context-bridge-memory 0.1.1

Shared neural memory middleware for multi-agent systems.

🧠 Context Bridge

Shared neural memory middleware for multi-agent systems

Stop passing giant transcripts between agents. Give them a shared memory and let each one recall only what it needs.

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Contents · Problem · Compare · Architecture · Features · Install · Demo · API · Config · Benchmark · Deploy · Roadmap

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The problem

Multi-agent frameworks route work by passing ever-larger context blobs from agent to agent. The shared history grows with every hop, so token cost climbs super-linearly and the genuinely relevant facts drown in noise.

        ❌ Without Context Bridge                  ✅ With Context Bridge

  Agent A ──[full history]──▶ Agent B          Agent A ─┐        ┌─ Agent B
     │                          │                       ▼        ▼
     └──[bigger history]──▶ Agent C             ┌─────────────────────┐
            tokens 📈📈📈 cost 💸               │   Shared memory pool │
                                                │  (write • recall)    │
   every agent re-reads everything              └─────────────────────┘
                                                each agent recalls only its
                                                task-scoped, budgeted slice

Context Bridge is a standalone service that turns agent memory into a shared, governed, queryable pool. Agents write their outputs into it and recall just the slice they need for the current task — under a strict token budget.

Why it's different

• Shared, not per-agent. Memory is a first-class service every agent reads from and writes to — with full provenance — not a private scratch buffer.
• Governed writes. Confidence gating, near-duplicate suppression and optional summarize-before-store stop the pool from poisoning itself.
• Token budgets are explicit. Every recall is bounded and reports exactly what it spent — the measurable cost-savings lever.

How it compares

	Transcript passing (AutoGen-style)	Per-agent memory (mem0 / Letta)	Context Bridge
Memory scope	none — re-sent each hop	private per agent	shared across agents
Token cost	grows ~quadratically	linear-ish	bounded by budget
Retrieval	—	dense only	hybrid + rerank + MMR
Provenance	—	partial	full (agent / task / session)
Write governance	—	varies	dedup + confidence + TTL
Multi-tenant	—	—	namespace RBAC

Context Bridge is not another agent framework — it's the memory layer any framework can point at.

Architecture

flowchart TB
    subgraph Agents
      A1[Agent A]
      A2[Agent B]
      A3[Agent C]
    end

    subgraph CB["Context Bridge API (/v1)"]
      MM[MemoryManager]
    end

    subgraph Tiers["Memory tiers"]
      W[("Working memory<br/>in-process / Redis")]
      S[("Semantic memory<br/>Qdrant · dense + sparse")]
      E[("Episodic / provenance<br/>SQL · SQLAlchemy")]
    end

    A1 & A2 & A3 -->|remember / recall| MM
    MM --> W
    MM --> S
    MM --> E

Tier	Purpose	Backing store
Working	Recent per-session scratchpad, ephemeral	In-process / Redis
Semantic	Long-term embedded knowledge	Qdrant (dense + sparse)
Episodic	Task graph & provenance ("who/what/when/why")	SQL (SQLite / Postgres)

Read & write pipelines

sequenceDiagram
    participant Agent
    participant API as Context Bridge
    participant V as Qdrant
    participant DB as SQL

    Note over Agent,DB: WRITE (governed)
    Agent->>API: remember(content, agent_id, namespace)
    API->>API: chunk → embed (dense+sparse)
    API->>API: dedup + confidence gate
    API->>V: upsert chunks
    API->>DB: store parent doc + episode

    Note over Agent,DB: RECALL (token-budgeted)
    Agent->>API: recall(query, token_budget)
    API->>V: hybrid search (RRF fusion)
    API->>API: rerank → MMR → assemble ≤ budget
    API-->>Agent: context + cited sources + tokens_used

Every provider sits behind a small Protocol — VectorStore, Embedder, Reranker, WorkingMemory, Summarizer — so backends are swappable without vendor lock-in.

