness.skills().

Example 1 — Create and list

from exemplar_harness import Harness

harness = Harness.from_env()
skills = harness.skills()

record = skills.create(
    name="refund-policy",
    instructions="# Refund policy\n\nReturns within 30 days.\n\nSee [references/policy.md](references/policy.md).",
    description="Refund workflow",
    tags=["support"],
    files={"references/policy.md": "# Policy\n\n30-day returns.\n"},
)
items = skills.list(limit=20)

Example 2 — Get, search, and install

fetched = skills.get("refund-policy")  # by ID or unique name
hits = skills.search("refund", top_k=5)

# Materialize SKILL.md + supporting files for agent runtimes
skills.install(".agents/skills", names=["refund-policy"])

Prompts

Base helper: harness.prompts().

Example 1 — Create and run

from exemplar_harness import Harness

harness = Harness.from_env()
prompts = harness.prompts()

record = prompts.create(
    name="support-summary",
    title="Support summary",
    messages=[
        {"role": "system", "content": "Be concise."},
        {"role": "user", "content": "Summarize topic: {{topic}}."},
    ],
    variables=["topic"],
)
result = prompts.run("support-summary", variables={"topic": "returns"})
print(result["content"])

Example 2 — Build for your own agent framework

Fetch a stored prompt, substitute {{variables}} locally, and hand messages to LangGraph, OpenAI, Haystack, etc. Does not call Exemplar’s model runner.

built = prompts.build("support-summary", variables={"topic": "returns"})
# built.messages -> [{"role": "system", ...}, {"role": "user", ...}]
# built.model    -> default model from the registry (if set)
# pass built.messages into your framework / LLM client

Or reuse an already-fetched record (no second network call):

record = prompts.get("support-summary")
built = record.build(variables={"topic": "returns"})

Example 3 — List and get

listed = prompts.list(limit=20)
fetched = prompts.get("support-summary")  # by ID or unique name
hits = prompts.search("support", top_k=5)

CLI (separate package)

Skills, prompts, and memory are also available as a standalone terminal package — not bundled with this SDK:


PyPI	`exemplar-cli`
Docs	`exemplar-cli/README.md`
Examples	`examples/cli/README.md`

pip install exemplar-cli
export EXEMPLAR_API_KEY="eis_your_org_api_key"
exemplar doctor

Advanced usage

One advanced pattern per offering. For full framework wiring, see examples/live/.

Session ingest — Session helper with auto judge

from exemplar_harness import Harness

harness = Harness.from_env()
session = harness.session(
    "sess-abc",
    agent_id="support-bot",
    source_app="my-app",
    auto_judge_run=True,  # run judge after each ingest through this session
)
session.ingest(
    "generic",
    event="turns",
    data={"turns": [{"input": "Hello", "output": "Hi!", "model": "gpt-4o"}]},
)

Memory — Generic hook in any agent loop

from exemplar_harness import Harness
from exemplar_harness.integrations.memory import MemoryHook

harness = Harness.from_env()
memory = harness.memory(user_id="user-123", session_id="chat-abc", app_id="my-app")
hook = MemoryHook(memory, recall_top_k=5, auto_add=False)

user_input = "What do you know about me?"
recall = hook.before_turn(user_input)  # prepend to system prompt
# ... run LLM ...
hook.after_turn(user_input, assistant_output)  # no-op unless auto_add=True

Skills — Install folders for agent runtimes

A skill is a directory rooted at SKILL.md (plus optional references/, scripts/, assets/). Install the whole folder for Cursor, Claude Code, ADK, Agno, Deep Agents, AG2, CrewAI, and similar agents — do not treat record.instructions as the full skill (that field is the markdown body only, useful for quick editor/MCP use).

Live skills demos also cover framework-native loaders:

Deep Agents: examples/live/deepagents_skills_demo.py (create_deep_agent(..., skills=[...]))
AG2: examples/live/ag2_skills_demo.py (SkillPlugin)
CrewAI: examples/live/crewai_skills_demo.py (Agent(..., skills=[...]))
Agno / OpenAI / Google ADK / Claude Agent: see Quick start sections above

from exemplar_harness import Harness

skills = Harness.from_env().skills()

# Materialize all active skills: dest/<name>/SKILL.md + supporting files
paths = skills.install(".agents/skills")

# Or one skill into Cursor project skills
skills.install(".cursor/skills", names=["refund-policy"])

CLI equivalent: exemplar skills install --all --dest .agents/skills (alias of pull).

Prompts — Publish, build for frameworks, or run inline

from exemplar_harness import Harness

prompts = Harness.from_env().prompts()

prompts.publish_version(
    "support-summary",
    messages=[
        {"role": "system", "content": "Be concise."},
        {"role": "user", "content": "Bullet summary for: {{topic}}."},
    ],
    change_notes="Use bullets",
)

# Hand off to your agent framework (local {{var}} substitution)
built = prompts.build("support-summary", variables={"topic": "returns"})

# Or execute via Exemplar without a stored prompt
inline = prompts.run_inline(
    messages=[
        {"role": "system", "content": "Be concise."},
        {"role": "user", "content": "Say hello."},
    ],
    model="openai/gpt-4o-mini",
)

