Client usage

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
 
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 = 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)
fetched = skills.get("refund-policy")
hits = skills.search("refund", top_k=5)
 
# Materialize SKILL.md + supporting files for agent runtimes
skills.install(".agents/skills", names=["refund-policy"])

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"])
 
# Local {{var}} substitution for your own agent framework (no Exemplar model call)
built = prompts.build("support-summary", variables={"topic": "returns"})
# built.messages -> [{"role": "system", ...}, {"role": "user", ...}]

inline = prompts.run_inline(
    messages=[
        {"role": "system", "content": "Be concise."},
        {"role": "user", "content": "Say hello."},
    ],
    model="openai/gpt-4o-mini",
)

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",
)

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

pip install "exemplar-harness-sdk[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",
            source_app="my-app",
        )
    ],
)
agent.run("Summarize our refund policy.")

poetry install --with dev,live --extras all
cp examples/.env.example examples/.env
# set EXEMPLAR_API_KEY in examples/.env
 
EXEMPLAR_API_KEY=... python -m examples.live.run_platform_demos --only skills
EXEMPLAR_API_KEY=... python -m examples.live.run_platform_demos --only prompts
EXEMPLAR_API_KEY=... python -m examples.live.run_platform_demos --only memory