Features

🔀 Hybrid retrieval	Dense + sparse vectors fused with Reciprocal Rank Fusion
🎯 Reranking + MMR	Cross-encoder precision, then diversity to kill redundancy
💰 Token budgeting	Recall is bounded and reports tokens_used
🧬 Small-to-big chunks	Match a small chunk, expand to its parent on demand
🛡️ Write governance	Dedup, confidence gating, summarize-before-store
🧾 Provenance	Every memory is attributable; full session timeline
⏳ TTL & decay	Expiry at query time + background/endpoint sweep
🏢 Multi-tenant RBAC	API keys scoped to namespace globs + read/write permissions
🔑 Auth & rate limit	Constant-time keys; in-memory or Redis limiter
🔌 Embedder choice	Local (FastEmbed), OpenAI, Cohere — all behind one protocol
📡 Streaming recall	Server-Sent Events: render context progressively
📊 Observability	Prometheus metrics, request IDs, structured errors
🐳 Production-ready	Dockerfile, CI, Alembic migrations, typed SDK
🔌 Pluggable	Swap Qdrant/embedder/reranker behind protocols

🧠 Cognitive layer (opt-in)

Beyond storage — the shared pool consolidates, reconciles and learns:

💤 Reflective consolidation	Cluster related memories and synthesize higher-order insights no single agent wrote
⚖️ Truth-maintenance	Detect contradictions between agents, flag and resolve them
🕸️ Knowledge graph	Extract entity/relation triples; multi-hop GET /v1/graph/neighbors
🔁 Learning recall	Outcome feedback re-ranks future retrieval
🔒 PII/secret redaction	Mask sensitive data before it enters the shared pool

🤝 Collective learning (the team compounds)

The pool doesn't just remember — the team gets better over time:

🏅 Agent reputation	Per-namespace profiles learn who is reliable at what (GET /v1/agents)
🎯 Outcome credit	A task's success/failure propagates to the memories and agents behind it (POST /v1/outcomes)
📒 Procedural memory	Reusable playbooks with success tracking, so solved problems aren't re-solved (/v1/procedures)
🧷 Salient distillation	Stream cheap turns; the system keeps only what was dwelled upon and carries it into future chats (/v1/sessions/{id}/distill)
📅 Temporal recall	Memories carry dates; recall with include_dates and since/until — remembers when, human-like
🧭 Ontology alignment	Agents that named the same entity differently converge on one canonical name — auto (POST /v1/graph/align) or by alias (POST /v1/graph/aliases)
📈 Collaboration-quality score	One 0-100 metric (GET /v1/quality) blends recall hit-rate, feedback positivity and conflict health, so a team can watch shared memory pay off
🧯 Failure memory	Capture a lesson from a mistake; the system proactively raises it as a guardrail before similar work, so the team stops repeating errors (/v1/lessons)
🧰 Preflight briefing	Before a task, get the lessons to avoid and the playbooks that worked in one call (POST /v1/preflight)
🔄 Belief revision	Resolving a contradiction decays the loser's trust; confidence-weighted recall sinks discredited memories and retires repeat losers — the pool changes its mind
⚗️ Auto lesson distillation	Cluster memories implicated in failures into auto-drafted lessons, no human in the loop (POST /v1/lessons/distill)
🔎 Recall explainability	Every recalled chunk carries per-signal scores and a plain-language why it was retrieved (match, feedback, confidence, age)
🩺 Memory health panel	One pulse-check per namespace: volume, trust distribution, conflicts, lessons, quality (GET /v1/namespaces/{ns}/health)
⚖️ Auto conflict resolution	Close contradictions automatically when one side decisively leads, leaving ambiguous ones for a human (POST /v1/conflicts/auto-resolve)
🧬 Belief timeline	A memory diff: how belief about a topic changed over time — which claim fell out of favour, and when (GET /v1/namespaces/{ns}/beliefs)
⏱️ Scheduled maintenance	One housekeeping cycle — sweep + auto-resolve + consolidate + distill — on demand or on a timer (POST /v1/maintenance/run)
📡 Event webhooks	Best-effort notifications on conflict.opened / conflict.resolved / lesson.created, never blocking a write (WEBHOOK_URLS)
📦 Namespace portability	Export a namespace to a portable, vector-free document and restore it anywhere — re-embedded on import (/v1/namespaces/{ns}/export · …/import)

Install

pip install context-bridge-memory                      # core
pip install "context-bridge-memory[fastembed,redis,postgres]"   # full local stack

The distribution is published as context-bridge-memory; the import package stays context_bridge (e.g. from context_bridge.sdk import ...).

