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Developer implementation guide for adding compact memory to an Agent — covers fork agent pattern for compaction, trigger strategy, summary format design, and memory restoration in subsequent sessions. Use when a developer asks how to implement compact memory, context compression,
Developer implementation guide for adding compact memory to an Agent — covers fork agent pattern for compaction, trigger strategy, summary format design, and memory restoration in subsequent sessions. Use when a developer asks how to implement compact memory, context compression, or memory persistence in their agent built with Claude Agent SDK or Anthropic API.
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A developer guide for building compact memory into an Agent: detect when to compress, fork a compactor sub-agent, produce a structured summary, and restore it in the next session.
Before designing anything, clarify:
This determines which pattern fits.
Three strategies, pick based on your session model:
1. Token threshold (recommended)
Check usage.input_tokens from the previous response. When it exceeds ~70–80% of your model's context limit, trigger compact.
COMPACT_THRESHOLD = 150_000 # adjust per model
if response.usage.input_tokens > COMPACT_THRESHOLD:
compact = compact_memory(history)
history = [] # reset — compact moves to system prompt
2. Turn count Compact every N turns. Simpler but less adaptive — misses sessions with a few very long turns.
COMPACT_EVERY_N = 30
if turn_count % COMPACT_EVERY_N == 0:
compact = compact_memory(history)
3. Phase boundary Compact at natural task boundaries (after research, before implementation). Requires the agent to detect phases. Produces summaries that align with meaningful milestones, but harder to implement reliably.
Recommended default: token threshold at 70%, with turn-count fallback at N=40.
The compactor is a separate agent call whose only job is to read the current state and return a structured summary. Fork it synchronously — the main agent waits for the result before continuing.
def compact_memory(history: list[dict]) -> dict:
response = client.messages.create(
model="claude-haiku-4-5-20251001", # cheaper model is fine for compaction
max_tokens=4096,
system=COMPACTOR_SYSTEM_PROMPT,
messages=[
{
"role": "user",
"content": format_history_for_compact(history),
}
],
)
return json.loads(response.content[0].text)
Why fork instead of self-compact:
{
"task": "What the agent is working on and why — the goal, not the steps",
"current_state": "Exact status at compaction point: what is done, what is not, what is in progress",
"key_decisions": [
{ "decision": "...", "reason": "...", "constraint": "..." }
],
"eliminated_approaches": [
{ "approach": "...", "reason_ruled_out": "..." }
],
"open_questions": ["..."],
"next_steps": ["..."],
"relevant_tool_results": {
"key": "Only results future steps will need — summarized, not raw dumps"
},
"compacted_at_turn": 42
}
You are a conversation compactor. Read the provided conversation and produce a JSON summary that captures everything a fresh agent needs to continue the work without asking what happened.
Include:
- Current task and goal (not the steps taken to get here)
- Exact current state — what is done and what is not
- Decisions made and WHY (reasoning, not just the choice)
- Approaches tried and ruled out with reasons (prevents re-exploration)
- Open questions and blockers
- Concrete next steps in priority order
- Tool results that future steps will need (summarize, don't dump raw output)
Omit:
- Intermediate reasoning that led nowhere
- Completed sub-tasks with no future relevance
- Raw tool output that has already been acted on
- Anything derivable by reading the code or running a command
Output valid JSON matching the schema provided. No prose outside the JSON.
def format_history_for_compact(history: list[dict]) -> str:
lines = ["Conversation to compact:\n"]
for msg in history:
role = msg["role"].upper()
content = msg["content"] if isinstance(msg["content"], str) else "[tool use]"
lines.append(f"[{role}]: {content[:2000]}") # cap very long messages
return "\n".join(lines)
The compact object becomes the "memory" for the next turn or session. Inject it into the system prompt so it's always visible to the agent.
MEMORY_BLOCK_TEMPLATE = """
## Restored memory (compacted at turn {turn})
**Task**: {task}
**Current state**: {current_state}
**Key decisions**:
{decisions}
**Ruled out approaches**:
{eliminated}
**Next steps**:
{next_steps}
Begin from current state above. Do not re-explore eliminated approaches.
