triage-agent 0.6.0

Classify why your agent failed. Recover intelligently.

triage

Classify why your agent failed. Recover intelligently.

pip install triage-agent

---

The problem

Current agent frameworks know that your agent failed. They don't know why — and without knowing why, every failure gets the same blunt response: retry from scratch or give up.

triage adds a classification-and-routing layer between the failure and the recovery:

agent fails → classify failure type → route to matching strategy → recover

It works with any async agent callable — OpenAI, LangGraph, CrewAI, raw LLM loops — without requiring you to change your framework.

---

Installation

# Core only
pip install triage-agent

# With framework adapters
pip install "triage-agent[langgraph]"
pip install "triage-agent[crewai]"
pip install "triage-agent[openai-agents]"
pip install "triage-agent[langchain]"

# With LLM-based classifier
pip install "triage-agent[anthropic]"

# With durable checkpoint storage
pip install "triage-agent[sqlite]"
pip install "triage-agent[redis]"

Python 3.10+ required. Core dependencies: anyio>=4.0, pydantic>=2.0.

---

Quick start

import triage
from triage.strategies.retry import retry_with_tool_manifest, backoff_and_retry
from triage.strategies.replan import replan
from triage.strategies.rollback import rollback_to_checkpoint
from triage.taxonomy import Step

# 1. Define your agent — it receives record_step and update_state callbacks
async def my_agent(task: str, *, record_step, update_state, _triage_hint=None, **kwargs):
    # ... your agent logic ...
    data = fetch_data(task)
    record_step(Step(index=0, action="called search", tool_called="search",
                     tool_input={"q": task}, tool_output=data))
    update_state({"data": data})   # persisted into checkpoints; restored on rollback
    return "done"

# 2. Declare a recovery policy
policy = triage.FailurePolicy(
    WRONG_TOOL_CALLED  = retry_with_tool_manifest(max_attempts=3),
    EXTERNAL_FAULT     = backoff_and_retry(max_attempts=5),
    LOOP_DETECTED      = replan(hint="Try a different approach."),
    HALLUCINATED_STATE = rollback_to_checkpoint(),
    default            = triage.FailurePolicy.escalate_by_default(),
)

# 3. Wrap and run
agent = triage.Agent(my_agent, policy=policy)
result = await agent.run("search for recent AI papers")

Or use the decorator form:

@triage.agent(policy=policy)
async def my_agent(task: str, *, record_step, **kwargs):
    ...

---

Framework adapters

Drop-in wrappers let you add triage to an existing agent without changing its internals.

LangGraph

from triage.adapters.langgraph import wrap_langgraph

agent = wrap_langgraph(compiled_graph, policy=policy)
result = await agent.run("your task")

Streams events via graph.astream_events(..., version="v2") to capture tool calls and LLM turns.

CrewAI

from triage.adapters.crewai import wrap_crewai

agent = wrap_crewai(crew, policy=policy)
result = await agent.run("your task")

Patches crew.step_callback for each run (original restored in finally).

OpenAI Agents SDK

from triage.adapters.openai_agents import wrap_openai_agents

agent = wrap_openai_agents(sdk_agent, policy=policy)
result = await agent.run("your task")

Uses Runner.run_streamed and iterates stream_events().

LangChain

from triage.adapters.langchain import wrap_langchain

agent = wrap_langchain(executor, policy=policy)
result = await agent.run("your task")

Injects a fresh BaseCallbackHandler per call via config={"callbacks": [...]}.

All adapters accept the same optional kwargs as triage.Agent: classifier, checkpoint_store, max_recovery_attempts, auto_checkpoint.

---

How it works

1. Record steps

Your agent calls record_step(Step(...)) for each observable action. triage injects the callback — you don't need to import or construct anything:

async def my_agent(task: str, *, record_step, **kwargs):
    result = call_tool("search", {"q": task})
    record_step(Step(
        index=0,
        action="called search tool",
        tool_called="search",
        tool_input={"q": task},
        tool_output=result,
    ))

2. Classify the failure

When your agent raises an exception, triage runs the classifier over the recorded trajectory and returns one of 10 FailureType values:

FailureType	Trigger	Default recovery
WRONG_TOOL_CALLED	Error matches "tool not found" / "no tool named"	Retry with correct manifest
CONSTRAINT_IGNORED	LLM output contains a forbidden string	Replan with constraint reminder
LOOP_DETECTED	Last 3 steps identical tool + input	Replan or rollback
HALLUCINATED_STATE	Agent asserts facts contradicting tool output	Rollback to checkpoint
PLAN_INCOMPLETE	Success declared but sub-goals incomplete	Resume from subgoal
SCHEMA_MISMATCH	Error matches "validation error" / JSON parse failure	Retry with schema hint
CONTEXT_OVERFLOW	Agent lost earlier context	Replan with compressed context
GOAL_DRIFT	Agent making progress toward the wrong goal	Replan with goal restatement
EXTERNAL_FAULT	HTTP 429 / 500 / 502 / 503 in error	Exponential backoff + retry
UNKNOWN	None of the above	Escalate to human