From source (for development):

git clone https://github.com/sa-aris/context-bridge && cd context-bridge
uv pip install -e ".[dev]"

Quick start

# 1. (optional) backing services — Qdrant + Postgres + Redis
docker compose up -d

# 2. run the API
context-bridge          # if installed from PyPI   ->  http://localhost:8000
make run                # from source (auto-reload) ->  http://localhost:8000/docs

Open http://localhost:8000 for the service banner, or /docs for interactive API docs.

Out of the box the defaults are dependency-light and fully offline: an in-process Qdrant (QDRANT_URL=:memory:), a deterministic hashing embedder and SQLite. Flip EMBED_PROVIDER=fastembed for production-quality local embeddings and point QDRANT_URL / DATABASE_URL at real services. See .env.example for every option.

Demo

Run the whole tour offline in one command — it uses real local embeddings (a small model is fetched once) and needs no external services:

python examples/quickstart.py

Or drive it over HTTP. Write two memories from two different agents, then recall just the relevant one:

# Agent 1 writes
curl -s localhost:8000/v1/memory/write -H 'content-type: application/json' -d '{
  "content": "The payment service uses Stripe and retries failed charges three times.",
  "agent_id": "billing-agent", "session_id": "run-42", "namespace": "project-x"
}'

# Agent 2 writes something unrelated
curl -s localhost:8000/v1/memory/write -H 'content-type: application/json' -d '{
  "content": "The office coffee machine is broken; a replacement is on order.",
  "agent_id": "ops-agent", "session_id": "run-42", "namespace": "project-x"
}'

# Another agent recalls — within a token budget
curl -s localhost:8000/v1/memory/query -H 'content-type: application/json' -d '{
  "query": "how does the payment service handle failed charges?",
  "namespace": "project-x", "token_budget": 256
}'

{
  "context": "The payment service uses Stripe and retries failed charges three times.",
  "tokens_used": 14,
  "chunks": [{ "id": "…", "agent_id": "billing-agent", "score": 0.83, … }],
  "sources": [{ "agent_id": "billing-agent", "session_id": "run-42", … }]
}

The coffee-machine note never shows up — only the budgeted, reranked, deduped slice the query actually needs, with provenance for every line.

From an agent (Python SDK)

from context_bridge.sdk import ContextBridgeClient

with ContextBridgeClient("http://localhost:8000") as cb:
    cb.remember(
        "The payment service retries failed charges three times.",
        agent_id="billing-agent", session_id="run-42", namespace="project-x",
    )

    result = cb.recall(
        "how are failed charges handled?",
        namespace="project-x", token_budget=512,
    )
    print(result["context"])   # budget-bounded, reranked, deduped
    print(result["sources"])   # provenance for every included chunk

An AsyncContextBridgeClient with the same surface is available for async agents.

API

Method	Path	Description
POST	/v1/memory/write	Chunk, embed, govern and store content
POST	/v1/memory/write_batch	Write many memories in one request
POST	/v1/memory/query	Hybrid recall within a token budget
POST	/v1/memory/query/stream	Hybrid recall streamed as Server-Sent Events
GET	/v1/memory	List / paginate records by namespace
GET · DELETE	/v1/memory/{id}	Fetch / remove a single record
DELETE	/v1/memory?namespace=&session_id=	Erase all memory for a namespace/session
POST	/v1/memory/summarize	Compress a session into a summary memory
POST	/v1/memory/feedback	Signal whether a recalled memory was useful
GET	/v1/sessions/{id}/timeline	Episodic / provenance view
POST · GET	/v1/sessions/{id}/turns	Append / read ephemeral conversational turns
POST	/v1/sessions/{id}/distill	Promote salient turns into durable cross-session memory
POST	/v1/maintenance/sweep	Delete TTL-expired memories
POST	/v1/maintenance/consolidate	Cluster & synthesize insights for a namespace
POST	/v1/maintenance/run	Run a full housekeeping cycle (sweep + per-namespace upkeep)
GET	/v1/conflicts · POST /v1/conflicts/{id}/resolve	Inspect / resolve contradictions
POST	/v1/conflicts/auto-resolve	Auto-close decisive contradictions (belief revision)
GET	/v1/graph/neighbors	Traverse the knowledge graph
POST	/v1/graph/aliases · /v1/graph/align	Map an alias · auto-merge entity variants
GET	/v1/agents	Agent reputation leaderboard
POST	/v1/outcomes	Credit a session's memories & agents by outcome
GET · POST	/v1/procedures · …/{id}/outcome	Playbooks with success tracking
GET · POST	/v1/lessons · …/{id}/confirm	Capture / list / confirm lessons from past mistakes
POST	/v1/lessons/distill	Auto-draft lessons from memories implicated in failures
POST	/v1/preflight	Pre-task briefing: lessons to avoid + playbooks that worked
GET	/v1/quality	Collaboration-quality score for a namespace
GET	/v1/namespaces/{ns}/health	Memory health panel for a namespace
GET	/v1/namespaces/{ns}/beliefs?query=…	Belief timeline (memory diff) for a topic
GET · POST	/v1/namespaces/{ns}/export · …/import	Back up / migrate a namespace's knowledge
GET	/health · /healthz · /metrics	Liveness · readiness · Prometheus