"""
def build_system_with_memory(base_system: str, compact: dict | None) -> str:
if compact is None:
return base_system
memory = MEMORY_BLOCK_TEMPLATE.format(
turn=compact["compacted_at_turn"],
task=compact["task"],
current_state=compact["current_state"],
decisions="\n".join(f"- {d['decision']} (because {d['reason']})"
for d in compact["key_decisions"]),
eliminated="\n".join(f"- {e['approach']}: {e['reason_ruled_out']}"
for e in compact["eliminated_approaches"]),
next_steps="\n".join(f"- {s}" for s in compact["next_steps"]),
)
return base_system + "\n\n" + memory
messages = [
{
"role": "user",
"content": f"[Resuming from compacted state — turn {compact['compacted_at_turn']}]\n"
f"{json.dumps(compact, indent=2)}\n\n"
f"Continue from the next steps listed above.",
}
]
import json, pathlib
MEMORY_DIR = pathlib.Path("memory")
MEMORY_DIR.mkdir(exist_ok=True)
def save_compact(session_id: str, compact: dict) -> None:
(MEMORY_DIR / f"{session_id}.json").write_text(json.dumps(compact, indent=2))
def load_compact(session_id: str) -> dict | None:
path = MEMORY_DIR / f"{session_id}.json"
return json.loads(path.read_text()) if path.exists() else None
def run_agent(session_id: str, user_input: str) -> str:
compact = load_compact(session_id)
system = build_system_with_memory(BASE_SYSTEM, compact)
history = []
turn = 0
while True:
response = client.messages.create(
model="claude-opus-4-7",
system=system,
messages=history + [{"role": "user", "content": user_input}],
max_tokens=8192,
)
# Trigger compact if context is growing too large
if response.usage.input_tokens > COMPACT_THRESHOLD:
compact = compact_memory(history)
save_compact(session_id, compact)
system = build_system_with_memory(BASE_SYSTEM, compact)
history = [] # reset history — compact is now in system
turn = 0
continue
if response.stop_reason == "end_turn":
return response.content[0].text
history.append({"role": "assistant", "content": response.content})
user_input = handle_tool_calls(response) # your tool dispatch
turn += 1
If a session resumes multiple times, don't stack compacts — re-compact instead:
COMPACTOR_WITH_PRIOR = """
You are updating an existing memory compact with new information from a continuation session.
Prior compact:
{prior_compact}
New conversation turns since last compact:
{new_turns}
Produce an updated compact that:
- Merges both sources
- Removes resolved items and completed steps
- Adds new decisions, eliminations, and open questions
- Keeps next_steps current
Output valid JSON. No prose outside the JSON.
"""
def compact_memory_with_prior(history: list[dict], prior: dict) -> dict:
prompt = COMPACTOR_WITH_PRIOR.format(
prior_compact=json.dumps(prior, indent=2),
new_turns=format_history_for_compact(history),
)
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=4096,
system=prompt,
messages=[{"role": "user", "content": "Update the compact."}],
)
return json.loads(response.content[0].text)
| Pitfall | Fix |
|---|---|
| Compact loses tool results needed later | Include summarized results in relevant_tool_results |
| Fresh session ignores compact | Inject into system prompt, not buried in messages |
| Compactor uses the same expensive model | Use Haiku for compaction, Opus for main work |
| Compact grows unbounded across sessions | Re-compact using "chaining compacts" pattern above |
| Compacting too often (every turn) | Use token threshold, not turn frequency |
| Compact JSON fails to parse | Add retry with explicit error feedback to compactor |
name: compact-memory-implementation description: Developer implementation guide for adding compact memory to an Agent — covers fork agent pattern for compaction, trigger strategy, summary format design, and memory restoration in subsequent sessions. Use when a developer asks how to implement compact memory, context compression, or memory persistence in their agent built with Claude Agent SDK or Anthropic API.
---
name: compact-memory-implementation
description: Developer implementation guide for adding compact memory to an Agent — covers fork agent pattern for compaction, trigger strategy, summary format design, and memory restoration in subsequent sessions. Use when a developer asks how to implement compact memory, context compression, or memory persistence in their agent built with Claude Agent SDK or Anthropic API.
---
# compact-memory-implementation
A developer guide for building compact memory into an Agent: detect when to compress, fork a compactor sub-agent, produce a structured summary, and restore it in the next session.
## Step 1 — Understand the setup
Before designing anything, clarify:
- **SDK / language**: Claude Agent SDK? Direct Anthropic API? Python or TypeScript?
- **Agent architecture**: single-agent loop, multi-agent, tool-calling?
- **Session model**: one long-running session or multiple short sessions?
- **What must survive compaction**: task state, decisions, tool results, conversation history?
This determines which pattern fits.
---
## Step 2 — When to trigger compact
Three strategies, pick based on your session model:
**1. Token threshold** (recommended)
Check `usage.input_tokens` from the previous response. When it exceeds ~70–80% of your model's context limit, trigger compact.
```python
COMPACT_THRESHOLD = 150_000 # adjust per model
if response.usage.input_tokens > COMPACT_THRESHOLD:
compact = compact_memory(history)
history = [] # reset — compact moves to system prompt
```
**2. Turn count**
Compact every N turns. Simpler but less adaptive — misses sessions with a few very long turns.