The default RulesClassifier is pattern-based and makes zero API calls. For semantic classification use LLMClassifier, or use HybridClassifier to get the best of both:

from triage.classifier.llm import LLMClassifier
from triage.classifier.hybrid import HybridClassifier

# LLM only — every failure classified by Claude
agent = triage.Agent(
    my_agent,
    policy=policy,
    classifier=LLMClassifier(model="claude-haiku-4-5-20251001"),
)

# Hybrid — rules first, LLM only when rules return UNKNOWN (~20% of failures)
agent = triage.Agent(
    my_agent,
    policy=policy,
    classifier=HybridClassifier(llm=LLMClassifier()),
)

LLMClassifier supports Anthropic and any OpenAI-compatible provider. Configure via constructor args or env vars:

# Anthropic (default)
ANTHROPIC_API_KEY=sk-ant-... python my_agent.py

# Ollama (local, no key)
TRIAGE_LLM_BASE_URL=http://localhost:11434/v1 TRIAGE_LLM_MODEL=llama3.2 python my_agent.py

# Groq
TRIAGE_LLM_BASE_URL=https://api.groq.com/openai/v1 TRIAGE_LLM_API_KEY=gsk_... TRIAGE_LLM_MODEL=llama-3.1-8b-instant python my_agent.py

Or pass explicitly:

LLMClassifier(base_url="http://localhost:11434/v1", model="llama3.2")

LLMClassifier falls back to UNKNOWN silently on any error. Requires pip install "triage-agent[anthropic]" for Anthropic, or pip install openai for any OpenAI-compatible provider.

3. Dispatch to a strategy

The policy maps each FailureType to a strategy callable. The strategy returns a RecoveryAction that tells triage what to do next.

4. Execute the recovery

triage executes the action and re-runs your agent with injected context:

Action	What happens
RETRY	Re-runs the agent; injects _triage_hint into kwargs
REPLAN	Re-runs the agent; injects _triage_hint with new plan instruction
ROLLBACK	Restores trajectory from checkpoint, re-runs agent
RESUME	Re-runs agent; injects _triage_subgoal pointing at incomplete subgoal
ESCALATE	Raises TriageEscalationError(message, context)
ABORT	Raises TriageAbortError(reason, context)

---

Failure policy

FailurePolicy is a plain dataclass — one field per FailureType:

policy = triage.FailurePolicy(
    WRONG_TOOL_CALLED  = retry_with_tool_manifest(max_attempts=3),
    CONSTRAINT_IGNORED = replan(hint="Re-read the task constraints carefully."),
    LOOP_DETECTED      = replan(max_replans=2),
    HALLUCINATED_STATE = rollback_to_checkpoint(),
    PLAN_INCOMPLETE    = resume_from_subgoal(),
    SCHEMA_MISMATCH    = retry_with_tool_manifest(max_attempts=2),
    EXTERNAL_FAULT     = backoff_and_retry(max_attempts=5),
    default            = triage.FailurePolicy.escalate_by_default(),
)

Any FailureType not explicitly listed falls through to default. If default is also unset, triage escalates automatically.

---

Built-in strategies

triage.strategies.retry

from triage.strategies.retry import retry_with_tool_manifest, backoff_and_retry

# Retry with a hint to use the correct tool manifest
retry_with_tool_manifest(max_attempts=3)

# Retry with exponential backoff (2^attempt seconds). Good for rate limits.
backoff_and_retry(max_attempts=5)

triage.strategies.replan

from triage.strategies.replan import replan, resume_from_subgoal

# Restart with a new plan, optionally injecting a hint
replan(hint="The previous approach used the wrong API endpoint.")

# Continue from the first incomplete sub-goal
resume_from_subgoal()

triage.strategies.rollback

from triage.strategies.rollback import rollback_to_checkpoint

# Restore to latest checkpoint (or a named one)
rollback_to_checkpoint()
rollback_to_checkpoint(checkpoint_id="before-api-call")

---

Checkpoints

Save agent state at key points so triage can roll back to them on failure.

In-memory (default)

from triage.checkpoint import InMemoryCheckpointStore

store = InMemoryCheckpointStore()
agent = triage.Agent(my_agent, policy=policy, checkpoint_store=store)

SQLite (persistent, single-process)

pip install "triage-agent[sqlite]"

from triage.checkpoint.sqlite import SQLiteCheckpointStore

store = SQLiteCheckpointStore("runs/checkpoints.db")
agent = triage.Agent(my_agent, policy=policy, checkpoint_store=store)

Redis (distributed)

pip install "triage-agent[redis]"

import redis.asyncio as aioredis
from triage.checkpoint.redis import RedisCheckpointStore

client = aioredis.Redis.from_url("redis://localhost:6379")
store = RedisCheckpointStore(client)
agent = triage.Agent(my_agent, policy=policy, checkpoint_store=store)

Auto-checkpoint

Enable automatic checkpointing after every successful step:

agent = triage.Agent(my_agent, policy=policy, checkpoint_store=store, auto_checkpoint=True)

Checkpoints are always awaited before run() returns or any recovery action executes, so a ROLLBACK always has a checkpoint available.