Interactive OpenAPI docs are served at /docs.

Configuration

Variable	Default	Description
QDRANT_URL	:memory:	Qdrant location or :memory:
EMBED_PROVIDER	hashing	hashing · fastembed · openai · cohere
RERANK_PROVIDER	identity	identity · fastembed · cohere
DATABASE_URL	sqlite+pysqlite:///./context_bridge.db	Episodic store
WORKING_PROVIDER	memory	memory or redis
SUMMARIZER_PROVIDER	extractive	extractive or llm
DEFAULT_TOKEN_BUDGET	2048	Default recall token budget
API_KEYS	(empty)	Comma-separated keys; empty = open
API_KEY_POLICIES	(empty)	JSON RBAC: key → namespace globs + read/write
RATE_LIMIT_PER_MINUTE	0	Per-identity limit; 0 = disabled
SWEEP_INTERVAL_SECONDS	0	Background TTL sweep; 0 = disabled

See .env.example for the complete list.

Proof: token savings benchmark

A runnable benchmark compares transcript passing vs. shared-memory recall over a synthetic multi-agent task:

python -m context_bridge.benchmark

 agents   transcript     bridge   savings
------------------------------------------
      4          216        222     -2.8%
      8        1,008        670     33.5%
     16        4,320      1,566     63.8%
     32       17,856      3,358     81.2%

With a handful of agents the transcript is cheap, but the advantage compounds: transcript passing grows ~quadratically while budgeted recall stays bounded, so by 32 agents Context Bridge uses ~81% fewer context tokens.

Deployment

Docker

docker build -t context-bridge .
docker run -p 8000:8000 \
  -e QDRANT_URL=http://qdrant:6333 \
  -e DATABASE_URL=postgresql+psycopg://user:pass@postgres/context_bridge \
  context-bridge

The image runs as a non-root user and ships a /health healthcheck. Apply database migrations with alembic upgrade head (the API also auto-creates tables for local development).

Kubernetes (Helm)

helm install cb deploy/helm/context-bridge \
  --set image.repository=ghcr.io/you/context-bridge \
  --set ingress.enabled=true

The chart includes a Deployment, Service, ConfigMap, ServiceAccount, optional Ingress and a CPU-based HorizontalPodAutoscaler.

Observability

/metrics exposes write/query/dedup counters, token-usage and chunk-count histograms, sweep totals and a request-latency histogram. Every response carries an X-Request-ID. Spin up a ready-made Prometheus + Grafana stack (a dashboard is auto-provisioned):

docker compose -f docker-compose.yml -f docker-compose.observability.yml up -d
# Grafana -> http://localhost:3000  (admin / admin)

For distributed tracing, install the otel extra and set TRACING_ENABLED=true with OTEL_EXPORTER_OTLP_ENDPOINT — the write/query pipeline stages emit spans via OTLP to Jaeger/Tempo/Grafana.

Development

make test        # pytest (hermetic: in-memory Qdrant + SQLite)
make lint        # ruff
make typecheck   # mypy
make fmt         # ruff format + autofix

See CONTRIBUTING.md. The test suite needs no network: it uses in-process Qdrant, SQLite and a deterministic embedder; model-dependent tests skip automatically when offline.

Roadmap

• Hybrid sparse vectors for OpenAI/Cohere providers (SPLADE side-car)
• gRPC API surface alongside REST
• Per-tenant usage accounting & quotas
• Managed cloud offering

License

Apache-2.0