```python
COMPACT_EVERY_N = 30
if turn_count % COMPACT_EVERY_N == 0:
compact = compact_memory(history)
```
**3. Phase boundary**
Compact at natural task boundaries (after research, before implementation). Requires the agent to detect phases. Produces summaries that align with meaningful milestones, but harder to implement reliably.
**Recommended default**: token threshold at 70%, with turn-count fallback at N=40.
---
## Step 3 — Fork agent for compaction
The compactor is a **separate agent call** whose only job is to read the current state and return a structured summary. Fork it synchronously — the main agent waits for the result before continuing.
```python
def compact_memory(history: list[dict]) -> dict:
response = client.messages.create(
model="claude-haiku-4-5-20251001", # cheaper model is fine for compaction
max_tokens=4096,
system=COMPACTOR_SYSTEM_PROMPT,
messages=[
{
"role": "user",
"content": format_history_for_compact(history),
}
],
)
return json.loads(response.content[0].text)
```
**Why fork instead of self-compact:**
- The main agent may have drifted in focus; the compactor starts fresh with the full picture
- Compaction is a different cognitive task — summarizing vs. executing
- A cheaper, smaller model (Haiku) can do compaction; save the expensive model for main work
- Clean separation makes the compact output easier to validate and test
---
## Step 4 — How to compact: format and prompt
### Compact output schema
```json
{
"task": "What the agent is working on and why — the goal, not the steps",
"current_state": "Exact status at compaction point: what is done, what is not, what is in progress",
"key_decisions": [
{ "decision": "...", "reason": "...", "constraint": "..." }
],
"eliminated_approaches": [
{ "approach": "...", "reason_ruled_out": "..." }
],
"open_questions": ["..."],
"next_steps": ["..."],
"relevant_tool_results": {
"key": "Only results future steps will need — summarized, not raw dumps"
},
"compacted_at_turn": 42
}
```
### Compactor system prompt
```
You are a conversation compactor. Read the provided conversation and produce a JSON summary that captures everything a fresh agent needs to continue the work without asking what happened.
Include:
- Current task and goal (not the steps taken to get here)
- Exact current state — what is done and what is not
- Decisions made and WHY (reasoning, not just the choice)
- Approaches tried and ruled out with reasons (prevents re-exploration)
- Open questions and blockers
- Concrete next steps in priority order
- Tool results that future steps will need (summarize, don't dump raw output)
Omit:
- Intermediate reasoning that led nowhere
- Completed sub-tasks with no future relevance
- Raw tool output that has already been acted on
- Anything derivable by reading the code or running a command
Output valid JSON matching the schema provided. No prose outside the JSON.
```
### Format history for compactor
```python
def format_history_for_compact(history: list[dict]) -> str:
lines = ["Conversation to compact:\n"]
for msg in history:
role = msg["role"].upper()
content = msg["content"] if isinstance(msg["content"], str) else "[tool use]"
lines.append(f"[{role}]: {content[:2000]}") # cap very long messages
return "\n".join(lines)
```
---
## Step 5 — How to use after compacting: memory restoration
The compact object becomes the "memory" for the next turn or session. Inject it into the system prompt so it's always visible to the agent.
### Pattern A — System prompt injection (recommended)
```python
MEMORY_BLOCK_TEMPLATE = """
## Restored memory (compacted at turn {turn})
**Task**: {task}
**Current state**: {current_state}
**Key decisions**:
{decisions}
**Ruled out approaches**:
{eliminated}
**Next steps**:
{next_steps}
Begin from current state above. Do not re-explore eliminated approaches.