---

Recovery context in your agent

Two callbacks are always injected, plus recovery context on retry:

async def my_agent(
    task: str,
    *,
    record_step,
    update_state,
    _triage_hint=None,
    _triage_subgoal=None,
    _triage_state=None,
    **kwargs,
):
    # On rollback, _triage_state contains the state saved at the checkpoint
    if _triage_state:
        data = _triage_state["data"]   # skip re-fetching, use restored state
    else:
        data = fetch_data(task)

    record_step(Step(index=0, action="fetch", tool_output=data))
    update_state({"data": data})       # saved into every auto_checkpoint

    if _triage_hint:
        print(f"Recovery hint: {_triage_hint}")
    if _triage_subgoal:
        task = _triage_subgoal

Key	Set when
record_step	Always — injected on every call
update_state	Always — injected on every call
_triage_hint	RETRY, REPLAN, or ROLLBACK action
_triage_subgoal	RESUME action
_triage_state	ROLLBACK action, when checkpoint has non-empty state

---

Attempt history

Strategies can inspect everything that was tried before they were called:

async def smart_strategy(ctx: triage.FailureContext) -> triage.RecoveryAction:
    # ctx.attempt_history is a list of (FailureType, action_kind) tuples
    replan_count = sum(1 for _, kind in ctx.attempt_history if kind == "replan")

    if replan_count >= 2:
        return triage.RecoveryAction.ESCALATE(message="Replanned twice, still failing.")
    return triage.RecoveryAction.REPLAN(hint="Try a different approach.")

policy = triage.FailurePolicy(GOAL_DRIFT=smart_strategy)

attempt_history is empty on the first failure and grows by one entry per recovery attempt. Each entry is (failure_type, action_kind) where action_kind is one of "retry", "replan", "rollback", "resume", "escalate", "abort".

---

Handling escalation and abort

try:
    result = await agent.run(task)
except triage.TriageEscalationError as exc:
    # exc.context is a FailureContext with the full trajectory and failure type
    print(f"Needs human review: {exc}")
    print(f"Failure type: {exc.context.failure_type.value}")
    print(f"Failed at step: {exc.context.critical_step_index}")
except triage.TriageAbortError as exc:
    print(f"Hard stop: {exc}")

---

Custom classifier

Any class implementing classify(trajectory, task) -> FailureType satisfies the protocol:

from triage.classifier.base import Classifier
from triage.taxonomy import FailureType
from triage.trajectory import Trajectory

class MyClassifier:
    def classify(self, trajectory: Trajectory, task: str) -> FailureType:
        ...

agent = triage.Agent(my_agent, policy=policy, classifier=MyClassifier())

---

Example: OpenAI tool-calling loop

See examples/raw_openai.py for a full working example. It deliberately triggers a WRONG_TOOL_CALLED failure on the first attempt and shows triage catching and recovering it automatically:

OPENAI_API_KEY=sk-... python examples/raw_openai.py

Expected output:

Task: What is 42 * 17?

[triage] wrong_tool_called detected at step 0
[triage] Dispatching: RecoveryAction.RETRY(hint='Re-run using only tools in the current manifest.', inject={'max_attempts': 3})
[triage] Attempt 1...

Result: 714

---

Project layout

triage/
  taxonomy.py        FailureType enum, Step, FailureContext
  trajectory.py      Trajectory (append / replay_from / last_n_steps)
  checkpoint/
    base.py          Checkpoint, CheckpointStore protocol, serialization helpers
    memory.py        InMemoryCheckpointStore
    sqlite.py        SQLiteCheckpointStore (requires aiosqlite)
    redis.py         RedisCheckpointStore (requires redis[asyncio])
  policy.py          RecoveryAction (6 constructors), FailurePolicy
  agent.py           Agent class, TriageEscalationError, TriageAbortError, @agent decorator
  classifier/
    base.py          Classifier protocol
    rules.py         RulesClassifier — 6 rules, sync, zero API calls
    llm.py           LLMClassifier — Anthropic or OpenAI-compatible backend
    hybrid.py        HybridClassifier — rules first, LLM fallback on UNKNOWN
  strategies/
    retry.py         retry_with_tool_manifest(), backoff_and_retry()
    replan.py        replan(), resume_from_subgoal()
    rollback.py      rollback_to_checkpoint()
  adapters/
    langgraph.py     wrap_langgraph() (requires langgraph)
    crewai.py        wrap_crewai() (requires crewai)
    openai_agents.py wrap_openai_agents() (requires openai-agents)
    langchain.py     wrap_langchain() (requires langchain)

---

License

MIT