Framework extras

Framework	Extra	Wire this
LangChain	`langchain`	`make_langchain_callback_handler` → `callbacks=[handler]`
LangGraph	`langgraph`	`HarnessLangGraphHandler.make_graph_callback_handler()`
Deep Agents	`deepagents`	`HarnessDeepAgentsHandler.make_graph_callback_handler()` / `record_run`
LiteLLM	`litellm`	`register_litellm_handler` + `metadata={"session_id": ...}`
OpenAI SDK	`openai`	`HarnessOpenAICallback.on_completion`
Anthropic SDK	`anthropic`	`make_anthropic_middleware`
Claude Agent SDK	`claude-agent`	`HarnessClaudeAgentHandler.run_query`
Portkey	`portkey`	`HarnessPortkeyCallback.on_completion`
Agno	`agno`	`harness_agno_post_hook` → `Agent.post_hooks`
Haystack	`haystack`	`HarnessHaystackHandler.run_and_record`
LlamaIndex	`llamaindex`	`register_llamaindex_handler`
AutoGen	`autogen`	`HarnessAutoGenHandler.on_agent_run_complete`
AG2	`ag2`	`HarnessAG2Handler.record_ask` / `record_chat_turn`
CrewAI	`crewai`	`make_crewai_listener`
Google GenAI SDK	`google-genai`	`instrument_google_genai_client`
Google ADK	`google-adk`	`ingest_adk_session`
Pydantic AI	`pydantic-ai`	`instrument_pydantic_ai_agent`
Semantic Kernel	`semantic-kernel`	`register_semantic_kernel_filter`
smolagents	`smolagents`	`harness_smolagents_step_callback`

Matching memory helpers live under exemplar_harness.integrations.memory.*.

# List / run live demos
python -m examples.live.run_all --list
python -m examples.live.run_memory_demos --list
python -m examples.live.run_platform_demos --list
HARNESS_EXAMPLES_SIMULATED=1 python -m examples.live.run_memory_demos --only memory_agno
HARNESS_EXAMPLES_SIMULATED=1 python -m examples.live.run_platform_demos --only skills_openai

Reference

Optional install extras

Extra	Module
`langchain`	`exemplar_harness.integrations.langchain`
`langgraph`	`exemplar_harness.integrations.langgraph`
`deepagents`	`exemplar_harness.integrations.deepagents`
`litellm`	`exemplar_harness.integrations.litellm`
`openai`	`exemplar_harness.integrations.openai`
`anthropic`	`exemplar_harness.integrations.anthropic`
`claude-agent`	`exemplar_harness.integrations.claude_agent`
`portkey`	`exemplar_harness.integrations.portkey`
`agno`	`exemplar_harness.integrations.agno`
`haystack`	`exemplar_harness.integrations.haystack`
`llamaindex`	`exemplar_harness.integrations.llamaindex`
`autogen`	`exemplar_harness.integrations.autogen`
`ag2`	`exemplar_harness.integrations.ag2`
`crewai`	`exemplar_harness.integrations.crewai`
`google-adk`	`exemplar_harness.integrations.google_adk`
`google-genai`	`exemplar_harness.integrations.google_genai`
`pydantic-ai`	`exemplar_harness.integrations.pydantic_ai`
`semantic-kernel`	`exemplar_harness.integrations.semantic_kernel`
`smolagents`	`exemplar_harness.integrations.smolagents`

Vercel AI SDK is not included in the Python package (TypeScript SDK path).

Envelope v1

Each ingest POSTs schemaVersion: 1 to POST /api/harness-ingest/v1/sessions with a sourceType and framework-native data payload. The Exemplar platform API maps envelopes to eval session turns. Harness judge runs use POST /api/harness-judge/v1/runs; per-turn session eval uses POST /api/harness-session-eval/v1/sessions/{sessionId}.

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Release history Release notifications | RSS feed

0.2.22

Aug 15, 2026

0.2.21

Aug 8, 2026

0.2.20

Aug 8, 2026

0.2.19

Aug 8, 2026

0.2.18

Aug 8, 2026

0.2.17

Jul 24, 2026

0.2.16

Jul 20, 2026

This release

0.2.15

Jul 20, 2026

0.2.14

Jul 15, 2026

0.2.13

Jul 15, 2026

0.2.12

Jul 15, 2026

0.2.11

Jul 15, 2026

0.2.10

Jul 15, 2026

0.2.9

Jul 14, 2026

0.2.8

Jul 6, 2026

0.2.7

Jul 6, 2026

0.2.6

Jul 6, 2026

0.2.5

Jul 6, 2026

0.2.4

Jul 4, 2026

0.2.3

Jul 3, 2026

0.2.2

Jun 29, 2026

0.2.1

Jun 29, 2026

0.2.0

Jun 25, 2026

0.1.2

Jun 25, 2026

0.1.1

Jun 25, 2026

0.1.0

Jun 25, 2026

exemplar-harness-sdk 0.2.15

exemplar-harness-sdk

Contents

Install

Quick start

Agno

Session ingest

Memory

Skills

Prompts

OpenAI SDK

Session ingest

Memory

Skills

Prompts

Google ADK

Session ingest

Memory

Skills

Prompts

Claude Agent SDK

Session ingest

Memory

Skills

Prompts

Session ingest

Example 1 — Framework helper (Agno)

Example 2 — Direct ingest (no framework)

Memory

Example 1 — Add and recall

Example 2 — Search and update

Skills

Example 1 — Create and list

Example 2 — Get, search, and install

Prompts

Example 1 — Create and run

Example 2 — Build for your own agent framework

Example 3 — List and get

CLI (separate package)

Advanced usage

Session ingest — Session helper with auto judge

Memory — Generic hook in any agent loop

Skills — Install folders for agent runtimes

Prompts — Publish, build for frameworks, or run inline

Framework extras

Reference

Optional install extras

Envelope v1

Project links

Key dates

1 maintainer

Credits

License

Requires

Tags

Classifiers

Download files

Source Distribution

Built Distribution

File details

File metadata

File hashes

File details

File metadata

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Release history Release notifications | RSS feed