"""
def build_system_with_memory(base_system: str, compact: dict | None) -> str:
if compact is None:
return base_system
memory = MEMORY_BLOCK_TEMPLATE.format(
turn=compact["compacted_at_turn"],
task=compact["task"],
current_state=compact["current_state"],
decisions="\n".join(f"- {d['decision']} (because {d['reason']})"
for d in compact["key_decisions"]),
eliminated="\n".join(f"- {e['approach']}: {e['reason_ruled_out']}"
for e in compact["eliminated_approaches"]),
next_steps="\n".join(f"- {s}" for s in compact["next_steps"]),
)
return base_system + "\n\n" + memory
```
### Pattern B — First message injection (for stateless API callers)
```python
messages = [
{
"role": "user",
"content": f"[Resuming from compacted state — turn {compact['compacted_at_turn']}]\n"
f"{json.dumps(compact, indent=2)}\n\n"
f"Continue from the next steps listed above.",
}
]
```
### Persistence across sessions
```python
import json, pathlib
MEMORY_DIR = pathlib.Path("memory")
MEMORY_DIR.mkdir(exist_ok=True)
def save_compact(session_id: str, compact: dict) -> None:
(MEMORY_DIR / f"{session_id}.json").write_text(json.dumps(compact, indent=2))
def load_compact(session_id: str) -> dict | None:
path = MEMORY_DIR / f"{session_id}.json"
return json.loads(path.read_text()) if path.exists() else None
```
---
## Step 6 — Full agent loop
```python
def run_agent(session_id: str, user_input: str) -> str:
compact = load_compact(session_id)
system = build_system_with_memory(BASE_SYSTEM, compact)
history = []
turn = 0
while True:
response = client.messages.create(
model="claude-opus-4-7",
system=system,
messages=history + [{"role": "user", "content": user_input}],
max_tokens=8192,
)
# Trigger compact if context is growing too large
if response.usage.input_tokens > COMPACT_THRESHOLD:
compact = compact_memory(history)
save_compact(session_id, compact)
system = build_system_with_memory(BASE_SYSTEM, compact)
history = [] # reset history — compact is now in system
turn = 0
continue
if response.stop_reason == "end_turn":
return response.content[0].text
history.append({"role": "assistant", "content": response.content})
user_input = handle_tool_calls(response) # your tool dispatch
turn += 1
```
---
## Step 7 — Chaining compacts across sessions
If a session resumes multiple times, don't stack compacts — re-compact instead:
```python
COMPACTOR_WITH_PRIOR = """
You are updating an existing memory compact with new information from a continuation session.
Prior compact:
{prior_compact}
New conversation turns since last compact:
{new_turns}
Produce an updated compact that:
- Merges both sources
- Removes resolved items and completed steps
- Adds new decisions, eliminations, and open questions
- Keeps next_steps current
Output valid JSON. No prose outside the JSON.
"""
def compact_memory_with_prior(history: list[dict], prior: dict) -> dict:
prompt = COMPACTOR_WITH_PRIOR.format(
prior_compact=json.dumps(prior, indent=2),
new_turns=format_history_for_compact(history),
)
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=4096,
system=prompt,
messages=[{"role": "user", "content": "Update the compact."}],
)
return json.loads(response.content[0].text)
```
---
## Common pitfalls
| Pitfall | Fix |
|---|---|
| Compact loses tool results needed later | Include summarized results in `relevant_tool_results` |
| Fresh session ignores compact | Inject into system prompt, not buried in messages |
| Compactor uses the same expensive model | Use Haiku for compaction, Opus for main work |
| Compact grows unbounded across sessions | Re-compact using "chaining compacts" pattern above |
| Compacting too often (every turn) | Use token threshold, not turn frequency |
| Compact JSON fails to parse | Add retry with explicit error feedback to compactor |
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"Stars/forks activity: 159 stars, 30 forks; issue activity unavailable in current metadata",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 79,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Permission surface may require sandboxing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Stars/forks activity: 159 stars, 30 forks; issue activity unavailable in current metadata",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 69,
"label": "Promising"
},
"supply": {
"track": "Design and creative production",
"scenario": "Design and creative",
"maintenance": "28d since push",
"risk": "Needs review"
},
"alternative_skills": [
{
"slug": "design-taste-frontend",
"name": "Taste Skill: Anti-Slop Frontend",
"url": "https://www.openagentskill.com/skills/design-taste-frontend",
"stars": 89359,
"install_command": "npx skills add Leonxlnx/taste-skill --skill design-taste-frontend",
"trust_score": 94,
"audit_score": 96
},
{
"slug": "emilkowalski-apple-design",
"name": "Apple Design",
"url": "https://www.openagentskill.com/skills/emilkowalski-apple-design",
"stars": 34452,
"install_command": "npx skills@latest add emilkowalski/skills",
"trust_score": 93,
"audit_score": 94
}
],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"high-compliance environments without internal security review",
"No major risk signals from current metadata",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Permission surface may require sandboxing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Stars/forks activity: 159 stars, 30 forks; issue activity unavailable in current metadata"
],
"agent_contract": {
"task_input": "Use compact-memory-implementation in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 75/100 Strong shortlist",
"Audit: 79/100 Needs review",
"Safety: 39/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "simbajigege-compact-memory-implementation (compact-memory-implementation)",
"install_command": "npx skills add simbajigege/book2skills --skill compact-memory-implementation",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "simbajigege-compact-memory-implementation",
"task": "Use compact-memory-implementation in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/simbajigege-compact-memory-implementation",
"api": "https://www.openagentskill.com/api/agent/skills/simbajigege-compact-memory-implementation",
"audit": "https://www.openagentskill.com/skills/simbajigege-compact-memory-implementation/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=simbajigege-compact-memory-implementation&task=Use%20compact-memory-implementation%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20compact-memory-implementation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20compact-memory-implementation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/simbajigege-compact-memory-implementation/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/simbajigege-compact-memory-implementation"
}
}Listing source
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Audit
79/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.